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

Abstract

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.

Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

“…High rain rates may cause flash floods, soil erosion, and urban waterlogging. Accurate rain rate information is essential for climate change models, water resources management, and assimilation into numerical weather prediction (NWP) models to improve rainfall forecasts [1,2].…”

Divergent Urban Signatures in Rainfall Anomalies Explained by Pre‐Storm Environment Contrast

“…The average storm duration is 12.7 hr, with 43% events exceeding 12 hr. These storms are frequently associated with the propagation of extratropical cyclones from the upper Yangtze River or landfalling tropical cyclones from the western Pacific (Ding & Sikka, 2006; Ding et al., 2001; Jiang et al., 2020; Tao & Ding, 1981; J. Wang et al., 2020).…”

Diurnal Precipitation Features over Complex Terrains along the Yangtze River in China Based on Long-Term TRMM and GPM Radar Products

“…In China, TRMM captures well the nocturnal peak over the eastern periphery of the Tibetan Plateau and the late-afternoon peak in southern China [38]. GPMs are assimilated to improve rainfall simulation associated with landfalling tropical cyclones [39], and they can exhibit microphysical features of precipitation [40]. Both TRMM and GPM show a high correlation coefficient with gauge observations over Central China [41].…”