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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model

A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50 deg. N - 50 deg. S at relatively high spatial (approximately 12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is approximately 0.9 and the false alarm ratio is approximately 0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30 deg. S - 30 deg. N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

global flood estimation↗
NASA NTRS2004

TRMM Common Microphysics Products: A Tool for Evaluating Spaceborne Precipitation Retrieval Algorithms

A customized product for analysis of microphysics data collected from aircraft during field campaigns in support of the TRMM program is described. These Common Microphysics Products (CMP's) are designed to aid in evaluation of TRMM spaceborne precipitation retrieval algorithms. Information needed for this purpose (e.g., particle size spectra and habit, liquid and ice water content) was derived using a common processing strategy on the wide variety of microphysical instruments and raw native data formats employed in the field campaigns. The CMP's are organized into an ASCII structure to allow easy access to the data for those less familiar with and without the tools to accomplish microphysical data processing. Detailed examples of the CMP show its potential and some of its limitations. This approach may be a first step toward developing a generalized microphysics format and an associated community-oriented, non-proprietary software package for microphysics data processing, initiatives that would likely broaden community access to and use of microphysics datasets.

Kingsmill, David E.↗
NASA NTRS2019

CloudSat Bias on Falling Snow Estimates Over the Daylight Only Operational Period (2012-2019)

Falling snow is a key component for the global atmospheric, hydrological and energy cycles, and its retrieval from space-based observations represents the best current capability to evaluate it globally. The Global Precipitation Measurement (GPM) Mission Core Observatory, launched in 2014, together with its constellation sensors, can provide quasi-global precipitation estimates every 30 minutes (for level 3 products). Evaluation and validation efforts for such products are crucial, and for global evaluations, one of the most suitable instruments is the Cloud Profiling Radar (CPR) on board CloudSat, which is sensitive to light rain and falling snow. However, due to a battery anomaly in 2011, during its period of overlapping observations with GPM the CPR has operated in a Daylight Only Operations mode (DO-Op) in which it makes measurements primarily during only the daylit portion of its orbit. The goal of this work is to estimate biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat's snowfall measurements during its Full Operations (Full-Op) period from 2006 to 2010 to evaluate the impact DO-Op mode would have had during this period. Results indicate that omitting the nocturnal component of the diurnal cycle of snowfall has nonnegligible impact on snowfall amounts in some regions. The lack of nighttime data during DO-Op biases the latitudinally averaged mean snowfall rates as well as some regional values. Hemispheric differences in bias may be due to more pronounced diurnal variability in the northern hemisphere owing to more prevalent land surface versus the southern hemisphere. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

Milani, Lisa↗
NASA NTRS2016

Evaluation of NASA's MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

MERRA↗
NASA NTRS2008

Global Precipitation Measurement (GPM) Ground Validation (GV) Science Implementation Plan

For pre-launch algorithm development and post-launch product evaluation Global Precipitation Measurement (GPM) Ground Validation (GV) goes beyond direct comparisons of surface rain rates between ground and satellite measurements to provide the means for improving retrieval algorithms and model applications.Three approaches to GPM GV include direct statistical validation (at the surface), precipitation physics validation (in a vertical columns), and integrated science validation (4-dimensional). These three approaches support five themes: core satellite error characterization; constellation satellites validation; development of physical models of snow, cloud water, and mixed phase; development of cloud-resolving model (CRM) and land-surface models to bridge observations and algorithms; and, development of coupled CRM-land surface modeling for basin-scale water budget studies and natural hazard prediction. This presentation describes the implementation of these approaches.

Petersen, Walter A.↗
NASA NTRS2021

Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat’s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by −10.16% and the mean snowfall rate changes by −8.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

Lisa Milani↗
NASA NTRS2021

Validation of Satellite-Based Precipitation Products from TRMM to GPM

The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.

