In this paper, seasonal prediction of spring dust weather frequency （DWF） in Beijing during 1982–2008 has been performed. First, correlation analyses are conducted to identify antecedent climate signals during last...In this paper, seasonal prediction of spring dust weather frequency （DWF） in Beijing during 1982–2008 has been performed. First, correlation analyses are conducted to identify antecedent climate signals during last winter that are statistically significantly related to spring DWF in Beijing. Then, a seasonal prediction model of spring DWF in Beijing is established through multivariate linear regression analysis, in which the systematic error between the result of original prediction model and the observation, averaged over the last 10 years, is corrected. In addition, it is found that climate signals occurring synchronously with spring dust weather, particularly meridional wind at 850 hPa over western Mongolian Plateau, are also linked closely to spring DWF in Beijing. As such, statistical and dynamic prediction approaches should be combined to include these synchronous predictors into the prediction model in the real-time operational prediction, so as to further improve the prediction accuracy of spring DWF in Beijing, even over North China. However, realizing such a prediction idea in practice depends essentially on the ability of climate models in predicting key climate signals associated with spring DWF in Beijing.展开更多
基金Supported by the Knowledge Innovation Project of the Chinese Academy of Sciences(KZCX2-YW-Q03-3)National Basic Research Program of China(2009CB421406)Special Public Welfare Research Fund of China for Meteorological Profession (GYHY200906018)
文摘In this paper, seasonal prediction of spring dust weather frequency （DWF） in Beijing during 1982–2008 has been performed. First, correlation analyses are conducted to identify antecedent climate signals during last winter that are statistically significantly related to spring DWF in Beijing. Then, a seasonal prediction model of spring DWF in Beijing is established through multivariate linear regression analysis, in which the systematic error between the result of original prediction model and the observation, averaged over the last 10 years, is corrected. In addition, it is found that climate signals occurring synchronously with spring dust weather, particularly meridional wind at 850 hPa over western Mongolian Plateau, are also linked closely to spring DWF in Beijing. As such, statistical and dynamic prediction approaches should be combined to include these synchronous predictors into the prediction model in the real-time operational prediction, so as to further improve the prediction accuracy of spring DWF in Beijing, even over North China. However, realizing such a prediction idea in practice depends essentially on the ability of climate models in predicting key climate signals associated with spring DWF in Beijing.
