With the advances of numerical weather simulation and reduced data assimilation updating cycle, surface observation data assimilation becomes more and more important in data assimilation systems It is widely accepted ...With the advances of numerical weather simulation and reduced data assimilation updating cycle, surface observation data assimilation becomes more and more important in data assimilation systems It is widely accepted that a better data assimilation system should contain the restriction of thermod^amic processes in the surface layer. Therefore, in this paper, a new surface wind observation operator is utilized in Global and Regional Assimilation PrEdiction System_3D-Variance （GRAPES_3D-Var）, with the restriction of thermodynamic process in the planetary boundary layer （PBL）. In order to research the ability of this new surface wind observation operator in assimilation and forecasting, a series of experiments are operated by using the GRAPES model. The main results indicate that this new method of surface wind observation operator has positive impact on the forecast with the GRAPES model.展开更多
Based on observations from 11 stations inside the GPS (global positioningsystem) observation network, study is performed both on adjustment of the MM5 initial humidity fieldby means of, and nudging assimilations of, G...Based on observations from 11 stations inside the GPS (global positioningsystem) observation network, study is performed both on adjustment of the MM5 initial humidity fieldby means of, and nudging assimilations of, G-PW (short for GPS-sensed atmospheric precipitablewater) for a rainfall event happening in the Yangtze delta during June 23-24, 2002. Results showthat adjusting the initial moisture field through G-PW will enhance pronouncedly the ability of theinitial field to depict vapor distribution, thereby harnessing errors of atmospheric PW predictionat an early stage of model integration to improve more markedly the prediction of 6-h rainfall and,in contrast, nudging assimilations of G-PW show insignificant amelioration of model prediction, withless effect on the result by using a bigger nudging coefficient. On the whole, compared tosuccessive nudging assimilations of G-PW into the MM5, greater amelioration occurs in 6-h rainfallprediction from the G-PW adjusted initial moisture field. Also, evidence suggests that theimprovement of 6-h rainfall prediction with G-PW in correcting the initial humidity field isrealized mainly through the amelioration of the ability of grid-scale rainfall prediction while thenudging scheme achieves the improvement largely by bettering sub-grid scale rainfall prediction.展开更多
基金National Natural Science Foundation of China (40675064)
文摘With the advances of numerical weather simulation and reduced data assimilation updating cycle, surface observation data assimilation becomes more and more important in data assimilation systems It is widely accepted that a better data assimilation system should contain the restriction of thermod^amic processes in the surface layer. Therefore, in this paper, a new surface wind observation operator is utilized in Global and Regional Assimilation PrEdiction System_3D-Variance （GRAPES_3D-Var）, with the restriction of thermodynamic process in the planetary boundary layer （PBL）. In order to research the ability of this new surface wind observation operator in assimilation and forecasting, a series of experiments are operated by using the GRAPES model. The main results indicate that this new method of surface wind observation operator has positive impact on the forecast with the GRAPES model.
基金funded by the Chinese Academy of Sciences "Innovation Program" under Grant KJCX2-SW-T1-3
文摘Based on observations from 11 stations inside the GPS (global positioningsystem) observation network, study is performed both on adjustment of the MM5 initial humidity fieldby means of, and nudging assimilations of, G-PW (short for GPS-sensed atmospheric precipitablewater) for a rainfall event happening in the Yangtze delta during June 23-24, 2002. Results showthat adjusting the initial moisture field through G-PW will enhance pronouncedly the ability of theinitial field to depict vapor distribution, thereby harnessing errors of atmospheric PW predictionat an early stage of model integration to improve more markedly the prediction of 6-h rainfall and,in contrast, nudging assimilations of G-PW show insignificant amelioration of model prediction, withless effect on the result by using a bigger nudging coefficient. On the whole, compared tosuccessive nudging assimilations of G-PW into the MM5, greater amelioration occurs in 6-h rainfallprediction from the G-PW adjusted initial moisture field. Also, evidence suggests that theimprovement of 6-h rainfall prediction with G-PW in correcting the initial humidity field isrealized mainly through the amelioration of the ability of grid-scale rainfall prediction while thenudging scheme achieves the improvement largely by bettering sub-grid scale rainfall prediction.
