A weather radar network relies on two or more transmitters and receivers for localized weather event monitoring. For a receiving aperture implemented as an every-element digital array radar, it provides opportunity to leverage convex optimization for coordinated beamforming and null forming, in order to perform weather metrology without impairments. This work describes weather radar network challenges on receive due to ground clutter. The use of second-order cone programming, a scalable form of nonlinear optimization, mitigates clutter interference by deriving array element weights for beamforming in the target direction and null-forming in the ground clutter directions. Convex optimization enables beamforming with more flexibility when compared to conventional null steering with low sidelobe array tapers. This is demonstrated through quantifying the signal-to-clutter ratio impact of nulling. This paper demonstrates how digital beamforming via convex optimization in weather radar networks improves the signal-to-clutter ratio for a volume resolution cell at low altitude when compared to conventional beamforming methods. A figure of merit is presented to show how these clutter mitigations can improve wind field estimates for weather radar network applications such as 3D wind field retrieval via multi-Doppler methods in the presence of ground clutter. The paper concludes with an evaluation of a well-known adaptive digital beamformer for clutter mitigation as well as a comparison of all methods considered for a surveillance region of interest.

Accurately estimating snowfall quantitatively plays a critical role in reducing the effects of winter storms on public safety. Conventional techniques, which mainly rely on radar reflectivity (Z), provide wide-range real-time data but face notable challenges in accurately capturing snowfall microphysics, resulting in reduced precision during heavy snowfall or mixed precipitation conditions. Recent innovations, including dual-frequency dual-polarization radar parameters—specific differential phase (Kdp), differential reflectivity (ZDR), and dual-frequency ratio (DFR)—along with machine learning techniques, have opened new avenues for enhancing snowfall estimation. This research examines and contrasts three empirical formulas (S-Z, S-Z-DFR, and S-Z-Kdp) with five machine learning models, including Convolutional Neural Networks (CNN), Back Propagation neural network (BP), Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory network (LSTM), to evaluate their effectiveness in estimating snowfall rates and accumulations. The findings indicate that the S-Z-Kdpmodel surpasses conventional reflectivity-based approaches, attaining the highest precision with an R2of 0.93 and the lowest RMSE (5.15) for snowfall accumulation at the YPO site, whereas CNN and LSTM models deliver the most accurate results among the machine learning models, with CNN showing an R2of 0.98 and an RMSE of 3.06, and LSTM yielding an R2of 0.97 and an RMSE of 3.56. RF, while showing promise, tends to underestimate, especially in estimating snowfall accumulation. The results highlight the advantages of models utilizing Kdpand underscore the potential of machine learning techniques, especially CNN and LSTM, for capturing snowfall variability. This thorough assessment provides insights into incorporating advanced radar parameters and machine learning to enhance quantitative snowfall estimation, with practical implications for weather forecasting.

During three consecutive winter seasons, between December 2021 and April 2024, several ground-based wintry precipitation measurement instruments were deployed at the University of Connecticut’s main campus. The instruments included an assortment of K-band and W-band profiling radars and Ka-Ku band scanning radars, weighing, and tipping bucket pluviometers, laser disdrometers, high-speed and high-resolution cameras for quantitative precipitation measurement, weather stations, and an unmanned aircraft system for environmental variables. The goal of this field campaign is to provide a dataset for validating NASA Global Precipitation Measurement (GPM) products, and to examine the error characteristics of co-located ground-based instruments. In this manuscript, we present the instrument suite and discuss possible uses of this unique set of measurements for remote sensing applications.

A vertical profile of hydrometeor type will be available for DPR resolution in full swath and this new feature will be implemented in the next version (V8) of GPM DPR level 2 algorithm. A vertical description of the profiles of precipitation is a long-term goal of atmospheric research and precipitation science. Although GPM DPR has fine vertical resolutions in dual-frequency observations, most of the algorithms or products developed are 2 dimensional with either a "flag" or "type" (or etc.) on a 2-dimentional surface. The products developed by our team in the classification module allow us to have the potential to take a big step forward adding vertical profile of hydrometeors for DPR full swath data.

The objective of this study is to compare microwave brightness temperature observations performed by the Temporal Experiment for Storms and Tropical Systems (TEMPEST) Space Test Program-Houston 8 (TEMPEST- H8) with those from the Global Precipitation Measurement (GPM) Microwave Imager (GMI). This study utilizes TEMPEST-H8 and GMI observations over tropical cyclones (TCs). Brightness temperature (TB) observations from the TEMPEST-H8 165 GHz channel and the GMI 166 GHz horizontal polarization channel were used in the cross-comparison study. Three tropical cyclones, including Tropical Cyclone Batsirai, Typhoon Mawar and Hurricane Hilary, occurring over a variety of oceanic regions, were analyzed. The cross-comparison results from all three cases showed that TC size and rain band structures appear similar in TEMPEST-H8 and GMI observations. The average correlation coefficient (r) values between the observations from the two instruments are 0.85 for TC Batsirai, 0.88 for Typhoon Mawar and 0.93 for Hurricane Hilary. These results demonstrate that the TEMPEST-H8 small-satellite sensor, which is nearly identical to TEMPEST-D, performs similarly to that of a traditional science mission sensor, i.e. GPM GMI.

Coherent Doppler lidar (CDL) for measuring wind by transmitting frequency-modulated pulse or continuous wave was proposed and demonstrated. A theoretical study showed that, while typical CDL uses single-frequency short-duration pulse by which distance and velocity resolutions are in a trade-off relationship, the new CDL allows us to specify the two resolutions independently. By using a prototype which can work as both typical and new CDLs, observations to clear or cloudy sky were carried out. Analyses of the observed data revealed that the independency was realized. Specifically, about five times finer velocity resolution than typical CDL was confirmed with no sacrifice of distance resolution.

In the San Francisco Bay Area, precipitation occurs in the wintertime, mostly as rain. Wintertime rainfall can be further classified into cold or stratiform rain with a typical radar bright band signature and warm orographic rain with absence of a radar bright band. Vertical Pointing S-Band profiler radar and disdrometer measurements from two of NOAA’s Hydrometeorology Testbed (HMT) sites in California are used to study the differences in microphysical properties between these two types of rain and their implications in radar rainfall estimation. A methodology has been developed to discriminate non bright band (NBB) rainfall from bright band (BB) rainfall using reflectivity (Z) and differential reflectivity (ZDR) computed from disdrometer data. Delineating the two rainfall types in this way allowed for an algorithm to be applied to the radar scans to identify rainfall types and apply appropriate reflectivity based and specific differential phase (KDP) based rainfall estimators. Recently, a gap-filling X-Band weather radar with dual-polarization capabilities was deployed in the San Francisco Bay Area in Santa Rosa to aid in weather monitoring and provide high resolution Quantitative Precipitation Estimation (QPE) products. When applied to real radar observations, this method shows great potential for improving the QPE compared to traditional operational products which more often tend to underestimate rainfall in the California coastal region.

There are current critical needs of NASA, NOAA, and other agencies carrying out Earth environmental monitoring for higher-performance observing systems (lower noise, finer resolution, broader coverage, etc.) but also smaller, lower-cost sensing platforms that offer flexibility in how they are deployed and used. To achieve these ambitions, it is necessary to consider the observing system, comprising not only the sensor, but also the concept of operations, processing, and potential for collaborative and synergistic observations. Here we present a new approach that enables dynamic, data-driven sensing and provides a way to test and evaluate the overall end-to-end system performance in the laboratory prior to launch with realistic Earth scenes. Recent technology advances now enable the utilization of new sensing concepts that reconfigure the sensor in real time to adjust where they are looking, their dwell time, their spatial resolution, and depending on the platform, their geometrical vantage point. For example, at frequencies spanning approximately 10-100 GHz, phased array and reflectarray observations of wind (speed and direction), wide-swath polarimetric imagery, soil moisture and sea surface temperature, and atmospheric thermodynamic state that are deemed critical by NASA’s strategic planning are now possible. These measurements would all be improved by this work, since the sensor would be configured for maximum resolution, coverage, and dwell time for regions in the scene that exhibit the highest variability and would thus benefit the most from high-fidelity sensing. This approach also efficiently optimizes use of a fixed set of resources.Here we describe two new systems recently funded by the NASA Earth Science Technology Office (ESTO) to improve present capabilities for high-resolution atmospheric sensing from small satellite platforms. First, the Configurable Reflectarray Wideband Scanning Radiometer (CREWSR) is a high-resolution, lightweight, low-power multiband (23, 31, and 50-58 GHz) radiometer with a deployable scanning reflectarray. It is envisioned to be fielded on an ESPA-class small satellite platform, with a stowed volume that fits within a 0.61 m x 0.71 m x 0.97 m envelope. Once in orbit, the platform will deploy a large Reconfigurable Reflective Surface (RRS), as well as a multi-feed antenna connected to a multiband radiometer. These components allow for an electronically-scanned beam for radiometric Earth observation. CREWSR would operate with a single, linear polarization, but fully polarimetric operation is also possible in principle. The reflectarray is also compatible with radar use, thus enabling wide-swath radar from a small satellite.Second, there is a critical need to enable development, test, and evaluation of Versatile, Intelligent, and Dynamic Earth Observation (VIDEO), and one key enabler is the recent emergence of metamaterials for use in high-performance blackbody radiometric targets. These materials are very thin (~200 microns) and lightweight (tens of grams), allowing them to be easily scaled up to realize very large targets (> 1 m^2) to subtend an entire sensor field of regard during laboratory measurements. Furthermore, the thin planar structure of the metamaterials provides a relatively small thermal mass, thereby permitting the projection of thermal features with very high spatial frequency content into the sensor field of view at the subpixel level. We will produce a 50 cm x 75 cm (20" x 30") Radiometric Scene Generator (RSG) operating near 54 GHz with very large thermal contrast at the subpixel level for a typical spaceborne microwave radiometer full-width-at-half-maximum (FWHM) beam width in the range of 1-3 degrees. The RSG will be used for two purposes: (1) to project spatial features into the radiometer field of regard that can be detected and acted upon by the intelligent processing algorithms, and (2) to spatially encode spectral information in the atmospheric sounding band used for temperature profiling. The intelligent processing algorithm will utilize feature detection and machine learning techniques to recognize regions of interest in the atmospheric scene and cause the sensor to react to the scene characteristics by changing the sensor response function. In this presentation, we will provide an overview of these new technologies and discuss how they address current unmet needs for high-resolution sensing from small satellites in the critical frequency bands spanning 20-60 GHz. In this paper, we present recent design and simulation results for the CREWSR prototyping effort.

The principal objective of this study is to develop machine learning (ML) models, in particular a random forest (RF) classifier and an artificial neural network (ANN) regression model, for estimating surface rain rates on a global basis using brightness temperature (TB) observations from the Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) CubeSat. The accuracy of the models is assessed by comparing the estimated rain rates with the Integrated Multi-satellitE Retrievals for GPM (IMERG) final run rain rate product, which also serves as the ground truth for ML model development. The ML models are developed using a dataset consisting of TEMPEST-D observations of 12 tropical cyclones (TC) and the corresponding IMERG products from various locations around the globe, including the Atlantic, eastern/western Pacific and Indian Oceans. To evaluate the performance of the ML models, independent validation is conducted using Hurricane Isaac and Typhoon Hagibis. The structural similarity index measure (SSIM) is used to assess the similarity between the ML estimated rain rates and the IMERG products. For Hurricane Isaac, an SSIM score of 0.8 is achieved, indicating a strong resemblance to the IMERG product. Similarly, for Typhoon Hagibis, the SSIM score is 0.73, indicating quite good agreement with the IMERG rain rate.

Weather radars play a prominent role in remote sensing of the atmosphere. Various fields, such as meteorology and hydrology, rely on accurate weather radar data as input for their models. Different hydrometeors present during a weather event influence the amount of attenuation encountered by the radar signal. Attenuation correction for dual-polarization weather radar data is necessary to improve the radar products and get accurate measurements. Most of the existing attenuation correction research is associated with rain hydrometeors. Currently, research that addresses the attenuation correction of snow in weather radars is limited. Although it is known that attenuation of radar signals when it encounters rain is much greater than that for snow, attenuation for all hydrometeors needs to be addressed for accurate radar estimates. In this research work, the attenuation of different hydrometeors is studied using signal simulations. Various factors which influence attenuation, such as the elevation angle and particle size distribution, are considered, and the results are presented. An attenuation correction algorithm that uses the hydrometeor classification and specific differential phase products from the DROPS2.0 algorithm is introduced. Signal simulations are employed to obtain the relationship between specific attenuation and specific differential phase for different hydrometeors used in the proposed algorithm. The attenuation correction method is applied to X-band and Ku-band radar data. Path integrated attenuation of about 8 dB was observed in the snow case discussed from Ku band radar data. The method proposed for attenuation shows promising results at both frequency bands.

Extreme precipitation such as hail has raised interest due to its high impact to human activities. In the new version of GPM DPR algorithm (version 7), a hail product is developed to identify hail along a vertical profile. The novelty of this algorithm offers the potential for retrieving a uniform and homogeneous hail dataset on the global scale from spaceborne radar sensor. The algorithm is built upon the precipitation type index (PTI) developed for the GPM DPR. PTI has been shown to be effective in separating various precipitation types such as snow, graupel and hail profiles. The capabilities of this algorithm to capture hail are validated by analyzing hail observations from various space and ground sources. These include ground validation radar NEXRAD, GMI based hail identification and multiple scattering effect from Trigger module of DPR level-2 algorithm. The global scale analysis demonstrates the good performance of the hail algorithm in identifying the world-wide high frequency hail regions and seasonal transitions.

The Global Precipitation Measurement (GPM) core observatory spacecraft has continuously provided us valuable observations on a global scale since launch in year 2014. The GPM core satellite is equipped with a dual-frequency precipitation radar (DPR) operating at Ku- (13.6 GHz) and Ka- (35.5 GHz) band. DPR on board the GPM core satellite improves our knowledge of precipitation processes by providing greater dynamic range, more detailed information on microphysics, and better accuracies in rainfall and liquid water content retrievals [1]. Figure 1 illustrates a conceptual diagram for GPM DPR observing various types of precipitation.

The inversion of satellite-based passive and active atmospheric measurements from radiometer and radiometers requires accommodation of highly nonlinear, non-deterministic, and even uncertain and evolving relationships between the satellite data and parameters of interest. In addition, the limitations in vertical, horizontal, and temporal resolution further confound our use of satellite data. Focusing on the estimation of cloud and precipitation parameters such as surface rain rate, integrated liquid and ice water content, phase distribution, we can trace a number of current developments back to the initial research of Dr. Gail Skofronick-Jackson in the early 1990s. As a NASA Gradute Student Fellow, Dr. Skofronick-Jackson recognized the key problems of nonlinearity, statistical non-determinism, and wavelength-dependent spatial resolution limitations in her development of performance simulations of the NASA proposed NASA Mutiband Imaging Microwave Radiometer (MIMR) instrument [Skofronick-Jackson, 1995]. This proposed six-band passive instrument using a 2-meter antenna provide a range of precipitation sensitive spatial resolutions over the bands of 6, 10, 18 23, 37, and 89 GHz along with mixed channel sensitivities to liquid water path and integrated ice content. In order to accommodate these effects she a nonlinear, multispectral, statistical extension of the CLEAN algorithm used in radio astronomy was developed and tested [Skofronick-Jackson and Gasiewski, 2000]. This iterative algorithm, termed NMS-CLEAN, showed improvements over simple linear methods and became the basis for further simulations exploring for the first time the application of simple two-layer single-perceptron artificial neural networks (ANNs) both in radiometric and radar applications [Xiao and Chandrasekar, 1997]. Perhaps unsurprising by our current understanding of them these ANNs improved upon the retrieval accuracy attainable using even the NMS-CLEAN algorithm within the range of training data available. These developments using MIMR instrument specifications supported the precipitation science essential to the eventual development and launch of AMSR-E, which operated from 2002 to 2017 [e.g., Surussavadee and Staelin, 2008].

The technology of Synthetic Aperture Radars (SAR) is growing at a rapid pace in the last decade. The advancements in SAR opens doors to explore more scientific problems which were considered impactical in the past. The study of meteorological targets using spaceborne SAR platforms is an emerging field of study. In the past, spaceborne monostatic SAR for observing precipitation echoes was studied. In this paper, a bistatic architecture of the SAR system is considered for studying the precipitation target using signal simulations. The precipitation radar parameters on-board the GPM core satellite is considered for the simulation study. The architecture of the bistatic SAR considered is introduced and the various factors considered for a bistatic SAR is studied in detail. This simulation work currently being researched on shows the initial results. The results presented will aid in understanding the precipitation echoes observed from spaceborne SAR platforms.

The objective of this study is to cross-validate observations of the Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) CubeSat mission with those observed by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) [1] over precipitating systems. The purpose of this paper is twofold: first, to show consistency between TEMPEST-D and GPM/GMI, and second, if the measurements are consistent, to demonstrate the potential to enhance temporal sampling when the TEMPEST-D and GPM/GMI observations are merged. This paper demonstrates both objectives and shows good agreement between TEMPEST-D and GPM/GMI observations.

Storm nowcasting relies on reasonably fast sampled radar data, and deep learning (DL) can be used to harness this vast amount of data. Despite all the publications on this topic over the past five years, there are stillad hocassumptions and a lack of standardization. This work addresses aspects that have not yet been analyzed on the development of DL models for nowcasting systems, such as the effects of different history lengths or using non-convex metrics during the training phase. For example, we show that even if the loss function is varied, it does not significantly influence the predictions, and that the number of predicted frames has a significant impact. We used the experiments’ results to propose different models and compare their performance against other DL models. The results show that the proposed models outperform, in many aspects, the existing implementations.

Deep learning is emerging as a powerful tool in scientific applications, such as radar-based convective storm nowcasting. However, it is still a challenge to extend the application of a well-trained deep learning nowcasting model, which demands to incorporate the learned knowledge at a certain location to other locations characterized by different precipitation features. This article designs a transfer learning framework to tackle this problem. A convolutional neural network (CNN)-based nowcasting method is utilized as the benchmark, based on which two transfer learning models are constructed through fine-tune and maximum mean discrepancy (MMD) minimization. The base CNN model is trained using radar data in the source study domain near Beijing, China, whereas the transferred models are applied to the target domain near Guangzhou, China, with only a small amount of data in the target area. The influence of a varying number of target data samples on the nowcasting performance is quantified. The experimental results demonstrate that the deep transfer learning models can improve the nowcasting skills.

This paper presents some of the first nearly simultaneous observations between TEMPEST-D and RainCube, two CubeSat missions supported by NASA for on-orbit validation of technology for studying the Earth's atmosphere. This paper presents simultaneous observations by a Ka-band radar and multi-frequency millimeter-wave radiometers over precipitation systems, in three widely dispersed locations over the globe. The first storm was near Mexico's Pacific coast, whereas the second storm was over the South Pacific Ocean near the Solomon Islands, and the third storm was near Houston, Texas, USA. The comparisons showed good physical consistency between the TEMPEST-D and RainCube observations.

Traditionally, polarimetric radar-based rainfall estimates are derived through empirical parametric relations obtained from nonlinear regression between rain rates and simulated radar data. The performance of such empirical relations is highly dependent on the variations of raindrop size distribution. In real applications, such empirical algorithms often need to be adjusted for different climate regimes and/or different rainfall types, and we do not have a simple parametric expression linking radar observables to rainfall intensity. Even if one can eliminate all the random errors associated with radar measurements, the parameterization uncertainty inherent in the empirical relations is hard to reduce. In addition, it is difficult to estimate surface rain rates with the radar measurements aloft, especially during the precipitation events characterized by dramatically changing vertical structures (i.e., precipitation particle sizes and distributions are changing during the falling process). In this paper, a convolutional neural network-based machine learning approach is developed to estimate surface rainfall rate from multidimensional polarimetric measurements. This deep learning algorithm can extract the complex relation from high dimensional input space (i.e., radar data) to the target space (i.e., surface rain rate). Polarimetric radar observations and rain gauge data collected near Melbourne, Florida are utilized for demonstration. The trained model is also extended to the whole radar coverage domain to provide complete rainfall mapping. Independent verification results show that this deep learning model has superior performance to conventional fixed-parameter rainfall relations based on either single- or dual-polarization radar measurements.

Synthetic Aperture Radars (SAR) deployed on spaceborne platforms has been a successful tool for imaging the earth. The potential for using SAR for meteorological applications is not fully explored. In this work, the mathematical framework corresponding to weather observations from a SAR is briefly introduced. A spaceborne SAR simulation program is developed which accounts for backscattering from weather present in the radar resolution volume. The satellite parameters of the GPM core satellite is considered and simulations are carried out in stripmap and spotlight mode of SAR. It is observed from the results that the spectral width of the precipitation particles have a direct impact on the azimuth resolution. As expected the spotlight mode of SAR performs better than the stripmap mode of SAR and the results are presented. The simulation studies which are presented in this work are carried out for a monostatic SAR and will aid in understanding observations of precipitation using SAR.

The profile classification module in GPM DPR level-2 algorithm has developed various products using rich information from the dual-frequency observations. These products perform functions in rain type classification, melting layer detection, identifying surface snowfall as well as graupel hail (GH). Extensive evaluation and validation activities have been performed on these products and illustrate excellent performance. Extreme precipitation such as hail has raised interest due to its huge impact on society. In the coming new version of GPM DPR algorithm, we will introduce a new hail product to identify hail along a vertical profile. Different from the existing “flagGraupelHail” product, which combines graupel and hail together, the new product will separate them. It should be noted here that presence of hail in the storm does not mean hail on the ground.

This paper presents a machine learning model to estimate surface rainfall from TEMPEST-D observations. An artificial neural network (ANN) was chosen to build the rainfall estimation model from TEMPEST-D measurements. TEMPEST-D brightness temperature (TB) observations performed at five frequencies (i.e. 87, 164, 174, 178 and 181 GHz) were used as inputs, and the Multi-Radar/Multi-Sensor System (MRMS) radar-only rain rate product at the surface was used as ground truth and target to train the ANN model. A spatial alignment algorithm was developed to align the TEMPEST-D observed storm with the storm measurement from ground radar. The training data set was generated from 14 storm events observed simultaneously by the ground radar network and TEMPEST-D over the continental U.S. Two storm events were used for independent testing. The testing showed that estimated rainfall matched well with the MRMS surface rainfall product in terms of rainfall intensity, area, and precipitation system pattern. The structural similarity index measure scores for the two independent test cases are 0.72 and 0.81.

Historical data discovery is a challenging task for any study in radar meteorology when the region of interest is recorded in weather observations. Weather radars exist in overlapping networks around the globe and are observing the atmospheric conditions around the clock. The observations are stored according to date, time, and location, as opposed to an indexing scheme based on the phenomena present in the scans themselves. Performing feature-based searches in these voluminous data sets is, at current, impossible. This research seeks to enable users seeking to study such phenomena as a mechanism for locating events of interest by leveraging recent progress from the fields of transfer learning and computer vision. Specifically, this work illustrates a methodology for performing image classification of precipitation regimes on colormapped weather radar scan images, as opposed to the raw data itself. This system reduces the data needed to perform this classification by two orders of magnitude, increasing throughput and democratizing usage of the deep learning tools for this task by allowing training and testing of the models on modest compute systems.

A GPM level-2 product of “flagGraupelHail” is implemented in the Experiment Module [1]. It is a Boolean product available for each Ku and Ka band matched precipitation profile of the dual-frequency precipitation radar DPR. The flag identifies whether graupel or hail exists along the vertical profile. Figure 1 is a cartoon plot illustrating this product. As shown in figure 1, for each Ku and Ka band matched footprint, graupel or hail exists when the flag equals 1, and 0 represents graupel or hail not existent. This identification algorithm is built upon a precipitation type index (PTI) defined in (1).

