ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Analysis of model forecast biases for the extreme dragon-boat precipitation in 2022 based on the MODE method
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Abstract:
During May 21 to June 21 in 2022, the strongest dragon-boat precipitation process in the last decade occurred in South China. The extreme dragon-boat precipitation process, with a large cumulative rainfall and frequent heavy rainfall processes, caused significant economic losses. In this paper, two operational models, TRAMS and ECMWF, which are commonly used by forecasters in South China, are selected to divide the torrential rain during dragon-boat precipitation into front-zone torrential rain and warm-sector torrential rain, and are verified and evaluated, in order to understand the characteristics of the two models’ biases for the front-zone torrential rain and warm-sector torrential rain under the background of the extreme dragon-boat precipitation. Compared with the traditional point-to-point method, the MODE method can effectively avoid the phenomenon of high false alarm ratio caused by precipitation position deviation in the model. Further analysis of the number, position, precipitation area and intensity of torrential rain objects based on MODE method shows that the high-resolution model TRAMS has better ability to identify and match torrential rain objects than the global model ECMWF. The position prediction of torrential rain by TRAMS mostly has a eastward bias, while ECMWF has a northward bias. The deviations in precipitation position are closely related to the differences in the forecast bias of the two models for low-level southwesterly flow. The area prediction of the front-zone torrential rain by TRAMS is close to the observation, while the forecast area of warm-sector torrential rain is large. The forecast areas of ECMWF for both front-zone torrential rain and warm-sector torrential rain are small. The prediction of torrential rain intensity and extreme value by TRAMS is closer to the observation than that by ECMWF, but it still underestimates the extreme precipitation. This study can provide new experience for forecasters to understand the prediction biases of different operational models for dragon-boat precipitation process. It also has reference value for model developers to further carry out research on error source diagnosis and technical improvement of TRAMS.
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