华南暴雨区域集合预报中不同同化方案的影响试验研究.

Based on the Global Ensemble Forecast System data, the present study uses the WRF model and the GEFS dynamic downscaling method to obtain the regional ensemble forecast initial states. Moreover, the assimilated analysis field is integrated to achieve the reginal ensemble forecast for a precipitation event during annually first rainy season in South China. Four tests, namely Hybrid, 3DVar, EnKF, and Ctrl, are carried out for a torrential rain process on June 10, 2019 in South China. We also explore the evolution characteristics of the ensemble disturbance and ensemble spread for the assimilation schemes with difference background error covariance matrices and evaluate the precipitation simulation performance of different tests. The results show that: (1) Hybrid can improve the initial field of the model, while 3DVar and EnKF failed. (2) For wind, temperature, and relative humidity, the forecast error of Hybrid in the early forecast is less than that of 3DVar and EnKF. In the middle and late forecast, the forecast error of 3DVar and EnKF is reduced and is smaller than that of Hybrid. As for the ensemble disturbance energy, Hybrid and Ctrl are better than 3DVar and EnKF in the early forecast, and 3DVar and EnKF are better than Hybrid and Ctrl in the middle and late forecasts. (3) According to the 24-hour cumulative precipitation scores, the assimilation test is better than Ctrl, 3DVar and EnKF are better than Hybrid; 3DVar scores the best in heavy and moderate rainfall and EnKF scores the best in torrential rain and above. (4) For 24-hour cumulative precipitation ensemble statistical analysis, the area under the curve value of the assimilation test is greater than that of Ctrl; 3DVar performs the best in the 10mm~100mm cumulative precipitation threshold and EnKF performs the best in the 125mm cumulative precipitation threshold. [ABSTRACT FROM AUTHOR]