摘要
The cold vortex is a major high impact weather system in northeast China during the warm season, its frequent activities also affect the short-term climate throughout eastern China. How to objectively and quantitatively predict the intensity trend of the cold vortex is an urgent and difficult problem for current short-term climate prediction. Based on the dynamical-statistical combining principle, the predicted results of the Beijing Climate Center's global atmosphereocean coupled model and rich historical data are used for dynamic-statistical extra-seasonal prediction testing and actual prediction of the summer 500-hPa geopotential height over the cold vortex activity area. The results show that this method can significantly reduce the model's prediction error over the cold vortex activity area, and improve the prediction skills. Furthermore, the results of the sensitivity test reveal that the predicted results are highly dependent on the quantity of similar factors and the number of similar years.
The cold vortex is a major high impact weather system in northeast China during the warm season, its frequent activities also affect the short-term climate throughout eastern China. How to objectively and quantitatively predict the intensity trend of the cold vortex is an urgent and difficult problem for current short-term climate prediction. Based on the dynamical-statistical combining principle, the predicted results of the Beijing Climate Center's global atmosphereocean coupled model and rich historical data are used for dynamic-statistical extra-seasonal prediction testing and actual prediction of the summer 500-hPa geopotential height over the cold vortex activity area. The results show that this method can significantly reduce the model's prediction error over the cold vortex activity area, and improve the prediction skills. Furthermore, the results of the sensitivity test reveal that the predicted results are highly dependent on the quantity of similar factors and the number of similar years.
基金
supported by the National Natural Science Foundation of China(Grant No.41375078)
the National Basic Research Program of China(Grant Nos.2012CB955902 and 2013CB430204)
the Special Scientific Research Fund of Public Welfare Profession of China(Grant No.GYHY201306021)