融合人工智能模型与多源数值预报的广东台风暴雨预报研究OA

Typhoon Danas,the 4th typhoon in 2025,made landfall in Zhejiang Province and was weakened into a remnant vortex,and moved southwestward across Fujian into Guangdong.Under the influence of this vortex,Guangdong Province experienced a widespread,extreme rainstorm on July 10-11.The extensive extreme rainstorm blanketed across the Pearl River Delta on July 10,when the center of the remnant vortex was over Eastern Guangdong.However,the torrential rains began to subside on July 11,as the remnant vortex center moved closer to the region.Traditional numerical models exhibited significant deviations in forecasting the location and temporal evolution of this heavy rainfall.To address the uncertainty in the typhoon rainstorm forecast,objective correction algorithms,such as frequency bias correction and the neighborhood method were applied to optimize the Bayesian model averaging(BMA).By leveraging the strengths of both artificial intelligence and traditional numerical models,a novel probabilistic precipitation forecast is developed based on the combination of the multiple forecasts.The probability of torrential rain predicted by the optimized BMA scheme successfully captured the major rainstorm area from the Pearl River Estuary to eastern Guangdong on July 10.On July 11,the likelihood of torrential rain decreased over the Pearl River Delta.The spatial distribution and temporal evolution of heavy rain probabilities exhibited better alignment with observations,effectively addressing the limitations of deterministic forecasts.A systematic verification of typhoon precipitation affecting Guangdong in 2025 shows that the optimized probabilistic precipitation forecast outperforms the ECMWF ensemble forecast in all verification metrics.These findings could serve as a valuable scientific basis for typhoon rainstorm prevention,mitigation,and emergency decision-making.