A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era

• Published in: Journal of hydrometeorology Vol. 17; no. 4; pp. 1101 - 1117
• Main Authors: Maggioni, Viviana, Meyers, Patrick C., Robinson, Monique D.
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01-04-2016
• Subjects: Algorithms; Atmospheric precipitations; Climate; Climate studies; Climatology; Cloud classification; Detection; Errors; Estimates; Floods; Hydrology; Interdisciplinary aspects; Kalman filters; Meteorological satellites; Morphing; Neural networks; Oceans; Precipitation; Precipitation estimation; Probability theory; Products; Rain; Rainfall; Real time; Regions; Satellites; Seasonal variations; Seasonality; Semi arid areas; Sensors; Topography (geology); Tropical climate; Tropical rainfall; Tropical Rainfall Measuring Mission (TRMM); Water resources; Water resources management; United States > US; Maryland; Japan
• ISSN: 1525-755X; 1525-7541
• DOI: 10.1175/jhm-d-15-0190.1

A great deal of expertise in satellite precipitation estimation has been developed during the Tropical Rainfall Measuring Mission (TRMM) era (1998–2015). The quantification of errors associated with satellite precipitation products (SPPs) is crucial for a correct use of these datasets in hydrological applications, climate studies, and water resources management. This study presents a review of previous work that focused on validating SPPs for liquid precipitation during the TRMM era through comparisons with surface observations, both in terms of mean errors and detection capabilities across different regions of the world. Several SPPs have been considered: TMPA 3B42 (research and real-time products), CPC morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP; both the near-real-time and the Motion Vector Kalman filter products), PERSIANN, and PERSIANN–Cloud Classification System (PERSIANN-CCS). Topography, seasonality, and climatology were shown to play a role in the SPP’s performance, especially in terms of detection probability and bias. Regions with complex terrain exhibited poor rain detection and magnitude-dependent mean errors; low probability of detection was reported in semiarid areas. Winter seasons, usually associated with lighter rain events, snow, and mixed-phase precipitation, showed larger biases.