Future drone and air taxi services will take place in the lowest parts of the atmosphere. In the United States, this region is vastly under sampled by existing atmospheric sensing systems and in-situ sensors. As a result, current and future vehicle operators may lack situational awareness of changing weather conditions, bringing uncertainty to aerial ride sharing, cargo delivery and emergency services, impacting up-time, business viability and safety of both vehicles and resources on the ground. This paper presents interviews with drone and air taxi operators on their weather related needs and current sources of weather information as a first step in defining requirements for integrative remote sensing solutions. The solution system is being prototyped and demonstrated in North Texas in collaboration with universities, government agencies and drone operators.

Recently, an X-Band radar with dual-polarization capabilities was deployed in Santa Rosa, California to aid in monitoring precipitation and to provide high resolution QPE by better understanding the rainfall processes in this region. The dominant type of rainfall occurring in this region is stratiform and it can be further classified into cold bright-band rain and warm orographic rain with absence of bright-band. S-Band profiler and disdrometer measurements from two NOAA Physical Sciences Division sites are used to study the microphysics of these two rainfall types. A methodology has been developed to discriminate these rainfall types using dual-polarization radar variables and apply appropriate rainfall estimators according to the rainfall type. When applied to operational radar data, the method shows great potential for improving the QPE estimation compared to traditional operational QPE products which more often tends to greatly underestimate rainfall in the California coastal region.

Remote sensing of precipitation is critical for regional, continental, and global weather, water, and climate research. This study develops a machine learning mechanism to link between point-wise rain gauge measurements, ground-based and spaceborne radar reflectivity observations. Two neural network models are designed to construct a hybrid rainfall system, where the ground radar is used to bridge the scale gaps between rain gauge and satellite. The first model is trained for ground radar using rain gauge data as target labels, whereas the second one is for spaceborne radar using ground radar estimates as training labels. Data from TRMM Precipitation Radar (PR) and GPM Dual-frequency Precipitation Radar (DPR) are utilized to illustrate the application of this hybrid rainfall system. Validation using independent ground-based observations as well as the standard PR and DPR products demonstrates the promising performance and generality of this innovative machine learning algorithm.

Microphysical retrievals in snow storms is a topic of active research. It gives us the opportunity to map the weather events correctly and make future predictions better. The primary objective of the ICE-POP field campaign in South Korea was to capture and understand winter precipitation. The dual-frequency, dual-polarization Doppler weather radar (D3R) radar along with several other weather remote sensing instruments were deployed in the campaign. Since D3R is a dual-frequency radar operating at Ku and Ka frequency bands, the dual frequency ratio (DFR) derived from them can be used to study ice hydrometeors. The DFR varies based on the axis ratio of the ice hydrometeor and this information can be used to link the amount of nonsphericity of the ice hydrometeors. The trend of reflectivity at Ku band versus DFR varies for different hydrometeors based on which we can infer the type of ice particle present. Simulations studies carried out using T-matrix method were used to study the changes in DFR when properties of the ice hydrometeor varied. Observations from D3R and other ground instruments are used to interpret the properties of ice particles based on the theoretical study.

Radar quantitative precipitation estimation (QPE) and forecast (QPF) over the coastal mountain area in Northern California can be very challenging due to the complex precipitation microphysics induced by land-ocean interaction in the coastal regions and orographic enhancement in the mountainous areas. A number of previous studies have documented the precipitation characteristics in this complex domain using data collected from remote sensors such as S-band profiling radar (S-PROF) and/or in situ measurements of raindrop size distributions (DSD) at the surface. This study extends the earlier work by including new DSD measurements both from the valley (Santa Rosa) and the mountain (Middletown), to characterize the orographic enhancement of precipitation and diagnose the bulk microphysical properties of rainfall. Two major rainfall types in this area, namely, non-bright band and bright band rain, are identified based on the observations from S-PROF. Several DSD and rainfall parameters are derived from the measured raindrop spectra, and the microphysical characteristics are investigated for different rainfall types and terrains (i.e., valley vs mountain).

In order to improve the quality of the dual-polarization radar data it is important to account for attenuation of the radar signal and correct it. Radars operating at very high frequencies are more affected by attenuation. Attenuation correction for radar data is necessary to improve the radar products and get accurate results. Most of the prior attenuation correction research for weather radars is primarily done for rain. Currently, limited research results are available for attenuation correction for ice hydrometeors especially in high frequency bands. An attenuation correction method using the hydrometeor classification and specific differential phase products from the DROPS2.0 algorithm for Ku band radars is presented in this work for use in dual-frequency, dual-polarization Doppler (D3R) Ku band radar. T-matrix simulations for the different hydrometeors are used in the algorithm to get the relationship between specific attenuation and specific differential phase. The main advantage of this method is that the attenuation correction can be done for ice hydrometeors as well. Rain and snow data from the D3R radar is considered here to show the performance of the proposed attenuation correction method. From the results it can be observed that the attenuation correction using this method works well and show promising results.

In this paper, we describe the evolution of a pair of polyphase coded waveform for use in second trip suppression in a weather radar. The polyphase codes were designed and tested on NASA weather radar. The NASA dual frequency, dual polarization Doppler radar (D3R) was developed primarily as a ground validation tool for the GPM satellite dual frequency radar [1]. Recently, the D3R radar was upgraded with new versions of digital receiver hardware and firmware, which supports larger filter lengths and multiple phase coded waveforms, and also newer IF sub-systems (see [2], [3] and [4]). It has enhanced the capabilities of this radar manifolds.

Precipitation is a critical element in global atmospheric circulation and ecosystem. However, it is challenging to monitor the global or even continental scale precipitation features using ground-based rain gauges due to the spatial coverage limitations. The Geostationary Operational Environmental Satellite-R (GOES-R) series provide new opportunities for continuous observation of precipitation at large scales. This paper presents a detailed cross-comparison of quantitative precipitation estimates (QPE) between GOES-R (GOES-16) and the ground radar-based rainfall product derived from the National Ocean and Atmospheric Administration's (NOAA) multi-radar multi-sensor (MRMS) system over the continental United States (CONUS). The precipitation detectability of GOES-R is investigated using MRMS products as references. In addition, uncertainties associated with the GOES-R precipitation estimates are quantified in different regions over the CONUS.

Space-based precipitation products are often used for regional and/or global hydrologic modeling and climate studies. A number of precipitation products at multiple space and time scales have been developed based on satellite observations. However, their accuracy is limited due to the restrictions on spatiotemporal sampling of the satellite sensors and the applied parametric retrieval algorithms. Similarly, a ground-based weather radar is widely used for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization capability and urban scale deployment of high-resolution X-band radar networks. Ground-based radars are often used for the validation of various spaceborne measurements and products. This article introduces a novel machine learning-based data fusion framework to improve the satellite-based precipitation retrievals by incorporating dual-polarization measurements from a ground radar network. The prototype architecture of this fusion system is detailed. In particular, a deep learning multi-layer perceptron (MLP) model is designed to produce the rainfall estimates using the geostationary satellite infrared (IR) data and low earth orbit satellite passive microwave (PMW)-based retrievals as inputs. The high-quality rainfall products from the ground radar network are used as the target labels to train this MLP model. An urban scale demonstration study over the Dallas-Fort Worth (DFW) metroplex is presented. In addition, the Climate Prediction Center morphing technique (i.e., CMORPH) is adopted for preprocessing of the satellite observations. Rainfall products from this deep learning system are evaluated using the standard CMORPH products. The results show that the proposed data fusion framework can be used for generating accurate precipitation estimates and could be considered as an alternative tool for developing future satellite retrieval algorithms.

Effective utilization of the changing precipitation microphysics in real-time radar quantitative precipitation estimation (QPE) is challenging, which requires dynamic adjustment of the radar reflectivity (Z) and rain rate (R) relations. This article develops and demonstrates two dynamic radar rainfall approaches using 16 Doppler weather radars and 4579 surface rain gauges deployed over the Eastern Jiang Huai River Basin (EJRB) in China. Both approaches are derived based on the radar-gauge feedback mechanism. Although the Z-R relations in both approaches are dynamically adjusted within a precipitation system, one is using a single global optimum (SGO) Z-R relation, while the other is using different Z-R relations for different storm cells identified by a storm cell identification and tracking (SCIT) algorithm. Four precipitation events featured by different rainfall characteristics are investigated to evaluate the performances of various QPE methodologies. In addition, the shortterm vertical profile of reflectivity (VPR) clusters is extensively analyzed to resolve the storm-scale characteristics of different storm cells. The evaluation results based on independent gauge observations show that both rainfall approaches with dynamic Z-R relations perform much better than the fixed Z-R relations. The adaptive approach incorporating the SCIT algorithm and real-time gauge measurements has the best performance since it can better capture the spatial variability and evolution of precipitation.

This article describes the design and implementation of intrapulse polyphase codes for a weather radar system. Algorithms to generate codes with good correlation properties are discussed. Thereafter, a new design framework is described, which optimizes the polyphase code and corresponding mismatched filter, using a cost/error function, especially for weather radars. It establishes the performance of these intrapulse techniques with specific application toward second trip removal. The developed code is implemented on NASA D3R, which is a dual-frequency, dual-polarization, Doppler weather radar system.

Short-range forecasts (nowcasts) of rainfall facilitate providing early warning of severe rainfall and flooding, which is particularly important in densely populated urban areas. Nowcasts are conventionally obtained by the extrapolation of radar echoes from a constant altitude plan position indicator (CAPPI) or lowest-angle plan position indicator (PPI). Lacking a model for growth or decay, this approach has a limited ability to forecast the summertime convective storms. In a previous attempt to address this shortcoming, called RadVil, the predicted surface rain rate is obtained from mass balance equations of vertically integrated liquid (VIL) retrieved from volumetric reflectivity measurements. Predicting the growth and decay has also been attempted by using an autoregressive (AR) model, which led to the development of spectral prognosis (S-PROG). A novel combination of these two methodologies, called ANVIL, is proposed. In this approach, the growth and decay of VIL are modeled by an autoregressive integrated (ARI) process. It is shown that the predictability of growth and decay is scale-dependent. Thus, the key idea of ANVIL is to decompose the VIL into multiple spatial scales and apply a separate ARI model to each scale. The operational feasibility of ANVIL is evaluated using the Next-Generation Radar (NEXRAD)/WSR-88D radar that covers the Dallas-Fort Worth metropolitan area. The evaluation is done using ten convective events in 2018 and 2019. Using several verification metrics, it is shown that ANVIL has up to 25% improved skill compared to conventional nowcasting techniques. The improvement is consistent for a wide range of spatial scales (1-11 km) and rain rate thresholds (5-20 mmh-1).

This paper describes the deployment and features of CSU-CHIVO radar during the RELAMPAGO campaign in Argentina. Intercomparison with GPM-DPR is done using Volume Matching. Vertical profile analysis of storms is also shown and a list of GPM overpasses and summary of the tallest storms during the campaign are also included. The results show that CHIVO agree well with GPM in terms of reflectivity and microphysical structure of clouds and show the value of CHIVO for ground validation.

TEMPEST-D is a 6U CubeSat with a payload of a 5-channel millimeter wave cross-track scanning radiometer. It is a technology demonstration mission with requirements of 2 K precision and 4 K absolute calibration. Since its deployment from the International Space Station in July of 2018, the TEMPEST-D team has focused efforts on validating the calibration of the instrument by comparing with similar well-calibrated operational sensors. Such comparisons have shown the instrument to be very well calibrated and stable, with very low noise, well within the requirements. Efforts have subsequently focused on demonstrating that the data can be used for various science applications, including water vapor and cloud water/ice retrievals and data assimilation.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was deployed in the International Collaborative Experiment during the PyeongChang 2018 Olympics and Paralympic winter games (ICE-POP 2018). The radar captured many rain and interesting snowfall events during the deployment. In this work, the snowfall events are studied in detail to interpret the microphysics from a radar's perspective. Interesting snow studies such as detecting the presence of pristine oriented ice crystals in the mixed phase snow clouds will be discussed in detail.

The increase in renewable energy has led to the expansion of wind farms in various locations and terrain. Accordingly, more wind turbines create more complex clutter targets for weather radars. Over the last few years, researchers have presented various methods to mitigate this problem of wind turbine clutter interfering with atmospheric observations. This paper investigates different methods as proposed by Keränen at el. [1], Y. Li et al. [2] and S.P.Sira et al. [3]. Performances of both fuzzy logic approach [1] and Generalized Likelihood Ratio Test [2] to detect the range gates affected by turbine clutter are discussed. There after algorithms based on spatial averaging and least squares approximation are proposed to mitigate the clutter while effectively maintaining the precipitation signal. The solutions presented are computationally efficient and works with common scanning strategies. An evaluation approach is also presented which combines clear air wind turbine data and precipitation data to provide a quantitative reference for the efficacy of the wind turbine clutter filtering.

One of the limiting factors in any research into meteorological phenomena is in locating data where the phenomena of interest is present. In the case of weather radar data, it is often required to aggregate notes, storm reports, and historical references to cross reference the vast repositories of image data available to the researcher, allowing the researcher to then manually examine the appropriate scans. In this work we present a method for classifying meteorological regimes by applying computer vision techniques, including a convolutional neural network, using weather radar imagery from the CASA DFW X-band radar network in Dallas-Fort Worth, TX. This work demonstrates the utility and validity of using Transfer Learning to apply image insights gained from careful and exhaustive training on photographic images to an unrelated data domain and achieve valuable results. This strategy is expected to assist scientists in reducing time and effort in data discovery and reducing time to science.

The NASA dual-frequency dual-polarization Doppler radar (D3R) serves as the ground validation tool for the Global Precipitation Measurement (GPM) mission. It is important to maintain the quality of the data from the radar. The D3R which operates at high frequency bands, has an unambiguous range of approximately 40 km. This unambiguous range of the radar is more prone to encounter second trip echoes in the received signal. In this work the method to detect and estimate the second trip signal parameters using the random phase codes is described. The D3R's Ku band data is considered here to show the performance of the random phase codes to detect and estimate the second trip echoes in the received signal.

In the current GPM DPR level-2 algorithm, there is a surface snowfall identification flag that detects surface snowfall using dual-frequency reflectivity profile from DPR. As shown in figure 1, for each Ku and Ka band matched footprint, surface snowfall exists when the flag equals 1, and 0 represents surface snowfall not existent. This identification algorithm is built upon a precipitation type index (PTI) defined in (1).

A simulator of weather radar signals can be exploited as a useful reference for many applications, such as weather forecasting and nowcasting models or for training artificial intelligence systems designed to optimize the trajectory of aircrafts with the purpose to reduce flight hazard and fuel consumption. However, before being used, it must be accurately examined under different operating conditions, in order to evaluate the consistency of the outputs produced. In this paper, we present a validation procedure for a newly developed polarimetric weather radar simulator (POWERS). The goal is to assess the ability of the simulator to deal with any kind of input data, be they simulated and real raindrop-size distributions, or outputs generated by numerical weather prediction models. Three different approaches are proposed, each providing a connection between meteorological inputs and the radar observables simulated by POWERS. The analysis is carried out in the case of rainfall, both at S- and X-bands.

The Global Precipitation Measurement (GPM) mission Core Observatory satellite, carrying the first space borne dual-frequency precipitation radar (DPR) operating at Ku/Ka band, and the GPM microwave imager (GMI), was launched on 27 February 2014 [1]. The GPM satellite extend the observation range attained by Tropical Rainfall Measuring Mission (TRMM) from tropics to most of the globe and provide accurate measurement of rainfall and snowfall. The observation of global precipitation plays an important role in improving the capabilities of weather, climate, and hydrological predictions.

A comparison between GPM Dual-frequency Precipitation Radar and ground-based radars located in South America and the Caribbean is presented. The analysis compares radar variables from both system during overpasses of GPM over ground-based radars in the region of interest. Attenuation and bias correction is performed to ground radar data. The results show the potential of GPM to calibrate and monitor weather radars and subsequently using them for ground validation in Latin America and the Caribbean.

The international collaborative experiment held during the PyeongChang 2018 Olympics and Paralympic winter games (ICE-POP 2018) is a large field campaign in which science instruments from across the world were brought together. This campaign aims to give a better understating of the dependence of precipitation on terrain features and improve the meteorological models for such complex terrain features such as in South Korea. The NASA dual-frequency, dual-polarization, Doppler radar (D3R) which operates in Ku and Ka frequency bands is a key instrument in this campaign for measurements of snow. This paper gives a overview of the deployment and performance of the D3R during the ICE-POP campaign and sample observations will be presented.

The Cloud-Hosted Real-Time Data Services for the Geosciences project (CHORDS) seeks to enable researchers collecting data in field experiments by providing a lightweight, cloud-based, and easy-to-use way to transfer, store, and visualize data in real-time from multiple time series sensor nodes. In preparation for the upcoming release of version 1.0, a set of specifications has been agreed upon for the ingest, storage, standards, and visualization of real-time image data, such as weather radar scans. This work serves to provide an update on CHORDS development and use cases, specifically focusing on the addition of two-dimensional image data and its applications in real-time visualization of the complex observation space, along with its integration with existing time series sensor functionality.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was developed to support development of algorithms and validation for the global precipitation measurement (GPM) missions dual-frequency precipitation radar (DPR). The D3R has participated extensively in various field campaigns in North America with geographic features covering both summer and winter climatic regimes. During the year 2017, D3R went through a major upgrade, specially with the digital receiver and waveform generation subsystems. In this work, the D3R systems upgrade will be discussed with a focus on key features of the new system. The new flexible architecture will enable new research capabilities that will be described.

The Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) instrument is a five-frequency millimeter-wave radiometer capable of observing thermal radiation from the Earth at 89, 165, 176, 180, and 182 GHz. The direct-detection architecture of the radiometer reduces its power consumption and eliminates the need for a local oscillator and mixer, reducing complexity. The instrument includes an ambient blackbody calibration target and a scanning reflector. The reflector rotates to scan the antenna beams in the cross-track direction so that the TEMPEST-D feed horn and receiver view first the blackbody calibration target, then the Earth over a range of nadir angles from −45ºto +45º, and finally the cosmic microwave background radiation at 2.73 K. This enables precision end-to-end calibration of the millimeter-wave receivers every scan period. The TEMPEST-D millimeterwave radiometers are based on 35-nm InP HEMT MMIC low-noise amplifiers and related technology developed under extensive investment by the NASA Earth Science Technology Office (ESTO).

The Temporal Experiment for Storms and Tropical Systems (TEMPEST) mission concept consists of a constellation of five identical 6U-Class nanosatellites observing at five millimeter-wave frequencies with five-minute temporal sampling to observe the time evolution of clouds and their transition to precipitation. The TEMPEST concept is intended to improve understanding of cloud processes, by providing critical information on the temporal development of cloud and precipitation microphysics and by improving our understanding of some of the largest sources of uncertainty in cloud process models. TEMPEST millimeter-wave radiometers are able to perform observations inside the cloud to observe changes as the cloud begins to precipitate or ice accumulates inside the storm. The TEMPEST Technology Demonstration (TEMPEST-D) mission is intended to reduce risk and demonstrate measurement capabilities for 6U-Class satellite constellations for Earth Science. The capabilities to be demonstrated include differential drag maneuvers to provide desired time separation in a 6U-Class satellites constellation. In addition, TEMPEST-D millimeter-wave radiometers will be cross-calibrated with space-borne radiometers with similar frequency channels. TEMPEST-D will provide radiometric observations at five millimeterwave frequencies from 89 to 183 GHz using a low-power, compact instrument that is highly suitable for deployment on 6U-Class satellites.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) [1] was conceived and developed to support ground validation (GV) operations of the Global Precipitation Measurement (GPM) mission [2]. The D3R operates in the same frequencies bands, Ku- and Ka-band, as GPM's dual-frequency precipitation radar enabling direct comparisons of microphysical observations of precipitation. The D3R radar is shown in Figure 1. To support the GPM GV mission, D3R substantively participated in four field campaigns in North America with diverse geographic features covering both winter and summer conditions.

In the post launch era of GPM (Global Precipitation Measurement) mission, classification module for DPR (dual-frequency radar) level 2 algorithm has been under extensively evaluation and continues to show promising results [1][2]. New feature such as surface snowfall identification has been added to the newly released version 5. In this paper, a brief review is provided for rain type classification and melting layer detection in dual-frequency classification module. More emphasis is put on the evaluation of the surface snowfall identification algorithm. Ground validation cases are shown with both NEXRAD radars and NPOL radar during OLYMPEX campaign. Further improvements are focused on developing algorithms to identify graupel and hail profiles using dual-frequency measurements.

The operating state of a wind turbine determines the characteristics of its time-varying radar signature. We demonstrate this using high-rate state telemetry from the NREL CART3 wind turbine to suppress its radar signature. We also consider the update frequency and accuracy requirements that are necessary to directly use state telemetry for the estimation or suppression of the radar signature of a wind turbine.

The Olympic Mountains Experiment (OLYMPEx) is a ground validation (GV) field campaign conducted, in part, to support the GV efforts of the U.S./Japan Global Precipitation Measurement (GPM) mission. The primary goal of OLYMPEx is to validate precipitation measurements in mid-latitude frontal systems moving from ocean to coast to mountains and to determine how remotely sensed measurements of precipitation by GPM can be applied to a range of hydrologic, weather forecasting and climate data. A variety of instruments were deployed for OLYMPEx, including ground radars, disdrometers, rain gauges, and airborne radars. This paper characterizes the microphysical properties of precipitation over the OLYMPEx regime, using observation from NASA dual-polarization radars and disdrometers. Particularly, statistical distribution of raindrop size distribution (DSD) parameters measured by a number of disdrometers is investigated. The dual-polarization radar observations at S- and Ku-band frequencies are used to classify precipitation types, and quantitatively estimate rainfall intensity.

Urban flash floods can occur immediately after heavy rainfall due to urban characteristics including impervious surface and complex drainage systems. Early warnings of such events, even by 5-10 min, are crucial in terms of protecting personal and property safety. Since 2012, the center for Collaborative Adaptive Sensing of Atmosphere (CASA), in collaboration with the National Weather Service (NWS) and North Central Texas Council of Governments (NCTCOG), has initiated the effort to deploy a dense radar network in Dallas-Fort Worth (DFW) for urban weather disaster detection and mitigation [1, 2]. The DFW Metroplex is one of the largest inland metropolitan areas in the U.S., and is also among the fastest growing major urban areas in the country. Every year the DFW area experiences a wide range of natural hazardous weather events including high wind, flash flood, tornado, and hail, etc. [2]. It is an ideal location to demonstrate the application of dense radar network for urban weather sensing and disaster management. As shown in Figure 1, centered in the DFW urban remote sensing network is the deployment of a NWS S-band radar system and eight dual-polarization X-band radars that can provide coverage to most of the 6.5 million people in this region. Based primarily on these radars, a number of product systems have been developed for real time warning operations, including the real-time multiple Doppler wind retrieval system [3], quantitative precipitation estimation (QPE) [4, 5, 6] and nowcast system.

This paper introduces an algorithm of radar quantitative precipitation estimation (RQPE) derived from a S-band dual-polarization weather radar network over southern China. The observations obtained from Zhuhai radar was used to demonstrate the performance of the RQPE algorithm. This initial regional study on RPQE in southern China provides reference on RPQE algorithm over China in near future when all weather radars will be upgraded with dual-polarization capability throughout China.

The San Francisco Bay Area is home to over 7 million people, the fifth largest population center in the United States. This region also supports one of the most prosperous economies in the U.S. A recent report by the State of California's Department of Water Resources has emphasized that the Bay Area is at risk of catastrophic flooding [1]. Monitoring heavy rainfall events and mitigation of their associated negative impacts are critical for protecting life and property in this region. To this end, accurate quantitative precipitation estimation (QPE) and forecast (QPF) are required to provide forecasters with sufficient understanding of rapidly changing weather conditions, and allow them to issue timely watches and warnings.

