Precipitation measurements are crucial for improving climate models and weather forecasting, with satellite-based radar systems, such as the Dual Precipitation Radar (DPR) on board the Global Precipitation Measurement Core Observatory (GPM CO), playing a pivotal role in enhancing observational coverage, especially in remote regions. In November 2023, an orbit boost maneuver raised the GPM CO’s altitude from 400 km to 435 km, potentially influencing the quality of DPR data. This study evaluates the impact of this altitude increase on the accuracy of DPR-derived precipitation products by comparing data acquired before and after the orbit boost. We assess the performance of DPR measurements against ground-based laser disdrometer data collected in Italy between May 2018 and November 2024. The results highlight the effects of the altitude change on rainfall retrievals, contributing to a better understanding of how orbital adjustments can affect satellite-based precipitation observations.

Abstract As part of NASA’s Global Precipitation Measurement (GPM) ground validation program, a multi-year winter field study in Storrs, Connecticut brought a unique opportunity to study the long-lasting problem of precipitation phase determination within retrieval algorithms. This study evaluates the phase algorithms of NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, the flagship GPM global precipitation product, and NOAA’s Multi-Radar Multi-Sensor (MRMS) dataset, which is frequently used to validate IMERG precipitation rates over the continental United States. The study selected ten long-lasting phase transition events where rain and snow were recorded for more than one hour with the presence of mixed precipitation and focused on two diverse events. Using the PARSIVEL disdrometer phase as a reference, phase transition occurs at around wet-bulb temperature of 0.5˚C, which agrees with prior studies. IMERG and MRMS phase algorithms performed better when using High Resolution Rapid Refresh (HRRR) and European Center for Medium-Range Weather Forecast Reanalysis (ERA5) temperatures than Modern-Era Retrospective Analysis for Research and Applications reanalysis (MERRA2) temperatures. Overall, IMERG Version 07 performed better than its predecessor Version 06. This study also evaluated HRRR deterministic and probabilistic phase algorithms, which performed better than HRRR temperature-based MRMS and IMERG algorithms, respectively.

The multisatellite precipitation product from the U.S. Global Precipitation Measurement (GPM) mission Science Team, the Integrated Multi-satellitE Retrievals for GPM (IMERG), is a widely used GPM product. It merges precipitation retrievals from passive microwave (PMW) and infrared (IR) sensors on board a collection of satellites operated by several agencies around the globe. IMERG retrospectively provides over two decades' global precipitation data at fine temporal-spatial resolutions. This overland study evaluates the satellite-based precipitation statistics in the latest versions of the IMERG (V06B and V07B) products over the conterminous United States. High-resolution, ground-based radar precipitation estimates from a quality-controlled version of the Multi-Radar Multi-Sensor system developed under the GPM Ground Validation effort are used as the reference product. The relative performance of the two IMERG versions is evaluated using volumetric and categorical statistical metrics. The precipitation retrieval errors are further separated into three individual components: hit bias, missed-precipitation bias, and false-precipitation bias, and all are traced back to the sensor-specific sources, as well as different levels of the IR input usage. The evaluation highlights the clear improvement in the IMERG V07B precipitation product for all seasons, especially for winter, with reduced systematic bias and uncertainty and increased precipitation detectability in comparison with V06B. The changes in V07B include the use of input data processed with improved algorithms for both PMW and IR retrievals, the addition of PMW retrievals over frozen surfaces, and upgrades in the intercalibration that address sources of bias in V06B.

