资源环境科技发展态势分析平台

The quality of probabilistic precipitation and zonal wind forecasts from National Centre for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Prediction System (NEPS-G) is investigated for Indian summer monsoon period between June–September 2018. Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG GPM) are used for verification of precipitation forecasts. The predictive skill of different categories of rainfall is examined with respect to daily climatology based on Tropical Rainfall Measuring Mission (TRMM) observations - and The Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis -. ERA Interim and IMDAA reanalysis daily climatologies are used to compute skill for the zonal wind forecasts at 850 hPa (u850). The model has a systematic tendency to over-predict the low level westerlies associated with the monsoon circulation. RMSE over Gangetic plains near Himalayan foothills is more in day-3 as compared to subsequent forecast lead times due to its overestimation of the easterly zonal wind flow. Spread in u850 is comparable to RMSE in day-1 forecast. The ensemble forecasting system is slightly under-dispersive for longer forecast lead times, since the rate of growth of forecast uncertainty is larger than that could be predicted by the ensemble system. Forecasts are sharper for lower thresholds of rainfall and exhibit more reliability and better discrimination of events over shorter lead times. Similar to reliability, the rank distribution depends on forecast lead time as well as ensemble spread. The positive Brier skill score and Continuous Ranked Probability Skill Score values above 0.4 for probabilistic wind as well as precipitation forecasts of light to moderate category, consistently show high predictive skill till day-7, with reference to the long-term climatology. NEPS-G could predict an extreme rainfall event with high probabilities of precipitation exceeding thresholds classified by India Meteorological department, which are in good correspondence with that of rainfall observed by GPM IMERG. A monsoon index based on large-scale features of monsoon circulation could be predicted by the EPS with high probabilistic skill during the peak monsoon months.