In this paper, a heavy rainfall process occurring in the Huaihe River Basin during 9-10 July 2005 is studied by the new generation numerical weather prediction model system-GRAPES, from the view of different initial f...In this paper, a heavy rainfall process occurring in the Huaihe River Basin during 9-10 July 2005 is studied by the new generation numerical weather prediction model system-GRAPES, from the view of different initial field effects on the prediction of the model. Several numerical experiments are conducted with the initial conditions and lateral boundary fields provided by T213 L31 and NCEP final analyses, respectively. The sensitivity of prediction products generated by GRAPES to different initial conditions, including effects of three-dimensional variational assimilation on the results, is discussed. After analyzing the differences between the two initial fields and the four simulated results, the memonic ability of the model to initial fields and their influences on precipitation forecast are investigated. Analyses show the obvious differences of sub-synoptic scale between T213 and NCEP initial fields, which result in the corresponding different simulation results, and the differences do not disappear with the integration running. It also shows that for the same initial field whether it has data assimilation or not, it only obviously influences the GRAPES model results in the initial 24 h. Then the differences reduce. In addition, both the location and intensity of heavy rain forecasted by GRAPES model Further is very close to the fact, but the forecasting area of strong torrential rain has some differences from the fact. For the same initial field when it has assimilation, the 9-12-, 12-24-, and 0-24-h precipitation forecasts of the model are better than those without assimilation. All these suggest that the ability of GRAPES numerical prediction depends on the different initial fields and lateral boundary conditions to some extent, and the differences of initial fields will determine the differences of GRAPES simulated results.展开更多
基金Supported by Anhui Meteorological Bureau Scientific Item under Grant No.0504,Anhui Meteorological Bureau General Project No.0601 and NKBRDPC No.2004CB418304.
文摘In this paper, a heavy rainfall process occurring in the Huaihe River Basin during 9-10 July 2005 is studied by the new generation numerical weather prediction model system-GRAPES, from the view of different initial field effects on the prediction of the model. Several numerical experiments are conducted with the initial conditions and lateral boundary fields provided by T213 L31 and NCEP final analyses, respectively. The sensitivity of prediction products generated by GRAPES to different initial conditions, including effects of three-dimensional variational assimilation on the results, is discussed. After analyzing the differences between the two initial fields and the four simulated results, the memonic ability of the model to initial fields and their influences on precipitation forecast are investigated. Analyses show the obvious differences of sub-synoptic scale between T213 and NCEP initial fields, which result in the corresponding different simulation results, and the differences do not disappear with the integration running. It also shows that for the same initial field whether it has data assimilation or not, it only obviously influences the GRAPES model results in the initial 24 h. Then the differences reduce. In addition, both the location and intensity of heavy rain forecasted by GRAPES model Further is very close to the fact, but the forecasting area of strong torrential rain has some differences from the fact. For the same initial field when it has assimilation, the 9-12-, 12-24-, and 0-24-h precipitation forecasts of the model are better than those without assimilation. All these suggest that the ability of GRAPES numerical prediction depends on the different initial fields and lateral boundary conditions to some extent, and the differences of initial fields will determine the differences of GRAPES simulated results.
