Abstract 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 reanalysis data from the Indian Monsoon Data Assimilation and Analysis (IMDAA). 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.

中文翻译：

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评估 NCMRWF 全球集合预报系统预测 2018 年印度夏季风的能力

摘要 研究了国家中期天气预报中心 (NCMRWF) 全球集合预报系统 (NEPS-G) 对 2018 年 6 月至 9 月之间印度夏季风期的概率降水和纬向风预报的质量。降水测量 (IMERG GPM) 用于验证降水预报。根据热带降雨测量任务 (TRMM) 观测和来自印度季风数据同化和分析 (IMDAA) 的再分析数据，根据日常气候学检查不同类别降雨的预测技能。ERA Interim 和 IMDAA 再分析每日气候学用于计算 850 hPa (u850) 纬向风预报的技能。该模型有系统的倾向，过度预测与季风环流相关的低层西风。与随后的预测提前期相比，喜马拉雅山麓附近恒河平原的 RMSE 在第 3 天更高，因为它高估了东风带状风流。u850 中的价差与第 1 天预测中的 RMSE 相当。对于较长的预测提前期，集合预报系统略微分散不足，因为预报不确定性的增长率大于集合系统可以预测的增长率。对于较低的降雨阈值，预测更清晰，并且在更短的提前期内表现出更高的可靠性和更好的事件识别能力。与可靠性类似，等级分布取决于预测提前期以及整体分布。正值 Brier 技能得分和连续排名概率技能得分高于 0.4 的概率风以及轻到中等类别的降水预测，参考长期气候学，在第 7 天之前始终显示出高预测技能。NEPS-G 可以预测极端降雨事件，降雨量超过印度气象部门划分的阈值的概率很高，这与 GPM IMERG 观测到的降雨量有很好的对应关系。基于季风环流大尺度特征的季风指数可以通过季风高峰期具有高概率技能的EPS进行预测。参考长期气候学。NEPS-G 可以预测极端降雨事件，降雨量超过印度气象部门分类的阈值的概率很高，这与 GPM IMERG 观测到的降雨量有很好的对应关系。基于季风环流大尺度特征的季风指数可以通过季风高峰期具有高概率技能的EPS进行预测。参考长期气候学。NEPS-G 可以预测极端降雨事件，降雨量超过印度气象部门分类的阈值的概率很高，这与 GPM IMERG 观测到的降雨量有很好的对应关系。基于季风环流大尺度特征的季风指数可以通过季风高峰期具有高概率技能的EPS进行预测。