This paper describes a measurement method for characterizing the reflectivity of blackbodies used as calibration devices in spaceborne instruments. The fundamental measurement principle is based on the scattering matrix theory. A monostatic apparatus has been developed to implement the reflectivity measurements up to 220 GHz. A few blackbody emitters made of metamaterials were measured at a number of radiometric frequencies. The results demonstrated the feasibility of using metamaterial blackbodies on miniaturized satellite platforms.

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.

We present the development of a hyperspectral microwave radiometer for high-altitude balloon deployment to better observe the Planetary Boundary Layer (PBL) with unprecedented sensitivity. The new hyperspectral radiometer is based on the existing High-frequency Airborne Microwave and Millimeter-wave Radiometer (HAMMR) instrument. Our key innovation from existing microwave sounders is increasing the number of channels around the oxygen and water-vapor absorption lines with high-resolution spectral sampling, thereby reducing profile retrieval errors and providing wide-band coverage over the frequency range of 18-200 GHz. In this paper, we present the overall system design, including the Stratollite subsystem, the full optical subsystem, and on-going RF and IF subsystems testing. We also show preliminary outdoor radiometric test results using a complete 48-72 GHz radiometer system. The hyperspectral HAMMR-HD balloon experiment will acquire up to 30 days of high-resolution PBL data, over a variety of weather conditions, and over land and ocean.

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.

In this work, we present metamaterial-based microwave absorbers fabricated on organic-based printed circuit boards as promising alternatives to traditional, bulky microwave absorbers for the calibration of microwave radiometers for atmospheric remote sensing. Their use is particularly attractive for onboard calibration of sensors on CubeSats and other small satellites. Planar metamaterials can be fabricated with near-unity absorption over a very broad frequency range, and are scalable by tuning the unit cell geometry. Specifically, we describe an approach and initial measurements toward designing a broadband metamaterial emitter operating at millimeter wave sounding channels from 50 GHz 230 GHz, enabling a thin, cost-effective calibration target for millimeterwave atmospheric remote sensing.

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.

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.

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.

Temporal Experiment for Storms and Tropical Systems-Demonstration (TEMPEST-D) is a 6U CubeSat satellite with a cross-track scanning millimeter-wave radiometer measuring at five frequencies from 87 to 181 GHz. It employs a direct-detection architecture with InP HEMT monolithic microwave integrated circuit (MMIC) low-noise amplifiers and related new technologies. An end-to-end two-point external calibration is performed every 2-s rotation of the scanning mirror, based on observations of the cosmic microwave background and an internal blackbody calibration target, with three thermistors to monitor the target physical temperature. Corrections for antenna pattern effects and cross-scan biases based on prelaunch measured values were updated using data from an on-orbit calibration pitch maneuver. Validation of the observed brightness temperatures ( TB) is performed by comparing to coincident nonprecipitating ocean observations from five well-calibrated on-orbit instruments, including Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) and four Microwave Humidity Sounder (MHS) sensors on board NOAA-19, MetOp-A, MetOp-B, and MetOp-C satellites. Absolute calibration accuracy is within 1 K for all channels, well within the 4-K requirement. Calibration precision, or stability over time, is within 0.6 K for all channels, also well within the 2-K requirement. The intrinsic noise of TEMPEST-D is lower than MHS, resulting in similar on-orbit noise equivalent differential temperatures (NEDTs), even though TEMPEST-D has a much shorter integration time of 5 ms as compared to 18 ms for MHS. As a result, although the TEMPEST-D radiometer is substantially smaller, lower power, and lower cost than similar current operational radiometers, it has comparable or better performance in terms of instrument noise, calibration accuracy, and calibration stability or precision.

The Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) instrument is a five-frequency millimeter-wave radiometer operating from 87 to 181 GHz. The cross-track scanning radiometer has been operating on a 6U CubeSat in low Earth orbit since September 5, 2018. The direct-detection architecture of the radiometer reduces its mass and power consumption by eliminating the need for a local oscillator and mixer, also reducing system complexity. The instrument includes a scanning reflector and ambient calibration target. The reflector rotates continuously to scan the antenna beams in the cross-track direction, first across the blackbody calibration target, then toward the Earth over the full range of incidence angles, and finally to cosmic microwave background radiation at 2.73 K. This enables precision end-to-end calibration of the millimeter-wave receivers during every 2-s scan period. The TEMPEST-D millimeter-wave radiometers are based on 35-nm indium phosphide (InP) high-electron-mobility transistor (HEMT) low-noise amplifiers. This article describes the instrument and its characterization prior to launch.