Diagnosing errors in spaceborne oceanic precipitation estimates is difficult due to complicated multisatellite algorithms and limited surface-based measurements. The Global Precipitation Measurement (GPM) mission helps to alleviate these challenges with NASA's Integrated Multi-satellitE Retrievals for GPM (IMERG) product, which is transparently designed to encourage community validation activities, and the GPM Validation Network, which collects observations across global precipitation regimes from over 100 ground-based weather radars to serve as reference datasets for the GPM precipitation products. This study uses the GPM Validation Network's oceanic precipitation observations from 32 island and coastal radars to diagnose the performance of IMERG V06B and V07B Final Run products during GPM Microwave Imager (GMI) overpasses (i.e., IMERG-GMI) in the period June 2014-September 2021. Errors are traced from the input level 2 (satellite footprint) Goddard profiling algorithm climate (GPROF-CLIM) GMI product through the successive gridding, calibration, and precipitation distribution restoration steps of IMERG's level 3 (gridded) algorithm. Results highlight that IMERG-GMI V07B outperforms V06B in detecting and quantifying oceanic precipitation, with a significant improvement over high-latitude ocean (V06B: +143%; V07B: +50%). Furthermore, there is a clear oceanic latitudinal trend in the mean relative bias of IMERG-GMI V07B (high latitude: +50%; midlatitude: +10%; tropical:-41%), which largely traces back to GPROF-CLIM V07 (high latitude: +22%; midlatitude:-8%; tropical:-44%), with bias differences driven by IMERG's passive microwave calibration scheme. This error tracing approach supports future IMERG algorithm developments by disentangling how algorithm steps enhance or mitigate errors. SIGNIFICANCE STATEMENT: Most precipitation occurs over the ocean, yet precipitation products are rarely evaluated there due to a lack of surface-based measurements. This study utilizes the GPM Validation Network's datasets from coastal and island radars to conduct the first oceanic evaluation of the NASA IMERG V07B multi-satellite precipitation product. The performance of IMERG at different successive algorithm steps is assessed to understand which processing steps reduce or increase errors and to support future algorithm improvements. The latest IMERG version provides more accurate and reliable precipitation estimates than the preceding version. However, IMERG-GMI is biased high by +50% and low by-41% over the high-latitude and tropical oceans, respectively.

Precipitation monitoring plays a key role in understanding Earth's climate system and its effects on sectors such as hydrology, water resource management, and agriculture. Satellite-based measurements, particularly through missions like the Global Precipitation Measurement (GPM), have significantly enhanced our ability to observe precipitation patterns globally. Onboard the GPM Core Observatory, the Dual-frequency Precipitation Radar (DPR), consisting of the Ku-band Precipitation Radar (KuPR), which operates at 13.6 GHz, and the Ka-band precipitation radar (KaPR) at 35.5 GHz. The DPR has proven to be an indispensable instrument for characterizing water cycle study applications. To extend the life of a satellite, in order to guarantee the continuity of observations, a common strategy is to increase the orbit altitude. For this reason, on November 7 and 8, 2023, the GPM Core Observatory performed two orbit boost maneuvers that raised its altitude from 407 km to 442 km. As a result of this orbital elevation, the observing parameters of the GPM DPR instruments underwent some changes, such as the increase of spatial resolution and of the minimum detectable rain rate, which has had an impact on some geophysical products. To ensure the accuracy and reliability of satellite data over time, the GPM mission supported a Ground Validation program, which aims to verify and improve precipitation retrieval algorithms over time using multiple ground based instruments. This study focuses on GPM DPR Level 2 Version 7, which is the first to incorporate a modified scan pattern for the KaPR, introduced on May 21, 2018. This adjustment enables the dual-frequency radar to operate across the full observation swath. This study compares the GPM DPR Version 7 products, specifically the earlier Version 7A (before the orbit boost) with Version 7C (after the orbit boost), over Italy, using data from a network of ground-based laser disdrometers networked by the GID (Gruppo Italiano Disdrometria, in Italian). The dual-frequency-based 2ADPR-FS, as well as the single-frequency-based 2AKa-FS and 2AKu-FS Version 7 Level 2 DPR products are used. GPM data from May 22, 2018, to November 30, 2024, were analyzed. The following variables have been investigated: reflectivity factors at the Ku and Ka bands corrected for attenuation, rainfall rate, and DSD parameters Dm and Nw. Statistical indices are used to assess the agreement between satellite observations and disdrometer data. After the orbit boost, dual frequency still presents a slightly better agreement with disdrometers with respect to single frequency products. Discrepancies, however, were noted in the performance of rainfall and microphysical parameters, especially in areas with complex terrain and disdrometers located at high altitudes. In general, the comparison of Version 7A and Version 7C products with disdrometers helped reveal the limited influence of the orbit boost on the quality of DPR products. The results suggest that an orbital adjustment, similar to those implemented for the GPM mission, can be effectively adopted by other missions aimed at reconstructing the 3D structure of clouds and precipitation, since extending the satellite's operational life results in only a negligible impact on the quality of the data products.