Advances in sensing, hardware, and wireless communications are allowing for an ever-increasing array of previously unmeasured weather phenomena at large scales. Integrating these new data with existing technologies and paradigms is of tantamount importance, in order to be able to respond quickly to emerging weather phenomena such as flash floods, tornadoes, and hail storms in real-time. Currently, integrating the massive amount of available data from various types of sensors, including rain gauges, hail sensors, radar, UAV, etc, is challenging and at times in possible, to analyze rapidly developing systems in real-time. This paper addresses this problem and presents a concept for integrating real-time weather radar data with ground sensors using CHORDS portals, allowing for the integration of large volumes of data in real-time using cloud computing technologies.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) is a weather radar operating at 13.91 GHz (Ku-band) and 35.56 GHz (Ka-band). The operational range of the D3R is 40 km, this relatively short operating range, along with the high sensitivity of D3R, are susceptible to increased observation of range ambiguous echoes, also referred to as “second-trip echoes”. In this work, by leveraging a staggered pulse repetition period and random transmitted phase codes, two methods are developed to detect and dealias the second trip contamination for the operational use with the D3R. Using these methods, the overlapping echoes are separated, and unambiguous range of the D3R is increased. Weather signals are simulated and used to quantitatively characterize the performance of moment estimator over a wide range of realistic weather scenarios. The D3R's Ku-band observations are presented to demonstrate the performance of algorithm with and without overlapping echoes from different ranges.

The NASA dual-frequency, dual-polarization, Doppler radar (D3R) is an important ground validation tool for the global precipitation measurement (GPM) missions dual-frequency precipitation radar (DPR). In this work, we start with the hardware modifications done to accomplish this upgrade, although the focus is not much on the hardware modifications. Overall system architecture is presented. The focus is more on the system sensitivity and spatial resolution. Also, the performance of the pulse compression waveforms is presented and compared with simulated results. Various trade-offs are discussed in context of range resolution, sensitivity and side lobe performance of these waveforms.

Currently, to observe entire Korean peninsula, two operational S-band radar networks have been operated by the KMA and the MOLIT. In this paper these two radar networks will be described in aspects of their goals and collaboration. In addition, two research X-band radar networks which will be deployed in Seoul and western sea side of Korean peninsula will be introduced.

The atmospheric science community is interested in using small satellites called CubeSats to provide new capabilities for global observations of temperature, humidity, clouds and precipitation. The use of CubeSats is steadily increasing and offers a less expensive (compared to traditionally-sized satellites) means of collecting atmospheric data to augment existing weather observations and provide additional global data to increase the fidelity of existing climate models. Due to the increased use of CubeSats, and the importance of the atmospheric data they are designed to collect, the risks need to be well understood and mitigated to the maximum extent possible. The atmospheric science community's interest in using CubeSats poses the question of what size CubeSat is sufficient to collect the required atmospheric data. The benefits and risks involved in using different sizes of CubeSats was analyzed along with representative cloud and precipitation data to confirm that system-level instrument requirements are achievable within a CubeSat form factor.

The Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) instrument is a five-frequency millimeter-wave radiometer capable of observing thermal radiation from the Earth at 89, 165, 176, 180, and 182 GHz. The direct-detection architecture of the radiometer reduces its power consumption and eliminates the need for a local oscillator and mixer, reducing complexity. The instrument includes an ambient blackbody calibration target and a scanning reflector. The reflector rotates to scan the antenna beams in the cross-track direction so that the TEMPEST-D feed horn and receiver view first the blackbody calibration target, then the Earth over a range of nadir angles from -45° to +45°, and finally the cosmic microwave background radiation at 2.73 K. This enables precision end-to-end calibration of the millimeter-wave receivers every scan period. The TEMPEST-D millimeter-wave radiometers are based on 35-nm InP HEMT MMIC low-noise amplifiers and related technology developed under extensive investment by the NASA Earth Science Technology Office (ESTO).

High resolution, networks of X-band radars can improve severe weather warning operations by observing the lower troposphere at very high spatiotemporal resolution. X-band networks provide unique information on storm features that complement existing radars such as NEXRAD and TDWR. In this paper, we examine the warning benefits of these small radars by looking at the performance of a network of 7 X-band CASA radars in the Dallas Fort Worth Metroplex, linked to real-time product generation and decision-making. By evaluating two severe weather episodes, a squall line and a mesoscale convective system, we begin to identify the strengths and weaknesses of the X-band radar networks, and propose future benefits to warning decision making.

The Temporal Experiment for Storms and Tropical Systems (TEMPEST) mission concept consists of a constellation of five identical 6U-Class nanosatellites observing at five millimeter-wave frequencies with five-minute temporal sampling to observe the time evolution of clouds and their transition to precipitation. The TEMPEST concept is designed to improve the understanding of cloud processes, by providing critical information on the time evolution of cloud and precipitation microphysics and by improving our understanding of the largest sources of uncertainty in cloud models. TEMPEST millimeter-wave radiometers are able to perform observations inside the cloud to observe changes as the cloud begins to precipitate or ice accumulates inside the storm. The TEMPEST Technology Demonstration (TEMPEST-D) mission will be deployed to demonstrate measurement capabilities required for a constellation of 6U-Class nanosatellites to directly observe the temporal development of clouds to understand the conditions that control their transition from non-precipitating to precipitating clouds. TEMPEST-D will provide observations at five millimeter-wave frequencies from 89 to 183 GHz using a single compact instrument that is well suited for the 6U-Class architecture.

A new method for three-axis wind field retrieval on multiple radar environment is proposed. The proposed method is designed especially to retrieve z-axis velocities in low altitudes with high accuracy by considering spatial correlation of wind velocities. A numerical simulation showed its advantage that the proposed method estimated z-axis velocities at a low altitude of 1500 m where the traditional method output an ambiguous wind field. It is expected that, even at low altitudes, the proposed method retrieves updrafts which are a key sign of thunderstorm initiation.

Unwanted radar echoes, colloquially referred to as “clutter,” impede the mission effectiveness of radar systems. Depending on radar's application, clutter examples can include weather, buildings, vegetation, and more. Techniques to mitigate clutter and improve the performance of radar systems are continuously being developed and refined. In this paper, wind turbines used for commercial power generation, which present a Doppler velocity signature that is time-varying, are considered a source of radar clutter. A wind turbine clutter mitigation technique is developed for fixed-pointing weather radar applications, approximating the turbine's radar signature as a cyclostationary process. The cyclostationary model for the wind turbine and the suppression technique is then validated using observations of wind turbines and precipitation.

Wind turbine observations and characterization efforts have treated the wind turbine as a noncooperative target. Similarly, suppression of the turbine's radar signature has been considered without the aid of state information from the wind turbine under observation. In this paper, X-band radar observations of a utility-scale wind turbine, with detailed turbine state telemetry, are investigated. From scattering theory, the wind turbine's physical structure has a deterministic radar cross section for a given observation geometry. Using the telemetry, the variation in the turbine's signature is considered over a range of operating states. The deterministic nature of a turbine's signature is demonstrated from radar observations, and a model is developed to isolate it. The turbine's radar signature, as it relates to changes in the operating state, is discussed with the intent of enabling future suppression techniques.

The optimal design of pulse compression waveform/filter pairs for use with near-nadir spaceborne radar in low earth orbit for the observation of clouds and precipitation is discussed. An optimization technique is introduced that considers performance metrics specific to the remote sensing of clouds and precipitation from such platforms. Specifically, the sensitivity of the radar to precipitation and clouds is maximized as close to the ground as required. The sensitivity of the radar near the surface is typically limited by the pulse compression range sidelobes from the surface's echo. Optimization of the waveform/filter pair's performance is facilitated by a time-domain radar scattering model to simulate radar reflectivity range profiles. The presented radar-scattering model accounts for the radar's configuration constraints and platform motion, as well as the spatial distribution and relative motion of the scatterers. In this paper, the optimization of both linear frequency modulation (LFM) and nonlinear frequency modulation (NLFM) waveforms is considered. It is demonstrated that the LFM waveforms provide superior performance over NLFM waveforms for application subject to unmitigated Doppler shifts.

In this paper, the development of a polarimetric phased array weather radar, which consists of a dual-polarized antenna with 2-D circular planar phase-array elements, is discussed. The radar is capable of measuring the 3-D rainfall distribution in less than several tens of seconds. Digital beamforming (DBF) is an important component in the development process of the phased array radar. In this paper, precipitation radar signal simulations are performed taking into consideration radar concepts in order to discuss the estimation accuracy of polarimetric precipitation profiles (differential reflectivity, specific differential phase, and copolar correlation coefficient) with two DBF methods that are based on Fourier and minimum mean-square error (MMSE) methods. A comparison of the performance of the two methods indicates that MMSE is superior in accuracy because of the effect of a stable and a robust main lobe and adaptively suppressed side lobes. MMSE also provides precipitation measurements eliminating the directional dependence of a beam pattern for improving the accuracy of measurements. It is also shown that the estimated accuracies of the precipitation profiles are almost independent of the number of pulses.

The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) core satellite has reflectivity measurements at two different frequency bands, namely, Ku- and Ka-bands. The dual-frequency ratio from these measurements has been used to perform rain-type classification and microphysics retrieval in the current DPR level 2 algorithm. In this paper, a surface snowfall identification algorithm is developed using GPM DPR observations. This algorithm provides a new approach to detect snowfall through radar observations, such as measured dual-frequency ratio. This algorithm is developed using GPM DPR data as well as Atmospheric Radiation Measurement (ARM) X/Ka-band radar data during the snowfall experiment. Several snow events observed by both DPR and ground radars are used in the algorithm validation, showing good comparisons.

The NASA D3R was successfully deployed and operated throughout the NASA OLYMPEx field campaign. A differential phase based attenuation correction technique has been implemented for D3R observations. Hydrometeor classification has been demonstrated for five distinct classes using Ku-band observations of both convection and stratiform rain. The stratiform rain hydrometeor classification is compared against LDR observations and shows good agreement in identification of mixed-phase hydrometeors in the melting layer.

Hydrometeor profile is a critical module in GPM DPR level 2 algorithm. The module has been extensively evaluated since GPM launch. Consistency has been achieved between dual-frequency classification algorithm and TRMM like single frequency classification algorithm. Melting region detection is cross-validated with ground radar and shows satisfactory results. Enhancement of dual-frequency classification module is focused on snow/rain detection.

How to eliminate the threats from tornadoes and high winds using advanced weather sensing techniques has been pursued for a few decades. However, conventional S-band radar based retrieval system is limited at warning against tornadoes and microbursts due to the combined effects of sampling limitation and Earth's curvature. This paper presents the rationale of using high-resolution X-band Doppler radar network for tornado detection and 3D wind velocity retrieval. The distributed collaborative adaptive sensing (DCAS) paradigm designed for the X-band radar network is able to reconfigure each radar node according to real-time weather changes. The wind retrieval system then selects the best radar pairs for multi-Doppler synthesis based on optimal beam-crossing angles in the target areas. The real-time wind products derived based on radar network observations in “Tornado Alley” have demonstrated the excellent performance of the designed multi-Doppler system. This real-time system is now being used for emergency warnings of severe weather by the local National Weather Service Forecast Offices.

Weather radar signals at high frequencies such as Ku-band are attenuated along the propagation path through rainfall. Hence, reflectivity and differential reflectivity measurements at such frequencies should be corrected for attenuation before any quantitative applications such as the retrieval of raindrop size distribution (DSD), which is a fundamental descriptor of rainfall microphysics. This paper presents the attenuation correction algorithm implemented for NASA Dual-frequency Dual-polarized Doppler Radar (D3R) Ku-band observations. The dual-polarization based correction performance is evaluated with the self-consistency criterion. In addition, the DSD parameters are estimated with the attenuation corrected observations, and the preliminary results are shown.

Dual-frequency precipitation radar (DPR) on board the GPM (Global Precipitation Measurement) core satellite has reflectivity measurements at two different frequency bands namely, Ku- and Ka- band. Dual-frequency ratio from these measurements has been used to perform rain type classification and melting region detection in the dual-frequency classification module in the current DPR level 2 algorithm. Beyond the applications that have been implemented, in this research, we focus on the enhancement of dual frequency classification module. We introduce and evaluate the algorithms to perform snow/rain separation and multiple scattering detection. These algorithms are candidates for future version of DPR algorithm.

TEMPEST-D will demonstrate technology for 6U-Class nanosatellites to advance NASA's Earth Science Goals. It will also reduce risk, cost, and development time for future constellations of small satellites to perform Earth Science measurements. It will raise the TRL of a millimeter-wave radiometer instrument from 6 to 7, representing the first on-orbit demonstration of 35-nm InP HEMT-based millimeter-wave radiometer front ends.

A probabilistic attenuation correction technique for differential reflectivity ZDR, based on the Bayesian theory, in a dual polarization networked environment is proposed. The proposed technique assumes a proportional relationship between specific differential attenuation ADPand specific differential phase KDP, and a power law relationship between backscattering differential phase δcoand ZDR. The algorithm maximizes a likelihood function by minimizing a cost function, and derives coefficients in the two relationship appropriately as well as a ZDRprofile. To evaluate the proposed technique, one dimensional simulation on X-band using S-band radar data is performed. In this paper, details of the algorithm of the proposed precipitation attenuation correction technique and evaluation results of the simulation are described.

Quantitative precipitation estimation (QPE) continues to be one of the principal objectives for weather researchers and forecasters. The purpose of this research is to present the development of a regional dual polarization QPE process known as the RAdar Multi-Sensor QPE (RAMS QPE). This scheme applies the dual polarization radar rain rate estimation algorithms developed at Colorado State University into an adaptable QPE system. The methodologies used to combine individual radar scans, and then merge them into a mosaic are described. The implementation and evaluation is performed over a domain that covers South Carolina for a severe rainfall event occurring October 2, 2015 through October 4, 2015. The QPE precipitation fields evaluated in this analysis will stem from the dual polarization radar data obtained from the local NWS WSR-88DP.

Linear depolarization ratio (LDR) is often difficult to measure in low and moderate signal-to-noise ratio conditions because the cross-polar echo power is typically two to three orders of magnitude weaker than the copolar echo power. For radars operating at attenuating frequencies such as X-, Ku-, and Ka-bands, differential attenuation must be accounted for to accurately estimate the LDR. A method for robust estimation of the LDR is introduced and evaluated that addresses both of these issues. In practice, the “enhanced” LDR offers robust LDR estimation over current estimation methods. The enhanced LDR is insensitive to noise, radar calibration error, and path-integrated attenuation. The proposed estimator is experimentally validated using Ku-band observations from the National Aeronautics and Space Administration dual-frequency dual-polarization Doppler radar (D3R).

The demand for renewable power production has fostered an exponential increase in the size and number of wind turbines. This expanding source of power generation is sometimes at odds with maintaining the effective operation of radar systems in air traffic control, defense, weather prediction, and severe-storm-tracking applications. With the recent upgrade of the NEXRAD weather radar system to enable dual-polarization observations, a dual-polarization characterization effort of wind turbines is warranted. Focusing on weather radar applications, a characterization of ground clutter, precipitation, and wind turbines is presented here using a consistent unified treatment. This characterization effort directly compares the dual-polarization radar signatures of these three classes of scatterers. The physical characteristics of wind turbines (particularly their cyclostationary behavior) are exploited to identify unique dual-polarization radar signatures.

For any quantitative applications that use reflectivity and/or differential reflectivity, radar observations need to be compensated for attenuation effects due to precipitation. This paper presents a robust attenuation correction system (ACS) for dual-polarization radars correcting the reflectivity factor as well as differential reflectivity profiles. The major advantage of the algorithm described in this paper is that the procedures are immune to the bias effect of reflectivity and differential reflectivity. In addition, this method is not very sensitive to the variation of temperature. The proposed ACS has been evaluated with X-band radar observations simulated from drop size distribution derived from high-resolution S-band measurements observed by the CSU-CHILL radar. The evaluation of the proposed retrieval algorithm shows that the retrieved reflectivity and differential reflectivity provide an improvement over the conventional self-consistent attenuation correction technique with the differential phase constraint.

A probabilistic attenuation correction technique for a single-polarization networked radar environment is proposed. The proposed technique, based on the Bayesian theory, makes a maximization of a likelihood function of Hitschfeld-Bordan (HB) reflectivity obtained by each radar node. A variance of the HB reflectivity (σHB2) is defined and regarded as instability of each HB reflectivity in the proposed technique. In the X-band simulation based on S-band real radar data, it is revealed that the corrected reflectivity obtained by the proposed technique has good accuracy, and the proposed technique works more stably than the HB technique. The proposed technique is also performed using CASA IP-1 dual-polarization radar network observations, and the corrected reflectivity by the proposed technique has a good agreement with differential phase (ΦDP)-based corrected reflectivity.

Vertical profiles of vertical air motion and raindrop size distributions (DSDs) within stratiform rain are estimated using two collocated vertically pointing radars (VPRs) operating at 3 and 35 GHz. Different raindrop backscattering cross sections occur at 3 and 35 GHz with Rayleigh scattering occurring for all raindrops at 3 GHz and Mie scattering occurring for larger raindrops at 35 GHz. This frequency-dependent backscattering cross section causes differently shaped reflectivity-weighted Doppler velocity spectra leading to radar transmit frequency-dependent radar moments of intrinsic reflectivity factor, mean Doppler velocity, and spectrum variance. The retrieval method described herein uses four radar moments as inputs to retrieve four outputs at each height within a precipitation column. The inputs include 3-GHz VPR mean Doppler velocity and unattenuated reflectivity factor and 35-GHz VPR mean Doppler velocity and spectrum variance. The outputs include vertical air motion and three parameters of a gamma-shaped DSD. To account for different VPR sample volumes, radar observations were accumulated over 45 s and over several range gates to represent time-space scales larger than either VPR sample volumes. Observed variability over this common time-space scale is used to estimate retrieval uncertainties. The retrieved air motions and DSD parameters compare well against retrievals from a collocated 449-MHz VPR that estimated air motions from Bragg scattering signals and DSD parameters from Rayleigh scattering signals.

The polarimetric radar parameter, linear depolarization ratio (LDR), provides microphysical insight into a scattering volume, particularly for mixed-phase and ice particles. A new estimator for improved estimation of LDR is presented. The NASA dual-frequency, dual-polarization, Doppler radar (D3R), which was recently upgraded to support operational linear depolarization ratio observations, was used as a testbed for evaluation of the new estimator. With D3R's Ku-band observations, the new LDR estimator is compared to conventional estimators and it is demonstrated that the new estimator is insensitive to attenuation, a number of radar system biases, and has increased immunity to noise for low SNR observations.

The Variational Doppler Radar Assimilation System (VDRAS) is applied to observations collected by X-band radars over the Dallas-Fort Worth metropolitan area. VDRAS is specifically designed to assimilate radar observations of Doppler velocity and reflectivity. In order to cope with attenuation issues affecting short-wavelength radars, the rainwater mixing ratio is here estimated using a technique based on dual-polarization observations. Preliminary results obtained from application to a severe storm case study are presented. The overall consistency of the resulting analyses demonstrates the feasibility of the proposed approach. The incorporation of observations from X-band systems sampling the lower atmosphere with high spatial and time resolution allows producing detailed analyses near the surface. In particular, the wind field reveals the capability to detect regions of low-level convergence associated with organized updrafts.

In order to investigate how well observations from precipitation-monitoring satellites match up to the best estimate of the true precipitation measured at ground level and how to use the collected precipitation data to evaluate models that describe and predict the hydrology, the Integrated Precipitation and Hydrology Experiment (IPHEx) was conducted in the southern Appalachian Mountains in the eastern United States from May 1 to June 15, 2014. The NASA dual-frequency dual-polarization Doppler radar (D3R), co-located with NASA NPOL radar, was deployed as part of the IPHEx field campaign to characterize precipitation properties at Ku- and Ka-band frequencies. This paper presents the deployment and performance of D3R during the IPHEx field experiment. Sample observations will be presented, with particular attention paid to cross-comparison between D3R and NPOL.

The Global Precipitation Measurement (GPM) mission was successfully launched in February 2014. It is the next satellite mission to obtain global precipitation measurements following success of TRMM (Tropical Rainfall Measuring Mission). The GPM core satellite is equipped with a dual-frequency precipitation radar (DPR) operating at Ku- (13.6 GHz) and Ka- (35.5 GHz) band. DPR on aboard the GPM core satellite is expected to improve our knowledge of precipitation processes relative to the single-frequency (Ku-band) radar used in TRMM by providing greater dynamic range, more detailed information on microphysics, and better accuracies in rainfall and liquid water content retrievals. New Ka- band channel observation of DPR helps to improve the detection thresholds for light rain and snow relative to TRMM PR [1]. The dual-frequency signals allow us to distinguish regions of liquid, frozen, and mixed-phase precipitation. In this paper, an evaluation of the profile classification module is presented. Cross validation is also presented with ground radar.

The light rain and snow are critical to the Earth's ecosystem due to the high occurrence rate, especially in middle and high latitude. However, it is challenging to use rainfall gauge to measure light rain due to the limitations of sampling time and bucket volume resolution. This paper presents the characterization and estimation of light rainfall using National Aeronautics and Space Administration (NASA) Dual-frequency Dual-polarization Doppler Radar (D3R) observations collected during the NASA Iowa Flood Studies (IFoodS) and Integrated Precipitation and Hydrology Experiment (IPHEx) field campaigns. Sample rainfall products are shown. Comparisons are performed between radar rainfall products and ground rainfall measurements from rain gauge and disdrometers. It is shown that the radar rainfall measurements agree with the disdrometer observations very well. The excellent performance for light rainfall estimation demonstrates one aspect of the capability of D3R as a ground validation tool for the Global Precipitation Measurement (GPM) satellite precipitation product evaluations.

Accurate, spatially specific, and temporally extended short-term automated forecasts (nowcasts) of lightning activity are of great interest to the preservation of life and resources for a multitude of applications. This paper presents a study to provide insight into the best manner and extent to nowcast lightning activity to a desired location. Radar quantities whose observations have previously been shown to be reliable precursors of lightning activity are nowcasted to introduce a concept of total lead time, whereby these nowcasts can potentially be used to nowcast lightning activity to a desired upstream site (e.g., airport, stadium, etc.). This study extends previous work by analyzing the correlation structure between nowcasts of radar and lightning quantities in Lagrangian space, giving insight into the manner and extent to which lightning activity at a desired location can be nowcasted.

Quantitative precipitation estimation (QPE) from radar measurements remains a principal objective for weather researchers and forecasters. The objective of this paper is to present the development of a regional dual polarization QPE process known as the RAdar Multi-Sensor QPE (RAMS QPE). This scheme applies the dual polarization radar rain rate estimation algorithms developed at Colorado State University into an adaptable QPE system. The methodologies to combine individual radar scans, and then merge them into a mosaic are described. The implementation and evaluation is performed over a domain that occurs near the Pigeon River Basin near Asheville, NC. In this mountainous locale, beam blockage, beam overshooting, orographic enhancement, and the unique climactic conditions complicate the development of reliable QPE's from radar. The QPE precipitation fields evaluated in this analysis will stem from the dual polarization radar data obtained from the local NWS WSR-88DP radars as well as the NASA NPOL research radar.

The presence of nonmeteorological radar signals, such as sea clutter, birds, and chaff, is a continuous challenge for meteorological services in different regions. In this paper, we assign membership functions to these signals using spectral decompositions of copolar correlation coefficient, differential reflectivity, and differential phase. Additionally, we apply the dual-polarization spectral decomposition technique to identify and suppress nonmeteorological echoes present in radar observations. The performance of the polarimetric spectral filter is illustrated in observations from the C-band Helsinki University Kumpula radar. The results show that the spectral polarimetric filter may be a suitable solution for the mitigation of these nonmeteorological signals.

