Recent advances in analyzing and predicting rapid intensity change in tropical cyclones suggest that the distribution and intensity of convective activity in the storm play an important role, particularly their occurrence with respect to the dynamically significant vortex structure. We developed a framework to detect and analyze these features from satellite observations of the condensed water, using it as a proxy for the distribution of the associated latent heating and, hence, the intensity of convective activity. Here we use passive microwave measurements of the condensed water from the GPM and TRMM constellations of conically-scanning radi-ometers and employ a low-wave-number decomposition of the 2D fields of columnar condensate to depict the radial distribution of the azimuthally-averaged fields (the magnitude of wave number 0 - WNO), as well as the radial distribution of the first order asymmetry that is captured by the magnitude and azimuthal orientation of the first harmonic in the Fourier decomposition (WN1). Our analyses of a number of hurricanes illustrate the potential predictive abilities of this satellite-based analysis framework, laying the ground for future investigations.

The sensitivity of PAZ’s Polarimetric Radio Occultation (PRO) observations to horizontally oriented frozen particles is assessed using coincident measurements from the Global Precipitation Measurement (GPM) core satellite’s radiometer GPM Microwave Imager (GMI) and ancillary information from Cloudsat. The difference between the horizontal and vertical polarizations of the GMI observed radiances at 166 GHz, indicative of horizontally oriented particles, is compared with the PAZ differential phase shift observations. A clear positive trend is observed, exhibiting a good correlation between the two observations. The radiometer absolute observations and polarization differences (PD) are then used to build a lookup table of ice water content (IWC) vertical profiles, using coincident observations between GPM and Cloudsat. The vertical profiles of IWC, along with the information of the radiometric PDs and some simple assumptions, are used to simulate the expected PAZ differential phase shift observations. The validation with the PAZ and GPM coincident measurements shows a high correlation between the simulated and observed differential phase shift, therefore proving the sensitivity of polarimetric radio occultations to horizontally oriented frozen particles. These results demonstrate that PRO can not only sense the precipitation near the surface (the original goal of the mission) but also the frozen particles near and well above the freezing level, providing additional detail of the cloud vertical structure.

The uncertainty of passive microwave retrievals of snowfall is notoriously high where the high-frequency surface emissivity is significantly reduced and varies markedly in response to changes of snowpack physical properties. Using the dense media radiative transfer theory, this article studies the potential effects of terrestrial snow-cover depth, density, and grain size on high-frequency channels 89 and 166 GHz of the radiometer onboard the Global Precipitation Measurement (GPM) core satellite, which are commonly used to capture the snowfall scattering signals. Integrating the inference across all feasible grain sizes, ranges of snowpack density and depth are identified over which the snowfall scattering signatures can be time varying and potentially obscured. Using 10 years of reanalysis data, the seasonal vulnerability of snowfall retrievals to changes of snowpack emissivity in the Northern Hemisphere is mapped in a probabilistic sense and connections are made with uncertainties of the GPM passive microwave snowfall retrievals. It is found that among different snow classes, relatively light Arctic tundra snow in fall, with a density below 260 kg m-3, and shallow prairie snow during the winter, with a depth of less than 40 cm, can reduce the surface emissivity and obscure the snowfall passive microwave signatures. It is demonstrated that, during the winter, the highly vulnerable areas are over Kazakhstan, and Mongolia with taiga and prairie snow. In the fall, these areas are largely over tundra and taiga snow in north of Russia and the Arctic Archipelagos as well as prairies in Canada and the Great Plains in the United States.

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on ana prioridatabase populated with the active–passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a$k$-nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mmh−1.

