Heat Waves in India during MAM 2019: Verification of ensemble based probabilistic forecasts and impact of bias correction

Abstract

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.