Accurate noise-power estimation in dual-polarization weather radar is necessary for the correction of all power-based moments in low signal-to-noise ratio environments. A method of real-time noise-power estimation for dual-polarization radar systems using copolar correlation and receiver power is introduced. This real-time noise-power estimation algorithm is implemented in the NASA dual-frequency dual-polarization Doppler radar, and the performance results are presented.

The light rain (less than or equal to a few mm hr-1) is critical to the Earth's ecosystem due to the high occurrence rate, particularly in middle and high latitude (over 80%). However, it is challenging to use rainfall gauge to measure light rain due to the sampling time and bucket volume resolution. Dual-polarization radar has become an important tool for quantitative precipitation estimation because of its relatively large covering area and ability to fill the sampling void. This paper presents the application of Ku-band dual-polarization radar for light rainfall estimation. The Ku-band radar rainfall algorithms and their error structure are described. The Ku-band observations from the National Aeronautics and Space Administration (NASA) Dual-frequency Dual-polarization Doppler Radar (D3R) during the NASA Iowa Flood Studies (IFoodS) field campaign are used to derive the rainfall products. The comparisons are performed between radar rainfall estimates and ground rainfall measurements from rain gauge and Autonomous Parsivel Unit (APU). It is shown here that the radar rainfall measurements at different timescales (i.e., 5, 10, and 15 min) agree with the APU observations very well. The normalized difference error is about 26.1%, 24.8%, and 23.7%, for 5-min, 10-min, and 15-min rainfall accumulations, respectively. The excellent performance of Ku-band rainfall algorithm for light rain estimation indicates the great potential of using D3R as a ground validation tool for the Global Precipitation Measurement (GPM) satellite precipitation product evaluations.

The Dynamic Radar Tracking of Storms (DARTS) model is a Lagrangian persistence-based nowcasting model that has previously shown utility in nowcasting a variety of weather radar data in severe weather and aviation decision support applications. DARTS is based on the discrete Fourier transform and thus provides an inherent means to perform interpolation. In this context, the model is modified such that interpolation can be accurately and efficiently performed by appropriately windowing the input data and evaluating an interpolating polynomial using the fast Fourier transform. The utility of this interpolation methodology for operational use is demonstrated, and its performance is compared with linear and cubic spline interpolation methods. The use of the original DARTS model to perform advection-based interpolation is also investigated. Rainfall rates derived from data collected by the Weather Service Radar-1988 Doppler S-band radar and the X-band radar at the Dallas-Fort Worth test bed were used for the analyses. The results show that the modified DARTS technique yielded normalized standard error values that were close to those of the forward-backward advection approach using the original DARTS model and ran about 2-4 orders of magnitude faster in terms of computation time. The error structure of the interpolation methods in the context of spatial variability and sampling of atmospheric scales represented by the data is also presented. In this sense, utility of the 1-2-km scales was shown, and the modified DARTS-based approach showed the ability to effectively utilize the value in these scales.

Radar observed differential Doppler velocity can be a proxy for the dual-frequency ratio for vertical pointing observations. Vertical pointing dual-frequency data was collected with the NASA D3R and the relationship between Ku- and Ka-band velocity measurements is discussed. It is shown that the differential Doppler velocity between frequencies provides insight into the microphysical properties of rain. The differential Doppler velocity can be used to estimate the median volume diameter, D0.

The Iowa Flood Studies (IFloodS) field experiment was conducted to better understand the strengths and limitations of Global Precipitation Measurement (GPM) mission satellite products in the context of hydrologic applications. The NASA dual-frequency dual-polarization Doppler radar (D3R), designed as part of the GPM ground validation program, participated in the IFloodS field campaign to characterize precipitation properties at Ku- and Ka-band frequencies. This paper presents the deployment of the D3R and summarizes the D3R observations during the IFloodS field campaign. The quality of the D3R measurements is evaluated by comparing with the NASA NPOL S-band radar observations. In addition, the capability for rainfall estimation using the D3R is also described and validated using ground gauge measurements.

Neural network (NN) is a nonparametric method to represent the relation between radar measurements and rainfall rate. The relation is derived directly from a dataset consisting of radar measurements and rain gauge measurements. Tropical Rainfall measuring Mission (TRMM) Precipitation Radar (PR) is known to be the first observation platform for mapping precipitation over the tropics. TRMM measured rainfall makes a significant contribution to the study of precipitation distribution over the globe in the tropics. Ground validation (GV) is a critical component in the TRMM system. However, the ground sensing systems have quite different characteristics from TRMM in terms of resolution, scale, sampling, viewing aspect, and uncertainties in the sensing environments. In this paper a novel hybrid NN model is presented to train ground radars for rainfall estimation using rain gauge data and subsequently the trained ground radar rainfall estimation to train TRMM/PR observation based neural networks. This hybrid NN model provides a mechanism to link between gauges on the ground, the ground radar observations and the TRMM/PR observations. The dual-polarization radar measurements from a ground WSR-88DP site in Dallas-Fort Worth region and local rain gauge data will be used for the demonstration purpose. The performance of the rainfall product derived for TRMM PR is then compared against TRMM standard rainfall products. In addition, a direct gauge comparison study is done to examine the improvement brought in by this hybrid neural networks approach.

The dual-frequency precipitation radar (DPR) on board the GPM (Global Precipitation Measurement) core satellite is equipped with two independent frequency channels, Ku- (13.6 GHz) and Ka- (35.5 GHz) band. This enables better retrieval of the DSD parameter and better accuracies in rainfall rate estimation than TRMM (Tropical Rainfall Measuring Mission) precipitation radar (PR). Le and Chandrasekar (2013) [1] developed a hybrid method to retrieve DSDs for GPM-DPR using airborne radar data. In this paper, precipitation rate is calculated using DSDs retrieved through the hybrid method based on assumptions of particle falling velocity. We also investigate potential relations between precipitation rate and dual-frequency parameters through direct parameterization. This analysis first goes through simulation procedure, and then applied to airborne data with attenuation corrected using method in [1]. Reasonable comparison of precipitation rate can be seen in between true precipitation rate, evaluation through DSDs and from direct parameterization.

The Global Precipitation Measurement (GPM) mission was successfully launched on February 27, 2014. It is the next satellite mission to obtain global precipitation measurements following success of TRMM (Tropical Rainfall Measuring Mission). The GPM core satellite is equipped with a dual-frequency precipitation radar (DPR) operating at Ku- (13.6 GHz) and Ka- (35.5 GHz) band. DPR on aboard the GPM core satellite is expected to improve our knowledge of precipitation processes relative to the single-frequency (Ku-band) radar used in TRMM by providing greater dynamic range, more detailed information on microphysics, and better accuracies in rainfall and liquid water content retrievals. New Ka-band channel observation of DPR will help to improve the detection thresholds for light rain and snow relative to TRMM PR [1]. The dual-frequency signals allow us to distinguish regions of liquid, frozen, and mixed-phase precipitation. In this paper, we summarize the vertical profile classification algorithm for GPM DPR with the focus on dual-frequency classification method.

Rainfall estimation using dual-polarization radars has shown a number of advantages over traditional single polarization radars. As the building blocks for deriving dual-polarization radar rainfall algorithms, the rain drop size distribution (DSD) has been studied over three decades. In this study, we present the estimation of DSD parameters using dual-polarization radar measurements at S-, X-, and Ku-band frequencies. Various DSD retrieval algorithms are implemented using the data collected by the S-band WSR-88DP/KFWS, X-band CASA/XUTA, and D3R Ku-band radars.

The dual-frequency precipitation radar onboard the Global Precipitation Measurement (GPM) core satellite has reflectivity measurements at two independent frequencies, i.e., Ku-band and Ka-band. Dual-frequency retrieval algorithms have been developed traditionally through forward, backward, and recursive approaches. However, these algorithms suffer from a “dual value” problem when they retrieve median volume diameter D0from a dual-frequency ratio (DFR) in the rain region. It has been shown in the literature that a linear constraint of the drop-size distribution along the rain profile is a reasonable assumption to avoid the “dual value” problem. In this paper, a hybrid method is proposed to retrieve DSDs by combining the forward method and the linear constraint. The forward method is applied to ice and melting ice regions, whereas the linear constraint is applied to the rain region. The method is evaluated using data-based simulation. Different error sources, including sensitivity of snow density, system bias, and attenuation from nonprecipitating particles, are considered. The hybrid method is compared with the surface reference with weak constraint method and the Hitschfeld-Bordan DFR method and shows reasonable comparisons, particularly for medium-to-heavy precipitation. Retrieval examples for Hurricane Earl are shown using the hybrid method.

A network of four weather radars was deployed as part of the Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA). The radars operate at the X-band frequency and are capable of polarimetric measurements. The rainfall polarimetric self-consistency principle for absolute calibration is evaluated and applied to the CASA radars' data to estimate any bias in the reflectivity (Z) measurements. Moreover, prior to the application of the self-consistency principle, bias error correction of differential reflectivity (ZDR) measurements is required. Two different approaches were evaluated for ZDRbias correction, the dry aggregated snow approach and the light rain approach. Results of ZDRcalibration show an accuracy of 0.2 dB or better in bias estimation for the two events analyzed when both methods are compared. Moreover, results show a calibration accuracy of 0.6 dB or better for the Z bias estimated using the self-consistency principle. For verification of results, Z bias estimates from the self-consistency principle are compared with Z bias estimated from the comparison between the CASA X-band and the two nearby WSR-88D S-band radars' data. Comparison of the two approaches shows a difference in the Z bias estimation of 0.61 dB or better, which validates the use of the self-consistency principle in rainfall for the absolute radar calibration of the CASA radars. Results show that the self-consistency principle provides a means to estimate bias errors in the radar power measurements from a specific observed medium with a set of space-time characteristics that are not taken into account in the radar hardware relative calibration.

The GPM core satellite will be ready to launch in February 2014, less than 7 months after the end of IGARSS2013 symposium. In the pre-launch era, several international validation experiments such as LPVEX (Light Precipitation Validation Experiment), MC3E (Midlatitude Continental Convective Clouds Experiment), and IFloodS (Iowa Flood Studies) have already generated a substantial set of measurements that continue to contribute to the development and test of pre-launch GPM algorithms. Following launch, it is expected that GPM ground validation will focus on evaluating precipitation data products, generated by a constellation of GPM satellites as well as assumptions made in algorithms. This paper presents simple concepts of dual-polarization radar observation strategies and products that can be generated for the post launch era of the GPM program, especially when there are satellite overpasses. The use of microphysical retrievals from ground-based radars and in-situ observations for validating the retrievals from space based observations is the main focus of this paper.

The Global precipitation mission is conceptually centered on the deployment of a “core” satellite with an active dual-frequency (Ka/Ku band) precipitation radar, and microwave imager capable of sensing the total precipitation within all cloud layers (http://gpm.gsfc.nasa.gov). Compared to the single-frequency TRMM (Tropical Rainfall Measuring Mission) precipitation radar (PR), the dual-frequency precipitation radar (DPR) onboard GPM core satellite is expected to enhance our understanding on microphysics, and provide more accurate retrieval of rainfall and liquid water content. The goal of the present study is to combine the TMI (TRMM's Microwave Imager), TRMM PR and a mesoscale WRF (Weather Research and Forecast) model to simulate the DPR reflectivity profiles. Prior studies to simulate the DPR profiles were done based on TRMM PR observation using frequency scaling arguments and hydrometeor classification [1]. First, the methodology to retrieve self consistent hydrometeor profiles from a combination of TMI, PR observations as well as WRF model is described. Using the TRMM constrained WRF model profiles, a procedure to simulate DPR observations is described. Attenuation from cloud water and cloud ice is considered. Simulation of cyclone Nargis shows reasonable results using TMI retrieved microphysics. Attenuations from both precipitation and non-precipitation are also compared.

The first of three proposed X-band dual polarization Doppler weather radar nodes of the Puerto Rico tropical weather testbed, known as the TropiNet network [1], has been deployed, and is in operation in Cabo Rojo, Puerto Rico since February 2012. The RXM-25, locally known as the TropiNet radar, is a polarimetric Doppler weather radar, operating in the X-band frequency range, designed to cover a scan range of 40 km, at high sampling resolutions. It has been well documented and reported, as in [2, 3], that the island of Puerto Rico experiences severe weather events that bring heavy rain. These rain events, compounded by the mountainous topography, urban development, and population density, adversely contribute to an increased rate of flash floods and mudslides that are potentially hazardous to life and property. This paper will present the axial node of the TropiNet radar network, its associated infrastructure, and selected first observations.

The dual-frequency precipitation radar (DPR) on board GPM core satellite collects Ku and Ka band vertical profiles which allow us to investigate the microphysics using the difference between two frequency observations (measured dual frequency ratio or DFRm). DFRm has been shown in the literature to be rich in information and can be used to perform melting layer detection and estimate drop size distribution (DSD) parameters. This paper summarizes the candidate algorithms to perform melting layer detection and DSD retrieval for GPM-DPR. Hurricane Earl observations collected by airborne precipitation radar were used to demonstrate the application.

The use of weather sensing radar measurements along with corresponding gauge data in multisensor applications seek to provide reliable estimates of rainfall rate and accumulation versus single radar. Radar rainfall estimators have a number of advantages over gauges including the ability to observe precipitation over wider areas within shorter timeframes and providing advanced warning of impending precipitation events. The radar reflectivity-rainfall (Z-R) relations are traditionally used for quantitative precipitation estimation (QPE).

Precipitation classification is a critical module in the retrieval algorithm set, for the dual-frequency precipitation radar (DPR) that will be onboard the global precipitation measurement (GPM) core satellite. Precipitation type classification namely stratiform, convective, and other rain type classification is an important part of the classification module. Characteristics of measured dual-frequency ratio ( DFRm), defined as the difference between measured reflectivity at two frequency channels (Ku- andKa- band), were studied for different rain types. This paper shows that DFRm can be used to separate stratiform and convective rain. In this paper, a precipitation type classification model is developed for DPR profile classification using characteristics ofDFRm. Data collected by the airborne PR (ARP-2) in NASA African Monsoon Multidisciplinary Analysis, Genesis and Rapid Intensification Processes, and Wakasa Bay campaigns are employed in model validation. The performance of the precipitation classification method for the GPM-DPR resolution is evaluated and is shown to be applicable to the GPM resolution.

Profile classification is a critical module in the microphysics retrieval algorithm for the dual-frequency precipitation radar (DPR) that will be onboard the Global Precipitation Measurement (GPM) Core satellite. Hydrometeor profile characterization (HPC or melting region detection) is an important part of profile classification. To accomplish this classification, characteristics of measured dual-frequency ratioDFRm, defined as the difference between measured reflectivity at two frequency channels (Ku- and Ka-bands), were studied for different hydrometeor phases. This paper shows that aDFRmprofile can be used to detect the frozen, mixed-phase, and liquid regions. An HPC model is developed in this paper for DPR profile classification usingDFRmand its range variability along the height. Data collected by the Second Generation Airborne Precipitation Radar (APR-2) in NASA African Monsoon Multidisciplinary Analysis, Genesis and Rapid Intensification Processes, and Wakasa Bay campaigns are employed in model validation. Signatures of Doppler velocity, as well as the linear depolarization ratio at Ku-band, available for APR-2 data, are used for cross-validation purpose. Comparison of the melting layer top and bottom between the HPC model and the velocity-based estimates shows that they compare well, with a 2% bias. The performance of the HPC method at GPM-DPR observation resolution is evaluated and is shown to be applicable to observation at GPM-DPR resolution. It can be inferred from the analysis presented that the methodology developed in this paper usingDFRmis a good candidate for HPC for GPM-DPR.

The 4-satellite constellation Cosmo Sky Med of the Italian Space Agency is providing high quality observations suitable for many applications in “all weather” conditions. However, Cosmo Sky Med SARs use a frequency at X-band (9.6 GHz) for which attenuation due propagation through precipitation is not negligible and can affect several SAR applications. In some conditions, especially heavy thunderstorms, precipitation effects can be significant, masking features of interest of the observed surface. During the two years of operation of Cosmo Sky Med, several images collected in stripmap, pingpong and scansar mode, both over sea and terrain and sea are analyzed and discussed by using simultaneous measurements collected by operational and research weather radar.

Dual wavelength precipitation radar (DPR) is planned to be deployed in the GPM core satellite. The DPR is expected to provide improved characterization of the raindrop size distribution ( DSD), as well as rainfall rate estimation from a combination of Ku band and Ka band radar measurement [1]. The Ku band radar is nearly same as the TRMM Precipitation radar. The Ka band provides higher sensitivity and can be useful in the measurement of snow and light rain. In contrast to TRMM the dual wavelength retrieval methods will use two DSD parameters to characterize the precipitation medium. The underlying precipitation structures, hydrometeors and DSDs dictate the type of models or retrieval algorithms that can be used to estimate precipitation. Having dual wavelength radar on the ground, with the potential for in-situ observations, or coordinated observations provide excellent opportunity to develop microphysical and system models for retrievals. Therefore a beam aligned dual-wavelength system consisting of Ku and Ka bands can be very useful as a ground validation tool. In addition if these systems can be dual-polarized, then these can be self-consistent cross validation tools. This paper describes the NASA Dual polarized, dual frequency Doppler radar, developed for the ground validation program.

Flooding is one of the most catastrophic disasters in the world. Radar rainfall estimation for flash flood forecasting in small, urban catchments is an important accomplishment of CASA. A Kdp(specific differential propagation phase) based rainfall algorithm was developed using the Kdpvalues of X-band radars. The performance of this rainfall algorithm is evaluated for all major rainfall events using a gauge network in the center of CASA IP1 test bed for a 5- year period. The cross comparison with gauge estimates shows great improvement compared to the current state-of-the-art. Since the beginning of 2012, CASA has been involved in developing the first urban weather demonstration network in Dallas-Fort Worth (DFW) area. This paper will summarize the performance of the radar rainfall product in the IP1 network. In addition, the implementation of CASA QPE (quantitative precipitation estimation) system in DFW Urban Demonstration Network will also be presented.

The Midlatitude Continental Convective Clouds Experiment (MC3E) was conducted in northern Oklahoma during the summer of 2011. This experiment provided a unique opportunity wherein a network of X-band radars was deployed within the range of dual polarized C-band and S-band radars. While the X-band network concept is popular, as can be seen by the success of the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, careful microphysical observations comparing X-band and non-attenuated S-band are rare. This paper shows preliminary results of X-band network retrievals of microphysical compared with S-band.

The dual precipitation radar (DPR) on board the GPM (Global precipitation measurement) core observatory satellite is expected to improve our knowledge of precipitation processes. DPR offers dual frequency observations (Ku and Ka band) along the vertical profiles which allow us to investigate the microphysics using the difference between two frequency observations (measured dual frequency ratio or DFRm). DFRm has been shown in the literature to be rich in information and can be used to perform precipitation type classification and hydrometeor type identification. This paper shows recent updates of the analysis on these two models. Analysis is focused on the availability of the algorithm to DPR vertical and horizontal resolution. The algorithm is also evaluated using off-nadir data from airborne precipitation radar (APR-2).

NASA Dual-Frequency Dual-Polarized Doppler Radar (D3R) is a ground-based system developed to enable ground validation for the Global Precipitation Measurement (GPM) Mission. The radar makes measurements at both Ku- and Ka-band frequencies in order to gain higher sensitivity towards light rain, drizzle and snow. D3R capitalizes on a solid-state transceiver which can considerably enhance the sensitivity of the radar by allowing implementation of pulse compression waveforms. However, the use of pulse compression techniques is accompanied by challenges to mitigate the blind zone, suppress range side-lobes and unavailability of wider bandwidth. D3R therefore employs a programmable wideband multi-channel digital receiver which implements a novel waveform to check the undesired consequences of pulse compression and meet unique requirements of D3R system. This paper discusses various considerations and challenges for design and realization of system requirements for the D3R system by employing several novel radar signal processing algorithms.

The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) and the North Central Texas Council of Governments (NCTCOG) are embarking on a five-year, project to create the Dallas Fort Worth (DFW) Urban Demonstration Network. The goals of the program are: 1) To develop high-resolution, three-dimensional mapping of the atmospheric conditions, focusing on the boundary layer, to detect and forecast severe wind, tornado, hail, ice, and flash flood hazards. 2) To create impacts-based, neighborhood-scale warnings and forecasts for a range of public and private decision-makers that result in measureable benefit for public safety and the economy. 3) To demonstrate the value of collaborative, adaptive X-band radar networks to existing and future National Weather Service sensors, products, performance metrics, and decision-making; and assess optimal combinations of observing systems. The centerpiece of the Dallas Fort Worth Urban Demonstration Network will be an 8-node, boundary-layer, dual polarized, multi-Doppler X-band CASA radar network. Additional in-situ and remote sensors will enable fusion of observations from all sensors. Data products will include single and multi-radar data, vector wind, Quantitative precipitation estimation, nowcasting, and analysis and numerical weather prediction products. Research and Research to operations in the DFW Urban Demonstration Network will occur in a quasi-operational environment. New technology and products will be integrated into operational platforms for evaluation by a variety of users during real-time weather events. Users include NWS forecasters and emergency managers; users from transportation, utilities, regional airports, arenas, and the media will be added in the near future. In this way, CASA's multidisciplinary team - engineers, computer scientists, social scientists (sociologists, geographer, economist), meteorologists, hydrologists - will conduct “end-to-end” research from sensor observation, to product development and validation linked to end user decision-making, response and value.

The Collaborative Adaptive Sensing of the Atmosphere (CASA) nowcasting system currently provides 0-30 min automated forecasts (nowcasts) of precipitation to National Weather Service forecasters, emergency managers, and researchers using composite X-band weather radar data. Nowcasting is accomplished in two steps. First, the Fourier-based Dynamic and Adaptive Radar Tracking of Storms (DARTS) technique computes a motion vector field representing precipitation pattern motion using a recently observed sequence of radar reflectivity fields. Then, future reflectivity fields are estimated by recursively advecting the latest observed or predicted field according to this motion vector field using a sine kernel-based method. This paper presents potential upgrades to the CASA nowcasting system. The performance of the current sine kernel-based advection method is compared to that of a backward mapping technique in terms of categorical (rain/no rain) assessments of accuracy. Because computational efficiency is an important concern given the high-resolution (0.5 km/1 min) nature of the CASA data, the respective computational efficiencies are also compared. A technique to perform temporal interpolation within the DARTS model with the potential application to data fusion is also presented and assessed.

This paper summarizes part of the work performed on the calibration and characterization of the Dual-frequency, Dual-polarized, Doppler Radar (D3R) system. The D3R makes use of pulse compression combined with a three pulse frequency separated waveform to achieve it's sensitivity while still maintaining a blind range similar to that of a single pulse weather radar. A slightly modified receiver calibration procedure to the one used on conventional weather radar was required to accommodate the use of multiple sub-channels. The use of the calibration loop in both transceivers is described and transmitter output power is shown for a twenty four hour period. Antenna beam (Ku to Ka) co-alignment verification using solar scans is also presented followed by sphere calibration results for the Ku-band radar. Finally, Ku-band reflectivity plots for two events during May 6th, 2012 and May 7th, 2012 are presented.

Raindrop size distribution (DSD) retrieval algorithm for an X-band dual-polarization weather radar is proposed. In this algorithm, DSD range profile is estimated to match the dual-polarization measurements, where the forward and back scatters are formulated simultaneously to avoid the two-step process of attenuation correction and DSD retrieval. For the optimization, the iterative maximum likelihood is applied, in which a posterior PDF of DSD parameters are calculated and then extended to radar network environment. Estimation accuracies of log(Nw) (Nw; mm-1m-3) and D0(mm) derived from single-radar numerical simulation are a mean bias (MB) of -0.02 and a standard deviation (SD) of 0.23, and an MB of 0.01 and an SD of 0.10, respectively.