Calibration plays an important role in improving the accuracy of the microwave and millimeter-wave radiometric measurements. Several calibration techniques have been used in radiometers including external calibration targets, vicarious sources, and internal calibrators such as noise diodes or matched reference load. A new calibration technique based on deep learning has recently been developed to calibrate microwave and millimeter-wave radiometers. The deep-learning calibrator has been previously demonstrated on a computer noise-wave modeled Dicke-switching radiometer. This article applies the new deep-learning calibration technique for the calibration of the high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) instrument. A deep-learning neural network model is built to calibrate the 2014 West Coast Flight Campaign antenna temperature measurements of the HAMMR. The deep-learning calibrator antenna temperature estimates are obtained from the radiometric measurements. The deep-learning calibration results are compared with the existing conventional calibration techniques used in HAMMR 2014 field campaign. The results have shown that the deep-learning calibrator is in agreement with the conventional calibration techniques. In this article, it is demonstrated that the deep-learning calibrator can be employed for calibrating the radiometers with high accuracy.

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.

Global observations with information content on water vapor content, ice water content and ice particle size distribution are needed to enhance knowledge of the impact of ice clouds on Earth's weather and climate. These observations may also help to reduce the uncertainty of global climate models. The Tropospheric Water and Cloud Ice (TWICE) microwave radiometer instrument has been designed to perform temperature and humidity sounding of the atmosphere near the 118.75, 183.31 and 380.20 GHz atmospheric absorption lines, as well as to retrieve ice cloud particle size information from radiometric measurements at 240, 310, 670 and 850 GHz. To acquire high-quality data, the TWICE instrument performs end-to-end, on-orbit calibration of all radiometer channels during each scan. The TWICE instrument is designed to fit within the mass, volume and power constraints of the 6U CubeSat platform.

Deep learning artificial neural network techniques can be applied for on-orbit calibration of microwave and millimeter-wave radiometer spaceborne instruments, including those for small satellites. The noise-wave model has been employed for noise characterization and validation of the proposed deep learning calibration technique for a synthetically generated Dicke-switching radiometer. The developed deep learning neural network radiometer calibrator produces high accuracy estimates of antenna temperatures from the measurements of radiometer output voltage and thermistor readings. Tests with noise-free and noisy samples of the developed model have shown that the proposed calibration method does not add any significant noise to the radiometer calibration. The performance of the proposed method does not degrade with increased nonlinearity for a radiometer, while nonlinearity is a challenging issue for conventional calibration techniques. The deep learning calibration model learns the radiometer noise characteristics from radiometer prelaunch measurements during thermal vacuum chamber testing. The neural network calibrator proposed in this paper has self-learning capability during the on-orbit operation of a radiometer that can be used to improve the performance of on-orbit calibration. The proposed technique is demonstrated by comparing the residual uncertainty of the deep learning calibration with the theoretical value. No numerical study is presented to compare the performance with conventional calibration techniques. The new method may be solely applied to calibrate the radiometer or applied along with conventional calibration techniques.

The Tropospheric Water and Cloud ICE (TWICE) millimeter and sub-millimeter radiometer instrument is being developed to enable global observations of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds. Global observations using the TWICE instrument are critically needed to reduce uncertainties in weather and climate models. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed and tested to control TWICE data acquisition and other subsystems. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, heavy-ion radiation testing has been performed for some critical commercial-off-the-shelf components to analyze radiation tolerance in low-Earth orbit. The C&DH prototype board meets the functional, noise and size, weight and power (SWaP) requirements for deployment on a 6U -Class satellite.

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.

Global measurements of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds are critically needed to reduce uncertainties in global weather and climate models. To address this need, the conically scanning Tropospheric Water and Cloud ICE (TWICE) millimeter and submillimeter radiometer instrument is being developed. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed to control TWICE data acquisition and other subsystems. The C&DH prototype board meets functional, noise and size, weight and power (SWaP) requirements for deployment in a 6U-class satellite. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, all of the components have been tested for radiation tolerance in low-Earth orbit.