Improved microwave retrieval of land and atmospheric state variables requires proper weighting of the information content of radiometric channels through their error covariance matrix. Inspired by recent advances in metric learning techniques, a new framework is proposed for a formal approximation of the channel error covariance. The idea is tested for the detection of precipitation and its phase over oceans, using coincidences of passive/active data from the Global Precipitation Measurement (GPM) and CloudSat satellites. The initial results demonstrate that the presented approach cannot only capture the known laws of radiative transfer equations, but also the surrogate signatures that can arise due to the co-occurrence of precipitation and other radiometrically active land-atmospheric state variables. In particular, the results demonstrate high precision (low error) for the low-frequency channels of 10-37 GHz in the detection of both rain and snowfall over oceans. Using the optimal estimate of the channel error covariance through the multi-frequency k-nearest neighbor (kNN) classification approach, without any ancillary data, it is demonstrated that the probability of passive microwave detection of snowfall (0.97) can be higher than that of the rainfall (0.88), when drizzle and light snowfall are the dominant form of precipitation. This improvement is hypothesized to be largely related to the information content of the low-frequency channels of 10-37 GHz that can capture the co-occurrence of snowfall with an increased cloud liquid water content, sea ice, and wind-induced changes of surface emissivity.

The paper discusses the hurricane intensity changes using machine learning technologies and data visualization.

The experiment called Radio-Occultation and Heavy Precipitation aboard PAZ (ROHP-PAZ) is the first attempt to perform spaceborne polarimetric radio-occultations (PRO) with signals transmitted by the Global Navigation Satellite Systems (GNSS). The GNSS payload aboard PAZ, initially intended for precise orbit determination, was extended with a dedicated two-polarization Radio-Occultation (RO) antenna pointing to the limb of the Earth and the receiver was upgraded to track occulting signals. The experiment is a proof-of-concept for heavy rain measurements using polarimetric GNSS RO. The PAZ satellite was launched in February 22, 2018. Preliminary results from the commissioning phase will be presented.

In its 2007 “Decadal Survey” (DS) of earth science missions for NASA [1] the U.S. National Research Council (NRC) recommended that a Doppler wind lidar be developed for a three-dimensional tropospheric winds mission (“3D-Winds”). It is expected that the next DS, currently under way, will put additional emphasis on the still pressing need for wind measurements from space. The first DS also called for a geostationary microwave sounder (GMS) on a Precipitation and All-weather Temperature and Humidity (PATH) mission. Such a sounder, the Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR), has been developed at the Jet Propulsion Laboratory (JPL). The PATH mission has not yet been funded by NASA, but a low-cost subset of PATH, GeoStorm was proposed as a hosted payload on a commercial communications satellite. Both PATH and GeoStorm will obtain frequent (every 15 minutes of better) measurements of tropospheric water vapor profiles, and they can be used to derive atmospheric motion vector (AMV) wind profiles even in the presence of clouds. We report on simulation studies of such wind vectors derived from a GMS or possibly from a cluster of low-earth-orbiting (LEO) small satellites (e.g., CubeSats).

It is now recognized that the enormous challenge of scientifically understanding the Earth system requires careful strategic decisions on what missions are deployed. In a recent report, the National Research Council developed a “value framework” for Earth observing systems with a focus on prioritizing NASA observations that merit long-term continuity. In this paper, we refer to this framework to discuss how high value observations arise from opportunistic use of signals generated by Global Navigation Satellite Systems (GNSS) such as GPS. The increasing number of GNSS constellations internationally, likely to be permanently deployed, suggests that the geosciences community will benefit by adopting these signals for a variety of remote sensing needs. We describe recent progress in using these observations scientifically and developing technology to exploit them. We conclude that dedicated constellations of GNSS science instruments in low Earth orbit capable of receiving both direct and reflected GNSS signals will provide excellent science return in a broad range of areas, and constitute a high value Earth observing system.

