Research on Quantitative Precipitation Correction Forecasting Based on the Improved U-Net Model in Hunan

ZHOU Li; XU Lin; CHEN He; LAN Mingcai; OU Xiaofeng; ZHOU Yue; XIE Yinan; XIAO Sihan

doi:10.16032/j.issn.1004-4965.2024.089

Abstract：This study presents a quantitative precipitation forecasting correction experiment in Hunan Province based on an improved U-Net model.Utilizing precipitation data from 1912 ground observation stations in Hunan during the rainy season from April to September between 2017 and 2022,along with the optimal factor set of the European Centre for Medium-Range Weather Forecast-Integrated Forecasting System(ECMWF-IFS)model,we developed an hourly precipitation forecasting correction model(SARU)on the basis of the U-Net model,integrating residual networks and attention mechanism networks.The model's forecast results for the 2023 rainy season were compared with those corrected by using the optimal threat score(OTS)method and those from the CMA-SH9 model.The results show that:(1)The SARU model's overall accuracy in clear/rainy forecasts,correlation coefficient,mean absolute error,and bias were 0.87,0.17,0.35,and 0.73,respectively,all of which outperformed the OTS model and the CMA-SH9 model,especially in central Hunan,where SARU showed a clear advantage in forecasting trends and magnitudes;its false alarm and missed forecast ratios were nearly equal,contrasting with the OTS model's significantly higher missed forecast ratio and the CMA-SH9 model's opposite trend.(2)The SARU model's forecasts of categorized precipitation frequency were closer to actual observations than the OTS model and the CMA-SH9 model,particularly for precipitation exceeding 20 mm,where it was under-predicted by 27.29%,significantly better than the OTS model's under-prediction of 85.54%and the CMA-SH9 model's over-prediction of 95.50%.(3)For hourly precipitation at levels of[5,10),[10,20),and≥20 mm,the SARU model achieved the best threat score(TS),probability of detection,false alarm ratio,and missed alarm ratio,particularly for short-term heavy precipitation,where it significantly outperformed the CMA-SH9 model,while the OTS model's forecasting capability was notably insufficient.(4)Nocturnal rainfall was pronounced in Hunan,with a noticeable increase in the frequency of short-term heavy rainfall during the night(0200-0800 BJT).The SARU model effectively captured the nocturnal characteristics of short-term heavy rainfall,with TS notably increasing at night,peaking around 0500 BJT at approximately 0.07,markedly outperforming the CMA-SH9 model and OTS model.