资源环境科技发展态势分析平台

The performance of the present operational global forecast system (GFS) at T1534 (~12.5 km) horizontal resolution with modified fractional cloud condensate to precipitation conversion parameter in the revised simplified Arakawa-Schubert (RSAS) convection scheme is evaluated for the summer monsoon seasons of 2018 and 2019 over the Indian region. The modified parameter has the form of an exponentially decreasing function of temperature above the freezing level. In contrast, below the freezing level, it is constant and is similar to default conversion parameter. The results reveal that the GFS T1534 with modified conversion parameter (EXPT) shows better fidelity in forecasting the mean summer monsoon rainfall over the Indian subcontinent region as compared to default GFS T1534 (CTRL). The rainfall probability distribution function analysis indicates a notable improvement in forecasting moderate and heavier category rainfall in EXPT as compared to CTRL. The improved distribution of total rainfall is found to be contributed by the proper forecasting of convective and large-scale rainfall in EXPT. It is likely that the reduced rate of conversion of cloud condensate to convective precipitation above the freezing level leads to a reduction in the convective rainfall, which eventually increases the moisture in the upper level through detrainment and hence enhances the large-scale precipitation. Further, EXPT shows relative improvement in forecasting outgoing longwave radiation, wind circulation, cloud fraction, dynamical-thermodynamical processes, and moist-convective feedback through improved lower tropospheric moistening over the Indian region. Finally, various skill score analyses suggest that EXPT shows better skill in predicting moderate and heavier category rainfall with longer lead time over the Indian subcontinent region. Considering the large socioeconomic impact of heavy and extreme precipitation over India, the modified conversion parameter can be incorporated in the present operational GFS T1534 model.