The main approach to probabilistic weather forecasting has been the use of ensemble forecasting. In ensemble forecasting, the probability information is generally derived by using several numerical model runs, with perturbation of the initial conditions, physical schemes or dynamic core of the numerical weather prediction (NWP) models. However, ensemble forecasting usually tends to be under‐dispersive. Statistical post‐processing has, therefore, become an essential component of any ensemble prediction system aiming to improve the quality of numerical weather forecasts as they seek to generate calibrated and sharp predictive distributions of future weather quantities. Different versions of the ensemble model output statistics (EMOS) and the Bayesian model averaging (BMA) post‐processing methods are used in the present paper to calibrate 24, 48 and 72 hr forecasts of 24 hr accumulative precipitation. The ensemble employs the weather and research forecasting (WRF) model with eight different configurations which were run over Iran for six months (September 2015–February 2016). The results reveal that the BMA and EMOS‐censored, shifted gamma (CSG) techniques are substantially successful at improving the raw WRF ensemble forecasts; however, each approach improves different aspects of the forecast quality. The BMA method is more accurate, skilful and reliable than the EMOS‐CSG method, but has poorer discrimination. Moreover, it has better resolution in predicting the probability of high‐precipitation events than the EMOS‐CSG method.

In this paper, the ensemble-weighted mean (ENSWM) technique is experimented for improving 24-to 72-hr precipitation forecasts over Iran during autumn and winter 2011 and 2012. The ensemble prediction system (EPS), used in this research, consists of nine different configurations of the weather research and forecasting model. In this technique, weights for each ensemble member at each grid point are assigned on the basis of the correlation coefficient (CC) between ensemble members and observed daily rainfall during a training period. Apart from ENSWM, precipitation forecasts using the simple ensemble mean (ENSM) are also generated and compared. Results showed that, in general, the forecast errors are relatively high along the coasts of the Caspian Sea in northern and at the Zagros mountainous areas located in western Iran. The skill of the rainfall forecasts of the ENSWM is examined against ENSM and individual members of the ensemble. The 24-to 72-hr forecasts are evaluated using common statistical scores including root mean-squared error (RMSE), and anomaly CC (ACC) for continuous forecasts and probability of detection (POD) score and threat score for categorical forecasts. The comparison reveals that the ENSWM is able to provide more accurate forecast of rainfall over Iran by taking the strength of each constituent member of the ensemble. It has been further found that the precipitation forecast skill of ENSWM is higher than ENSM and each ensemble member in the short-range time scale over Iran. The rainfall prediction skill over Iran was improved significantly using the weighted ENSWM technique. Results clearly show the advantage of using an EPS for the prediction of precipitation over the country vs. a single deterministic forecast for operational purposes. The RMSE of 24-, 48-and 72-hr forecasts in ENSWM relative to ENSM is reduced by 2, 2 and 5%, respectively. The CC increased by 15% in the ENSWM relative to ENSM.

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