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).

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.

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.

The electromagnetic spectrum is a valued shared resource. Its scientific use allows us to learn about our universe, measure and monitor our planet, and communicate scientific data. The use of the spectrum is managed by national, regional, and global regulatory frameworks. There are increasing demands for new or extended allocations because of vast technological advances in the past few years. Understanding spectrum management is important in the successful planning and execution of missions and instruments, as well as in determining the potential source of radio frequency interference in existing data and instruments, and in working to ameliorate its impact. This paper provides a summary of this framework for radio scientists and engineers.

The 12th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2012) was held at Villa Mondragone, University of Rome "Tor Vergata," near Frascati, Italy, on March 5-9, 2012. The objective of MicroRad 2012 was to provide an open forum to report and discuss recent advances in the field of microwave radiometry, particularly with application to remote sensing of the environment. The meeting was highly successful, with more than 120 attendees representing 20 countries. There were 76 oral presentations and more than 40 posters. From the papers presented at MicroRad 2012 and others submitted specifically for this special issue, 12 were selected for inclusion in the special issue. The papers were carefully peer reviewed with the usual standards of the IEEE TGRS. As is evident from the table of contents, these papers span a broad range of microwave radiometry and remote sensing applications and reflect the interest in MicroRad and the vitality of research in this area.

In recent decades, atmospheric scientists have been interested in measuring thermodynamic variables such as tropospheric water vapor and temperature with increasing temporal and spatial resolution due to their importance on the climate modeling. For this purpose, microwave radiometers have been used to measure columnar integrated water vapor. The radiative transfer equation (RTE) has been used to retrieve the contributions of individual atmospheric layers, assuming a stratified atmosphere. In recent years, significant advances have been made toward retrieval of these parameters in 2-D, 3-D and 4-D (3-D + time) distributions. This work presents a new radiometric concept to directly measure the contribution of each pixel (avoiding the use of the RTE inversion) by using pencil-beam antennas and interferometric techniques. This new approach has the potential to improve the quality of the retrieved thermodynamic variables to meet the research goals of atmospheric science.

Spaceborne interferometric synthetic aperture radar (SAR) (InSAR) imaging has been used for over a decade to monitor tectonic movements and landslides, as well as to improve digital elevation models. However, InSAR is affected by variations in round-trip propagation delay due to changes in ionospheric total electron content and in tropospheric humidity and temperature along the signal path. One of the largest sources of uncertainty in estimates of tropospheric path delay is the spatial and temporal variability of water vapor density, which currently limits the quality of InSAR products. This problem can be partially addressed by using a number of SAR interferograms from subsequent satellite overpasses to reduce the degradation in the images or by analyzing a long time series of interferometric phases from permanent scatterers. However, if there is a sudden deformation of the Earth's surface, the detection of which is one of the principal objectives of InSAR measurements over land, the effect of water vapor variations cannot be removed, reducing the quality of the interferometric products. In those cases, high-resolution information on the atmospheric water vapor content and its variation with time can be crucial to mitigate the effect of wet-tropospheric path delay variations. This paper describes the use of a ground-based microwave radiometer network to retrieve 3-D water vapor density with fine spatial and temporal resolution, which can be used to reduce InSAR ambiguities due to changes in wet-tropospheric path delay. Retrieval results and comparisons between the integrated water vapor measured by the radiometer network and satellite data are presented.

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is a well established technique useful in many land applications, such as monitoring tectonic movements and landslides or extracting digital elevation models. One of its major limitations is the atmospheric variability, and in particular the high water vapor spatial and temporal variability, which introduces an unknown delay in the signal propagation. On the other hand, these effects might be exploited, so as InSAR could become a tool for highresolution water vapor mapping. This paper describes the approach and some preliminary results achieved in the framework of an ESA funded project devoted to the mitigation of the water vapor effects in InSAR applications. Although very preliminary, the acquired experimental data and their comparison give a first idea of what can be done to gather valuable information on water vapor, which play a fundamental role in weather prediction and radio propagation studies.

The 29 papers in this special issue were originally presented at the 2008 International Geoscience and Remote Sensing Symposium (IGARSS'08), held from July 6 to 11 in Boston, MA.

