摘要
In this paper, a local record severe rainfall since1949 occurring in the Shanghai urban area on 5-6 August,2001 is investigated by using non-conventional observationaldata provided by the "973" basic research project of China,including automatic meteorological stations data, wind pro-filer data, Doppler radar echoes and GMS5 satellite data andNCEP data. By analyzing, it is revealed: (1) the heavy rain-fall, caused by a serious of mesoscale βconvective cloudclusters developing inside the landing tropical depression(TD), occurred suddenly with the typical characteristics ofurban heavy rainfall disaster; (2) the landing tropical de-pression, moved eastward to Shanghai and re-intensifiedbefore entering the sea, was very favorable for the occur-rence of the heavy rainfall in Shanghai; (3) there may existsthe interaction of different scale systems between the tropicaldepression and mesoscale convective cloud clusters; and (4)the various advanced intensive data contribute importantlyto detect earlier and predict successfully the urban meteoro-logical disasters.
In this paper, a local record severe rainfall since 1949 occurring in the Shanghai urban area on 5-6 August, 2001 is investigated by using non-conventional observational data provided by the '973' basic research project of China, including automatic meteorological stations data, wind pro- filer data, Doppler radar echoes and GMS5 satellite data and NCEP data. By analyzing, it is revealed: (1) the heavy rain- fall, caused by a serious of mesoscale βconvective cloud clusters developing inside the landing tropical depression (TD), occurred suddenly with the typical characteristics of urban heavy rainfall disaster; (2) the landing tropical de- pression, moved eastward to Shanghai and re-intensified before entering the sea, was very favorable for the occur- rence of the heavy rainfall in Shanghai; (3) there may exists the interaction of different scale systems between the tropical depression and mesoscale convective cloud clusters; and (4) the various advanced intensive data contribute importantly to detect earlier and predict successfully the urban meteoro- logical disasters.
