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Open AccessArticle

Data Assimilation of High-Resolution Satellite Rainfall Product Improves Rainfall Simulation Associated with Landfalling Tropical Cyclones in the Yangtze River Delta

1
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
2
School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 276; https://doi.org/10.3390/rs12020276
Received: 3 November 2019 / Revised: 5 January 2020 / Accepted: 10 January 2020 / Published: 14 January 2020
(This article belongs to the Special Issue Weather Forecasting and Modeling Using Satellite Data)
Floods caused by heavy rainfall events associated with landfalling tropical cyclones (TCs) represent a major risk for the Yangtze River Delta (YRD) region of China. Accurate extreme precipitation forecasting, at long lead times, is crucial for the improvement of flood prevention and warning. However, accurate prediction of timing, location, and intensity of the heavy rainfall events is a major challenge for the Numerical Weather Prediction (NWP). In this study, high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) are evaluated at the hourly timescale, and the optimal Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product is selected and applied to directly assimilate into the Weather Research and Forecasting (WRF) model via the four-dimensional variational (4D-Var) method. The TC Jondari and Rumbia events of August 2018 are evaluated to analyze the performance of the WRF model with the 4D-Var method assimilated IMERG precipitation product (DA-IMERG) and the conventional observation (DA-CONV) for real-time heavy rainfall forecasting. The results indicate that (1) IMERG precipitation products were larger and wetter than the observed precipitation values over YRD. By comparison, the performance of “late” run precipitation product (IMERG-L) was the closest to the observation data with lower deviation and higher detection capability; (2) DA-IMERG experiment substantially affected the magnitude of the WRF model primary variables, which changed the precipitation pattern of the TC heavy rain. (3) DA-IMERG experiment further improved the forecast of heavy rainbands and relatively reduced erroneous detection rate than CTL and DA-CONV experiments at the grid scale. Meanwhile, the DA-IMERG experiment has a better fractions skill score (FSS) value (especially in the threshold of 10 mm/h) than DA-CONV for TC Jondari and Rumbia at the spatial scale, while it shows a lower performance than CTL and DA-CONV experiments when the threshold is lower than the 5 mm/h for the TC Rumbia. View Full-Text
Keywords: TC Jondari and Rumbia; GPM IMERG; four-dimensional variational (4D-Var) method TC Jondari and Rumbia; GPM IMERG; four-dimensional variational (4D-Var) method
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MDPI and ACS Style

Wang, J.; Xu, Y.; Yang, L.; Wang, Q.; Yuan, J.; Wang, Y. Data Assimilation of High-Resolution Satellite Rainfall Product Improves Rainfall Simulation Associated with Landfalling Tropical Cyclones in the Yangtze River Delta. Remote Sens. 2020, 12, 276.

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