Winter precipitation forecasts of phase and amount are challenging, especially in Northeast United States where mixed precipitation events from various synoptic systems frequently occur. Yet, there are not enough quality observations of winter precipitation, particularly microphysical properties from falling snow or mixed phase precipitation. During the winters of 2021-2022, 2022-2023, and 2023-2024, the NASA Global Precipitation Measurement (GPM) Ground Validation (GV) program conducted a field campaign at the University of Connecticut (UConn). The goal of this campaign was to observe various phases of winter precipitation and winter storm types to validate the GPM satellite precipitation products. Over the three winters at UConn, a total of 40 instruments were deployed across two observing sites that captured 117 precipitation events, including 19 phase transition events as indicated by the PARSIVEL(2). These instruments included scanning and vertically pointing radars, along with suites of in-situ sensors. In addition, an unmanned aircraft system has been deployed in 2023-2024. Here, an overview of the different field deployments, instrumentation, and the datasets collected are presented. To showcase the observations, this article features a wide-ranging set of measurements collected from the instrument suite for the 28 February 2023 storm, during which six to eight inches of snow accumulated at the two different observing sites. Also included is a discussion on how these observations can be combined with other datasets to validate ground-based and remote sensing measurements and highlight important atmospheric processes that impact winter precipitation phase and amount. The datasets collected from this GPM GV field campaign are available at 10.5067/GPMGVUCONN/DATA101 (Cerrai et al., 2025).

A comprehensive understanding of various microphysical processes underlying precipitation formation can be achieved through simultaneous measurements from ground-based and airborne radar systems at different frequencies. The study presented in this paper primarily centers on the analysis of collective observations of winter precipitation obtained from various radars deployed during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) conducted by the National Aeronautics and Space Administration (NASA). This work focuses on precipitation observing remote sensing instruments, namely the Dual-frequency Dual-polarized Doppler Radar (D3R) operating at Ku/Ka-bands, airborne radars at X, Ku, Ka, and W-band frequencies aboard NASA's ER-2 flight, and instruments on NASA's P-3 flight, including probes and dropsondes. Coordinated Range Height Indicator (RHI) scans from the D3R along the flight track are used for simultaneous observation of a snowstorm event on 28 th February 2023. Cross-validation procedures are performed, accounting for differences in spatial resolution and viewing geometry through volume matching. The ground based disdrometer is used to project the level of inter-comparison expected between radar measurements at different frequencies. The inter-comparison between the D3R and the aircraft-borne radar systems revealed consistent measurements. Additionally, this study provides an assessment of distinct ice crystal habits observed during the storm based on radar measurements, corroborated by corresponding observations from microphysics probes aboard the P-3 flight.

AbstractMicrophysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

NASA's multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Inte-grated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network's island-based radars to assess IMERG when the GPM Core Observatory's Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 cli-mate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the esti-mates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm's calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1 degrees eastward in the latitude band 75 degrees N-75 degrees S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach en-ables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the impor-tance of collaboration between evaluation studies and algorithm developers.

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

Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA's Global Precipitation Measurement (GPM) mission ground validation pro-gram as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is de-rived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are de-rived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relation-ships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relation-ships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.

