Application of Generalized Moist Potential Vorticity to Analysis and Forecast of the Torrential Rain over the Changjiang－Huaihe River Basin

The generalized moist potential vorticity (GMPV), adopted by Gao et al.(2004b), is studied in this paper. It is found that GMPV, which contains information of interactions among wind, temperature and moisture, can be used in dynamic identification of heavy rain systems. Utilizing the retrieved horizontal wind from the observation data of China new generation weather radar (CINRAD-SA radar) with 1h interval, the sounding with 12 h interval and the surface observational data with 3 h interval, and the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) 1°×1° grid data, with the four-dimensional data assimilation system, the output data of the meso-scale model MM5 with fine resolution are used to test the ability of GMPV and its tendency anomalies in dynamic identification and prediction of the torrential rain occurring over the Changjiang－Huaihe River Basin during 8－9 July 2003. Two sets of diagnoses are performed. One is the precipitation evolution at one single station, and the other is the locations and movements of surface heavy rain centers. Both show good correspondences between precipitation and change of GMPV and its tendency. The analyses of GMPV over the Yangtze River basin in the Meiyu period of 1999 and over the Huaihe River basin during the torrential rain period of 2007 indicate that the change of the GMPV anomaly is in agreement with that of precipitation, and the anomaly generally occurrs at mid-lower levels (under 500 hPa), which is similar to the distribution of moisture in the air. The tendency of GMPV under 850 hPa is the same as that of precipitation intensity, namely, the positive (negative) tendency of GMPV always indicates that the precipitation would increase (decrease), which is useful for the intensity forecast of torrential rain. The GMPV and its tendency not only can be taken as dynamical variables to analyze and forecast the location and intensity tendency of torrential rain, but also can reflect the distribution and concentration of water vapor, which are useful for the analysis of heavy rain cases.