Evaluation of Precipitation Estimates by at-Launch Codes of GPM/DPR Algorithms Using Synthetic Data from TRMM/PR Observations

Kubota, Takuji; Yoshida, N.; Urita, Shinji; Iguchi, Toshio; Seto, Shûichi; Meneghini, R.; Awaka, Jun; Hanado, Hiroshi; Kida, Sigeo; Oki, Riko

doi:10.1109/jstars.2014.2320960

Cited by 46 publications

(30 citation statements)

References 53 publications

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“…With regards to the DPR, a diagnosed side‐lobe clutter issue was corrected (Kubota et al ., ), and overall performance demonstrated in Kubota et al . (), Toyoshima et al . () and Iguchi et al .…”

Section: Observational Goals For Gpm Precipitation: Microphysical To mentioning

confidence: 99%

The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations

Skofronick-Jackson

Kirschbaum

Petersen

et al. 2018

Quart J Royal Meteoro Soc

142

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Precipitation represents a life‐critical energy and hydrologic exchange between the Earth's atmosphere and its surface. As such, knowledge of where, when and how much rain and snow falls is essential for scientific research and societal applications. Building on the 17‐year success of the Tropical Rainfall Measurement Mission (TRMM), the Global Precipitation Measurement (GPM) Core Observatory (GPM‐CO) is the first US National Aeronautical and Space Administration (NASA) satellite mission specifically designed with sensors to observe the structure and intensities of both rain and falling snow. The GPM‐CO has proved to be a worthy successor to TRMM, extending and improving high‐quality active and passive microwave observations across all times of day. The GPM‐CO launched in early 2014, is a joint mission between NASA and the Japanese Aerospace Exploration Agency (JAXA), with sensors that include the NASA‐provided GPM Microwave Imager and the JAXA‐provided Dual‐frequency Precipitation Radar. These sensors were devised with high accuracy standards enabling them to be used as a reference for inter‐calibrating a constellation of partner satellite data. These inter‐calibrated partner satellite retrievals are used with infrared data to produce merged precipitation estimates at temporal scales of 30 min and spatial scales of 0.1° × 0.1°. Precipitation estimates from the GPM‐CO and partner constellation satellites, provided in near real time and later reprocessed with all ancillary data, are an indispensable source of precipitation data for operational and scientific users. Advances have been made using GPM data, primarily in improving sensor calibration, retrieval algorithms, and ground validation measurements, and used to further our understanding of the characteristics of liquid and frozen precipitation and the science of water and hydrological cycles for climate/weather forecasting. These advances have extended to societal benefits related to water resources, operational numerical weather prediction, hurricane monitoring, prediction, and disaster response, extremes, and disease.

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Section: Observational Goals For Gpm Precipitation: Microphysical To mentioning

confidence: 99%

The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations

Skofronick-Jackson

Kirschbaum

Petersen

et al. 2018

Quart J Royal Meteoro Soc

142

View full text Add to dashboard Cite

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“…Chen et al [70] found that due to incorrect rain type classification, overcorrection of radar signals, poor performance of the Surface Reference Technique (SRT) module, ground clutter and geometric effects, TRMM PR overestimates the frequency of moderate rain rates. GPM DPR was found to underestimate precipitation rates for rates smaller than 10 mm/h and overestimate for rates above 10 mm/h by using at-launch code and synthetic data based on the TRMM PR data [71]. and some other regions compared with ground radars [68,69].…”

Section: Comparison Of Overall Precipitationmentioning

confidence: 99%

Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling

2017

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Spaceborne precipitation radars are powerful tools used to acquire adequate and highquality precipitation estimates with high spatial resolution for a variety of applications in hydrological research. The Global Precipitation Measurement (GPM) mission, which deployed the first spaceborne Ka-and Ku-dual frequency radar (DPR), was launched in February 2014 as the upgraded successor of the Tropical Rainfall Measuring Mission (TRMM). This study matches the swath data of TRMM PR and GPM DPR Level 2 products during their overlapping periods at the global scale to investigate their similarities and DPR's improvements concerning precipitation amount estimation and type classification of GPM DPR over TRMM PR. Results show that PR and DPR agree very well with each other in the global distribution of precipitation, while DPR improves the detectability of precipitation events significantly, particularly for light precipitation. The occurrences of total precipitation and the light precipitation (rain rates < 1 mm/h) detected by GPM DPR are~1.7 and~2.53 times more than that of PR. With regard to type classification, the dual-frequency (Ka/Ku) and single frequency (Ku) methods performed similarly. In both inner (the central 25 beams) and outer swaths (1-12 beams and 38-49 beams) of DPR, the results are consistent. GPM DPR improves precipitation type classification remarkably, reducing the misclassification of clouds and noise signals as precipitation type "other" from 10.14% of TRMM PR to 0.5%. Generally, GPM DPR exhibits the same type division for around 82.89% (71.02%) of stratiform (convective) precipitation events recognized by TRMM PR. With regard to the freezing level height and bright band (BB) height, both radars correspond with each other very well, contributing to the consistency in stratiform precipitation classification. Both heights show clear latitudinal dependence. Results in this study shall contribute to future development of spaceborne radar precipitation retrievals and benefit hydrological and meteorological research.

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“…A 'Core' satellite in the GPM mission is used to measure the rainfall rate from space and the obtained data serves as a reference to consolidate rainfall measurements from an international network of satellites. The measurement instruments include a multi-channel GPM Microwave Imager (GMI) and the first space-borne Ku/Ka-band Dual-frequency Precipitation Radar (DPR) both of which are carried on the 'Core' satellite [25]. The GMI scans a swath of 885 km and has 13 microwave radiometer channels covering a frequency range of 10-183 GHz; the DPR includes Ku-band and Ka-band precipitation radars with 13.6 GHz and 35.5 GHz frequencies, respectively [26].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

et al. 2017

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Abstract:The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to −7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of −60.91% (−58.19%, −65.30%, and −63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (−72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (−2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region.

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Evaluation of Precipitation Estimates by at-Launch Codes of GPM/DPR Algorithms Using Synthetic Data from TRMM/PR Observations

Cited by 46 publications

References 53 publications

The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations

The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations

Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling

Comprehensive Evaluation of Two Successive V3 and V4 IMERG Final Run Precipitation Products over Mainland China

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