Quantitative precipitation forecasting is currently limited by the paucity of observations on sufficiently fine temporal and spatial scales. Three-dimensional water vapor fields can be retrieved with improved spatial coverage from measurements obtained using a network of scanning microwave radiometers. To investigate this potential, an observation system simulation experiment was performed in which synthetic examples of retrievals using a network of radiometers were compared with results from the Weather Research and Forecasting model at a grid scale of 500 m. These comparisons show that the 3-D water vapor field can be retrieved with an accuracy of better than 15%-20%. A ground-based demonstration network of three compact microwave radiometers was deployed at the Atmospheric Radiation Measurement Southern Great Plains site in Oklahoma. Results using these network measurements demonstrated the first retrieval of the 3-D water vapor field in the troposphere at fine spatial and temporal resolutions.

Quantitative precipitation forecasting is currently limited by the paucity of observations of thermodynamic variables in the troposphere, including water vapor. Specifically, measurements of 3-D water vapor fields are needed at sub-meso-gamma scales in pre- storm conditions. This can be achieved using a network of remote sensors to retrieve the water vapor field with high spatial and temporal resolution. Such measurements may be used for assimilation into and validation of numerical weather prediction (NWP) models. Conventional measurements of water vapor density profiles are obtained using in-situ probes on-board weather balloons, including radiosondes. Remote sensing techniques to retrieve moisture profiles include ground-based networks receiving Global Navigation Satellite Systems (GNSS) signals, including GPS, and GPS receivers aboard the COSMIC satellite constellation for atmospheric occultation. These methods provide measurements with high vertical resolution but with coarse horizontal resolution. Differential Absorption Lidars (DIAL) can retrieve water vapor with comparable resolution to that of radiosonde observations. However, these lidars are expensive, and their operation is limited to clear-sky conditions due to the high opacity of clouds at optical wavelengths. Inversion of brightness temperatures measured by upward- looking, ground-based microwave radiometers allows the estimation of vertical profiles with high temporal resolution in both clear and cloudy conditions. However, assimilation of retrieved 3-D water vapor fields with improved spatial coverage into NWP models in pre-storm conditions has the potential for substantial impact on numerical weather prediction of convective storm activity. Measurements using a network of multi-frequency microwave radiometers can provide the necessary information to retrieve the 3-D distribution of water vapor in the troposphere.

Wind speed has been retrieved reliably from SSM/I microwave radiometric measurements since 1990 with a precision of better than 2 m/s. However, operational requirements of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) dictate retrieval of the ocean surface wind vector, i.e. both speed and direction. This requires improved understanding and forward modeling of the physical processes governing ocean surface microwave emissivity. Although the microwave brightness temperature over the ocean exhibits a relatively strong dependence on wind speed, the dependence on wind direction with respect to the azimuthal angle of observation is at most a few Kelvin peak-to-peak. Although it is known that breaking waves and foam significantly affect the microwave emission of the ocean surface at 6.8, 10.8, 19 and 37 GHz, the effect of foam on the azimuthal dependence of sea surface emissivity is not well understood. This understanding is critical to improve forward models to achieve the accuracy required for ocean surface wind vector retrievals. Recently, electromagnetic modeling of the emissivity of foam generated by breaking waves has been improved by considering the vertical and horizontal heterogeneity of thick layers of foam at the ocean- atmosphere interface [1]. In this work, we compare foam emissivities both calculated using this model with those inferred from microwave brightness temperature measurements performed during the Polarimetric Observations of the Emissivity of Whitecaps Experiment (POEWEX'04).

Quantitative precipitation forecasting is limited by the paucity of observations of water vapor in the troposphere. In particular, severe storms have been observed to develop in regions of strong and rapidly evolving moisture gradients. Conventional measurements of water vapor density profiles are obtained using in-situ probes on-board weather balloons, including radiosondes. These in-situ profile measurements have high vertical resolution, but have severe limitations in both temporal and spatial sampling. Lidars use differential absorption techniques to estimate water vapor with comparable resolution to that of radiosonde observations. However, lidars are expensive, and their operation is limited to clear-sky conditions due to the high opacity of clouds at optical wavelengths. Inversion of brightness temperatures measured by upward- looking, ground-based microwave radiometers allows the estimation of vertical profiles with high temporal resolution in both clear and cloudy conditions. However, assimilation of retrieved water vapor fields with improved spatial coverage has the potential for more substantial impacts on numerical weather prediction of convective storm initiation. Measurements using a network of multi-frequency microwave radiometers can provide information to retrieve the 3-D distribution of water vapor in the troposphere.

