Assessment of Near-Real-Time Satellite Precipitation Products from GSMaP in Monitoring Rainfall Variations over Taiwan

Abstract

This study assessed four near-real-time satellite precipitation products (NRT SPPs) of Global Satellite Mapping of Precipitation (GSMaP)—NRT v6 (hereafter NRT6), NRT v7 (hereafter NRT7), Gauge-NRT v6 (hereafter GNRT6), and Gauge-NRT v7 (hereafter GNRT7)— in representing the daily and monthly rainfall variations over Taiwan, an island with complex terrain. The GNRT products are the gauge-adjusted version of NRT products. Evaluations for warm (May–October) and cold months (November–April) were conducted from May 2017 to April 2020. By using observations from more than 400 surface gauges in Taiwan as a reference, our evaluations showed that GNRT products had a greater error than NRT products in underestimating the monthly mean rainfall, especially during the warm months. Among SPPs, NRT7 performed best in quantitative monthly mean rainfall estimation; however, when examining the daily scale, GNRT6 and GNRT7 were superior, particularly for monitoring stronger (i.e., more intense) rainfall events during warm and cold months, respectively. Spatially, the major improvement from NRT6 to GNRT6 (from NRT7 to GNRT7) in monitoring stronger rainfall events over southwestern Taiwan was revealed during warm (cold) months. From NRT6 to NRT7, the improvement in daily rainfall estimation primarily occurred over southwestern and northwestern Taiwan during the warm and cold months, respectively. Possible explanations for the differences between the ability of SPPs are attributed to the algorithms used in SPPs. These findings highlight that different NRT SPPs of GSMaP should be used for studying or monitoring the rainfall variations over Taiwan for different purposes (e.g., warning of floods in different seasons, studying monthly or daily precipitation features in different seasons, etc.).

A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

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Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

“…(3) FPR = FP FP + TN (4) TSS = TPR − FPR landslide/no-landslide-triggering rainfall events), 0.5 < AUC < 0.7 is considered poor, 0.7 ≤ AUC < 0.8 is considered acceptable, 0.8 ≤ AUC < 0.9 is considered excellent, and AUC ≥ 0.9 is considered outstanding (Hosmer et al 2013).…”

Advantages of GSMaP Data for Multi‐Timescale Precipitation Estimation in Luzon

“…In contrast, satellite‐based rainfall estimation (SRE) with high spatial coverage can monitor a wider spatial range of rainfall variations (Da Silva et al., 2021; Huang et al., 2023). Therefore, SRE is often used for various research purposes (e.g., climate studies, validating model performance, and other studies) (Huang et al., 2016; Huang, Liu, Hsu, et al., 2021; Huang, Hsu, et al., 2022; Koralegedara et al., 2023).…”