Raindrop size distribution (DSD) retrieval algorithm for a weather radar network consisting of X-band dual-polarization weather radars is proposed. This algorithm is based on a DSD retrieval method for a single-radar (SRR), which is elaborated in our paper of the SRR, “RAINDROP SIZE DISTRIBUTION (DSD) RETRIEVAL FOR X-BAND DUAL-POLARIZATION RADAR” in this conference. The SRR outputs an ML solution of the two DSD parameters of the normalized Gamma DSD, Nwand D0. The proposed algorithm calculates posterior probability of the DSD profile with the use of a prior probability, and then, the posterior probabilities on different polar coordinates are integrated to that on a common Cartesian grid by Bayesian theorem. An example of the numerical simulation shows that the fluctuated DSD profiles of the SRRs are improved to less fluctuated DSD profiles on a common Cartesian grid with spatially homogeneous quality.

When synthetic aperture radars (SAR) operating above 5 GHz began acquiring data clearly showing attenuation and backscatter from storms in the images, the notion that SAR is truly an “all weather” technology was challenged. With the recent launch of several dual-polarization X-band SAR systems, the capability of characterizing this impact became reality; however, a complete model describing SAR observations during precipitation is required to do this. Using real storm observations by fully polarimetric ground radars and microphysical models of electromagnetic scattering from hydrometeors, a quantitative characterization of the impact of precipitation on high-frequency SAR is presented here. The methodology is described to simulate X-band SAR observations of real storms from ground-based weather radars with an example of a squall line observed by the CSU-CHILL weather radar added to a TerraSAR-X image acquired at a different time. By conditioning the simulation on real data, the variability of radar observations is greatly reduced and more realistic than simulating from pure theoretical parameters. Given the challenges involved in characterizing the propagation effects, the results demonstrate the model capabilities well, and the results will apply to higher frequency systems for the future.

Differential reflectivity from dual-polarization radars at frequencies such as S- and C-bands has been used to estimate the median drop diameter. Similarly, the dual-frequency ratio (DFR) can also be used to estimate median drop diameter. This paper presents an algorithm for estimating the raindrop size distribution parameters of median drop diameter (D0) and equivalent intercept parameter (Nw) using a dual-polarization dual-frequency ground radar operating at Ku- and Ka-bands. The retrieval philosophy is based on combining attributes of the DFR, historically used in spaceborne radar systems and dual-polarization approaches used in ground radar algorithms. The estimator ofD0as well asNw(in the logarithmic scale) are evaluated using simulated radar observations in the presence of different error sources. It is shown that normalized bias and standard error for two estimators are both within ±2% and 16% when system noise is added. The situation when Ka-band observations are extinct during heavy precipitation is also considered.

Effects of rain in SAR images have been noted since early missions. For SARs at X-band such as the satellites of the Italian Space Agency constellation COSMO SkyMed or the DLR TerraSAR-X, SAR returns from surface are attenuated by precipitation that gives a specific signature in SAR images that depend on polarization. Attenuation, jointly with a reduced space resolution determined by the Doppler spectrum of precipitation returns, limits the capability of detecting surface features during storms. On the other hand, such precipitation signature could be used for atmospheric studies or precipitation measurements. Characteristics induced by precipitation on polarimetric COSMO SkyMed images collected have been detected and analyzed. The analysis methodology exploits also the availability of coincident measurements of precipitation from ground based radars, used to reconstruct the component of SAR return due to precipitation.

When synthetic aperture radars (SAR) operating above 5 GHz began acquiring data clearly showing attenuation and backscatter from storms in the images, the notion that SAR is truly an "all weather" technology was challenged. With the recent launch of several dual-polarization X-band SAR systems, the capability of characterizing this impact became reality; however, a model to demonstrating SAR observations in precipitation is necessary to accomplish this. Using storm observations by fully polarimetric ground radars and microphysical models of electromagnetic scattering from hydrometeors, a quantitative characterization of the impact of precipitation on high frequency SAR can be created. Model validation is presented using data from two cases: 1. X-SAR storm observations simulated from archived polarimetric S-band ground radar data incorporated with TerraSAR-X acquisitions over central Florida, U.S.A. and 2. dual-polarized C-band ground radar with simultaneous co-polar COSMO-SkyMed X-band SAR channels over northern Italy in June 2010. Given the challenges involved in characterizing the propagation effects, the results demonstrate the model capabilities well.

The dual precipitation radar (DPR) on board the GPM (Global precipitation measurement) core observatory satellite is expected to improve our knowledge of precipitation processes. DPR offers dual frequency observations (Ku and Ka band) along the vertical profiles which allow us to investigate the microphysics using the difference between two frequency observations (measured dual frequency ratio or DFRm). DFRm has been shown in the literature to be rich in information and can be used to perform precipitation classification and hydrometeor identification. In this paper, extensive analysis is focused on validating the classification criteria from DFRm using other auxiliary information such as velocity and linear depolarization ratio (LDR) based on airborne precipitation data. The cross comparisons show good agreement and the proposed classification method provides critical information needed for developing profile classification algorithms.

The melting layer attenuation is studied by fitting the modeled reflectivity factor and the reflectivity-weighted fall velocity to measured values at both Ku- and Ka-bands. In the fitting process the optimized snow density and velocity parameters are defined and the dependence of the modeled attenuation on these parameters is investigated. The study shows that for Ku-band the implemented model can describe the melting layer with sufficient accuracy, but for Ka-band there is discrepancy between the measured and modeled values despite the fitting process. At Ka-band the attenuation of water vapor and cloud water influence more than at Ku-band and this should be considered also in the model. The sensitivity study of the modeled attenuation shows that in the snow velocity power-law function V = αDβthe changes in the factor α induces a significant deviation in the modeled attenuation. This is true for all of the tested effective permittivity models and at both frequency bands. The permittivity model of a coated sphere solution is out of the tested models the most susceptible on changes of snow related parameters.

A rain/ice mixture identification algorithm from dual polarization radar observations is described. The method uses fuzzy logic technique and three two-dimensional Beta membership functions constructed by the power of copolar correlation coefficient, the normalized coherent power, and the texture of differential propagation phase in terms of the signal-noise variable. The proposed technique is tested for two different frequency band radars such as the S-band CSU-CHILL radar and the X-band CASA-IP1 radar (KCYR) observations.

Nowcasting refers to short-term automated forecasting (0-6 hours or less) of high-impact weather events such as heavy rainfall that can produce severe flooding. Accurate and efficient nowcasting can be used to assist emergency managers in the decision-making process. This paper presents an evaluation of nowcasting performance within the Collaborative Adaptive Sensing of the Atmosphere (CASA) system from the 2009-2010 Integrative Project 1 (IP1) experiment. The nowcasting methodology consists of the Dynamic and Adaptive Radar Tracking of Storms (DARTS) method for motion estimation and a sine kernel-based advection method. Radar reflectivity fields were predicted up to 10 min into the future. Previous analysis is extended to include data over a two year period using Critical Success Index (CSI), False Alarm Ratio (FAR), Probability of Detection (POD), and Mean Absolute Error (MAE) scores for evaluation. Analysis of the categorical scores (i.e., CSI, POD, FAR) relative to scoring threshold and neighborhood is also presented.

Motivated by the use of a Ku/Ka-band radar in the Global Precipitation Measurement core satellite due to launch in 2013, we have studied the use of techniques to simulate radar observations of snowfall at these bands from data at C/W-bands by using scattering simulations to derive the approximate relationships of radar observables at the C/W-bands and those at Ku/Ka-bands. In this paper, we form the cross-band relationships by simulating radar reflectivity and attenuation in snowfall. The relations can be used to simulate observations at given bands from measurements at other bands. When measurements are available at multiple frequencies, the consistency of the model and the measurements can be used as a measure of the validity of the underlying assumptions of snowflake shape and constitution, which have been an important topic in recent snowfall remote sensing research. We also present mapping functions that can be used to perform cross-band analysis of snowfall observations and examine the use of these for practical cases of combined ground- and space-based (CloudSat) radar measurements as well as airborne radar data from the 2003 Wakasa Bay experiment.

Precipitation patterns exist on a continuum of scales where generally larger scale features facilitate longer useful prediction times at the expense of coarser resolution. Favorable measurement range and resolution make weather radar observations an attractive choice for input to automated short-term weather prediction (nowcasting) systems. Previous research has shown that nowcasting performance can be improved by spatially filtering radar observations and considering only those precipitation scales that are most representative of pattern motion for prediction or filtering those scales from predicted fields deemed unpredictable by remaining past their lifetimes. It has been shown that an improvement in nowcasting performance can be obtained by first applying a nonlinear elliptical spatial filter to observed Weather Surveillance Radar 88 Doppler vertically integrated liquid water fields to predict motion of larger scale features believed to better represent the motion of the entire precipitation pattern for forecast lead times up to 1 h. It has also been shown in the literature that wavelet transform can be used to develop measures of predictability at each scale and adaptive wavelet filters can be designed to remove perishable scales from predicted continental-scale reflectivity fields according to prediction lead time. This paper investigates the adaptation of both of these approaches and Fourier filtering to evaluate the effects of scale filtering on nowcasting performance using a Fourier-based nowcasting method and high-resolution Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere radar reflectivity data. A maximum improvement of approximately 18% in terms of Critical Success Index was observed by applying Fourier filtering in the context of truncating Fourier coefficients within the prediction model to the observed sequence of reflectivity fields used for assimilation. In addition, applying Fourier filtering to the resulting predictions showed a maximum reduction in mean absolute error of approximately 14%.

Both the space borne radar and the radiometer suite on the TRMM satellite observe the same column of precipitation and derive rain rates, however at different spatial resolutions. The observations from TRMM PR and TMI are fundamentally different measurements. While the radar provides a backscatter measurement resolved in the vertical direction, the radiometer is a passive instrument obtaining integrated observations over the full depth of the cloud. In addition, they respond to different physical mechanisms. Nevertheless, they observe the same precipitation medium and retrieve the same output products. Therefore, it begs the question, what type of information about radiometric observations can be directly retrieved from radar observations. This question can be further focused and stated as: To what extent can one predict the radiometric observations from radar observations? This question can be answered in several ways, and one of them is an informational theoretic approach using neural networks which is described in this paper.

Conventional radar networks are limited at warning against low-altitude wind hazards such as tornadoes and micro-bursts due to the combined effects of their long range, far spacing, and Earth's curvature. The Collaborative Adaptive Sensing of the Atmosphere (CASA) Engineering Research Center aims to solve these limitations through dual-Doppler systems operating in densely-spaced short range radar networks. The CASA test bed radar network incorporate the Distributed Collaborative Adaptive Sensing (DCAS) model in order to optimize scanning and retrieval for fast Doppler wind field products. Under the DCAS environment, fast coordinated sector scans are made by each radar based on weather detection and competitive end-user needs. The retrieval subsystem then makes the best pair selection for dual-Doppler synthesis based on optimal beam-crossing angles for target areas. Together, they create a fast, accurate, and robust dual-Doppler system suitable for weather emergency warnings.

As an integral part of Global Precipitation Measurement (GPM) mission, Ground Validation (GV) program proposes to establish an independent global cross-validation process to characterize errors and quantify uncertainties in the precipitation measurements of the GPM program. A ground-based Dual-Frequency Dual-Polarized Doppler Radar (D3R) that will provide measurements at the two broadly separated frequencies (Ku- and Ka-band) is currently being developed to enable GPM ground validation, enhance understanding of the microphysical interpretation of precipitation and facilitate improvement of retrieval algorithms. The first generation D3R design will comprise of two separate co-aligned single-frequency antenna units mounted on a common pedestal with dual-frequency dual-polarized solid-state transmitter. This paper describes the salient features of this radar, the system concept and its engineering design challenges.

Dual polarization radar measurements of rainfall parameters are based on the assumption of a mean shape-size relationship of raindrops. This paper focuses on retrieving and interpreting the mean raindrop shape-size relation from polarimetric radar observations. A procedure to retrieve the drop shape-size relation that governs the polarimetric radar observations of reflectivity (Zh) differential reflectivity (Zdr) and specific differential propagation phase (Kdp) is presented. The mean drop shape-size relations retrieved from measurements collected by the NCAR SPOL radar during three campaigns are analyzed to explore whether the natural raindrop shape-size relation can be described by a unique model.

A dual-frequency precipitation radar (DPR) will be deployed aboard GPM (Global Precipitation Measurement) core satellite in order to enhance our knowledge of precipitation microphysics. A ground based dual-frequency (Ku and Ka band) and dual-polarization radar D3R is being built to perform cross validation with GPM-DPR which helps provide insight into the physical basis of the retrieval algorithm. This paper is the follow up study of the author's previous paper where a new drop size distribution (DSD) retrieval algorithm was proposed. In this paper, the algorithm evaluation is extended to the complete region including rain, melting ice and ice based on simulation data. A possible method to classify the hydrometeor identification for dual-frequency and dual-polarization ground radar is also proposed which might be applied to D3R.

Global Precipitation Measurement (GPM) is poised to be the next generation observations from space after Tropical Rainfall Measuring Mission (TRMM). The GPM mission concept is centered on the deployment of a core observatory satellite with an active dual-frequency radar (DPR), operating at Ku and Ka bands. The DPR is expected to improve our knowledge of precipitation processes relative to single-frequency radar on microphysics retrievals. Hydrometeor classification method is a key part of any microphysical retrieval algorithm. This paper is focused on the hydrometeor classification method which might be applied to GPM DPR and maps the results to Zdr- DFR plane to cross verify with the pixel based hydrometer identification method. In addition a comparison is made between the DSD retrieval algorithm proposed by the author and other existing algorithm.

Electromagnetic waves backscattered from a common volume in networked radar systems are attenuated differently along the different paths. A network based attenuation correction system for a network of dual polarization radars can be developed by combing the conventional network approach and the conventional differential propagation phase based attenuation correction technique. The network based attenuation correction system proposed here has been evaluated by data from the Adaptive Sensing of the Atmosphere (CASA) Integrated Project 1 (IP1) radars, which is a radar network that can observe a weather event simultaneously by multiple radars in different locations. The preliminary results show that the network based attenuation correction algorithm retrieves reflectivity and differential reflectivity properly.

Lately, the continuing expansion of wind energy industry has led to the installation of several wind farms which are often in the vicinity of the weather radars. This is a source of growing concern for the weather radar community since wind turbines interfere with the normal operation of the weather radars. The wind turbine tower can drive the receivers into saturation and the Doppler shift from the moving blades can introduce errors in the estimation of wind speed, reflectivity and rainfall rates. The radar cross-section of the wind turbines has a large temporal and spatial variation which poses additional difficulties for traditional clutter filtering algorithms. This paper presents a first-order theoretical model of the radar signature of a wind turbine that can be helpful in deducing its unique features to be incorporated in filtering out the wind turbine clutter. A comparison with the observations from an S-band radar is made later in the paper.

The Student Led Test Bed (STB) is part of the NSF Engineering Research Center CASA and is currently focused in developing low-cost and low infrastructure radar networks to fill lower atmosphere gaps not covered by current technology. The first radar node, which is part of a small region radar network, will significantly improve the time and spatial resolution of the radar data measured for the lower atmosphere. This paper describes the development of an Off-The-Grid (OTG) X-band radar node that requires minimum infrastructure for its deployment and can operate using solar energy and wireless communication links. The OTG radar was developed for meteorological applications modifying a commercially available marine radar. Hardware modifications for meteorological purposes were performed as well as the design and implementation of a photovoltaic system to power the radar using solar energy. The system was moved to the Colorado State University (CSU)-CHILL National Weather Radar facility for a cross-calibration and system evaluation. Satisfactory results were obtained where it was demonstrated that the OTG radar can provide precipitation measurements with improved spatial and temporal resolution, both necessary to have better lower troposphere measurements. This OTG node is the first prototype of a low infrastructure X-band weather radar network to aid forecasts in the western region of Puerto Rico.

This paper presents a preliminary evaluation of short-term prediction (nowcasting) of rainfall fields estimated from specific differential phase fields derived from Collaborative Adaptive Sensing of the Atmosphere X-band radar data. A Fourier-space, linear system-based nowcasting method used these rainfall fields as input to generate rainfall forecasts up to 20 min. The results show the extent to which specific differential phase-derived rainfall fields can be predicted and the utility of such predictions to be approximately 15 min.

The Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) has deployed a Distributive, Adaptive and Collaborative Sensing (DCAS) network of four radars in central Oklahoma. The radars operate at the X-band frequency and are capable of polarimetric and Doppler measurements. The radar network is being evaluated for Quantitative Precipitation Estimation (QPE). QPE algorithms based on radar power measurements (e.g. ZHand ZDR) require bias correction. The polarimetric self-consistency principle is applied to the CASA radar data to estimate any bias in ZH. Results show a ZHcalibration accuracy of 0.6 dBZ or less for two the analyzed events. ZHbias estimates from the self-consistency principle in rainfall are compared and validated with ZHbias estimated from the comparison of the X-band and the S-band radars' data. Comparison of the two approaches shows a difference in the ZHbias estimation of 0.61 dBZ or less and validates the use of the self-consistency principle in rainfall for the absolute radar calibration of the CASA radars.

This paper describes some of the novel technologies adopted in the realization of the NASA Dual-frequency Dual-polarized Doppler Radar (D3R) system for to be used by the GPM ground validation program. A description of the transceivers and major trades that lead to a solid-state architecture is presented. Other aspects enabling the design such as the waveform design and generation and the digital receiver is also described. Data measured from a similar power amplifier was used to estimate the expected range side lobe performance. An estimate of the expected sensitivity based on the transceiver parameters also presented.

Recent advances in synthetic aperture radar (SAR) technology have revived meteorological applications with this type of radar. SARs are designed for surface imaging, but now that several X-band multipolarization SAR satellites are in orbit, the attenuation and backscatter caused by precipitation can be better studied. The results presented here demonstrate some of the possibilities by analyzing observations from dual-polarization (HH, VV) TerraSAR-X (TSX) acquisitions over central Florida surrounding severe storms in August 2008. Simultaneous to the SAR acquisitions, WSR-88D ground weather radars in Melbourne and Tampa Bay, FL, collected reflectivity and radial velocity data; the observed strong precipitation cells from convective storms are colocated with severe attenuation in the corresponding SAR images. The observations from SAR measurements are explained quantitatively by converting ground radar reflectivity into spaceborne radar attenuation via a theoretical model. In addition, polarization analysis comparing the SAR image to two additional TSX acquisitions 11 days apart and without rain provides an indication of storm-induced propagation effects on X-band SAR. Specifically, the copolar ratio Z_dr and the copolar correlation differences exhibit behavior that is better explained by the precipitation impact versus surface changes. Multiple regions with varying ground cover, including urban, and storm characteristics are analyzed to highlight the complexity of meteorological research using SAR while revealing a potential use of the technology to investigate the storm structure.

Adequate sensitivity of weather radars using low-powered solid-state transmitter is achieved by using pulse compression waveforms. However, pulse compression waveforms have drawbacks of blind zone and range side lobes. In this paper, we present a methodology to address the major challenges in designing the waveforms for an X-band dual polarization Doppler radar operating with a solid-state transmitter. Here, frequency diversity wideband waveforms are proposed to mitigate low sensitivity of solid-state transmitters and the range eclipsing problem associated with pulse compression. An analysis of the performance of pulse compression using mismatched compression filters is reported. The performance of the proposed system is also quantified using signal and system simulations.

In recent years, it becomes increasingly possible to move the operating frequency of weather radar systems from non-attenuating lower frequencies, such as at S-band, to attenuating higher frequencies, such as at X-band. However, wave is more easily extinct in rain at higher frequencies in which case there will be missing observations. Therefore, rain attenuation is one of the important metrics in radar system design and an extra attenuation margin needs to be applied to the allocation of power budget to meet the required sensitivity. The NSF Engineering Research Center (ERC) for Collaborative and Adaptive Sensing of the Atmosphere (CASA) is advancing a new sensing paradigm using networked short-range radar systems to avoid problematic earth curvature blockage. The CASA ERC has developed a networked radar test bed-Integrated Project 1 (IP1)-in southwestern Oklahoma, using 4 X-band radar of 40 km range to cover an area of 7, 000 km2. In this paper the attenuation margin performance are analyzed in the network context and the metric to design a networked radar system is formed.

Spaceborn precipitation radars acquire atmospheric measurements using beams that scan through nadir. Naturally, the reflection from the surface is typically quite strong, especially at nadir. This surface observation can be used to estimate the attenuation caused by precipitation provided there is a reasonable value of the non-attenuated surface return leading to an estimate of the rain rate. Over land, the backscatter characteristics are usually very dynamic and change in the presence of water due to its high dielectric constant. In addition, the viewing geometry results in very large beam footprints on the ground containing highly variable scattering mechanisms. Using a high resolution Synthetic Aperture Radar, the surface characteristics within this footprint can be examined with respect to how precipitation changes the backscatter. This research investigates both of these radar types, plus ground based weather radars, to quantitatively analyze surface dynamics within the footprint of a precipitation radar.

A tropical weather testbed, inspired by the CASA IP1 radar network [1], is to be constructed on the western coast of the island of Puerto Rico. This new radar network testbed will address Quantitative Precipitation Estimation (QPE) in a tropical environment. The testbed will consist of three low-power, short-range, dual polarized X-band Doppler radars. Special considerations were taken in the design of the radar, as related to the tropical seaside environment of the proposed site locations, where high temperatures, humidity, and elevated salinity in the air are common. The goal of the network is to study tropical weather events in the lower 2 km of the troposphere, where reduced accuracy of precipitation estimates by conventional weather radar can occur. This paper will describe the characteristics of the network radar nodes.

Radar systems with a drifting transmitter frequency (such as those employing magnetron transmitters) typically use an Automatic Frequency Control system to keep the receiver down-conversion settings tuned to the transmitted frequency. This paper presents a method to incorporate the Automated Frequency Control information in measurements affected by changing receiver frequency down-conversion settings, such as phase measurements to obtain change in refractivity fields.

The dual-frequency precipitation radar (DPR) aboard the GPM (Global Precipitation Measurement) core satellite is expected to improve our knowledge of precipitation processes. Ground validation is an integral part of all satellite precipitation missions which helps provide insight into the physical basis of the retrieval algorithm. A dual-frequency (Ku and Ka band) and dual-polarization ground radar will be built in near future to perform cross validation with GPM. This paper presents a new algorithm to retrieve parameters of the drop size distribution from this dual-frequency and dual-polarization ground. The method is based on combination of DFR (dual frequency ratio) and dual-polarization approach. Attenuation correction is solved within the retrieval process. The proposed algorithm is evaluated based on simulated Ku and Ka band realistic observations, for rain, melting layer and ice parts.

Shortterm weather forecasting (nowcasting) performance can be improved by spatially filtering radar images of precipitation patterns and either predicting only those precipitation scales most representative of pattern motion or removing unpredictable scales after prediction. Previous research has shown that improvement in nowcasting precision can be obtained by first applying an elliptical spatial filtering procedure to observed WSR-88D Vertically Integrated Liquid water data to predict larger-scale features believed to better represent the motion of precipitation patterns. Another study used the wavelet transform to develop measures of predictability at each scale and designed adaptive wavelet filters to remove perishable scales from predicted continental-scale reflectivity data. This study investigated the adaptation of both approaches and Fourier filtering to examine the effects of scale filtering on nowcasting performance using a spectral-based nowcasting method and high-resolution Collaborative Adaptive Sensing of the Atmosphere radar reflectivity data.

The Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) has deployed a Distributive, Adaptive and Collaborative Sensing (DCAS) network of four radars in central Oklahoma working as a closed-loop system since 2006. The radars operate at the X-band frequency and are capable of polarimetric and Doppler measurements. The radar network is being evaluated for Quantitative Precipitation Estimation (QPE). QPE algorithms based on radar power measurements (e.g. ZHand ZDR) require bias correction. ZDRcalibration is required prior to any application of the self-consistency principle. Two different methods were evaluated for ZDRbias correction. The intrinsic properties of dry aggregated snow present above the melting layer and light rain measurements close to the ground are used for the study. Results show a ZDRcalibration accuracy of 0.2 dB or less for both analyzed events when both methods are compared.