We examine several different features of DSDs based on data and observations from two mid-latitude coastal locations: (a) the Delmarva peninsula, USA, and (b) Incheon, South Korea. In each case, the full DSD spectra were obtained from two collocated disdrometers. Two events from location (a) and one event from location (b) are presented. For (a), observations and retrievals from NASA’s S-band polarimetric radar are included in the analyses as well as retrieved DSD parameters from the dual-wavelength precipitation radar onboard the Global Precipitation Measurement satellite. For (b), the disdrometer-based DSD data are compared with measurements from another sensor. Our main aim is to examine the underlying shape of the DSDs and their representation by the generalized gamma model.

The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars-WSR-88D network-provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA-JAXA spaceborne radar-the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)-has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA's WSR-88D network and NASA-JAXA's spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA-JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed.

The NASA POLarimetric (NPOL) Radar is based at NASA's Goddard Space Flight Center(GSFC) Wallops Flight Facility (WFF) in Wallops Island, Virginia, and is physically located about 38 km northeast in Newark, MD (38.263N, 75.342W). NPOL is NASA's flagship weather radar and provides well-calibrated high-quality data for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. NPOL is an S-band, Doppler, dual-polarimetric radar that measures reflectivity, radial velocity, and spectrum width, as well as differential reflectivity, differential phase, and co-polar correlation. Subsequent data processing provides retrievals of rain rate, specific differential phase, and particle size distribution parameters.A discussion of the NPOL system is provided in Section 6.1. The various means of both engineering and data-based calibration of the radar are given in Section 6.2. A summary of the software tools that have been developed and used by GPM GV personnel is provided in Section 6.3. A brief review of the NPOL-based GPM field campaigns, as well as NPOL's contributions to the WFF Precipitation Research Facility, is given in Section 6.4. Finally, Section 6.5 provides some examples that utilize NPOL for validation.

Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment-PyeongChang Olym-pic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter Dmass and the normalized intercept parameter NW, have median values of 1.15-1.31 mm and 2.84-3.04 log(mm21 m23), respec-tively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx-Deq, and the relationship between the Reynolds and Best numbers, Re-X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW, resulting in well -fitted power-law relationships between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameterm ranging from 22 to 10. NASA's Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes m 5 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with m, where the functional dependence on m depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23-0.34 when m 5 2 and by 0.29-0.40 when m 5 3, whereas the MFB of SWER had a much narrower range (from 20.03 to 0.04) for the same m values.

We present a case study that showcases the analysis of four snowstorms that occurred during the winter season of 2021‐2022 at Wallops Flight Facility. The study includes data collection and analysis from various instruments ranging from delicate optical, electronic, and mechanical surface instrumentation to state-of-the-art radars. Surface observations of geometrical and microphysical properties of winter precipitation coupled with radar scattering measurements and computations are crucial for the advancement of our understanding of snow events, numerical weather prediction models, and correct interpretation of data from the national network of weather radars.

The BiLateral Operational Storm-Scale Observation and Modeling (BLOSSOM) project was initiated in order to establish routine storm-scale polarimetric radar observations and cloud-process modeling at NASA GSFC Wallops Flight Facility (WFF), where various continental and maritime convective systems are being observed. The ultimate goals of BLOSSOM include: * Establish a long-term super site to improve understanding of cloud physical states and processes over the WFF site through bilateral storm-scale observations and modeling. * Provide routine meteorological large-scale forcing input to support cloud-resolving models (CRMs), large-eddy simulation (LES) models, and single-column models (SCMs) for the improvement of cloud microphysics and convection parameterizations. * Provide routine storm-scale cloud-precipitation simulations as well as storm-scale measurements using ground-based polarimetric Doppler radar and in-situ data. * Collect and organize value-added data from the cloud-process simulations, ground-based polarimetric radar, and NASA satellite observations for the community. This presentation will highlights a few case studies to test the concept of BLOSSOM, including the creation of ensemble large-scale forcing, configuring and performing cloud-process simulations with different bulk microphysics using the Goddard Cumulus Ensemble (GCE) model, organizing and streaming NASA S-band dual-POLarimetric radar (NPOL) and other WFF instrument data, and validating the ensemble GCE simulations through formulating statistical composites by comparing observed and simulated polarimetric radar signals using the POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS). Different spatial grid spacing (1km vs 250m) of the GCE simulations will be also evaluated to examine resolution impact on representing time-series as well as time-integrated composites of polarimetric radar signals.

