Uncertainties of GPM Microwave Imager Precipitation Estimates Related to Precipitation System Size and Intensity

Abstract The uncertainties in the version 5 Global Precipitation Measurement (GPM) Microwave Imager (GMI) precipitation retrievals are evaluated via comparison with the radar–radiometer (so-called “Combined”) retrievals between 40°S and 40°N. Results show the precipitation estimates are close (~7% GMI overestimation) globally. However, some specific regions, such as central Africa, the Amazon, the Himalayan region, and the tropical eastern Pacific, show a large overestimation (up to 50%) in GMI retrievals when compared to Combined retrievals. The uncertainties are further evaluated based on precipitation system properties, such as size and intensity of the system. GMI tends to underestimate precipitation volume when the system is relatively warm (>250 K) and small (<200 km2) due to the lack of ice scattering signatures. However, for large systems (>2000 km2), GMI-derived precipitation is typically higher than Combined over all surfaces. Based on the system properties, a simple bias correction methodology is proposed to implement in the Goddard Profiling Algorithm (GPROF) to reduce GMI biases. GMI precipitation volume is adjusted in each precipitation system based on the size and minimum 89 GHz polarization-corrected temperature (PCT) over land and ocean separately. The overall GMI bias is reduced to 3%, with significant improvement over land. The GMI biases (up to 50%) over the previously mentioned regions are significantly or partially removed, becoming less than 20%. This method also shows effectiveness in removing zonal and seasonal biases from GMI estimates. These results suggest the importance of utilizing the information of whole precipitation systems instead of individual pixels in the precipitation retrieval.

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Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0064.1 ◽

2020 ◽

Author(s):

Lisa Milani ◽

Mark S. Kulie ◽

Daniele Casella ◽

Pierre E. Kirstetter ◽

Giulia Panegrossi ◽

...

Keyword(s):

Ad Hoc ◽

A Priori ◽

Precipitable Water ◽

The United States ◽

Lake Effect ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

Microwave Imager ◽

Quantitative Precipitation Estimates ◽

Global Precipitation

AbstractThis study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the United States lower Great Lakes region. GPM Microwave Imager (GMI) high frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard PROfiling (GPROF) QPE retrievals produce inconsistent results when compared against the Multi-Radar/Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not conform with MRMS observations. Ad-hoc precipitation rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-meter temperature, total precipitable water, and background surface type) used to constrain the GPROF a-priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using snow cover a-priori database in the locations of originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a-priori databases to improve intense lake-effect snow detection and retrieval performance.

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Comparative Strengths of SCaMPR Satellite QPEs with and without TRMM Ingest versus Gridded Gauge-Only Analyses

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-053.1 ◽

2013 ◽

Vol 14 (1) ◽

pp. 153-170 ◽

Cited By ~ 10

Author(s):

Yu Zhang ◽

Dong-Jun Seo ◽

David Kitzmiller ◽

Haksu Lee ◽

Robert J. Kuligowski ◽

...

Keyword(s):

False Alarm ◽

Tropical Rainfall Measuring Mission ◽

Detection Rates ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

Trmm Precipitation ◽

Microwave Imager ◽

Quantitative Precipitation Estimates ◽

Global Precipitation Measurement ◽

Global Precipitation

Abstract This paper assesses the accuracy of satellite quantitative precipitation estimates (QPEs) from two versions of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm relative to that of gridded gauge-only QPEs. The second version of SCaMPR uses the QPEs from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and Microwave Imager as predictands whereas the first version does not. The assessments were conducted for 22 catchments in Texas and Louisiana against National Weather Service operational multisensor QPE. Particular attention was given to the density below which SCaMPR QPEs outperform gauge-only QPEs and effects of TRMM ingest. Analyses indicate that SCaMPR QPEs can be competitive in terms of correlation and CSI against sparse gauge networks (with less than one gauge per 3200–12 000 km2) and over 1–3-h scale, but their relative strengths diminish with temporal aggregation. In addition, the major advantage of SCaMPR QPEs is its relatively low false alarm rates, whereas gauge-only QPEs exhibit better skill in detecting rainfall—though the detection skill of SCaMPR QPEs tends to improve at higher rainfall thresholds. Moreover, it was found that ingesting TRMM QPEs help mitigate the positive overall bias in SCaMPR QPEs, and improve the detection of moderate–heavy and particularly wintertime precipitation. Yet, it also tends to elevate the false alarm rate, and its impacts on detection rates can be slightly negative for summertime storms. The implications for adoption of TRMM and Global Precipitation Measurement (GPM) QPEs for NWS operations are discussed.

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The GPM Combined Algorithm

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-16-0019.1 ◽

2016 ◽

Vol 33 (10) ◽

pp. 2225-2245 ◽

Cited By ~ 67

Author(s):

Mircea Grecu ◽

William S. Olson ◽

Stephen Joseph Munchak ◽

Sarah Ringerud ◽

Liang Liao ◽

...