MRMS↗
NASA NTRS2022

Precipitation over the U.S. Coastal Land/Water Using Gauge-Corrected Multi-Radar/Multi-Sensor System and Three Satellite Products

The weather and climate over the coastal regions have received increasing attention because of substantial population growth, the rising sea level, and extreme weather. Satellite remote sensing provides global precipitation estimates (including coastal land/ocean). While these datasets have been extensively evaluated over land, they have rarely been assessed over coastal ocean. As precipitation radars cover both coastal land and ocean, we used the Multi-Radar/Multi-Sensor System (MRMS) gauge-corrected precipitation product from 2018 to 2020 to evaluate three widely used satellite-based precipitation products over the U.S. coastal land versus the ocean (and the water over the Great Lakes). These products included the Integrated Multi-satellite Retrievals for GPM (IMERG), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center Morphing technique (CMORPH). The MRMS data showed a precipitation climatology difference between the coastal land and the ocean that was higher in the winter and lower in the summer and autumn. IMERG and CMORPH performed best over land and water, respectively, while PERSIANN was the most consistent in its performance over land versus water. Heavy precipitation was overestimated by the three products, with larger overestimates over water than over land. These results were not affected by the MRMS uncertainties due to the gauge correction or by the use of different versions.

coastal precipitation↗
NASA NTRS2022

A Multi-sensor Evaluation of Precipitation Uncertainty for Landslide-triggering Storm Events

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall-triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select among a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity-duration thresholds. The average intensity, peak intensity, duration, and NOAA-Atlas return periods are compared ahead of reported landslides across the continental US and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real-time and post-processed satellite (IMERG), radar (MRMS), and gauge-based (NLDAS-2). Landslide-triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296mm with an average range of 51mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8262745mm/hr and as much as 57mm/hr. The two products more reliant upon ground-based observations (MRMS and NLDAS-2) performed better at identifying landslides according to published intensity duration storm thresholds, but all products exhibited hit-ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually-verified landslide locations. We recommend practitioners consider low-latency products like MRMS for investigating landslides, given their near-real time data availability and good performance in detecting landslides. Practitioners would be well-served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.

Precipitation inter-comparison↗
NASA NTRS2013

Statistical and Hydrological Evaluation of TRMM-Based Multi-Satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the Latest Satellite Precipitation Products 3B42V7 Ready for Use in Ungauged Basins?

The objective of this study is to quantitatively evaluate the successive Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products and further to explore the improvements and error propagation of the latest 3B42V7 algorithm relative to its predecessor 3B42V6 using the Coupled Routing and Excess Storage (CREST) hydrologic model in the mountainous Wangchu Basin of Bhutan. First, the comparison to a decade-long (2001-2010) daily rain gauge dataset reveals that: 1) 3B42V7 generally improves upon 3B42V6s underestimation both for the whole basin (bias from -41.15 to -8.38) and for a 0.250.25 grid cell with high-density gauges (bias from -40.25 to 0.04), though with modest enhancement of correlation coefficients (CC) (from 0.36 to 0.40 for basin-wide and from 0.37 to 0.41 for grid); and 2) 3B42V7 also improves its occurrence frequency across the rain intensity spectrum. Using the CREST model that has been calibrated with rain gauge inputs, the 3B42V6-based simulation shows limited hydrologic prediction NSCE skill (0.23 in daily scale and 0.25 in monthly scale) while 3B42V7 performs fairly well (0.66 in daily scale and 0.77 in monthly scale), a comparable skill score with the gauge rainfall simulations. After recalibrating the model with the respective TMPA data, significant improvements are observed for 3B42V6 across all categories, but not as much enhancement for the already well-performing 3B42V7 except for a reduction in bias (from -26.98 to -4.81). In summary, the latest 3B42V7 algorithm reveals a significant upgrade from 3B42V6 both in precipitation accuracy (i.e., correcting the underestimation) thus improving its potential hydrological utility. Forcing the model with 3B42V7 rainfall yields comparable skill scores with in-situ gauges even without recalibration of the hydrological model by the satellite precipitation, a compensating approach often used but not favored by the hydrology community, particularly in ungauged basins.