Recent evidence suggests that the tropics have expanded over the last few decades by a very rough 10per decade. Until now, understanding the mechanisms of that expansion has been confined to models and proxies because of the unavailability of systematic observations of the large-scale circulation. Scatterometer-derived ocean surface vector winds, provide for the first time, an accurate depiction of the large-scale circulation and allow the study of the Hadley cell evolution through analysis of its surface branch. In this study we determine the extent of the Hadley cell as defined by the subtropical zero-crossing of the zonally-averaged zonal wind component. We use scatterometer observations from a number of missions, covering ~13 years. Our analyses reveal seasonal and interannual variability, as well as a long-term trend for expansion of the Hadley cell width. More interestingly, our results show an apparent discontinuity in the signal when the data source changes from one observing system to another. This raises the question about the significance of the unresolved diurnal signal. Indeed, analyses of observations from tandem missions support this notion. Fortunately, the RapidScat mission makes it possible to resolve, for the first time, the details of the diurnal signal. Our preliminary analyses of the RapidScat observations show the presence of a clear semidiurnal signal in the width of the Hadley cell. This helps explain previously found discrepancies. More importantly, this points to a clear need to understand and resolve the diurnal signal before merging wind observations from different missions to form a consistent climate record.

Spaceborne scatterometer observations, especially at Ku-band, are affected by rain in several ways and these effects need to be corrected to avoid errors in wind retrievals. In this work we propose a model to derive the surface backscattering coefficient in presence of both wind and rain. Our approach consists in the development of an ocean surface wind wave spectrum accounting for two effects due to raindrops impact on the surface: the rain-induced wave damping and the generation of ring waves. The results show that this extended spectrum is able to model the ocean surface wave modifications due to rain so that it can be used for further study in physically representing the scatterometer observations in presence of both wind and rain.

A Global Navigation Satellite System (GNSS) radio occultation (RO) experiment is being accommodated in the Spanish low Earth orbiter for Earth Observation PAZ. The RO payload will provide globally distributed vertical thermodynamic profiles of the atmosphere suitable to be assimilated into weather numerical prediction models. The Ground Segment services of the U.S. National Oceanographic and Atmospheric Administration and standard-RO processing services by University Corporation for Atmospheric Research (USA) will be available under best effort basis. Moreover, the mission will run, for the first time, a double-polarization GNSS RO experiment to assess the capabilities of polarimetric GNSS RO for sensing heavy rain events. This paper introduces the Radio-Occultation and Heavy Precipitation experiment aboard PAZ and performs a theoretical analysis of the concept. The L-band GNSS polarimetric observables to be used during the experiment are presented, and their sensitivity to moderate to heavy precipitation events is evaluated. This study shows that intense rain events will induce polarimetric features above the detectability level.

The joint National Aeronautics and Space Administration and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) mission will provide considerably more observations over complex and dynamically changing land backgrounds. A physically based precipitation retrieval using GPM's satellite constellation of passive microwave (PMW) observations has to accommodate the spatially and temporally varying radiometric signature of the land surface to constrain the set of candidate rainfall solutions. The challenge for retrieval algorithms is to identify and isolate precipitation profiles whose simulated observations agree with the satellite observations and are also representative of the surface conditions. Microwave emissivity modeling results are presented from a physically based land algorithm that retrieves soil moisture, vegetation water content, and surface temperature, along with the emissivity using polarized 10, 18, and 37 GHz channel measurements from the WindSat sensor onboard the Coriolis satellite, and results from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The emissivity mean, coefficient of variation, covariance, and correlation slope are examined for the range of clear-scene surface properties observed by WindSat and TRMM between 2003-2012 and 2002-2011, respectively, under a range of seasons, time of day, rain events, etc. These joint data provide a means to examine the extent to which the surface geophysical properties control the microwave land surface emissivity covariability, to better utilize these lower frequency observations in overland PMW-based precipitation retrievals.

