Based on The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） dataset, using various verification methods, the performances of four typical ensemble predi...Based on The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） dataset, using various verification methods, the performances of four typical ensemble prediction systems （EPSs） from the China Meteorological Administration （CMA）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, the US National Centers for Environmental Pre- diction （NCEP）, and the Japan Meteorological Agency （JMA） are compared preliminarily. The verification focuses on the 500-hPa geopotential height forecast fields in the mid- and higb-latitude Eurasian region during July 2007 and January 2008. The results show that for the forecast of 500-hPa geopotential height, in both summer and winter, the ECMWF EPS exhibits the highest forecast skill, followed by that of NCEP, then by JMA, and the CMA EPS gets in the last. The better system behaviors benefit from the better com- bination of the following： data assimilation system, numerical models, initial perturbations, and stochastic model perturbations. For the medium-range forecast, the ensemble forecasting can effectively filter out the forecast errors associated with the initial uncertainty, and the reliability and resolution （the two basic attri- butions of the forecast system） of these EPSs are better in winter than in summer. Specifically, the CMA EPS has certain advantage on the reliability of ensemble probabilistic forecasts. The forecasts are easy to be underestimated by the JMA EPS. The deficiency of ensemble spread, which is the universal problem of El＇S, also turns up in this study. Although the systems of ECMWF, NCEP, and JMA have more ensemble mem- bers, this problem cannot be ignored. This preliminary comparison helps to further recognize the prediction capability of the four EPSs over the Eurasian region, provides important references for wide applications of the TIGGE dataset, and supplies useful information for improving the CMA EPS.展开更多
基金Supported by the China Meteorological Administration Public Welfare Research Fund (GYHY200706001 and GYHY200906007)the Priority Academic Development Project of Jiangsu Higher Education Institutions
文摘Based on The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） dataset, using various verification methods, the performances of four typical ensemble prediction systems （EPSs） from the China Meteorological Administration （CMA）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, the US National Centers for Environmental Pre- diction （NCEP）, and the Japan Meteorological Agency （JMA） are compared preliminarily. The verification focuses on the 500-hPa geopotential height forecast fields in the mid- and higb-latitude Eurasian region during July 2007 and January 2008. The results show that for the forecast of 500-hPa geopotential height, in both summer and winter, the ECMWF EPS exhibits the highest forecast skill, followed by that of NCEP, then by JMA, and the CMA EPS gets in the last. The better system behaviors benefit from the better com- bination of the following： data assimilation system, numerical models, initial perturbations, and stochastic model perturbations. For the medium-range forecast, the ensemble forecasting can effectively filter out the forecast errors associated with the initial uncertainty, and the reliability and resolution （the two basic attri- butions of the forecast system） of these EPSs are better in winter than in summer. Specifically, the CMA EPS has certain advantage on the reliability of ensemble probabilistic forecasts. The forecasts are easy to be underestimated by the JMA EPS. The deficiency of ensemble spread, which is the universal problem of El＇S, also turns up in this study. Although the systems of ECMWF, NCEP, and JMA have more ensemble mem- bers, this problem cannot be ignored. This preliminary comparison helps to further recognize the prediction capability of the four EPSs over the Eurasian region, provides important references for wide applications of the TIGGE dataset, and supplies useful information for improving the CMA EPS.
