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
This paper describes the procedure and methodology to formulate the convective weather potential （CWP） algorithm. The data used in the development of the algorithm are the radar echoes at 0.5° elevation from Gu...This paper describes the procedure and methodology to formulate the convective weather potential （CWP） algorithm. The data used in the development of the algorithm are the radar echoes at 0.5° elevation from Guangzhou Doppler Radar Station, surface observations from automatic weather stations （AWS） and outputs of numeric weather prediction （NWP） models. The procedure to develop the CWP algorithm consists of two steps： （1） identification of thunderstorm cells in accordance with specified statistical criteria; and （2） development of the algorithm based on multiple linear regression. The thunderstorm cells were automatically identified by radar echoes with intensity greater than or equal to 50 dB（Z） and of an area over 64 square kilometers. These cells are generally related to severe convective weather occurrences such as thunderstorm wind gusts, hail and tornados. In the development of the CWP algorithm, both echo- and environment-based predictors are used. The predictand is the probability of a thunderstorm cell to generate severe convective weather events. The predictor-predictand relationship is established through a stepwise multiple linear regression approach. Verification with an independent dataset shows that the CWP algorithm is skillful in detecting thunderstorm-related severe convective weather occurrences in the Pearl River Delta （PRD） region of South China. An example of a nowcasting case for a thunderstorm process is illustrated.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.
文摘This paper describes the procedure and methodology to formulate the convective weather potential （CWP） algorithm. The data used in the development of the algorithm are the radar echoes at 0.5° elevation from Guangzhou Doppler Radar Station, surface observations from automatic weather stations （AWS） and outputs of numeric weather prediction （NWP） models. The procedure to develop the CWP algorithm consists of two steps： （1） identification of thunderstorm cells in accordance with specified statistical criteria; and （2） development of the algorithm based on multiple linear regression. The thunderstorm cells were automatically identified by radar echoes with intensity greater than or equal to 50 dB（Z） and of an area over 64 square kilometers. These cells are generally related to severe convective weather occurrences such as thunderstorm wind gusts, hail and tornados. In the development of the CWP algorithm, both echo- and environment-based predictors are used. The predictand is the probability of a thunderstorm cell to generate severe convective weather events. The predictor-predictand relationship is established through a stepwise multiple linear regression approach. Verification with an independent dataset shows that the CWP algorithm is skillful in detecting thunderstorm-related severe convective weather occurrences in the Pearl River Delta （PRD） region of South China. An example of a nowcasting case for a thunderstorm process is illustrated.
