A multi-model ensemble tool for predicting districts level monsoon rainfall and extreme rainfall events over India


  • MT Bushair India Meteorological Department (IMD), MoES
  • DR Pattanaik India Meteorological Department (IMD), MoES
  • M Mohapatra India Meteorological Department (IMD), MoES




Extended range forecast, Monsoon Onset over Kerala, Multi-model ensemble, Coupled model, District level forecast


The southwest (SW) monsoon season from June to September (JJAS) is the major rainfall period over most parts of Indian regions. Accurate rainfall forecast is one of the most crucial and least predictable parameters of the numerical weather prediction (NWP) models because of its uneven distribution and patterns over the globe. During the last decade many studies have been carried out using different NWP models to predict rainfall incidents, and it is found that the forecast skill has been improved considerably. In the present study, a multi-model ensemble (MME) based tool has been developed for the prediction of SW monsoon rainfall at the district level over India for five days. The precipitation forecasts from five operational NWP modelling systems, viz., (i) Global Forecast System (GFS) and          (ii) Global Ensemble Forecasting System (GEFS) running at India Meteorological Department, (iii) Global Forecast System model running at National Centre for Environment Prediction (NCEP), (iv) Unified Model running at National Centre for Medium-Range Weather Forecasting (NCMRWF) and (v) Global Spectral Model (GSM) running at Japan Meteorological Agency (JMA) have been used for developing the MME forecasts for SW monsoon 2021. The prediction skill of the MME and the individual model forecast is evaluated against observed district rainfall.  The district-level heavy rainfall forecast from individual models and MME is also evaluated against the observed rainfall events useful for warning services. Different verification scores like Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Bias, Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Treat Score (ETS), Critical Success Index (CSI), etc. are calculated for the verification purpose. The different verification score shows that MME rainfall forecast has performed well than the individual models in different spatial domains and temporal scales. The CC between observed rainfall and day 1 MME forecast is 0.58, whereas GFS, GEFS, NCEP, NCUM and JMA are showing 0.43, 0.47, 0.49, 0.49 and 0.46 respectively. The RMSE observed for MME, GFS, GEFS, NCEP, NCUM and JMA are 12.7, 15.2, 14.1, 14.3, 16.6 and 14.1 mm/day respectively when compared with IMD observed rainfall. The inter-comparison of the model forecasts reveal that the MME method can generate skillful district rainfall forecast over India for operational use during the monsoon season.




How to Cite

M. Bushair, D. . Pattanaik, and M. Mohapatra, “A multi-model ensemble tool for predicting districts level monsoon rainfall and extreme rainfall events over India”, MAUSAM, vol. 74, no. 2, pp. 429–454, Mar. 2023.




Most read articles by the same author(s)