Multiple timescale assessment of wet season precipitation estimation over Taiwan using the PERSIANN family products

Wan Ru Huang*, Pin Yi Liu, Jie Hsu

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

This study evaluates the performance of five satellite precipitation products (SPPs) from the Precipitation Remotely Sensed Information using Artificial Neural Networks (PERSIANN) family for depicting precipitation changes in Taiwan over multiple timescales. Rain gauge data provided by the Central Weather Bureau (CWB) of Taiwan were used as a reference for evaluation, which focused on the wet seasons (May to October) during the period 2003–2019. All SPPs were found to have good ability in expressing the temporal phase changes over most of Taiwan on all the timescales examined, with significant temporal correlation coefficients (TCC) observed between the SPPs and the CWB data. We further evaluated the performance of the SPPs in making quantitative precipitation estimates based on the root mean square error (RMSE). For all examined timescales, the comparison between the best and worst performance shows greater normalized differences in quantitative estimates (i.e., RMSE) than in temporal phase depiction (i.e., TCC). In general, all SPPs tend to underestimate precipitation over most of Taiwan; however, two relatively new products (PDIR-Now and PERSIANN-CCS-CDR) have better RMSE performance than other SPPs on different timescales. PDIR-Now is the best product for quantitatively estimating precipitation on interannual, annual, and seasonal timescales, while PERSIANN-CCS-CDR is superior for daily and diurnal timescales. The findings also highlight that the performance of the PERSIANN-family in quantitatively estimating Taiwan precipitation does not depend primarily on the spatiotemporal resolution of SPPs, but may be related to the use of the cloud patch approach and the inclusion of weather station information in producing PDIR-Now and PERSIANN-CCS-CDR.

原文英語
文章編號102521
期刊International Journal of Applied Earth Observation and Geoinformation
103
DOIs
出版狀態已發佈 - 2021 12月 1

ASJC Scopus subject areas

  • 全球和行星變化
  • 地表過程
  • 地球科學電腦
  • 管理、監督、政策法律

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