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.

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 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.

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).

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).

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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).

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.

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.

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.

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.

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.