Passive microwave (PMW) satellite-based precipitation over land algorithms rely on physical models to define the most appropriate channel combinations to use in the retrieval, yet typically require considerable empirical adaptation of the model for use with the satellite measurements. Although low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes since the low-frequency methods are limited to the highly variable land surface background, whose radiometric contribution is substantial and can vary more than the contribution of the rain signal. Thus, emission techniques are generally useless over the majority of the Earth's surface. As a first step toward advancing to globally useful physical retrieval schemes, an intercomparison project was organized to determine the accuracy and variability of several emissivity retrieval schemes. A three-year period (July 2004-June 2007) over different targets with varying surface characteristics was developed. The PMW radiometer data used includes the Special Sensor Microwave Imagers, SSMI Sounder, Advanced Microwave Scanning Radiometer (AMSR-E), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Advanced Microwave Sounding Units, and Microwave Humidity Sounder, along with land surface model emissivity estimates. Results from three specific targets in North America were examined. While there are notable discrepancies among the estimates, similar seasonal trends and associated variability were noted. Because of differences in the treatment surface temperature in the various techniques, it was found that comparing the product of temperature and emissivity yielded more insight than when comparing the emissivity alone. This product is the major contribution to the overall signal measured by PMW sensors and, if it can be properly retrieved, will improve the utility of emission techniques for over land precipitation retrievals. As a more rigorous means of comparison, these emissivity time series were analyzed jointly with precipitation data sets, to examine the emissivity response immediately following rain events. The results demonstrate that while the emissivity structure can be fairly well characterized for certain surface types, there are other more complex surfaces where the underlying variability is more than can be captured with the PMW channels. The implications for Global Precipitation Measurement-era algorithms suggest that physical retrievals are feasible over vegetated land during the warm seasons.

The 15 papers in this special issue focus on the Chinese Fengyun (FY)-3 Satellite instrument calibration and applications.

We have developed a methodology to enhance an infrared-based high resolution rainfall retrieval algorithm by intelligently calibrating the rainfall estimates using space-based observations. Our approach involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) feature extraction using a wavelet-based method; 3) clustering and classification of cloud patches; and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The results show that using wavelet features along with other features increase the performance of rainfall estimate in terms of quantitative rain/no rain area estimates. In addition, using lightning data as a feature improves the estimates as well.

Passive microwave (PMW) radiometric observations of tropical cyclones (TCs) from the special sensor microwave imager/sounder (SSMIS) continue the legacy monitoring capabilities initiated with the special sensor microwave/imager (SSM/I) that began in 1987. The SSMIS has the following several important differences that should be factored into applications when compared to SSM/I data: 1) channel changes from 85 to 91 GHz result in a 2-8-K brightness temperature depression for many TC inner core scenes; 2) the inclusion of bore-sighted 150-GHz data can help in detecting rapidly growing convective cells that are frequently obscured by upper level clouds in visible and infrared data and are often associated with rapid intensification; and 3) the sensor swath increases by 300 km and permits enhanced spatial and temporal coverage of global TCs. All three attributes can be incorporated to maintain and/or enhance the satellite analyst's ability to monitor critical TC structure via these PMW observations.

Passive microwave (PMW) radiometric observations of clouds from multichannel imaging sensors onboard low Earth-orbiting environmental satellites are now a vital operational dataset. The first operational passive microwave sensor was the Special Sensor Microwave/Imager onboard the Defense Meteorological Satellite Program satellites, which has been gathering hydrological data records since 1987, and continued with the Tropical Rainfall Measuring Mission (TRMM) and the Advanced Microwave Scanning Radiometer onboard Aqua. These sensors view the underlying scene with an Earth incidence angle near 53/spl deg/ and with a variable azimuthal angle, depending upon the orbit direction and scan position. The WindSat sensor onboard the Coriolis satellite, launched in January 2003, is a five-channel polarimetric PMW radiometer designed to optimize ocean surface wind vector retrievals. While it does not have 85-GHz channels, an added feature is its unique fore-aft viewing capability across a portion of its fore scan swath. This provides a view of the underlying scene from two separate azimuthal directions, which provides added information on the three-dimensional (3-D) structure of clouds and their evolution. In this paper, we compare WindSat and TRMM Precipiation Radar observations of tropical cyclones (TCs) with Monte Carlo radiative transfer simulations performed on idealized 3-D convective cloud structures. The TC 3-D structure and possible tilt in the convective cloud structure are inferred from the difference between the 37-GHz equivalent blackbody brightness temperatures (T/sub B/) from the corresponding fore and aft view observations. The information gained from this analysis is important since asymmetries in the cloud vertical and horizontal structure may be an indication of upper level wind shear, which plays a major role in influencing changes of the TC intensity.

