Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China

Abstract

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB.

Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

“…To date, there are three popular pathways for measuring precipitation (i.e., direct observations with various gauges, retrievals from ground‐based radar signals, and estimations based on satellite techniques) (Sun et al ., 2021). Of them, direct measurements of precipitation with rain gauges are considered to be the most accurate observations with a high temporal frequency (Petersen et al ., 2005; Guo et al ., 2016) and have been extensively utilized by different branches (e.g., agriculture, industry, and forestry) and various scientific disciplines (e.g., climate change studies; Kidd et al ., 2003; Wentz et al ., 2007).…”

“…In addition, the period of radar observations is relatively short or even unavailable in some regions, mainly because of the high cost of radar installation and maintenance. For example, radar estimates over distant oceans are relatively sparse (Westrick et al ., 1999; Sun et al ., 2021).…”