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Article

GPM Annual and Daily Precipitation Data for Real-Time Short-Term Nowcasting: A Pilot Study for a Way Forward in Data Assimilation

1
Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2
Department of Planning, Wujiang Water Bureau, Wujiang District, Suzhou 215200, China
3
Department of Urban Planning and Design, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
*
Author to whom correspondence should be addressed.
Academic Editors: Renato Morbidelli and David Dunkerley
Water 2021, 13(10), 1422; https://doi.org/10.3390/w13101422
Received: 9 March 2021 / Revised: 22 April 2021 / Accepted: 6 May 2021 / Published: 20 May 2021
(This article belongs to the Special Issue Urban Catchment: Rainfall–Runoff Issues and Responses)
This study explores the quality of data produced by Global Precipitation Measurement (GPM) and the potential of GPM for real-time short-term nowcasting using MATLAB and the Short-Term Ensemble Prediction System (STEPS). Precipitation data obtained by rain gauges during the period 2015 to 2017 were used in this comparative analysis. The results show that the quality of GPM precipitation has different degrees efficacies at the national scale, which were revealed at the performance analysis stage of the study. After data quality checking, five representative precipitation events were selected for nowcasting evaluation. The GPM estimated precipitation compared to a 30 min forecast using STEPS precipitation nowcast results, showing that the GPM precipitation data performed well in nowcasting between 0 to 120 min. However, the accuracy and quality of nowcasting precipitation significantly reduced with increased lead time. A major finding from the study is that the quality of precipitation data can be improved through blending processes such as kriging with external drift and the double-kernel smoothing method, which enhances the quality of nowcast over longer lead times. View Full-Text
Keywords: rainfall; satellite precipitation; GPM; hydroclimatic changes; nowcasting; STEPS; Mexico rainfall; satellite precipitation; GPM; hydroclimatic changes; nowcasting; STEPS; Mexico
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MDPI and ACS Style

Wang, K.; Kong, L.; Yang, Z.; Singh, P.; Guo, F.; Xu, Y.; Tang, X.; Hao, J. GPM Annual and Daily Precipitation Data for Real-Time Short-Term Nowcasting: A Pilot Study for a Way Forward in Data Assimilation. Water 2021, 13, 1422. https://doi.org/10.3390/w13101422

AMA Style

Wang K, Kong L, Yang Z, Singh P, Guo F, Xu Y, Tang X, Hao J. GPM Annual and Daily Precipitation Data for Real-Time Short-Term Nowcasting: A Pilot Study for a Way Forward in Data Assimilation. Water. 2021; 13(10):1422. https://doi.org/10.3390/w13101422

Chicago/Turabian Style

Wang, Kaiyang, Lingrong Kong, Zixin Yang, Prateek Singh, Fangyu Guo, Yunqing Xu, Xiaonan Tang, and Jianli Hao. 2021. "GPM Annual and Daily Precipitation Data for Real-Time Short-Term Nowcasting: A Pilot Study for a Way Forward in Data Assimilation" Water 13, no. 10: 1422. https://doi.org/10.3390/w13101422

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