This paper examines WindSat wind retrievals from two perspectives. The first is a statistical analysis, comparing both WindSat and QuikSCAT to model output. The second is an analysis geared toward weather forecasters based on individual case studies.

The multispectral sensing capabilities afforded by the 36-channel Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Earth Observing System (EOS) Terra and Aqua satellites have the potential to improve satellite-derived cloud and precipitation products. Included in this channel suite are spectral bands having particular sensitivity to both cloud vertical distribution and near cloud-top microphysics. EOS Aqua, the local afternoon crossing satellite, carries in addition to MODIS the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), a conically scanning passive microwave (PMW) instrument with 12 channels between 6.9 and 89 GHz. While limited by revisit time from low-earth orbit, Aqua provides an ideal test bed for investigating high refresh-rate, geostationary-based blended PMW/optical-spectrum techniques related to improved cloud characterization and precipitation. As the need for improved subdaily quantitative precipitation analysis has grown in recent years, blended-satellite techniques have taken on added relevance. In this paper, we present an application of the MODIS/AMSR-E sensor combination related to potential improvements to the Naval Research Laboratory (NRL)-developed blended-satellite precipitation technique, involving the characterization of cirrus clouds. The presence of cirrus clouds above and nearby to both convective and stratiform precipitation imposes a limit to the utilization of longwave (>10 /spl mu/m wavelength) thermal infrared channels for precipitation techniques. The established "split window" technique involving the 11-12-/spl mu/m brightness temperature difference (BTD) perform well for thin cirrus. We demonstrate that the 1.38- /spl mu/m channel (and the 3.7-11-/spl mu/m BTD at night) on MODIS, when combined with additional channels, is capable of decoupling thin surrounding cirrus from thicker ice clouds. This information may be useful for screening thin cirrus that is often falsely interpreted as light precipitation. Radiative transfer simulations are used to demonstrate the theoretical basis for the selected multispectral channel combinations, and examples involving daytime and nighttime Aqua overpasses are presented.

The objective of this paper is to investigate how the complementarity between low earth orbit (LEO) microwave (MW) and geostationary earth orbit (GEO) infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne IR measurements on sufficiently limited subregions and time windows. The proposed methodologies are focused on new statistical approaches, namely the multivariate probability matching (MPM) and variance-constrained multiple regression (VMR). The MPM and VMR methods are rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy and computing efficiency. In order to demonstrate the potentiality of the proposed MW-IR combined rainfall algorithm (MICRA), three case studies are discussed, two on a global scale on November 1999 and 2000 and one over the Mediterranean area. A comprehensive set of statistical parameters for detection and estimation assessment is introduced to evaluate the error budget. For a comparative evaluation, the analysis of these case studies has been extended to similar techniques available in literature.

The objective of this paper is to investigate how the synergy between low-earth-orbit (LEO) microwave (MW) and geostationary earth orbit (GEO) infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is, at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be sufficiently accurate, to calibrate spaceborne IR measurements on limited sub-regions and time windows. The proposed methodology is focused on a new statistical approach, namely the multivariate probability matching (MPM). The MPM method is rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy.

The Naval Research Laboratory Marine Meteorology Division has designed a dynamically scalable Web-based interface to serve as portal for rapid transition of its cutting-edge satellite applications. Multispectral value-added products, leveraging high spectral/spatial datasets, populate the "Satellite Focus" Web page in near real-time. Satellite Focus has played an integral and ongoing role in Naval operations during the War on Terror, including Operation Enduring Freedom and most recently the Operation Iraqi Freedom campaigns. A structural overview precedes a case study detailing the role of Satellite Focus in supporting Coalition assets during a heavy dust outbreak.