Multi-sensor data fusion latency in Internet-based sensor applications is analyzed. The probabilistic estimation of periodic backlog (PEPB) technique presented takes into account the nature of sensor data generation and the time-scale invariant burstiness (i.e., self-similarity) of network traffic. The fusion application considered requires synchronizing a set of correlated data before fusion processing can begin. PEPB is used first to model the single-hop delay. Analysis is then extended to a network with multi-hop communication and multiple sensor nodes. Comparison with simulation-based results demonstrates the accuracy of the model.

A networked waveform system is developed to overcome the fundamental limitation of a single pulsed Doppler radar in resolving ambiguities. The networked radar system uses the principle that the underlying intrinsic properties of the precipitation medium remain consistent in a network. The ambiguity in range and velocity is resolved by jointly processing the measurements from all the radars in the network. In this paper results for networked waveform system are shown for simulated data as well as data collected with first generation CASA (The Center for Collaborative Adaptive Sensing of the Atmosphere, an engineering research center established by the National Science Foundation) radars deployed in Oklahoma.

Neural network is a nonparametric method to represent the relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. Tropical Rainfall measuring Mission (TRMM) Precipitation Radar (PR) is known to be the first observation platform for mapping precipitation over the tropics. TRMM measured rainfall is important in order to study the precipitation distribution all over the globe in the tropics. TRMM ground validation is a critical important component in TRMM system. However, the ground sensing systems have quite different characterizations from TRMM in terms of resolution, scale, viewing aspect, and uncertainties in the sensing environments. In this paper a novel hybrid Neural Network model is presented to train ground radars for rainfall estimation using rain gage data and subsequently using the trained ground radar rainfall estimation to train TRMM PR based Neural networks. One year of ground data from Melbourne Florida and Houston Texas are used to demonstrate this hybrid approach. The performance of the rainfall product estimated from TRMM PR is compared against TRMM standard products. A direct gage comparison study is done to demonstrate the improvement brought in by the neural networks.

A dense weather radar network is an emerging concept advanced by the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). A major goal of CASA is to develop an entirely new paradigm, referred to as Distributed Collaborative Adaptive Sensing (DCAS), for improving the coverage of the lowest portion of the atmosphere through coordinated scanning of low-power, short-range, networked radars. The CASA enterprise designs, develops, and deploys system-level test beds to integrate underlying scientific and technical advances and demonstrate the potential to observe, understand, predict and respond to hazardous atmospheric phenomena-with end users involved from the outset. The first one of DCAS test-beds was deployed in south-west Oklahoma, named as Integrated Project 1 (IP1). It is an end-to-end system of a network of four, low-power, short-range, dual polarization, Doppler radar units, aimed at severe weather and hazardous wind sensing. In this paper, a number of aspects in developing a DCAS radar network for these specific applications are reviewed.

A real-time refractivity retrieval platform for the CASA IP-1 [1] testbed is currently being developed at the University of Oklahoma. From our previous efforts in the 2007-2008 KTLX/KFDR refractivity experiment [2], a software module to produce refractivity products has been developed and is ported over to the IP-1 testbed this year. One of the challenges for refractivity using the IP-1 radars is the use of X-band systems, which results in more rapid phase wrapping across ranges. In this work, a theoretical explanation will be presented to show that X-band is not a limiting factor to refractivity retrieval. Another significant challenge is the use of magnetron-based transmitter, which changes the effective wavelength being applied for measuring the propagation phase. This question remains open but it will be shown that through the use of differential refractivity technique, scan-to-scan refractivity can still be useful in practice.

Efforts to estimate the near-surface moisture field using weather radar returns from coherent ground targets has been successfully demonstrated in recent years. A very crucial step to make the moisture retrieval successful is the identification of appropriate targets. The estimation process also depends on adequate spatial distribution of these targets, so it is actually more important to have a good distribution than to have every target be extremely coherent. Presently, however, there is no fully automated technique to identify the targets. The currently accepted method is to manually examine phase difference scans that span approximately an hour to identify a time period where the phase gradient is approximately the same across all azimuths. Unfortunately, this is not simple and improper selection can have drastically different results. In order to design an algorithm to automate this process, one must first understand the phase behavior of ground reflections for this purpose. Previous analysis of ground returns were for the purpose of eliminating them, but the work presented here aims to identify suitable targets for refractivity estimation.

One of the most promising applications of synthetic aperture radar (SAR) imagery is change detection. However, the success of change detection algorithms is highly dependent on the type of change being detected. With the advent of multi-channel SAR systems (multi-frequency and/or polarimetric) new algorithms to improve change detection are being developed to take advantage of the additional information. However, these algorithms are still hindered by the speckle phenomena of radar imaging. Adaptive neighborhood filtering techniques have been shown to be effective to reduce speckle, but the impact on change detection has not been explored. The research presented explores the impact of an adaptive filtering algorithm on the probability of detection for synthesized changes.

Pulse compression is a useful technique for weather radar, as an enabling technology to facilitate use of low-power solid state transmitters. It also has the benefit of improving the dynamic range and range resolution of the radar, permitting rapid scanning of a volume. The nonlinear FMpulse waveform described produces the low sidelobe levels required for weather radar applications, while remaining Doppler-tolerant within the range of radial velocities expected for weather radar (±100 m/s). Traditional pulse compression waveforms must be modified to reduce their range sidelobes to levels suitable for use in weather radar. The use of pulse compression involves some changes to the methods used during radar calibration, and places some restrictions on the design and implementation of the RF and IF components of the radar.

X-band weather radar systems present several advantages mainly related to their lower cost and smaller size relative to their S and C band counterparts. The main drawback of X-band is the attenuation suffered by the electromagnetic wave propagating through precipitation that reduces the reliability of rainfall estimates based on power measurements. Developments in dual-polarization techniques have provided solutions to mitigate this problem and have revived the interest on X-band radar systems for operational applications. Because of this growing interest, there is a need to evaluate the performance of X-band polarimetric radars for quantitative rainfall estimation. At X-band, attenuation affects any radar parameter based on backscatter power measurements such as Zh, while differential attenuation affects parameters based on differential power measurement such as Zdr. Consequently, Zhand Zdrmust be corrected prior to use in quantitative applications such as rainfall estimation. However, correction procedures can introduce additional errors that impact on rain estimation and therefore must be applied with caution. When using the X-band rain algorithm based on Zhand Zdr, the biases due to attenuation and differential attenuation nearly cancel each other and result in a small bias of the estimated rainfall rate, so that correction of Zhand Zdrmay not be needed. This paper investigates this property of rain rainfall estimation based on X-band dual-polarization measurements.

The Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is investigating the use of dense networks of short-range radars for weather sensing. A first test-bed of this new paradigm is currently deployed in southwest Oklahoma. The potential benefits of closely deployed, overlapping, short-range weather radars are easy to see intuitively amounting to a greater ability to measure at lower beam heights (mitigating the effects of the Earth curvature), an increased spatial and temporal resolution in the measurements, and the capability of optimally and adaptively tasking the individual radars according to the meteorological scene. In this paper formulations for radar network design are provided, with various parameters such as number of radars with overlapping coverage, network coverage area, number of radars in a network, and number of elemental cells in a network, and applied to the design of a radar network based on system specifications such as detection sensitivity, beam size, minimum beam height, and overlapping coverage.

Global precipitation measurement (GPM) is poised to be the next generation precipitation observations from space after the TRMM mission. The GPM will carry a dual-frequency precipitation radar (DPR), operating at Ku-band, and Ka-band frequencies. Since spaceborne precipitation observations have never been done in Ka-band before, extensive research work on dual-frequency radar, including electromagnetic wave propagation characteristics from space and retrieval algorithms are essentially required in developing system design and instrument performance evaluations. This paper presents a simulation-based study of Ku-and Ka-band radar observation of precipitations.

The first generation testbed of the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA-IP1) is currently operational in Oklahoma. The CASA-IP1 system is a radar network observing a weather event simultaneously by four radars. Within the CASA, a network-based reflectivity retrieval technique has been developed and evaluated extensively. This paper presents the evaluation of the network-based reflectivity retrieval by comparing retrieval results using CASA-IP1 data with WSR-88D observations. This paper also describes the design and implementation of an architectural framework for real-time processing of the network-based retrieval algorithm. Experimental results show that the implementation meets the real-time requirement of CASA.

This paper presents an adaptive time domain filter for ground clutter filtering and signal parameter estimation for dual-polarization capable weather radars. The auto-covariance function of radar signal can be expressed as a sum of auto-covariance functions of the clutter, precipitation and noise that follow Gaussian forms. The filter matrix is designed such as when it is applied to the time series data, clutter component in the signal will be transformed to noise (i.e. the auto-covariance matrix is diagonal). However, weather echoes overlap clutter are also suppressed. An interpolation procedure then is used to recover the transformed part of the weather. The proposed design overcomes limitations of current spectral processing method caused by finite length of the data. A unique filter can be designed to use for both H and V channels for dual-pol parameter estimation. This way ensures the correlation between the two channels and minimizes estimate errors. In addition, the filter can be directly extended for staggered PRT 2/3 sampling scheme. The filter performance analysis was done using simulated time series radar data and CSU-CHILL measurements.

The NSF engineering research center for Collaborative and Adaptive Sensing of the Atmosphere has developed a networked weather radar test-bed to monitor and respond to severe thunderstorms and severe wind. For acquisition of radar reflectivity and other scalar fields, each radar is an independent unit and only the radar range equation is of primary concern. However, with respect to the acquisition of wind velocity, pairs or triplets of radar need to be considered as units and the perspective observation angles are of primary concern, besides the constraint in range. Multiple candidate pairs exist for most of the test-bed coverage and choice has to be made in scan strategy. In addition, the whole network is required to update the observations within a finite time period. This further limits the number of sweeps depending on the sector width to be scanned. All these constraints are consolidated together in generating the scan strategy based on the best dual-Doppler pair. In this paper, the method to generate dual Doppler scan strategy is presented. The method is tested in field experiments for scan evaluation and the results are presented.

IP1 is a prototype CASA radar sensor network located in southwestern Oklahoma whose goal is to detect severe weather in the lower part of the atmosphere. At the center of this system's control loop is its Meteorological Command and Control (MC&C). In this paper, we presented the overall control architecture for the IP1 network and highlight new features that have recently been added to the MC&C. We also present an analysis of the MC&C performance based on measurement data from a 5-day operation period. In addition, we introduce a distributed version of the MC&C.

Oversampling weather radar signals in range and then whitening these signals has been shown to improve the accuracy of spectral moments. For dual-polarized radar, the polarimetric variables depend upon information gleaned from the cross correlation of the different received signals. Theoretical improvements to the polarimetric variables have been provided to date, but experimental evidence of improvements through whitening has been limited. This paper provides an analysis of the effects of whitening on the estimated cross correlation along with experimental results of whitening applied to polarimetric variables. Different whitening transformations based solely on covariance matrix inversions will be shown to affect the copolar correlation of the whitened data. A Hermitian symmetric whitening transformation will be shown to produce better estimates of polarimetric variables obtained from whitened data than the original whitening transformation defined for use with range oversampling.

The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), an engineering research center (ERC) established by the National Science Foundation (NSF) deployed its first generation network of four low-power, short-range, X- band, dual-polarized Doppler weather radars known as NE- TRAD. The short range CASA radars will have range overlay and velocity folding problems with conventional pulse-pair processing. The first testbed of X-band radar systems (developed within the ERC) in central Oklahoma called IP-1 (Integrated Project 1) have a low unambiguous velocity due to their short wavelength, and increasing the PRF will result in multiple trip overlays since storms can extend over a large distance. In addition the radar observations at short ranges are contaminated by ground clutter. This paper describes the waveforms for the individual radar nodes based on NETRAD operational requirements such as scan speeds, volume coverage pattern and system/hardware limitations to resolve range and velocity ambiguities. A dual PRF waveform has been suggested for operational use based on a simulation study. This paper presents an evaluation of the waveform from data collected by the first generation CASA radars.

Spectral decompositions of dual polarization observation allows for extending of analysis of polarimetric radar data to the new dimension, namely Doppler frequency domain. In this paper it is shown how this new paradigm can be used for improving quality of radar data. On an example of the CSU- CHILL observations it is demonstrated that this analysis can be used to design a spectral filter that allows for both ground clutter mitigation and radar sensitivity improvement.

The retrieval of the surface-layer moisture field can be obtained by estimating the refractive index of air, measured in parts per million and referred to as refractivity. A technique developed to estimate the refractivity using radar has been demonstrated with validated results from field experiments [1], [2]. This technique utilizes the measured change in phase from stationary ground targets, which will be primarily due to changes in refractivity at warmer temperatures given a stable radar frequency. While this technique has been successfully demonstrated on individual radars, there is no clearly defined method of combining multiple radar estimations beyond gridding and averaging. Recently, however, a new algorithm was proposed as an alternative approach, especially when dealing with multiple radars [3]. The new algorithm uses a minimum least squared error approach, with a smoothing constraint and a method to address phase wrapping. This paper will explore the implementation of this constrained least squares (CLS) approach with data collected during a refractivity field experiment during the summer of 2006 in Colorado. It will also explore the implications of running the CLS algorithm in real-time, and briefly discuss future directions.

Rain microphisycs retrieval has been proposed and demonstrated mainly at S-band where attenuation effects are usually negligible. Recent advances in attenuation correction techniques enable rain microphisycs retrieval also using attenuating frequencies, such as X- and C-band. Most up to date attenuation correction methodologies are based on the use of a constraint represented by the total amount of the attenuation encountered along the path, which is distributed over each range bin contained in the path. The inner self-consistency of radar measurements can be exploited to improve the attenuation estimate obtained using those techniques. In fact, based on the self-consistency principle, an optimization procedure can be devised to obtain the best estimate of specific and cumulative attenuation as well as of specific and cumulative differential attenuation. The main goal of the study is the examination of the drop size distribution retrieval from X-band radar measurements after attenuation correction. The study is based both on simulated and measured data. Simulations based on the use of profiles of gamma drop size distribution parameters obtained from S-band observations are used for quantitative analysis. Examples of the DSD parameter retrievals using radar measurements corrected for attenuation and differential attenuation are also shown.

The use of dense networks of small radars for weather sensing is being investigated by the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere, with a first test-bed of this new paradigm well underway. The potential benefits of closely-deployed, overlapping, short-range weather radars are easy to see intuitively, and can be summarized as a greater ability to mitigate the effects of the Earth curvature and sense close the ground in all of the network domain, an increased spatial and temporal resolution, the capability of performing multiple-radar measurements, and the capability of adaptively tasking the individual radars according to the meteorological scene, while using less complex radar units. Virtually all of these potential benefits are governed by the trade-offs generated by the characteristics of the particular radar units employed and their spatial distribution, creating different data outcomes depending on the individual radar capabilities, the radar network layout, and the resulting number of radars with overlapping coverage.

Megha 2700, S-band ground-based Doppler weather radar (DWR) located on the east coast of India, is capable of providing long range detection and characterization precipitation and severe storm such as tropical cyclone over Bay of Bengal. The location of the radar is in coverage area of Tropical Rainfall Measurement Mission (TRMM) satellite. Precipitation radar (PR) operating at Ku-band (13.8 GHz) abroad the TRMM has capability of providing vertical structure of tropical cyclones. Although tropical cyclones can be frequently observed by PR, and their vertical structure can be studied, it is not so frequent that they are observed by most existing Ground based weather Radars (GR). The authors take this opportunity for performing inter comparison between the two radars and inter-compare vertical profile of reflectivity (VPR) of tropical cyclone which were simultaneously observed by the Megha 2700 DWR and PR This work can be useful not only in cross-monitoring severe tropical cyclone activity in a long range over Bay of Bengal, but also as ground validation study of TRMM, and even to the future global space based precipitation mission such as Global Precipitation Measurement (GPM), which is planed to fly over the globe in the near future.

A network-based reflectivity retrieval technique has been developed within the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). The concept of a networked- radar system is simultaneous observations of the same precipitation event by multiple radars operating at the attenuating frequency such as X-band and scanning in a low elevation plane. This paper presents the preliminary demonstration of the network-based retrieval using data from the first Integration Project (IP1) radar network in Oklahoma. Electromagnetic waves backscattered from a common volume in a networked radar system are attenuated differently along the different paths. The CASA networked-retrieval method is based on a set of governing integral equations describing the backscatter and propagation of common volume with constraints of total path attenuation. The method has been implemented in a multiprocessor environment, which operate simultaneously and collaboratively to meet the real time requirement of CASA. The performance of the implemented retrieval algorithm such as computation requirement will be presented. Comparison of the CASA networked retrieval is made against the conventional attenuation correction based on the principle of coupling the specific attenuation, differential propagation phase and reflectivity. The preliminary results show good agreement with conventional differential phase base attenuation correction.

This paper reports on a real-time three-dimensional radar mosaic technique for Collaborative and Adaptive Sensing of the Atmosphere (CASA) Integrated Project 1 (IP1) testbed. The technique exploits the dual-polarization capability of the IP1 radar network for the improvement of multi-radar composite fields. In particular, the copolar correlation and the attenuation derived from the differential propagation phase are injected to the interpolation process to derive a weighting scheme and a data quality index field for the composite fields. This technique is also served as a front-end that drives the composite reflectivity fields to the CSU DART now-casting algorithm.

Numerous space-borne X-band Synthetic Aperture Radars (X-SAR) systems will be launched by European agencies in the coming decade commencing this year. Those X-SARs can measure precipitation over land, thereby significantly augmenting the sensors that comprise the Global Precipitation Mission (GPM). This will incur relatively little incremental cost because they have already been funded. X-SAR measurements are especially beneficial over land where rainfall is difficult to measure by means of microwave radiometers that depend on scattering by frozen hydrometeors associated with that rain. The improved horizontal resolution of the retrievals will match the higher spatial resolution of mesoscale and general circulation models that will become available in the coming decade.

In this paper, a method for clutter filtering and the estimation of spectral moments from non-uniformly sampled Doppler weather radar signals is presented. The method uses a parametric model for the received signal. The spectral moments of both clutter and precipitation echoes are estimated using the maximum likelihood estimator. Unlike the current frequency domain techniques where non-uniformly sampled signals are zero-padded to be uniform sequences, the likelihood function used in this algorithm can be constructed directly from the non- uniformly sampled data. Therefore an accurate estimation of spectral moments even in case of clutter - to - signal ratio as high as 60 dB can be obtained. The proposed method is illustrated on simulated radar signal time series and measurements collected using the staggered pulse repetition time (PRT) and dual pulse repetition frequency (PRF) transmission schemes from the CSU- CHILL and collaborative adaptive sensing of atmosphere (CASA) IP1 radars.

The Tropical Rainfall Measuring Mission (TRMM) is NASA's first mission dedicated to observing and understanding tropical rainfall and its effects on global climate. The Precipitation Radar in TRMM is the first spaceborne instrument designed to obtain three-dimensional maps of precipitation reflectivity. Such measurements yield information on the intensity and distribution of rain, rain type and storm depth. An advantage of space radar is that the scattering volume has similar size at any location. However, it has been a challenge to compare data to the one that is collected from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) for varying scan angles. Intercomparisons between ground radar and spaceborne radar on a point-by-point basis can be a difficult task. Errors result from the mismatch between ground radar and spaceborne radar resolution volume, spatial alignment, and operating frequencies as well as the limited number of the data set collected instantaneously by both instruments. Differences in viewing aspects and resolution that result from the measurement of return signals from different volumes of the precipitation medium contribute to the intercomparison error. A study of the characteristics of the region of the bright band from TRMM-PR vertical profile measurements on a global scale indicates that while bright band height varies widely, the distribution of bright band structure does not vary around the globe. The results show that the average profile of the bright band vertical profile using bright band height as a reference point around the globe has unique profile and do not changes around the globe for large data set. This unique profile can be used to adjust the radar observation error due to different parameters. The TRMM-PR vertical resolution becomes poorer with increasing distance of the TRMM-PR samples from the nadir. Studying the model profile at the nadir profile and other profiles that are off-nadir ray can be used to build the statistical model that can be used to adjust the effect of the scanning cross-track at angle far from the nadir-ray, and the results are presented.

Systems engineering constitutes a group of processes and methods to design and implement a system for optimal performance given limited time, technology, or resources. As with any system, it is important to understand which subcomponents are most important and which are less important so that appropriate resource allocations may be made. An example of a complex system is the Tropical Rainfall Measuring Mission (TRMM). Its subsystems include the satellite vehicle, the precipitation radar (PR), the ground validation system, and the retrieval algorithms. Each of these subsystems contributes to the overall success of the mission. Sensitivity analysis (SA) is a method whereby the output response from a model can be linked back to the variability in the input parameters. This paper describes a method of performing SA on a TRMM PR-like (TL) rainfall retrieval algorithm (based on the TRMM 2A25 algorithm) to better describe how the uncertainty in the model output can be apportioned to the uncertainty in the input factors and gain greater understanding as to the relative importance of each factor. For example, assuming a model with several input factors, if one factor is found to be the dominating cause of model error, and the others contribute relatively little, then resources can be devoted to improving the accuracy of one factor, thereby improving the overall model accuracy. This paper is based on global SA using a variance decomposition technique. Analyses are done and results are presented for factor importance for cases over both ocean and land. Results for the simple TL algorithm considered in this paper show that at low rain rate, the a and b coefficients in the R=aZebrelationship contribute the greatest amount to the output variance. At higher rain rates, above about 8 mm/h, the error from Deltasigmadeg is the greatest contributor to error in algorithm output

The variability of the drop size distribution (DSD) is one of the factors that must be considered in understanding the uncertainties in the retrieval of oceanic precipitation from passive microwave observations. Here, we have used observations from the Precipitation Radar on the Tropical Rainfall Measuring Mission spacecraft to infer the relationship between the DSD and the rain rate and the variability in this relationship. The impact on passive microwave rain rate retrievals varies with frequency and rain rate. The total uncertainty for a given pixel can be slightly larger than 10% at the low end (ca. 10 GHz) of frequencies commonly used for this purpose and smaller at higher frequencies (up to 37 GHz). Since the error is not totally random, averaging many pixels, as in a monthly rainfall total, should roughly halve this uncertainty. The uncertainty may be lower at rain rates less than about 30 mm/h, but the lack of sensitivity of the surface reference technique to low rain rates makes it impossible to tell from the present data set.

Natural variations in raindrop size distribution (DSD) were studied for simulated X band radar response from October 2004 to July 2005 (normal no-storm conditions) and during September 15th to 16th, 2004 when the tropical storm Jeanne passed over the island of Puerto Rico. Three types of estimators of rain rates were examined: A classical estimator R(ZH) and two polarimetric radar estimators R(KDP) and R(ZH,ZDR) . According to simulation results, the normalized errors (NEs) with respect data disdrometer of R(ZH),R(KDP) and R(ZHZDR) for all DSD samples in October 2004 to July 2005 data are 40.85%, 14.73%, and 15.83% respectively, while for the tropical storm Jeanne they are 23.39%, 9.35% and 14.53%. The results show that the estimator R(ZH) is the most sensitive to variations in DSD. Calibrated R-Z relations where developed for X-band for two conditions: storm/no-storm. Data from NASA TRMM satellite and rain gauges, the rain rate per hour was used for pinpointing areas of heaviest rain and for validation.

