This paper deals with the analysis of the ability of the operational global ensemble prediction system (EPS) at the National Centre for Medium Range Weather Forecasting (NCMRWF, NEPS in predicting the probabilities of the maximum 2 m temperature (TMAX). The mean of the TMAX forecasts from NEPS are bias corrected (1st moment) using the technique of Decaying Average. This method does not lead to any correction in the spread of the EPS. Therefore the method of Variation Inflation is used to correct the spread (2nd moment) of NEPS. The forecasts of TMAX from the raw and the bias corrected data are then compared for March to May 2019 using standard verification metrics for probabilistic forecasts like Brier Score (BS), Brier Skill Score (BSS), ROC and Reliability Diagrams as well as the Value score. A case by case comparison of several heat wave cases during March to May 2019 is also performed in order to assess the day-to-day performance of the model.

The forecasts after bias correction in the mean (1st moment) show a large improvement in terms of reduced false alarms and increased hit rate. However these forecasts show a reduced reliability in terms of under forecasting events with low probabilities and over forecasting events with high probabilities. The analysis of the forecast reliability after the correction of both the mean and the spread (1st and 2nd moments) shows an improvement in reliability. The forecasts with correction in the spread show a slight decrease in the ROC (i.e., increased false alarms) as compared to forecasts with just the corrected mean but the improvement in the reliability is appreciable. Thus the study demonstrates improved skill in probabilistic forecasts of TMAX over India during MAM 2019.

本文分析了美国国家中距离天气预报中心（NCMRWF，NEPS）的全球综合预报系统（EPS）在预测最高2 m温度（TMAX）概率方面的能力。 NEPS的TMAX预测采用“衰减平均”技术进行了偏差校正（第一时刻），该方法不会对EPS的价差进行任何校正，因此，使用变差通货膨胀方法来校正EPS的价差（第二时刻）。 NEPS。然后使用标准验证指标对概率预测进行比较，比较原始数据和经偏差校正后的TMAX预测值，例如Br​​ier分数（BS），Brier技能分数（BSS），ROC和可靠性图以及价值得分。为了评估模型的日常性能，还对2019年3月至2019年5月期间的几个热浪案例进行了逐案比较。

均值（第一时刻）偏差校正后的预测显示出在减少误报和增加命中率方面有很大的改善。但是，这些预测在低概率的低预测事件和高概率的高预测事件方面显示出降低的可靠性。对均值和展宽（第一个和第二个矩）进行校正后，对预测可靠性的分析显示出可靠性的提高。与仅具有校正平均值的预测相比，价差得到校正的预测显示ROC略有下降（即，虚假警报增加），但是可靠性的提高是可观的。因此，该研究证明了在2019年MAM期间印度TMAX概率预测中技巧的提高。