The fabrication of a miniaturized ground-based water vapor profiling radiometer demonstrates the capability of monolithic microwave and millimeter-wave integrated circuit technology to reduce the mass and volume of microwave remote sensing instrumentation and to reduce substantially the necessary operational power consumption and size of the radio-frequency and intermediate-frequency sections. Since those sections comprise much of the mass and volume of current microwave receivers, the fabrication of this system represents an important contribution to the design of microwave radiometers. This miniaturized radiometer implementation is particularly well suited to benefit from the cost savings associated with mass production. The small size of the radiometer (24times18times16 cm) reduces the power required by the temperature control system and allows a rapid warm-up to the temperature set point as well as maintenance of a highly stable internal temperature. Exhibiting very similar statistical properties, the four channels of the radiometer have measured Allan times of greater than 40 s. Measurement results demonstrate that the instrument achieves a sensitivity of better than 0.2 K for 3 s of integration time. Preliminary comparisons of measured brightness temperatures with simulation results based on radiosonde data show good agreement, which are consistent with previously reported results.

The 38 papers in this special issue were originally presented at the 9th Specialist Meeting on Microwave Radiometry and Remote Sensing Applications (MicroRad '06). These papers are organized into topical areas and applications, which are in the general order of the MicroRad technical sessions: Radiometer Calibration and RFI Mitigation (4); Synthetic Aperture Radiometry (3); Land and Vegetation (6); Ocean Salinity (5); Ocean Wind (4); Atmosphere (3); Temperature and Humidity Sounding (8); and Precipitation (5).

WindSat, the first satellite polarimetric microwave radiometer, and the NPOESS Conical Microwave Imager/Sounder both have as a key objective the retrieval of the ocean surface wind vector from radiometric brightness temperatures. Available observations and models to date show that the wind direction signal is only 1-3 K peak-to-peak at 19 and 37 GHz, much smaller than the wind speed signal. In order to obtain sufficient accuracy for reliable wind direction retrieval, uncertainties in geophysical modeling of the sea surface emission on the order of 0.2 K need to be removed. The surface roughness spectrum has been addressed by many studies, but the azimuthal signature of the microwave emission from breaking waves and foam has not been adequately addressed. Recently, a number of experiments have been conducted to quantify the increase in sea surface microwave emission due to foam. Measurements from the Floating Instrumentation Platform indicated that the increase in ocean surface emission due to breaking waves may depend on the incidence and azimuth angles of observation. The need to quantify this dependence motivated systematic measurement of the microwave emission from reproducible breaking waves as a function of incidence and azimuth angles. A number of empirical parameterizations of whitecap coverage with wind speed were used to estimate the increase in brightness temperatures measured by a satellite microwave radiometer due to wave breaking in the field of view. These results provide the first empirically based parameterization with wind speed of the effect of breaking waves and foam on satellite brightness temperatures at 10.8, 19, and 37 GHz.

WindSat, the first polarimetric radiometer on orbit, launched in January 2003, provides the promise of passive ocean wind vector retrievals on a continuous basis, simultaneous with the retrieval of many other geophysical variables such as sea surface temperature, atmospheric water vapor, cloud liquid water, and sea ice extent and concentration. WindSat also serves as risk reduction for the upcoming National Polar-orbiting Operational Environmental Satellite System (NPOESS) Conical Scanning Microwave Imager/Sounder (CMIS). Since the dependence of microwave brightness temperatures on wind direction is small relative to that of other parameters such as wind speed, wind direction retrieval relies on increasingly accurate knowledge of the ocean surface microwave emission, which depends upon surface properties such as roughness and foam due to wave breaking. Coordinated near-surface measurements of ocean surface microwave emission and air-sea interaction parameters are needed to quantify the effects of the processes mentioned above in surface emission models to improve the accuracy of wind vector retrievals. Such coordinated observations were performed during the Fluxes, Air-Sea Interaction, and Remote Sensing (FAIRS) experiment conducted on the R/P Floating Instrument Platform (FLIP) in the northeastern Pacific Ocean during the Fall of 2000. X- and Ka-band partially polarimetric radiometers were mounted at the end of the port boom of R/P FLIP to measure ocean surface emission at incidence angles of 45/spl deg/, 53/spl deg/, and 65/spl deg/. A bore-sighted video camera recorded the fractional area of foam in the field of view of the radiometers. Air-sea interaction parameters that were measured concurrently include wind speed, friction velocity, heat fluxes, and significant wave height. The measured dependence of ocean surface emissivity on wind speed and friction velocity is in good agreement with, and extends, earlier observations and empirical models based on satellite data. Concurrent radiometric measurements and fractional area foam coverage data strengthen the possibility of retrieval of sea surface foam coverage using airborne or spaceborne radiometry. The dependence of emissivity on atmospheric stability is shown to be much smaller than the dependence of emissivity on wind speed. Analysis of emissivity dependence on atmospheric stability alone was inconclusive, due to the variation in atmospheric stability with wind speed. The effect of long-wave incidence angle modulation on sea surface emissivity for near-surface measurements was found to be negligible when emissivity measurements were averaged over tens to hundreds of long waves.