TRMM↗
NASA NTRSDate not supplied

Evaluating the Performance of GPM IMERG Products for Extreme Precipitation Estimation in Volta River Basin of Ghana

The Volta River basin in West Africa, covering a substantial area of Ghana, faces significant water management challenges due to highly variable rainfall and limited availability of high temporal resolution rain gauges. This study, developed under the 2024 Global Precipitation Measurement (GPM) Mission mentorship program, evaluates precipitation in the Volta River basin using high-resolution satellite-derived data from the Integrated Multi-satellite Retrievals for GPM (IMERG). In the absence of sub-daily rainfall data, comparisons were made between daily, monthly, and seasonal precipitation from local gauges and IMERG V06 and V07 (early and final) runs. Results indicated a very weak correlation between daily precipitation estimates from IMERG and gauge data, but a significantly improved correlation for monthly and seasonal estimates. Additionally, IMERG data were used to identify heavy rainfall events and drought periods, which were compared to known occurrences in the region. By addressing these objectives, this study contributes to more accurate extreme precipitation estimation in the Volta River basin, thereby enhancing disaster risk management and climate resilience in Ghana.

Jessica Sutton↗
NASA NTRS2023

Robustness of Gridded Precipitation Products for Vietnam Basins using the Comprehensive Assessment Framework of Rainfall

The use of satellite–based precipitation products (SPPs) have become increasingly prevalent as key inputs to provide regional rainfall for improving hydrological simulations in data–spare regions. This study introduces a new approach – Comprehensive Assessment Framework of Rainfall (CAFR) to evaluate six satellite–based precipitation products (SPPs) for eleven basins with different sizes across Vietnam (2007–2015). These SPPs include the Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final run Version 6 (GPM IMERGF V6), Multi–Source Weighted–Ensemble Precipitation (MSWEP) V2.2, Soil Moisture to Rain (SM2RAIN) – Advanced SCATterometer (ASCAT) V1.5, Asian Precipitation–Highly–Resolved Observational Data Integration Towards Evaluation (APHRODITE) V1901, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) V2.0, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) – Climate Data Record (CDR) V1.0. With the proposed CAFR: (1) IMERGF is suggested to have the best performance overall, especially when simulating flood peaks; (2) SM2RAIN–ASCAT demonstrates the best skills in metrics related to the dry season; (3) For streamflow simulations, SPPs' performance is sensitive to basin size, with larger basins showing better performance skills. In this study, we demonstrate the capability of our proposed framework to better understand SPP applications in hydrological modeling.

Comprehensive Assessment Framework of Rainfall (CA↗
NASA NTRS2021

Evaluation of GEOS Precipitation Flagging for SMAP Soil Moisture Retrieval Accuracy

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can produce either 1) an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain) or 2) an unnecessary nonretrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG), version 06, Early Run (latency of ~4 h) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 1800 local time (LT) ascending and 0600 LT descending SMAP overpasses over the first five years of the mission (2015–20). Consisting of blended precipitation measurements from the Global Precipitation Mission (GPM) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include (i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, (ii) the IMERG product has a higher frequency of light precipitation amounts, and (iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement [standard deviation of the unbiased RMSE (ubRMSE) less than 0.04 cu. m/cu. m].

IMERG-precipitationCal↗
NASA NTRS2021

Validation of Satellite-Based Precipitation Products from TRMM to GPM

The global precipitation measurement mission (GPM) has been in operation for sevenyears and continues to provide a vast quantity of global precipitation data at finer temporospatialresolutions with improved accuracy and coverage. GPM’s signature algorithm, the integratedmultisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expectedfor wide variety of research and operational applications. This study evaluates the latest version(V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatelliteprecipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensorsystem (MRMS) precipitation products over the conterminous United States (CONUS). The spatialdistributions of all products are analyzed. The error characteristics are further examined for 3B42 andIMERG in winter and summer by an error decomposition approach, which partitions total bias intohit bias, biases due to missed precipitation and false precipitation. The volumetric and categoricalstatistical metrics are used to quantitatively evaluate the performance of the two satellite-basedproducts. All products show a similar precipitation climatology with some regional differences.The two satellite-based products perform better in the eastern CONUS than in the mountainousWestern CONUS. The evaluation demonstrates the clear improvement in IMERG precipitationproduct in comparison with its predecessor 3B42, especially in reducing missed precipitation inwinter and summer, and hit bias in winter, resulting in better performance in capturing lighter andheavier precipitation.