The first testbed of X-band radar systems deployed by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), in central Oklahoma called IP-1 (Integrated Project 1) will have a low unambiguous velocity due to their short wavelength, and increasing the PRF will result in multiple trip overlays since storms can extend over a large distance. The range-velocity ambiguity is more severe for X-band radars compared to the conventional S-band. However, low cost radars limit the ability to support complex waveforms due to hardware requirements. In addition the radar observations at short ranges are contaminated by ground clutter. This paper describes the waveforms for the individual radar nodes based on operational requirements such as scan speeds, volume coverage pattern and system/hardware limitations to resolve range and velocity ambiguities along with clutter suppression.

The surface-layer refractivity, i.e., the refractive index of air near the earth's surface, can be retrieved from radar using a technique developed by Frederic Fabry, et al. In warm weather, the equation for the index of refraction humidity dominates over temperature and pressure, which has a significant impact on the phase of propagating electromagnetic waves from a radar. Thus, the refractive index can be measured by observing the phase change between any two stationary ground targets along a radial from the radar at roughly the same ground level. This index, in turn, can be used to estimate the water vapor near the surface. With this data, the evolution of the near- surface boundary layer moisture field leading up to convective storm initiation and storm evolution can be detected to enhance quantitative precipitation forecasts. Stationary ground targets are those that return strong radar echoes and do not produce rapid phase changes under slowly varying humidity levels, unlike vegetation, for example, that adds a significant random component to the measured phase as it moves in the wind. The calibration stage, which determines the stationary targets and reference phase, is a critical step, currently requiring manual selection of scans, ideally under conditions of uniform humidity in the radar coverage space. This procedure was recently performed using the dual-polarized CSU-CHILL S-band radar to estimate the refractivity. Prior to this experiment, only single polarization had been used for this estimation.

This paper describes how the synchronization and networking capabilities of the transmit and receive chain used at the CSU-CHILL and CSU-Pawnee radars are used to form a bistatic radar system capable of observing clear air echoes from atmospheric boundary layer. An overview of the bistatic radar geometry and resolution volume are presented, along with a discussion of the methods used to achieve timing coherence. Some preliminary results from clear-air observations are included.

Dual polarization weather radars have brought in significant advancement to precipitation observation, as rainfall rate estimation, microphysical characterization, and hydrometeor classification. The improvements have been mostly demonstrated at S-band frequency where attenuation effects are usually negligible. In Europe C-band is largely adopted in operational and research radars because of larger differential phase measurements, reduced antenna size and an overall lower cost with respect to that of S-band systems. The major disadvantage is that the signal attenuation is not negligible. In the context of GPM Ground Validation, techniques to compensate the reflectivity measurements for propagation effects are thus necessary to obtain GV products from ground-based C-band radars. The attenuation correction methodology using differential phase shift as constraint has shown a good performance. One of the advantages of polarimetric radar measurements is their self-consistency. Starting from the initial guess of attenuation correction provided by the rain profiling algorithm, self-consistency is used in an iterative technique to improve the accuracy of attenuation correction at C-band. The obtained accuracy is evaluated in terms of bias and standard error using C-band profiles generated from S-band dual polarization observations.

The CASA NSF ERC is using cutting edge radar technology to create a network of short range weather radar to observe the lower regions of the atmosphere that are currently out of sight. Range oversampling and whitening is one of the newest technologies available for increasing the accuracy of weather radar moment estimates. The CASA mission has incorporated range oversampling into the design of one the first prototype radars created for the mission. Due to several constraints, the MA-1 radar has several hardware design differences from the large state-of-the-art S-band weather radars that oversampling and whitening was developed and verified with. The oversampling and whitening process is validated with the suboptimal MA-1 radar configuration by producing results that adhere to theoretical expectations.

Precipitation radar (PR) on Tropical Rainfall Measuring Mission (TRMM) satellite provides high resolution vertical profile of reflectivity (VPR) of tropical storms. Three- dimensional downward-looking observations of tropical storms are very useful to study Hurricanes and Typhoons. The increased reflectivity measured in bright band (BB) region can lead to rainfall overestimate. It is also known that VPR of BB holds extensive information on the types of precipitation and their variability. Better knowledge of VPR of storms is important to understand cloud dynamics and microphysical processes, and to improve satellite retrieval algorithm. Because of a large number of VPR observation, it is of interesting to classify the VPR into characteristic profiles so that it can be useful in studying and comparing different vertical reflectivity profiles. In this study, Self Organizing Map (SOM) Neural Network is used as a method to study and classify VPR of Hurricanes and Typhoons. SOM is unsupervised neural network. It forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships of VPR. Similarity relationships within the VPR data and its vertical structure can be visualized and interpreted. Preparation of vertical profile of reflectivity used as input vectors of SOM algorithm is one of the most vital steps. In total eleven Hurricanes and forty Typhoons are studied. VPR of Hurricanes and Typhoons are classified into characteristic profiles. The result of classification shows a distribution that indicates location of each characteristic profile within a storm when viewed from the PR. Percentages of contribution of each characteristic profile to Hurricanes and Typhoons can also be determined. By using SOM, VPR can be classified into various numbers of classes up to one hundred. In this study, VPR is classified into four classes. Two simple operations were performed. Firstly, SOM was applied to all VPR data regardless of rain type. Secondly, stratiform and convective portion of VPR was applied to SOM separately. For stratiform portion of Hurricanes and Typhoons, the bright band (BB) properties including the height of BB peak, BB thickness, reflectivity of BB peak and BB sharpness index of Hurricanes and Typhoons are investigated and compared to those of generic oceanic storm. Comparison of those BB properties of Hurricanes, Typhoons and generic oceanic storm reveals similarities and differences among them.

Many dual-frequency DSD retrieval algorithms have been proposed for space-borne radars. A self-consistent backward iterative algorithm, based on non-Rayleigh scattering has been studied recently[1][2][3]. This algorithm is based on a certain DSD model of rain, for which the attenuation caused by the ice or mixed-phase particles should be extracted accurately. Any error in the attenuation correction at one of the two frequencies would affect the accuracy of the DSD retrievals for rain region. This paper examines how the DSD retrieval for rain is affected by the reflectivity correction for attenuation due to the ice or mixed-phase particles in bright band. First, a non-coalescence and non-break up (N-N) model, with an adjustable thickness and the DSD at the bottom of the melting layer is used to generated the reflectivity and specific attenuation profiles. The profiles for varying DSD (Nwfrom 1000 to 8000, D0from 1.0 mm to 1.75 mm) are used to derive the alpha and beta coefficients for k- Z relationships for different heights, for a certain thickness of the bright band. Then the k-Z relationships are incorporated in Hitschfeld-Bordan method to evaluate the two way attenuation at the two frequencies. Last, the reflectivities in rain region, considered of the attenuation correction error, are made of use of by the self-consistent backward iterative algorithm [2] to retrieve the DSD. The simulation shows that the accuracy of the attenuation correction for Ka-band, the higher frequency, is crucial for the dual-frequency iterative algorithm to correctly retrieve the DSD. While the attenuation correction error at Ku-band remain negligiblely small, the error at Ka-band could be as large as 0.47 dB, and this error would have been too large for backward iterative method to correctly retrieve the DSD. The method presented in his paper can be used to evaluate any rain DSD retrieval algorithms proposed for GPM.

In this study the problem of precipitation signal spectral moments estimation in case of clutter contamination is considered. It is proposed to use a parametric model to estimate spectral moments of precipitation echoes and clutter. To estimate these spectral moments the maximum likelihood estimator based on the properties of Gaussian joint distribution of complex time series is used. The main advantage of this approach is that it does not suppress any part of the signal and the properties of weather echoes and clutter are estimated simultaneously. The performance of the proposed method is evaluated based on simulations of radar signals and compared to the performance of GMAP (Gaussian model adaptive processing). The proposed procedure is also applied to measurements collected by CSU- CHILL radar collected during summer 2004.

It has always been a challenge to compare data that is collected from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) for varying scan angles. Intercomparisons between ground radar and spaceborne radar on a point-by-point basis can be a difficult task. Errors result from the mismatch between ground radar and spaceborne radar resolution volume, spatial alignment, and operating frequencies as well as the limited number of the data set collected instantaneously by both instruments. Differences in viewing aspects and resolution that result from the measurement of return signals from different volumes of the precipitation medium contribute to the intercomparison error. Searching for a model profile from one of the atmospheric phenomenon that has unique characteristics can help to adjust the error in the collected data and can serve as an intercomparison data set. The TRMM-PR produces unprecedented high-resolution vertical profiles of precipitation that can be used in study the characteristics of the precipitation. One of the most widely known radar signatures is Bright band. A study of the characteristics of the region of the bright band from TRMM- PR vertical profile measurements for one year on a global scale indicates that while bright band height varies widely, the distribution of bright band structure does not vary around the globe (1). This result is examined in this paper by comparing the vertical profile of reflectivity at bright band region using bright band height as a common reference from different regions around the globe. The results show that the average profile of the reflectivity vertical profile of stratiform rain type with bright band using bright band height as a common reference around the globe has unique profile and does not change around the globe for a large data set. This unique profile can be used to adjust the radar observation error due to different parameters. This result can be used to adjust the effect of the scanning cross-track at angle far from the nadir-ray by intercomparison the model profile at the nadir ray with other rays profile at the scan edge.

In many emerging sensing applications, random network losses may lead to drop of critical information, rendering partially received data useless for the end users. A packet-marking scheme based on the application content is proposed that enables application-aware processing of the data within the overlay network. A token-bucket based rate control algorithm in conjunction with the proposed packet-marking scheme enables on-the-fly selection of data for forwarding/drop to a particular end user at the desired transmission rate. We demonstrate the effectiveness of the packet-marking and token-bucket based rate control scheme in simultaneously meeting heterogeneous QoS requirements of the multiple end users for content quality and bandwidth for a weather monitoring sensing application.

A new attenuation correction algorithm based on profiles of reflectivity, differential reflectivity, and differential propagation phase shift is presented. A solution for specific attenuation retrieval in rain medium is proposed, which solves the integral equations for reflectivity and differential reflectivity with cumulative differential propagation phase shift constraint. The conventional rain profiling algorithms that connect reflectivity and specific attenuation can retrieve specific attenuation values along the radar path assuming a constant intercept parameter of the normalized drop size distribution. However, in convective storms, the drop size distribution parameters can have significant variation along the path. This paper presents a dual-polarization rain profiling algorithm for horizontal looking radars incorporating reflectivity as well as differential reflectivity profiles. The dual-polarization rain profiling algorithm has been evaluated with X-band radar observations simulated from drop size distribution derived from high-resolution S-band measurements collected by the Colorado Statue University CHILL radar. The analysis shows that the retrieved specific attenuation, differential attenuation, reflectivity, and differential reflectivity from the dual-polarization rain profiling algorithm provide significant improvement over the current algorithms.

A new Dual-Frequency Precipitation Radar (DPR) will be included on the Global Precipitation Measurement (GPM) core satellite which will succeed the highly successful Tropical Rainfall Measuring Mission satellite launched in 1997. New dual-frequency drop-size distribution (DSD) and rain-rate estimation algorithms are being developed to take advantage of the enhanced capabilities of the DPR. It has been shown previously that a backward-iteration algorithm can be embedded within a single-loop feedback model to retrieve the rain rate. However, the single-loop model is unable to correctly estimate DSD profiles for a significant portion of global median-volume-diameter, D/sub o/, and normalized DSD intercept parameter, N/sub w/, combinations in rain because of a multiple-value solution space. For the remaining D/sub o/,N/sub w/ pairs, another retrieval method is necessary. This paper proposes a dual-loop model, in which the intercept parameter, N/sub w/, of the DSD is constrained in its vertical profile, to guide the algorithm to correct convergence. This allows an additional constraint on the DSD values estimated by the iterative algorithm and helps to retrieve correct DSD values in the regions where the iterative approach alone fails. To demonstrate feasibility of the proposed method, three test cases representative of many DSD and profile combinations are discussed. The first case is a constant vertical profile of the DSD parameters. The second case examines linear variation of the DSD parameters, and the third case examines how measurement error affects the retrieval process. In each case, the proposed constraint on the intercept parameter is implemented, and the results are discussed. Using the constraint, the dual-loop algorithm is able to retrieve reasonable values for the DSDs and rain-rate profiles and extend the convergence region of the algorithm.

The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dual-polarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift values

A dual-polarization radar system operating in simultaneous transmission mode of both horizontal and vertical polarization states is a viable implementation if only copolar measurements are needed. The simultaneous transmission of horizontal and vertical polarizations results in an arbitrary elliptical polarization state transmitted, whereas the reception states in horizontal and vertical polarizations are neither copolar nor cross-polar to the transmitted state. Because of this, it is often referred as the hybrid mode. Previous studies have shown that the hybrid mode in the linear horizontal and vertical polarization basis is capable of providing measurements similar to the alternate transmission mode for most measurement conditions. These findings are based on the assumption of perfect sensing systems. This paper presents the results of radar system limitations on hybrid mode measurements that in turn are converted to system requirements. It is shown that the polarization purity requirement is more stringent for the hybrid mode compared to the alternate mode of operation.

Abstract — Precipitation is an important environmental parameter which affects the hydrology of the land surface, coastal processes, terrain stability, and climate and global heat circulation. Understanding rainfall distribution and intensity can improve protection of environmental and human resources, and knowledge of geophysical process of land, ocean and atmosphere. Rain measurements have been historically verified using traditional rain-gauges in high detail or microwave radars that cover vast areas. Nevertheless, in order to develop more accurate rainfall forecast algorithms and validate them, the drop size distribution (DSD) of rainfall events need to be studied. Using measurements from NASA TRMM satellite and rain gauges, the raindrop size distribution will be studied and used in analyzing disdrometer rain retrieval. The comparison took place on September 15th to 17th, 2004 in San Juan, Puerto Rico; when the tropical storm Jeanne passed by the island of Puerto Rico, in the Caribbean. Only 4 out of 21 locations worldwide where 2DVDs have been deployed in the past are in the tropics, therefore we expect this work to provide further insight into the rainfall statistics of tropical regions. I.

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The recently established National Science Foundation Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) will be deploying the first generation of an automated network of four low-power, short-range, X-band, polarimetric, Doppler radars, known as NetRad, in central Oklahoma in late 2005. This network is developed with the goal of tracking tornadoes with high spatial and temporal resolution as well as mapping severe weather events in the lowest 2 km of the troposphere. Each radar node has been developed to accomplish this system goal through the coordinated interaction with other radars in the network via a real-time, closed-loop software control system. This paper will describe the characteristics of the individual radar nodes in the system, with emphasis on those aspects of the design that lend themselves toward operation as a coordinated network. Calibration results and performance characteristics of the single node radar of the first generation system will also be presented.

2034-2037

Bright band is perhaps one of the most widely known radar signature for a long time. Extensive research has been reported in the literature, documenting various features of bright bands. Nevertheless it has always been a challenge to obtain a clear classification of the underlying structure of the vertical profile of reflectivity from radar observations. An understanding of the vertical structure of bright band region is important because it holds information on the type of precipitation and its variability. A better understanding of the vertical structures of bright band region and its radar reflectivity structures is important for understanding micro- physical processes of stratiform precipitation. Observation of the vertical profile of precipitation over the global tropics is a key objective of the Tropical Rainfall Measuring Mission. Tropical Rainfall Measuring Mission Precipitation Radar produces unprecedented high-resolution vertical profiles of precipitation. This paper presents a methodology to analyze bright band developed based on Self Organizing Maps. This method is used to identify the characteristics of the region of the bright band from TRMM-PR vertical profile measure- ments for one year on a global scale. Subsequently the data are summarized in a new way that can be useful in comparing the vertical profile of reflectivity from different regions around the globe as well as different seasons.

An operational radar rainfall estimation system based on the adaptive radial basis function (RBF) neural network is developed. During the process of training and cross validation, the rainfall estimation was computed only at the gauge locations. Once the training is done, the radar rainfall estimation based on neural networks is applied to the full coverage area of the radar. Such large-scale application of the rainfall estimate poses several questions in the context of operational applications. This letter addresses two of those questions, namely: 1) the feasibility of adaptively updating RBF neural network models on a daily basis and 2) the ability of neural network radar rainfall estimation at high spatial resolution within reasonable and practical time frame for operational applications. Using the datasets collected by WSR-88D radar located in Melbourne, FL, it is demonstrated that radar-based rainfall estimation using an adaptive RBF neural network is feasible. The results show that 73% of overnight updating for the RBF neural network can be completed within 2 h, and the estimation over an area of 100 km/spl times/100 km can be generated within the time frame (a few tens of seconds-150 s), which is much smaller than the average radar volume scan time.

The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar uses surface reference method to estimate the attenuation encountered in the observation of radar reflectivity. The cumulative attenuation estimated from the surface reference method can be distributed along the radar range using a power law relation between the specific attenuation (k) and reflectivity factor (Z). A physical interpretation of the variability in the k-Z relation can be provided with the normalized drop size distributions. This paper describes an algorithm to estimate the drop size distribution (DSD) parameters from the measured attenuation and reflectivity values obtained from TRMM precipitation radar observations. Coincident data collected with ground polarimetric radar during the TRMM field campaigns is used to cross-validate the estimates of drop size distribution parameters obtained from the TRMM precipitation radar. The results of cross validation show fairly good agreement with the drop size distribution parameters retrieved from TRMM precipitation radar and the ground-radar-based estimates. The algorithm is subsequently used to generate monthly global maps of DSD. The global distribution of DSDs is critically important for development of retrieval algorithms used by the Global Precipitation Mission Radiometers.

A hydrometeor classification system based on a fuzzy logic technique using dual-polarization radar measurements of precipitation is presented. In this study, five dual-polarization radar measurements (namely horizontal reflectivity, differential reflectivity, specific differential phase, correlation coefficient, and linear depolarization ratio) and altitude relating to environmental melting layer are used as input variables of the system. The hydrometeor classification system chooses one of nine different hydrometeor categories as output. The system presented in this paper is a further development of an existing hydrometeor classification system model developed at Colorado State University (CSU). The hydrometeor classification system is evaluated by comparing inferred results from the CSU CHILL Facility dual-polarization radar measurements with the in situ sample data collected by the T-28 aircraft during the Severe Thunderstorm Electrification and Precipitation Study.

A systems approach to the development of spaceborne dual-frequency radar retrievals is presented. The necessary integral equations governing the backscatter and forward scatter propagation of electromagnetic waves are redeveloped through the parameters of the drop-size distribution (DSD) namely the median drop diameter, D/sub o/, and normalized intercept parameter N/sub w/. The backward iteration algorithm using these equations is reviewed and cast in terms of a closed-loop control-system model with feedback on measured radar reflectivity values. This model is preferred over the fixed-point method because the closed-loop model calculates and iterates based on the input variables for measured reflectivity instead of using attenuation as a loop control variable. The closed-loop model is examined and analyzed in terms of sensitivity to measurement error and DSD parameters and is used to describe and develop the convergence region of the retrievals in the DSD space. The impact of the convergence region is evaluated to define the relative contribution of the methodology in a global sense. It is shown that about half the rainfall falls into the convergence zone demonstrated in this paper.

A new stable backward iterative technique to correct for path attenuation and differential attenuation is presented here. The technique named, neural network iterative polarimetric precipitation estimator by radar (NIPPER), is based on a polarimetric model used to train an embedded neural network, constrained by the measurement of the differential phase along the rain path. Simulations are used to investigate the efficiency, accuracy, and the robustness of the proposed technique. The precipitation is characterized with respect to raindrop size, shape, and orientation distribution. The performance of NIPPER is evaluated by using simulated radar volumes scan generated from S-band radar measurements. A sensitivity analysis is performed in order to evaluate the expected errors of NIPPER. These evaluations show relatively better performance and robustness of the attenuation correction process when compared with currently available techniques.

Monitoring of precipitation using high frequency radar systems such as X-band is becoming increasingly popular due to their lower cost compared to their counterpart at S-band. Meteorological radar systems operating at S-band frequencies are not affected by attenuation due to precipitation. The S-band radar systems are typically expensive with large antennas, and high power transmitters. Recently, network of meteorological radar systems at higher frequencies such as X band are being pursued, especially for low cost and targeted applications, such as coverage over a city or a small basin. Attenuation correction of the signal returns from these radars is important for quantitative applications. In order to design the radar systems as well as evaluate algorithm development, it is useful to have simultaneous X-band observation with and without the impact of path attenuation. The only way to collect such data is from dual frequency radar system with matched beams. This paper presents a methodology to generate realistic range profiles of radar observations at attenuating frequencies, such as X band, for rain medium. Fundamental microphysical properties of precipitation, namely size and shape distribution information are used to generate realistic profiles of X-band starting with S-band observation. Conditioning the simulation from S-band maintains the natural distribution of rainfall microphysical parameters. Data form the Colorado State University CHILL radar and the National Center for Atmospheric Research S-POL radar are used to simulate X-band radar observations. The two procedures and sample applications are presented.

The cumulative attenuation (A) along TRMM PR path given by the surface reference technique (SRT) can be distributed along the radar range using a power law relation between the specific attenuation (k) and reflectivity factor (z) written as, k=aZ/sup /spl beta//. A commonly used approximation is that /spl beta/ is constant and /spl alpha/ changes according to the raindrop size distribution (DSD). More recently physical interpretation of /spl alpha/ has been provided with the normalized drop size distributions. This work evaluates the method to estimate the DSD parameters from TRMM PR observations. Coincident data collected with polarimetric radar on the ground during the TRMM field campaigns was used to compare the estimates of DSD retrieved from TRMM PR. This work also introduces a new methodology for comparing DSD estimates from widely varying sampling volumes. The cross validation shows fairly good agreement, thereby validating the DSD retrieval process.

This paper describes a methodology for reflectivity and attenuation retrieval in a networked radar environment. Electromagnetic waves backscattered from a common volume are attenuated differently along the different paths. Solution of the specific attenuation distribution is proposed by solving the integral equation for reflectivity, in a manner similar to that used with a differential phase constraint. The set of governing integral equations describing the backscatter and propagation of common resolution volume are solved simultaneously with constraints on observed total path attenuation. The algorithms developed are evaluated on simulated X-band radar observations in rain obtained from S-band measurements by CSU-CHILL radar. Retrieved reflectivity and specific attenuation using the iterative method show good agreement with intrinsic reflectivity and specific attenuation

Weather radars that operate at frequencies higher than about 3 GHz can he affected adversely by rain attenuation. However, for spaceborne applications, where the size and mass of the antenna are limited, adequate spatial resolution can be obtained only by increasing the frequency. For the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), the use of 13.8-GHz frequency with a 2-m antenna size represents a compromise between the desire to minimize the antenna size and rain attenuation and maximize the spatial resolution. As demonstrated by Hitschfeld and Bordan (1954) in their classic study, estimates of rain rate from a single-attenuating wavelength radar are unstable when the path attenuation is large, unless the radar constant and the drop size distribution are known to a high degree of accuracy. Because these conditions are seldom met, an alternative strategy is needed to complement the Hitschfeld-Bordan method for moderate and high rain rates. One of the proposed ways of estimating attenuation is the surface reference technique (SRT). In this work, the data from TRMM PR is used to study the statistical characteristics of the surface return (s0) as a function of the surface type (ocean/land) and incident angles during rain and rain-free times, this analysis is further used to study the effectiveness of using surface return as the reference.

The global precipitation measurement (GPM) mission is dedicated to improving the understanding of the global water cycle by measuring and mapping precipitation throughout the globe. The core GPM satellite will incorporate two separate precipitation radars: one operating at Ku-band (13.6 GHz) and the other at Ka band (35.6 GHz). Each radar beam will be steered such that they both point to the same location in the atmosphere. The main purpose of the dual-frequency radar system is to resolve the DSD in precipitation as well as discriminate between rain and ice. With the two beams collocated on the same precipitation volume, new algorithms are being developed to reliably estimate attenuation and rain rate. Any algorithm is based on models of precipitation. In addition, the GPM system assumes collocated beams and matched resolution volumes. Electromagnetic and microphysical models have been developed based on ground-based dual-frequency radar data at S-band to simulate Ku- and Ka-band results for comparison with the new GPM algorithms. This paper evaluates the dual-frequency inversion algorithm with synthesized S-band and known perfect data and presents results. Results show the expected performance of the new dual-precipitation radar algorithms with the potential for guiding algorithm and system improvements.