Keyword(s):

Optimal Estimation ◽

Environmental Data ◽

Dual Frequency ◽

Precipitation Measurement ◽

Deconvolution Technique ◽

Precipitation Estimates ◽

Microwave Imager ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Combined Algorithm

AbstractIn this paper, the operational Global Precipitation Measurement (GPM) mission combined radar–radiometer algorithm is thoroughly described. The operational combined algorithm is designed to reduce uncertainties in GPM Core Observatory precipitation estimates by effectively integrating complementary information from the GPM Dual-Frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) into an optimal, physically consistent precipitation product. Although similar in many respects to previously developed combined algorithms, the GPM combined algorithm has several unique features that are specifically designed to meet the GPM objectives of deriving, based on GPM Core Observatory information, accurate and physically consistent precipitation estimates from multiple spaceborne instruments, and ancillary environmental data from reanalyses. The algorithm features an optimal estimation framework based on a statistical formulation of the Gauss–Newton method, a parameterization for the nonuniform distribution of precipitation within the radar fields of view, a methodology to detect and account for multiple scattering in Ka-band DPR observations, and a statistical deconvolution technique that allows for an efficient sequential incorporation of radiometer information into DPR precipitation retrievals.

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Evaluation of V05 Precipitation Estimates from GPM Constellation Radiometers Using KuPR as the Reference

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0144.1 ◽

2020 ◽

Vol 21 (4) ◽

pp. 705-728 ◽

Cited By ~ 1

Author(s):

Yalei You ◽

Veljko Petkovic ◽

Jackson Tan ◽

Rachael Kroodsma ◽

Wesley Berg ◽

...

Keyword(s):

Low Frequency ◽

Evaluation Process ◽

Advanced Technology ◽

Lower Priority ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

Priority Order ◽

Microwave Imager ◽

Global Precipitation ◽

Level 2

AbstractThis study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHSs)]—are evaluated over the 65°S–65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low-frequency channels. Furthermore, AMSR2 is clearly superior to SSMISs, likely due to the finer footprint size. Over land all sensors perform similarly except the noticeably worse performance from ATMS and SSMIS-F17. Second, we include the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) into the evaluation process, contrasting it against other sensors in the SAPHIR latitudes (30°S–30°N). SAPHIR has a slightly worse detection capability than other sounders over ocean but comparable detection performance to MHSs over land. The intensity estimates from SAPHIR show a larger normalized root-mean-square-error over both land and ocean, likely because only 183.3-GHz channels are available. Currently, imagers are preferred to sounders when level-2 estimates are incorporated into level-3 products. Our results suggest a sensor-specific priority order. Over ocean, this study indicates a priority order of AMSR2, SSMISs, MHSs and ATMS, and SAPHIR. Over land, SSMIS-F17, ATMS and SAPHIR should be given a lower priority than the other sensors.

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Ground Validation and Error Sources Identification for GPM IMERG Product over the Southeast Coastal Regions of China

Remote Sensing ◽

10.3390/rs12244154 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4154

Author(s):

Xinxin Sui ◽

Zhi Li ◽

Ziqiang Ma ◽

Jintao Xu ◽

Siyu Zhu ◽

...

Keyword(s):

Coastal Areas ◽

Probability Of Detection ◽

Surface Type ◽

Error Sources ◽

Precipitation Measurement ◽

Ground Validation ◽

Precipitation Estimates ◽

Microwave Imager ◽

Type Factor ◽

Global Precipitation

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) has been widely evaluated. However, most of these studies focus on the ultimate merged satellite-gauge precipitation estimate and neglect the valuable intermediate estimates which directly guide the improvement of the IMERG product. This research aims to identify the error sources of the latest IMERG version 6 by evaluating the intermediate and ultimate precipitation estimates, and further examine the influences of regional topography and surface type on these errors. Results show that among six passive microwave (PMW) sensors, the Microwave Humidity Sounder (MHS) has outstanding comprehensive behavior, and Special Sensor Microwave Imager/Sounder (SSMIS) operates advanced at precipitation detection, while the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) has the worst performance. More precipitation events are detected with larger quantitative uncertainty in low-lying places than in highlands, in urban and water body areas than in other places, and more in coastal areas than in inland regions. Infrared (IR) estimate has worse performance than PMW, and the precipitation detectability of IR is more sensitive to the factors of elevation and the distance to the coast, as larger critical successful index (CSI) over lowlands and coastal areas. PMW morphing and the mixing of PMW and IR algorithms partly reverse the conservative feature of the precipitation detection of PMW and IR estimates, resulting in higher probability of detection (POD) and false alert ratio (FAR). Finally, monthly gauge calibration improves most of the statistical indicators and reduces the influence of elevation and surface type factor on these errors.

