An Analysis for the Applicability of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Data in Typhoons

Abstract

This study selected examples of 17 typhoons that landed in Fujian after passing through Taiwan. The study evaluated the precipitation in different time scales and the spatial distribution of daily precipitation of varying magnitudes in the southeastern coastal area by comparing satellite precipitation estimation products with meteorological observation station data. The evaluation used a correlation coefficient, mean relative error, relative bias, and graded assessment indexes (probability of detection, false alarm rate, and critical success index). Correlation coefficient analysis revealed that maximum daily precipitation performed best, followed by process total precipitation. The relative bias indicates that the precipitation estimated by the satellite is lower than the rainfall recorded by the automatic weather station. Mean relative error analysis showed that hourly precipitation had the highest error, followed by maximum daily precipitation. The GPM IMERG precipitation products’ retrieval of daily precipitation of varying magnitudes was assessed using three indicators. The assessment revealed that the satellite had a low under-reporting rate for light rain events but a high under-reporting rate for torrential rain events, especially extremely heavy rainstorm events, in terms of probability of detection. For the false alarm rate, the satellite had a small probability of false predictions for light rain events, while extremely heavy rainstorm events had the highest probability. For the critical success index, the satellite’s estimation of light rain events was basically consistent with reality; however, its ability to estimate precipitations above rainstorm levels was low. The results of the spatial assessment of heavy precipitation show that the satellite’s ability to detect heavy precipitation’s structure, intensity, and location is fair and has some reference value, especially for regions where conventional information is scarce.