Abstract
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.
Highlights
The evaluation demonstrates the clear improvement in integrated multisatellite retrievals for GPM (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
Reliable precipitation data are critical for a wide variety of applications such as water budget studies and prevention or mitigation of natural hazards caused by extreme precipitation events
The passive microwave precipitation estimates are intercalibrated to the products, the validation is conducted using an approach that matches and temporally combined (TCI)
Summary
Reliable precipitation data are critical for a wide variety of applications such as water budget studies and prevention or mitigation of natural hazards caused by extreme precipitation events. Precise precipitation measurements are always a challenge because of its large spatiotemporal variability and inherent errors of various measuring instruments. Traditional rain gauges provide direct rainwater measurements, and often serve as the reference for validation of radar- and satellite-based precipitation products [1,2,3,4,5]. Gauges can only make what are essentially point-measurements at a specific site. The areal distribution of rain gauge networks is usually sparse, irregular, incomplete, and insufficient for accurately describing the spatial variability of precipitation [1,3]. Groundbased weather radars estimate precipitation from reflectivity measurements over relatively large areas. The implementation and retrofitting of dual-polarization to many new weather radars leads to a more accurate precipitation estimation [6,7].
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