The Precipitation Radar (PR) from the Tropical Rainfall Measuring Mission (TRMM) produces high resolution vertical profiles of precipitation. Extensive information about the type of storm is contained in its vertical structure. This paper develops classification methodology precipitation radar profiles using self organizing maps. Reflectivity observation of vertical rain profile on global scale obtained from TRMM PR is classified with Self-Organizing Maps. The methodology is demonstrated by computing a SOM for a month of TRMM radar data around the globe. A sample application of the methodology is provided to study the difference between east and west Pacific Ocean.

Classification of precipitation into convective and stratiform type is an important product of Tropical Rainfall Measuring Mission (TRMM) data set. This work presents the results of a new algorithm, used to classify precipitation into convective and stratiform types, developed using two dimensional wavelet transform. The methodology analyzes the three dimensional radar profile of precipitation in both horizontal and vertical directions (with respect to the surface of the earth). The rain type is determined by analyzing the wavelet coefficients of the horizontal and vertical details of each plane. The data structure used in each plane is rebuilt using the suitable wavelet coefficients to extract the proper signal components at the selected plane. Data from TRMM-PR are analyzed to produce precipitation type classification. The results are compared against ground validation inferences.

The Tropical Rainfall Measuring Mission (TRMM) program is dedicated to observing and understanding the impact of tropical rainfall. Two important products of the TRMM precipitation radar include the classification of precipitation into convective and stratiform type as well as determination of the height of the brightband. Currently, TRMM uses an algorithm to arrive at these products based on a characterization of horizontal and vertical variability of reflectivity. This paper presents a new algorithm for classifying precipitation type into convective or stratiform as well as detect the height of brightband developed using wavelet analysis. The results obtained from wavelet analysis are compared against simultaneous ground validation products from the TRMM program. The results show that the wavelet-based algorithms provide fairly accurate results for both convective/stratiform classification as well as brightband determination.

Gigabit networking makes possible the remote access to expensive and specialized facilities that were inaccessible in the past due to limited bandwidths. VCHILL project for digitized radar data transfer is one such real-time application that will tap the next generation Internet technology to provide interactive access to real-time and stored data generated by weather radars, thus revolutionizing the way experiments are carried out. The design and implementation of the deployment of an efficient congestion control algorithm for this application are presented. The TCP-friendly rate adaptation based on loss (TRABOL) algorithm is a source-based rate control mechanism that controls the transmission rate based on the feedback about losses experienced by the client station. The performance results show that the deployment of this algorithm makes the application TCP-friendly.

The precipitation radar uses surface reference method to estimate the attenuation encountered in the obser- vation of radar reflectivity. The cumulative attenuation (A) estimated from the surface reference method can be distributed along the radar range using a power law relation between the specific attenuation (k) and reflectivity factor (Z) written as, k = αZ β . A commonly used approximation is that β is constant and α changes according to the drop size distribution. More recently physical interpretation of the α has been provided with the normalized drop size distributions. This paper describes a procedure to estimate the DSD parameters from the measured attenuation and reflectivity values obtained from TRMM precipi- tation radar observations. Coincident data collected with ground radar during the TRMM field campaigns have been used to cross- validate the estimates of DSD parameters obtained from TRMM precipitation radar measurements with those obtained from ground polarimetric radar observations. The results of cross- validation show fairly good agreement with the DSD parameters retrieved from TRMM precipitation radar and the ground radar based estimates. The algorithm is also used to generate monthly global maps of DSD parameters.

Ground validation is a critical part of the European contribution (EGPM) to the Global Precipitation Measurement mission (GPM). According to the EGPM approach, the objective of the ground validation activities extends beyond the traditional measurement of rainfall at ground, to include the generation of a 3-Dimensional data set to be used for validating the microphysical models as well as the assumptions made in the retrieval algorithms. The complexity of the Italian geography and the diversity of its climatology, especially for the distribution of precipitation, suggests the need of a distributed ground validation site which can be effectively realized using the permanent structures where the instrumentation is, or will be in the next future, deployed and operated routinely. The resulting distributed Italian site would be an essential and integral part of the more complex European EGPM supersite. The paper introduces basic concepts to develop plans for the Italian contribution to the ground validation program for EGPM.

Rainfall on the ground is dependent on the four dimensional distribution of precipitation aloft. In principle one can obtain a functional relation between the rain rate on the ground and the four-dimensional radar observations aloft. However it is difficult to express this in a useful form. Neural networks provide a mechanism to solve this complex problem. Using ground measurements of rain rate as the target output neural networks have been developed in the past that use the radar measurements as input and produce rainfall rates on the ground. Several topics related to neural network based radar rainfall estimates are addressed in this paper. This paper investigates the input vector types and sizes that are useful in a radar rainfall estimation context. Similarly, the neural network is trained with an initial data set, but updated adaptively. Various updating mechanisms are investigated with respect to accuracy of rainfall estimation. Two years of data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and a network of gages from Melbourne, Florida are used to evaluate the topics listed here.

The TRMM mission is dedicated to observing and understanding the impact of tropical rainfall. Two of the important products of the TRMM mission are classification of precipitation into convective and stratiform type as well determination of the height of the bright band. Currently TRMM uses an algorithm to arrive at these products based on a characterization of horizontal and vertical variability of reflectivity. This paper presents result of a new algorithm developed using wavelets transform. The algorithms for wavelet analysis based products of both convective/stratiform classification as well as bright band detection are described. The results obtained from wavelet algorithms are compared against both the current products as well as ground radar inferences. The results show that the wavelet-based analysis provides fairly accurate results for both convective/stratiform classification as well as bright band determination.

Implementing a TCP-friendly congestion control mechanism is imperative for emerging UDP-based real-time, high-bandwidth applications such as multimedia. VCHILL project for digitized radar data transfer over the Internet is one such application for which a source based TCP-friendly rate adaptation based on loss (TRABOL) algorithm is deployed and shown to be TCP-friendly over applicable timescales. Comparison of the TRABOL-based radar application with other non-congestion controlled UDP flows shows that the application performs better with TRABOL and is fair towards neighboring TCP flows. Implementation of a simple memory-based mechanism as an add-on to the TRABOL algorithm enhances the performance of the application.

Ground-based polarimetric radar observations along the beam path of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), matched in resolution volume and aligned to PR measurements, are used to estimate the parameters of a gamma raindrop size distribution (RSD) model along the radar beam in the presence of rain. The PR operates at 13.8 GHz, and its signal returns can undergo significant attenuation due to rain, which requires compensation to adequately assess the rain rate. The current PR algorithm used for attenuation correction of the reflectivity is cross-validated using ground-based dual-polarization radar measurements. Data from the Texas and Florida Underflights (TEFLUN-B) campaign and TRMM Large-scale Biosphere Atmosphere (LBA) experiment are used in the analysis. The statistical behavior of the raindrop size distribution parameters are presented along the vertical profile through the rain layer, which is used to evaluate the PR attenuation correction and rainfall algorithms. The PR rain rate estimates are compared to ground radar estimates. The standard error of the difference between the rainfall estimates from PR and ground radar was within the error of the rainfall estimates from the two instruments. Though no systematic differences between PR attenuation-corrected reflectivity and ground radar reflectivity measurements are observed, there may exist some undercorrection and overcorrection on a beam-by-beam basis. Comparison of the normalized reflectivity versus rainfall relation between PR and ground polarimetric radar is also presented.

The propagation of radio waves for Earth-space slant path at C-band and higher frequencies is dominated by precipitation in the atmosphere. At a given frequency, attenuation depends on the length of the radio path, the size distribution, and the phase state of the hydrometeor profile. Using the observations from the Tropical Rainfall Measuring Mission (TRMM) spaceborne Ku-band (13.8 GHz) radar at low Earth orbit of 350 km above Earth, global attenuation maps are produced at the Ku-band frequency. A simple microphysical model for precipitation developed using hydrometeor size distributions and thermodynamic phase state is used to estimate attenuation and reflectivity observations at Ka-band (35 GHz) where numerous high-bandwidth satellite applications are being planned including the next-generation space-based radar for the Global Precipitation Mission (GPM). Differences in the microphysical structure in convective and stratiform precipitation are also incorporated in the model. The results show substantial attenuation variation in a 12-month period at both Ku- and Ka-bands over the various regions of the globe, including the contrast between land and ocean. The estimates of attenuation made at Ku- and Ka-band will be useful in the design and development of spaceborne systems.

Estimation of the raindrop size distribution (RSD) is made along the beam-path of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The PR is a nadir-pointing spaceborne radar that operates at 13.8 GHZ frequency. It is currently being used, along with other instruments aboard the TRMM satellite, for meteorological research. Short wavelength systems are used to reduce cost while maintaining the resolution near the Earth's surface required for meteorological sampling. The limitations are the high signal degradation of the return signal caused by scattering and absorption of the precipitating medium that it is intended to probe. Correction of the TRMM PR return echo is based on a single-frequency technique, which is evaluated using ground-based polarimetric radar observations. Quantitative estimation of a 3-parameter gamma model is made of the RSD along the PR beam. PR attenuation correction is compared to the median values of the RSD parameters. In particular, variations of the median drop size diameter (DO) and normalized intercept parameter (N/sub w/) with PR correction is made along the PR vertical profile in the rain layer. Data collected during the TExas and FLorida UNderflights (TEFLUN-B) experiment and TRMM Large Biosphere-Atmosphere experiment in Amazonia (LBA) field campaign is used in the evaluation.

The propagation of radio waves for Earth/space slant path at C-band and higher frequencies are dominated by precipitation in the atmosphere. At a given frequency, attenuation depends on the length of the radio path, the size distribution and the phase state of the hydrometeor profile. Using the observations from the TRMM spaceborne Ku-band (13.8 GHz) radar at Low Earth Orbit of 350 km above Earth, global attenuation maps are produced at the Ku-band frequency. A simple precipitation microphysical model developed using hydrometeor size distributions and phase state is used to scale this observed attenuation to Ka-band (35 GHz) where numerous high bandwidth satellite applications are being planned including the next generation space based radar for the Global Precipitation Mission (GPM). In this study, three layers of precipitation that consist of aggregate, dry graupel and rain are considered. Using this precipitation model the attenuation and backscatter reflectivity relations between Ka- and Ku-band frequencies are developed for scaling the specific attenuation and backscatter reflectivity along the observation path. The results show substantial attenuation variation in a 12-month period at both Ku- and Ka-bands over the various regions of the globe. These results can help in the design and development criteria for satellite applications at Ka-band.

Presents results from comparing space radar and ground radar to cross-calibrate the Kwajalein polarimetric radar (KPOL) with the spaceborne TRMM Precipitation Radar (PR). Use of self-consistency of polarimetric measurements is implemented to check the absolute calibration of KPOL and is checked against the cross-calibration results of PR and KPOL. Differential reflectivity (Z/sub dr/) calibration is also done and compared with vertical looking scans through rain.

This paper presents a comparison of S- and X-band algorithms for rainfall estimate. This topic is fairly extensive and only limited results are presented for brevity. The results demonstrate that R(K/sub dp/) at X-band can be useful at much lower rainrates compared to S-band.

The neural network is a nonparametric method for representing the relationship between radar measurements and rainfall rate. Recent research had demonstrated that neural network techniques can be successfully used for ground rainfall estimation from radar measurements. An adaptive neural network has been developed to estimate rainfall rate from vertical profiles of reflectivity that gradually adapts itself over time, without retraining from the beginning. Such a network is also computationally stable. The performance of the neural network is evaluated by conducting tests on data sets using WSR-88D over Melbourne, FL and surface gage network data during 1998, 1999. The results show that the adaptive neural network can estimate rainfall fairly accurately and consistently.

The essential idea of a clustered radar system is to replace a single and large radar system with a group of small radars. Various operational concepts of multiple radar systems in a networked environment, which provide various advantages compared to the conventional single radar operation, have been introduced (see Chandrasekar, V. and Jayasumana, A.P., Proc. SPIE, vol.4527, p.142-7, 2001). This paper proposes an operational model, network model and node architecture to realize the idea of the clustered radar system over a currently available data network with the aim of providing high QoS real-time radar data to end-users. The network model and the end node architecture developed use satellite and terrestrial links via the Internet. The validity of the network model and the effect of critical node and network parameters on the performance are examined by simulation.

Quantitative use of C-band radar measurements of reflectivity (Z/sub h/) and differential reflectivity (Z/sub dr/) demands the use of accurate attenuation-correction procedures, especially in convective rain events. With the availability of differential phase measurements (/spl Phi//sub dp/) with a dual-polarized radar, it is now possible to improve and stabilize attenuation-correction schemes over earlier schemes which did not use /spl Phi//sub dp/. The recent introduction of constraint-based correction schemes using /spl Phi//sub dp/ constitute an important advance. In this paper, a self-consistent, constraint-based algorithm is proposed and evaluated which extends the previous approaches in several important respects. Radar data collected by the C-POL radar during the South China Sea Monsoon Experiment (SCSMEX) are used to illustrate the correction scheme. The corrected radar data are then compared against disdrometer-based scattering simulations, the disdrometer data being acquired during SCSMEX. A new algorithm is used to retrieve the median volume diameter from the corrected Z/sub h/, corrected Z/sub dr/, and K/sub dp/ radar measurements which is relatively immune to the precise drop axis ratio versus drop diameter relation. Histograms of the radar-retrieved D/sub 0/ compared against D/sub 0/ from disdrometer data are in remarkable good agreement lending further validity to the proposed attenuation-correction scheme, as well as to confidence in the use of C-band radar for the remote measurement of rain microphysics.

Detection of rain/no-rain condition on the ground is important for application of radar rainfall algorithms. A radial basis function neural network-based scheme for rain/no-rain determination on the ground using vertical profiles of radar data is described. Evaluation based on WSR-88D radar over central Florida indicates that the rain/no-rain condition can be inferred fairly accurately.

Documenting simultaneous multiparameter radar observations of precipitation in conjunction with in situ hydrometeor sampling is important for the interpretation of multiparameter radar observations. In situ observation using aircraft-mounted probes is one of the best ways to collect such data. In situ observation of hail and graupel in convective storms is complicated due to adverse environment of flight and low concentration of large particles that are difficult to sample. This paper presents one of the first observations of simultaneous multiparameter radar observations and in situ samples of wet hail and graupel in convective storms. The observations are unique because of the excellent coordination between aircraft samples and radar scanning, as well as relatively large sample volumes of aircraft data. Multiparameter radar observations (namely reflectivity, differential reflectivity, linear depolarization ratio, copolar correlation coefficient, and specific differential phase) are documented in graupel and wet hail. The observations indicate that the linear depolarization ratio and copolar correlation measurements, in conjunction with reflectivity levels, can be used to distinguish between graupel and hail. A simple procedure is developed to estimate the average bulk density of graupel and wet hail, comparing radar and in situ observations.

The joint distribution characteristics of size and shape of raindrops directly translate into features of polarization diversity measurements in rainfall. Theoretical calculations as well as radar observations indicate that the three polarization diversity measurements, namely, reflectivity, differential reflectivity, and specific differential propagation phase, lie in a constrained space that can be approximated by a three-dimensional (3D) surface. This feature as well as the vertical-looking observation of raindrops are used to determine biases in calibration of the radar system. A simple procedure is developed to obtain the bias in the absolute calibration from polarization diversity observation in rainfall. Simulation study as well as data analysis indicate that calibration errors can be estimated to an accuracy of 1 dB.

Multiparameter radar measurements of rainfall, namely, reflectivity factor Z/sub H/, differential reflectivity Z/sub DR/, and specific differential phase K/sub DP/ lie in a constrained three-dimensional (3D) space and therefore measurement of Z/sub H/, Z/sub DR/, or, K/sub DP/ should be consistent with the other two. This self-consistency relationship between Z/sub H/, Z/sub DR/, and K/sub DP/ is valid when the radar resolution volume is homogeneous. When there are reflectivity gradients within the radar resolution cell, the self-consistency relation is perturbed. This perturbation can be utilized to detect the presence of gradients in the radar resolution volume. This paper presents a technique to detect and estimate reflectivity gradients in the resolution cell. The technique is evaluated using theoretical analysis as well as experimental data collected by the NCAR CP-2 radar. It is demonstrated that the presence of reflectivity gradients larger than a few decibels can be detected using the algorithm developed in this paper.

Eleven penetrations of rainshafts by the University of Wyoming King Air (WKA) aircraft equipped with a two-dimensional (2D) optical array probe are studied in coordination with multiparameter radar measurements from the National Center for Atmospheric Research (NCAR) CP-2 radar collected in a multicellular storm that occurred on August 8, 1991, of the Convective and Precipitation/Electrification (CaPE) experiment. A comparison is made between the mass-weighted mean diameter (D/sub m/) and rainrate (R) computed from the nine-size spectra and their estimates from multiparameter radar algorithms based on Z/sub dr/, and Z/sub h/. It was found that D/sub m/ could be estimated with a mean bias of 0.07 mm and a standard deviation of 0.35 mm. Rainrates (in the range of 10-60 mmh/sup -1/) could be estimated from Z/sub h/, and Z/sub dr/ with a mean bias of 1-4% and fractional standard error (FSE) of 30-40% depending on the estimator used. Raindrop axis ratios are analyzed as a function of volume equivalent spherical diameter (D/sub eq/) in the range 2-6 mm. The mean axis ratio versus the D/sub eq/ relationship was found to be consistent with previous data from the High Plains (from Colorado and Montana). A study of fluctuations of axis ratio (about their mean value) showed that most drops have axis ratios close to their mean values with oscillation amplitudes to be typically /spl plusmn/10% in axis ratio, again consistent with the earlier High Plains results.

Wideband waveform techniques, such as pulse compression, allow for accurate weather radar measurements in a short data acquisition time. However, for extended targets such as precipitation systems, range sidelobes mask and corrupt observations of weak phenomena occurring near areas of strong echoes. Therefore, sidelobe suppression is extremely important in precisely determining the echo scattering region. A simulation procedure has been developed to accurately describe the signal returns from distributed weather targets, with pulse compression; waveform coding. This procedure is unique and improves on earlier work by taking into account the effect of target reshuffling during the pulse propagation time which is especially important for long duration pulses. The simulation procedure is capable of generating time series from various input range profiles of reflectivity, mean velocity, spectrum width, and SNR. Results from the simulation are used to evaluate the performance of phase coded pulse compression in conjunction with matched and inverse compression filters. The evaluation is based on comparative analysis of the integrated sidelobe level and Doppler sensitivity after the compression process. Pulse compression data from the CSU-CHILL radar is analyzed. The results from simulation and the data analysis show that pulse-compression techniques indeed provide a viable option for faster scanning rates while still retaining good accuracy in the estimates of various parameters that can be measured using a pulsed-Doppler radar. Also, it is established that with suitable sidelobe suppression filters, the range-time sidelobes can be suppressed to levels that are acceptable for operational and research applications.

Using radar to measure snowfall accumulation has been a research topic in radar meteorology for decades. Traditionally, a parametric reflectivity-snowfall (Z-S) relationship is used to estimate ground snowfall amounts based on radar observations. However, the accuracy and reliability of Z-S relationship are limited by the wide variability of the Z-S relationship with snowfall type. In this paper, the authors introduce a neural network based approach to address the problem of snowfall estimation from radar by taking into account the vertical structure of precipitation. The motivation for using a multilayer feedforward neural network (MFNN), such as the radial-basis function (RBF) network, is the good universal function approximation capability of the network. The network is trained using vertical reflectivity profiles averaged over a 9-km/sup 2/ area as the input and ground snowfall amounts as the target output. Separate data, which are not part of the training data, are used to test the generalization performance of the RBF network after the training is done. Radar reflectivity data collected by the CSU-CHILL multiparameter radar and ground snowfall measurements recorded by snowgages located at the Stapleton International Airport (SIA), Stapleton, CO, and the Denver International Airport (DIA), Denver, CO, during the Winter and Icing and Storms Projects (WISP94) were used for this study. The snowfall estimates from the RBF network are shown to be better than those obtained from conventional Z-S algorithms. The neural network based approach provides an alternate method to the snowfall estimation problem.

Rainfall estimation based on radar measurements has been an important topic in radar meteorology for more than four decades. This research problem has been addressed using two approaches, namely a) parametric estimates using reflectivity-rainfall relation (Z-R relation) or equations using multiparameter radar measurements such as reflectivity, differential reflectivity, and specific propagation phase, and b) relations obtained by matching probability distribution functions of radar based estimates and ground observations of rainfall. In this paper the authors introduce a neural network based approach to address this problem by taking into account the three-dimensional (3D) structure of precipitation. A three-layer perceptron neural network is developed for rainfall estimation from radar measurements. The neural network is trained using the radar measurements as the input and the ground raingage measurements as the target output. The neural network based estimates are evaluated using data collected during the Convection and Precipitation Electrification (CaPE) experiment conducted over central Florida in 1991. The results of the evaluation show that the neural network can be successfully applied to obtain rainfall estimates on the ground based on radar observations. The rainfall estimates obtained from neural network are shown to be better than those obtained from several existing techniques. The neural network based rainfall estimate offers an alternate approach to the rainfall estimation problem, and it can be implemented easily in operational weather radar systems.

Repeated changes associated with lightning have been observed with multiparameter radar in the echoes from the tops of Florida thunderstorms. These lightning-related radar signatures are interpreted as changes in the orientation of ice crystals being preferentially aligned parallel to the in-cloud electric field. The changes occur at intervals on the order of 10 s and are easily observed in the signatures of the differential propagation phase shift and the linear depolarization ratio which are sensitive to propagation effects caused by the oriented ice crystals. The orientation of ice crystals aloft has been previously observed using circularly polarized radar while the simultaneous differential phase shift and linear depolarization measurements reported were obtained with a dual-linear polarized radar. The observations indicate crystal orientation angles greater than 450 and occasionally near vertical. In one case, the crystals were found to be oriented in a layer near radar cloud top spanning a 20-km range and 3 km in depth.

Polarization diversity measurements of rainfall, namely the reflectivity factor, differential reflectivity, and specific differential propagation phase, vary in a constrained three-dimensional space. Algorithms are derived to quantify this self-consistency of measurements. In particular, estimation of the specific differential propagation phase shift based on reflectivity and differential reflectivity is analyzed in detail. Theoretical simulation as well as radar observations of rainfall at S (CSU-CHILL) and C (Polar 55C) bands are used to demonstrate that the range profiles of differential propagation shift can be constructed from measurements of reflectivity and differential reflectivity.

Absolute calibration of a weather radar is critical for quantitative estimation of rainfall. This is a very difficult measurement especially for beam-filled targets like precipitation. In this paper we discuss a method that uses the properties of rain medium itself to obtain accurate system gain calibration. This technique is based on the principle that the rainfall rate measured using absolute reflectivity (Z) and differential reflectivity (ZDR) is the same as that obtained from specific differential phase (KDP). The measurements required for this technique are Z.ZDR and KDP. We compare the rainfall rate estimates RDP obtained from Z and ZDR with the estimates RDP obtained from KDP. The scatter plot between the two rainfall estimates should lie close to a 1:1 line and any systematic deviation from this line can then be removed by appropriately adjusting the system gain. It is noted here that ZDR can be calibrated accurately because it is a differential power measurement and KDP is obtained from differential phase measurement which is unaffected by system calibration. The sensitivity and accuracy of this technique are studied in this paper. We present theoretical and simulation results evaluating the accuracy of this technique for C-band frequencies. Our analysis indicates that by sampling over few data sets of rain, the calibration can be done to an accuracy of 0.6 dB.