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Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1751.1 ◽

2005 ◽

Vol 22 (7) ◽

pp. 909-929 ◽

Cited By ~ 69

Author(s):

Hirohiko Masunaga ◽

Christian D. Kummerow

Keyword(s):

A Priori ◽

Three Dimensional ◽

Tropical Rainfall Measuring Mission ◽

Dimensional Structure ◽

Retrieval Algorithm ◽

Brightness Temperatures ◽

Precipitation Measurement ◽

Microwave Imager ◽

Trmm Microwave Imager ◽

Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

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Understanding performance of GPROF precipitation retrievals over the Netherlands in relation to precipitation characteristics as derived from ground-based dual-polarization radars

10.5194/egusphere-egu21-6141 ◽

2021 ◽

Author(s):

Linda Bogerd ◽

Hidde Leijnse ◽

Aart Overeem ◽

Remko Uijlenhoet

Keyword(s):

The Netherlands ◽

Background Radiation ◽

A Priori ◽

Dual Frequency ◽

Dual Polarization ◽

Ground Clutter ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

Global Precipitation ◽

Precipitation Events

The Global Precipitation Measurement mission (GPM) is one of the recent efforts to provide satellite-based global precipitation estimates. The GPM Profiling Algorithm (GPROF) converts microwave radiation measured by passive microwave (PMW) sensors onboard constellation satellites into precipitation. Over land, precipitation estimates are obtained from high frequency PMW-channels that measure the radiance scattered by ice particles in rain clouds. However, due to the limited scattering related to shallow and light precipitation, it is challenging to distinguish these signals from background radiation that is naturally emitted from the Earth&#8217;s surface.Increased understanding of the physical processes during precipitation events can be used to improve PMW-based precipitation retrievals. This study couples overpasses of GPM radiometers over the Netherlands to two dual-polarization radars from the Royal Netherlands Meteorological Institute (KNMI). The Netherlands is an ideal setting for this study due to the availability of high-quality ground-based measurements, the frequent occurrence of shallow events, the absence of ground-clutter related to mountains, and the varying background emission related to its coastal location.The coupling of overpasses with ground-based precipitation radars provides the opportunity to relate GPROFs performance to physical characteristics of precipitation events, such as the vertical reflectivity profile and dual-polarization information on the melting layer. Furthermore, simultaneous radiometer estimates and space-based reflectivity profiles from the dual-frequency precipitation radar (DPR) onboard the GPM core satellite are coupled to the ground-based reflectivity profiles for selected case studies. Because the a-priori database implemented in the GPROF algorithm is based on observations from the DPR, the comparison of the reflectivity profiles further unravels discrepancies between GPROF and ground-based estimates.

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The Contribution of Rain Gauges in the Calibration of the IMERG Product: Results from the First Validation over Spain

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0116.1 ◽

2020 ◽

Vol 21 (2) ◽

pp. 161-182 ◽

Cited By ~ 4

Author(s):

Francisco J. Tapiador ◽

Andrés Navarro ◽

Eduardo García-Ortega ◽

Andrés Merino ◽

José Luis Sánchez ◽

...

Keyword(s):

Rain Gauge ◽

Surface Precipitation ◽

Rain Gauges ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

Level 3 ◽

Hydrologic Impacts ◽

A Minor ◽

Global Precipitation Measurement ◽

Global Precipitation

AbstractAfter 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ≈ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

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Validation of the First Years of GPM Operation over Cyprus

Remote Sensing ◽

10.3390/rs10101520 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1520 ◽

Cited By ~ 13

Author(s):

Adrianos Retalis ◽

Dimitris Katsanos ◽

Filippos Tymvios ◽

Silas Michaelides

Keyword(s):

High Resolution ◽

Prediction Models ◽

Weather Prediction ◽

Monthly Basis ◽

Rain Gauges ◽

Precipitation Measurement ◽

Precipitation Estimates ◽

First Results ◽

Global Precipitation Measurement ◽

Global Precipitation

Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

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Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Remote Sensing ◽

10.3390/rs11060697 ◽

2019 ◽

Vol 11 (6) ◽

pp. 697 ◽

Cited By ~ 18

Author(s):

Fenglin Xu ◽

Bin Guo ◽

Bei Ye ◽

Qia Ye ◽

Huining Chen ◽

...

Keyword(s):

Tropical Rainfall Measuring Mission ◽

Accurate Estimation ◽

Detection Capability ◽

Satellite Precipitation ◽

Precipitation Measurement ◽

Daily Precipitation Data ◽

Precipitation Estimates ◽

Global Precipitation Measurement ◽

Global Precipitation ◽

Precipitation Events

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

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