Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines

• Published in: Earth and space science (Hoboken, N.J.) Vol. 9; no. 9
• Main Authors: Aryastana, Putu, Liu, Chian‐Yi, Jong‐Dao Jou, Ben, Cayanan, Esperanza, Punay, Jason Pajimola, Chen, Ying‐Nong
• Format: Journal Article
• Language: English
• Published: Hoboken John Wiley & Sons, Inc 01.09.2022; American Geophysical Union (AGU)
• Subjects: Algorithms; Datasets; Disasters; Extreme weather; Gauges; heavy precipitation; Hydrologic data; Hydrology; Performance evaluation; Philippines; Precipitation; Rain; Rain gauges; Rainfall intensity; Rainfall measurement; Satellites; Topography; typhoon; Typhoons; Wind speed; wind terrain; Philippines
• ISSN: 2333-5084; 2333-5084
• DOI: 10.1029/2022EA002382

Extreme weather events, such as typhoons, have occurred more frequently in the last few decades in the Philippines. The heavy precipitation caused by typhoons is difficult to measure with traditional instruments, such as rain gauges and ground‐based radar because these instruments have an uneven distribution in remote areas. Satellite precipitation data sets (SPDs) provide integrated spatial coverage of rainfall measurements, even for remote areas. However, the speed and direction of the wind has the interaction with terrain, which leads the uncertainty of the SPDs. This study performed sub‐daily assessments of near‐real‐time and high resolution SPDs (i.e., IMERG, GSMaP, and PERSIANN data sets) during five typhoon‐related heavy precipitation events in the Philippines, with the analysis under the impact due to wind and terrain effect. The aforementioned assessments were performed through a point‐to‐grid comparison by using continuous and volumetric statistical validation indices for the 34‐knot wind radii of the typhoons, rainfall intensity, terrain, and wind velocity effects. The results revealed that the IMERG exhibited good agreement with rain gauge measurements and exhibited high performance in detecting rainfall. The GSMaP data set overestimated the gauge observations during peak rainfall, while the IMERG and PERSIANN data sets considerably underestimated rainfall. The GSMaP exhibited the best performance for detecting heavy rainfall at high elevations, whereas IMERG exhibited the best performance for rainfall detection at low elevations. The IMERG exhibited a strong ability to detect heavy rainfall under various wind speeds. Key Points Satellite precipitation data sets (SPDs) were evaluated during typhoon‐related heavy precipitation events in the Philippines for the first time The high spatial resolution SPDs (0.04° and 0.1°) were assessed in this study The performance of SPDs varies clearly with terrain complexity