Heavy Rainfall Forecast of Landfalling Tropical Cyclone Over China With an Upgraded DSAEF_LTP Model

Abstract

Since the release of the Dynamical‐Statistical‐Analog Ensemble Forecast for Landfalling Typhoon Precipitation (DSAEF_LTP) model, a series of upgrades has been made to improve its heavy rainfall forecast performance for different cases and regions of China. However, little effort has been given to systematic evaluation of its multi‐upgraded version with a large sample size, especially for all the coastal regions of China. This study evaluates the performance of Version 1.0 (V1.0) and its improved Version 1.1 (V1.1) of the DSAEF_LTP model in simulating heavy rainfall amounts associated with 76 landfalling tropical cyclones (LTCs) over China during 2004–2016. The optimized schemes from these simulations are then applied to the heavy rainfall forecasts of 37 LTCs during 2017–2020. To further improve the forecast performance of V1.1, a subregional re‐ensemble scheme, referred to as V1.1R, is introduced after examining its performances over three coastal subregions of China. A comparison of the forecast performances of V1.0, V1.1, V1.1R, and four operational numerical weather prediction (NWP) models for the 37 LTCs shows that the performance of V1.1 is much better than that of V1.0, with a 65% higher summed threat score (TS) for over 250‐mm (TS250) and 100‐mm (TS100) rainfall; and that V1.1R's TS100 and TS250 values are further improved by 8% and 20%, respectively, as compared to V1.1. These values are superior to those of the four operational NWP models. Additionally, case studies reveal that the track distributions and intensities of LTCs may significantly impact the forecast performance of heavy rainfall.

Rainfall Differences and Possible Causes of Similar-Track Tropical Cyclones Affected and Unaffected by Binary Tropical Cyclones (BTCs) in China

“…The results indicated statistically significant differences (p < α), supporting our expectation that BTC-affected TCs exhibit higher maximum daily (process) rainfall compared to general TCs. Since the track of TC reflects, to some extent, the large-scale circulations and underlying topography where the TC situated [39,41], the target TC and its historical analogs share similar topography and large-scale circulation systems that jointly influence precipitation. Hence, to emphasize the BTC effect more distinctly, we compare similar-track TCs with and without the BTC influence.…”

“…Due to the extensive coastline in China, the primary influencing factors on TC rainfall may vary across different regions [41,67]. As pointed out by Wang et al (2020), extreme rainfall associated with the BTC effect in China is prominent in three typical regions-the Pearl River Delta, Northern Fujian Province, and Taiwan Island [33].…”