Satellite data from various sensors and platforms are being used to develop automated algorithms to assist in U.S. Navy operational weather assessment and forecasting. Supervised machine learning techniques are used to discover patterns in the data and develop associated classification and parameter estimation algorithms. These methods are applied to cloud classification in GOES imagery, tropical cyclone intensity estimation using SSM/I data, and cloud ceiling height estimation at remote locations using appropriate geostationary and polar orbiting satellite data in conjunction with numerical weather prediction output and climatology. All developed algorithms rely on training data sets that consist of records of attributes (computed from the appropriate data source) and the associated ground truth.

The complementarity between Sun-synchronous microwave (MW) and geo-stationary infrared (IR) radiometry for rain detection and estimation is analyzed. A systematic analysis of different statistical integration methods is carried out in order to use MW-based rainrate estimates to calibrate IR measurements. Multivariate probability matching and nonlinear multiple regression algorithms are investigated in terms of relative detection and estimation accuracy and computing efficiency. In order to demonstrate the potentiality of the techniques proposed, three case studies are discussed: one focused over the Mediterranean area and two focused on a global scale. The analysis of these case studies has been extended to similar techniques, available in literature, for a comparative evaluation.

A series of validation statistics are presented from a operational global, minimum 3-hourly updating precipitation analysis. The technique was developed at the Naval Research Laboratory (NRL) for use in numerical weather prediction (NWP) model data assimilation and now casting operations. The technique is an adaptive statistical/hybrid technique which incorporates data from all current operational infrared-based geostationary satellites and from all (currently) available low-Earth orbiting microwave-based sensors, the Special Sensor Microwave Imager (SSMI), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Sounding Unit (AMSU-B). Knowledge of the rain estimation error statistics are needed in order to properly utilize precipitation observations in NWP variational assimilation techniques. Validation statistics are presented from comparisons with operationally supported raingauge networks in Australia and Korea in a two-dimensional fashion, where the spatial and temporal dimensions are each individually varied.

The objective of this paper is to evaluate the potential of a Bayesian inversion algorithm using microwave multisensor data for the retrieval of surface rainfall rate and cloud parameters. The retrieval scheme is based on the maximum a posteriori probability (MAP) method, extended for the use of both spaceborne passive and active microwave data. The MAP technique for precipitation profiling is also proposed to approach the problem of the radar-swath synthetic broadening; that is, the capability to exploit the combined information also where only radiometric data are available. In order to show an application to airborne data, two case studies are selected within the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). They refer to a stratiform storm region and an intense squall line of two mesoscale convective systems, which occurred over the ocean on February 20 and 22, 1993, respectively. The estimated rainfall rates and columnar hydrometeor contents derived from the proposed algorithms are compared to each other and to radar estimates based on reflectivity-rainrate (Z-R) relationships. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are also discussed. A database of combined measurements acquired at nadir during various TOGA-COARE flights, is used for applying the radar-swath synthetic broadening technique in the case of an along-track radar-failure countermeasure. A simulated test of the latter technique is performed using the case studies of February 20 and 22, 1993.

The NASA airborne Advanced Microwave Precipitation Radiometer (AMPR) and the National Center for Atmospheric Research (NCAR) CP-2 multiparameter radar were jointly operated during the 1991 Convection and Precipitation/Electrification experiment (CaPE) in central Florida. The AMPR is a four channel, high resolution, across-track scanning total power radiometer system using the identical multifrequency feedhorn as the widely utilized Special Sensor Microwave/Imager (SSM/I) satellite system. Surface and precipitation feature are separable based on the T/sub B/ behavior as a function of the AMPR channels. The radar observations are presented in a remapped format suitable for comparison with the multifrequency AMPR imagery. Striking resemblances are noted between the AMPR imagery and the radar reflectivity at successive heights, while vertical profiles of the CP-2 products along the nadir trace suggest a storm structure consistent with the viewed AMPR T/sub B/.>