The International Collaborative Experiment during the PyeongChang Olympics and Paralympic winter games 2018 took place in the PyeongChang region of South Korea. The main goal of this field campaign was to study winter precipitation in an environment that has complex terrain. The NASA dual-frequency, dual-polarization, Doppler radar (D3R) was calibrated and deployed in this field campaign. The positioning error of the radar was calibrated to be within 0.1°. The D3R was deployed for more than four months and was able to capture many interesting snowfall events along with a few rain events. In this article, the deployment and performance of the D3R during the campaign are discussed. The snowfall events captured by the D3R are discussed in detail to interpret the microphysics from a radar's perspective. The reflectivity–snowfall rate relationship is derived at the Ku band, and the snow accumulation computed is in good agreement with a precipitation gauge that was deployed near the radar. The benefit of the dual-frequency ratio for identifying the precipitation particle types is briefly introduced using the data from a large snow event on 28th February 2018. The vertical profile D3R data for this snow event are studied for detecting the presence of pristine-oriented ice crystals in the mixed hydrometeor phase conditions. Various other instruments, such as X-band radar and disdrometers, were deployed in the campaign. The D3R data are compared with the MxPOL X-band radar, and the reflectivity values match within a couple of dB in the common volume region.

The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, Virginia, has been established as a semipermanent supersite for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research-quality precipitation instruments, including NASA's S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual-Frequency Precipitation Radar (DPR), combined DPR-GPM Microwave Imager (GMI) and GMI level II precipitation retrievals compared to WPRF ground observations from a 6-yr collection of satellite overpasses. Multisensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (D-m), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 min after overpass time but are not consistent between cases. In addition, GPM Core Observatory D-m retrievals are within level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (< 1.0 mm h(-1)). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multisensor DPR/GMI combined algorithms outperform single-sensor DPR algorithm. SIGNIFICANCE STATEMENT: Satellites are beneficial for global precipitation surveillance because extensive ground instruments are lacking, especially over oceans. Ground validation studies are required to calibrate and improve precipitation algorithms from satellite sensors. The primary goal of this study is to quantify the differences between satellite raindrop size and rain-rate retrieval with ground-based observations. Rainfall-rate algorithms require assumptions about the mean raindrop size. Results indicate Global Precipitation Measurement (GPM)/satellite-based mean raindrop size is within acceptable error (& PLUSMN;0.5 mm) with respect to ground measurements. In addition, GPM satellite measurements overestimate light rain (< 1.0 mm h(-1)), which is important during the winter months and at high latitudes. Illuminating the challenges of GPM satellite-based precipitation estimation can guide algorithm developers to improve retrievals.

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]A Python-based Radar Data Processing System for the NASA GPM Ground Validation ProgramAuthorsJasonPippittiDDavidWolffiDDavidMarksCharanjitPablaBrandonGardnerSee all authors Jason PippittiDCorresponding Author• Submitting AuthorNASA Goddard Space Flight CenterSSAIiDhttps://orcid.org/0000-0002-6698-0756view email addressThe email was not providedcopy email addressDavid WolffiDNASA Goddard Space Flight Center Wallops Flight FacilityiDhttps://orcid.org/0000-0003-0005-3754view email addressThe email was not providedcopy email addressDavid MarksScience Systems and Applications, Inc.view email addressThe email was not providedcopy email addressCharanjit PablaScience Systems and Applications, Inc.view email addressThe email was not providedcopy email addressBrandon GardnerNASA Goddard Space Flight Center Wallops Flight Facilityview email addressThe email was not providedcopy email address