At present, the vast majority of ground-based and airborne microwave remote sensing instrumentation is produced using waveguide-based or connectorized discrete microwave components, which are high in cost and large in volume. Recent maturation of monolithic microwave and millimeter-wave integrated circuit (MMIC) technologies developed for the wireless communications and defense industries is expected to enable development of a new generation of radiometers. This work describes the design of a prototype miniaturized water vapor profiler for the 3D measurement of tropospheric water vapor using a four-sensor network, including the fabrication and initial performance evaluation of its RF and IF sections.

WindSat, the first polarimetric microwave radiometer on orbit, has as its primary objective the demonstration of robust retrieval of the sea surface wind vector from measured brightness temperatures. The ocean surface wind vector is one of the key environmental data records for the NPOESS Conical Microwave Imager/Sounder (CMIS) instruments, first planned for launch in 2009. To date, aircraft and satellite measurements, as well as modeling results, indicate that brightness temperature variations with wind direction are small, on the order of 1-3 K peak-to-peak. Therefore, quantitative understanding of the dependence of the ocean surface emissivity on properties such as surface roughness and wave breaking is critical for wind vector retrieval. Despite the importance of this, some basic physical properties such as the azimuthal angle dependence of the microwave emission from foam have not been well characterized to date. Recent measurements from the R/P FLIP indicated that the increase in ocean surface emission due to breaking waves may depend on both the incidence and azimuthal angles. The need to quantity this dependence motivated systematic measurement of the emissivity of reproducible breaking waves at varying incidence and azimuthal angles. Results from these recent field measurements provide the first parameterization with wind speed of the change in brightness temperatures due to breaking waves.

Soil Moisture and Ocean Salinity (SMOS) is an Earth Explorer Opportunity Mission from the European Space Agency with a launch date in 2007. Its goal is to produce global maps of soil moisture and ocean salinity variables for climatic studies using a new dual-polarization L-band (1400-1427 MHz) radiometer Microwave Imaging Radiometer by Aperture Synthesis (MIRAS). SMOS will have multiangular observation capability and can be optionally operated in full-polarimetric mode. At this frequency the sensitivity of the brightness temperature (T/sub B/) to the sea surface salinity (SSS) is low: 0.5 K/psu for a sea surface temperature (SST) of 20/spl deg/C, decreasing to 0.25 K/psu for a SST of 0/spl deg/C. Since other variables than SSS influence the T/sub B/ signal (sea surface temperature, surface roughness and foam), the accuracy of the SSS measurement will degrade unless these effects are properly accounted for. The main objective of the ESA-sponsored Wind and Salinity Experiment (WISE) field experiments has been the improvement of our understanding of the sea state effects on T/sub B/ at different incidence angles and polarizations. This understanding will help to develop and improve sea surface emissivity models to be used in the SMOS SSS retrieval algorithms. This paper summarizes the main results of the WISE field experiments on sea surface emissivity at L-band and its application to a performance study of multiangular sea surface salinity retrieval algorithms. The processing of the data reveals a sensitivity of T/sub B/ to wind speed extrapolated at nadir of /spl sim/0.23-0.25 K/(m/s), increasing at horizontal (H) polarization up to /spl sim/0.5 K/(m/s), and decreasing at vertical (V) polarization down to /spl sim/-0.2 K/(m/s) at 65/spl deg/ incidence angle. The sensitivity of T/sub B/ to significant wave height extrapolated to nadir is /spl sim/1 K/m, increasing at H-polarization up to /spl sim/1.5 K/m, and decreasing at V-polarization down to -0.5 K/m at 65/spl deg/. A modulation of the instantaneous brightness temperature T/sub B/(t) is found to be correlated with the measured sea surface slope spectra. Peaks in T/sub B/(t) are due to foam, which has allowed estimates of the foam brightness temperature and, taking into account the fractional foam coverage, the foam impact on the sea surface brightness temperature. It is suspected that a small azimuthal modulation /spl sim/0.2-0.3 K exists for low to moderate wind speeds. However, much larger values (4-5 K peak-to-peak) were registered during a strong storm, which could be due to increased foam. These sensitivities are satisfactorily compared to numerical models, and multiangular T/sub B/ data have been successfully used to retrieve sea surface salinity.