Jianxin Wang↗
NASA NTRS2021

Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions

We present results from an evaluation of precipitable water vapor (W) over remote oceanic areas as derived from global reanalysis models and from satellites against observations from the Maritime Aerosol Network (MAN) for cloudless skies during the period of 2004–2017. They cover polar, mid latitude and tropical oceanic regions and represent a first effort to use MAN observations for such evaluation. The global reanalysis model products evaluated in this study are from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA I), and the Climate Forecast System Reanalysis (CFSR) model. The satellite products evaluated are from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Polarization and Directionality of the Earth's Reflectances (POLDER), the Global Ozone Monitoring Experiment (GOME-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and the Atmospheric Infra-red Sounder (AIRS). Satellite retrievals of W are based on the attenuation of solar reflected light by water vapor absorption bands, except those from AIRS that rely on brightness temperature measurements. A very good agreement is observed between the model estimates and MAN, with mean differences of ~5% and standard deviations of ~15%. These results are within the uncertainties associated with the models and the measurements, indicating the skill of the reanalysis models to estimate W over oceans under clear sky conditions. Mean differences of W between the satellite and MAN products are ~11, 6.7, 12, −7, and 3% for MODIS, POLDER, GOME-2, SCIAMACHY and AIRS respectively, while their standard deviations are 31, 29, 28, 20 and 17%. These differences reveal the need to address inconsistencies among different satellite sensors and ground-based measurements to reduce the uncertainties associated with the retrievals.

Daniel Pérez-Ramírez↗
NASA NTRS2021

How Well Do Multisatellite Products Capture the Space–Time Dynamics of Precipitation? Part I: Five Products Assessed via a Wavenumber–Frequency Decomposition

As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space–time features are captured for use in hydrologic modeling, climate studies, and other applications. Here we propose a space–time Fourier spectral analysis and define a suite of metrics that evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space–time scales at which a satellite product reproduces the variability of a reference “ground-truth” product (“effective resolution”). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space–time structure. We then evaluate five high-resolution multisatellite products (CMORPH, GSMaP, IMERG-Early, IMERG-Final, and PERSIANN-CCS) over a period of 2 years over the southeastern United States. All five satellite products show generally consistent space–time power spectral density when compared to a reference ground gauge–radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively “smooth.” The products also show low levels of spectral coherence with the gauge–radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space–time spectral coherence, the IMERG-Final product shows superior ability in resolving the space–time dynamics of precipitation down to 200-km and 4-h scales compared to the other products.

Clement Guilloteau↗
NASA NTRS1999

TRMM Precipitation Radar Reflectivity Profiles Compared to High-Resolution Airborne and Ground-Based Radar Measurements

In this paper, TRMM (Tropical Rainfall Measuring Mission Satellite) Precipitation Radar (PR) products are evaluated by means of simultaneous comparisons with data from the high-altitude ER-2 Doppler Radar (EDOP), as well as ground-based radars. The comparison is aimed primarily at the vertical reflectivity structure, which is of key importance in TRMM rain type classification and latent heating estimation. The radars used in this study have considerably different viewing geometries and resolutions, demanding non-trivial mapping procedures in common earth-relative coordinates. Mapped vertical cross sections and mean profiles of reflectivity from the PR, EDOP, and ground-based radars are compared for six cases. These cases cover a stratiform frontal rainband, convective cells of various sizes and stages, and a hurricane. For precipitating systems that are large relative to the PR footprint size, PR reflectivity profiles compare very well to high-resolution measurements thresholded to the PR minimum reflectivity, and derived variables such as bright band height and rain types are accurate, even at high PR incidence angles. It was found that for, the PR reflectivity of convective cells small relative to the PR footprint is weaker than in reality. Some of these differences can be explained by non-uniform beam filling. For other cases where strong reflectivity gradients occur within a PR footprint, the reflectivity distribution is spread out due to filtering by the PR antenna illumination pattern. In these cases, rain type classification may err and be biased towards the stratiform type, and the average reflectivity tends to be underestimated. The limited sensitivity of the PR implies that the upper regions of precipitation systems remain undetected and that the PR storm top height estimate is unreliable, usually underestimating the actual storm top height. This applies to all cases but the discrepancy is larger for smaller cells where limited sensitivity is compounded by incomplete beam filling. Users of level three TRMM PR products should be aware of this scale dependency.

Heymsfield, G. M.↗