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.