In a recent experiment the microwave emissivity of foam on calm water was measured to be 0.75 to 0.95 and dependent on polarization. Microwave radiometric measurements of breaking waves on the open ocean showed that the emission due to wave breaking varies with the time dynamics of the wave, as well as with radiometer polarization and viewing angle. However, the inherent intermittency and sparseness of breaking waves makes it very difficult to perform repeatable measurements on the open ocean. Therefore, the authors conducted a wave basin experiment in which reproducible breaking waves were generated every 1-2 seconds. This paper reports preliminary results of the combined observations of polarimetric brightness temperatures and physical characteristics of these waves and foam. These and future results will provide input parameters to bound numerical electromagnetic models for prediction of foam emissivities.

This paper describes the deployment of a fully polarimetric K-band radiometer in the Rough Evaporation Duct (RED) experiment, which was conducted during August and September of 2001. The calibration of the four Stokes parameters is described, along with a comparison of the measurements with results of both the Klein-Swift and Ellison et al. sea surface dielectric models. The purpose of the experiment was to improve physical forward models of the ocean surface emission in order to improve wind vector retrieval algorithms.

The foam-covered ocean surface is treated as densely packed air bubbles coated with thin layers of seawater. We apply Monte Carlo simulations of solutions of Maxwell's equations to calculate the absorption, scattering, and extinction coefficients at 10.8 and 36.5 GHz. These quantities are then used in dense-media radiative transfer theory to calculate the microwave emissivity. Numerical results of the model are illustrated as a function of foam parameters. Results of emissivities for both horizontal polarization and vertical polarizations at 10.8 and 36.5 GHz are compared with experimental measurements.

Sea surface salinity can be measured by passive microwave remote sensing at L-band. In May 1999, the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Opportunity Mission to provide global coverage of soil moisture and ocean salinity. To determine the effect of wind on the sea surface emissivity, ESA sponsored the Wind and Salinity Experiment (WISE 2000). This paper describes the field campaign, the measurements acquired with emphasis in the radiometric measurements at L-band, their comparison with numerical models, and the implications for the remote sensing of sea salinity.

Radiometric measurements of the microwave emissivity of foam were conducted during May 2000 at the Naval Research Laboratory's Chesapeake Bay Detachment using radiometers operating at 10.8 and 36.5 GHz. Horizontal and vertical polarization measurements were performed at 36.5 GHz; horizontal, vertical, +45/spl deg/, -45/spl deg/, left-circular, and right-circular polarization measurements were obtained at 10.8 GHz. These measurements were carried out over a range of incidence angles from 30/spl deg/ to 60/spl deg/. Surface foam was generated by blowing compressed air through a matrix of gas-permeable tubing supported by an aluminum frame and floats. Video micrographs of the foam were used to measure bubble size distribution and foam layer thickness. A video camera was boresighted with the radiometers to determine the beam-fill fraction of the foam generator. Results show emissivities that were greater than 0.9 and approximately constant in value over the range of incidence angles for vertically polarized radiation at both 10.8 and 36.5 GHz, while emissivities of horizontally polarized radiation showed a gradual decrease in value as incidence angle increased. Emissivities at +45/spl deg/, -45/spl deg/, left-circular, and right-circular polarizations were all very nearly equal to each other and were in turn approximately equal to the average values of the horizontal and vertical emissivities in each case.