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Based on the daily 24-168 h ensemble precipitation forecasts over China from 1 May to 31 August in 2016 from the global ensemble models of ECMWF, JMA, NCEP, CMA and UKMO extracted from the TIGGE archives, the frequency matching method is tested to calibrate the precipitation frequency of each ensemble member. Then results of multi-model ensemble forecasts before and after calibration, including Kalman filter(KF), multi-model super-ensemble (SUP) and bias-removed ensemble mean(BREM), are analyzed in order to improve the prediction of precipitation based on numerical weather forecast data. Results show that precipitation forecasts calibrated by the frequency matching method, which uses the moderate precipitation to correct light and heavy precipitation, can effectively improve the problem of the underestimation of heavy precipitation caused by ensemble mean forecast and improve the positive deviation of the ensemble forecasting system, so that the precipitation forecast category is closer to the observation. However, the frequency matching method can barely improve the prediction of precipitation area. Different from frequency matching method, multi-model ensemble forecasts can extract and consider features of each model, therefore the prediction of precipitation area is more accurate than each single model, but the result is not as good as the frequency matching method in terms of the prediction of precipitation category. Among different multi-model methods, because of the updated weights over time, the result of Kalman filter forecast is superior to SUP and BREM in terms of threat scores, root mean square error (RMSE) and anomaly correlation coefficient (ACC). Furthermore, combining advantages of the above two methods, the multi-model ensemble precipitation after calibration based on ensemble members is more effective in the prediction of heavy precipitation category and area, which is closer to the observation. Results improve the threat score (TS) of the precipitation in all forecast lead times, especially in the heavy precipitation with the TS of 24 h forecast reaching 0.26, indicating a lower false alarm rate and missing rate compared with single model. Results also improve ACC and RMSE of the heavy precipitation and this method produces the best results among all the other methods, especially in the coastal areas in the south of China. In terms of the prediction of precipitation area, results effectively optimize the area of heavy and light precipitation, making the multi-model ensemble precipitation after calibration best in predicting heavy precipitation processes.

To improve the refined forecasting skill of surface wind field of Zhengzhou, based on the surface wind field (3~60 h) forecasting data from ECMWF (European centre for medium-range weather forecasts), JMA (Japan meteorological agency), CMA (China meterological administration) and observational data at automatic weather stations in Zhengzhou in the duration from 2012-06-01 to 2012-09-30, the multi-model ensemble forecasts of the surface wind field of 11 stations in Zhengzhou were firstly carried out, and then the statistical downscaling of multi-model ensemble results were studied. In view of the difficulty of the surface wind field forecasting, the U and V component of the wind field (method 1) and the full wind speed and wind direction (method 2) were tested. Finally, the RMSE(root mean square error) was used to evaluate the results of the 2 methods. The results show that the method for the full wind speed and wind direction is better than that of the U and V components of the wind field in the 2 methods, and the SUP(superensemble forecasts) of this method has smaller forecast error and becomes the best scheme. In the statistical downscaling experiments for the multi-model ensemble, the multi-model ensemble for the full wind speed and wind direction can further reduce the forecast error, which is better than that for the U and V components of the wind field. Compared to the refined forecasting of multi-model ensemble, the statistical downscaling forecasting can further reduce the forecast error and improve the forecast accuracy of the surface wind field. [ABSTRACT FROM AUTHOR]