Abstract

Weather forecasts by any forecast system are verified using either distributions-oriented or measures-oriented forecast verification measures. Both the forecast verification schemes represent different aspects of the forecast quality, and advantages of them can be utilized to get better insight and to identify particular strengths (deficiencies) in the forecast performance of any forecast system. Keeping this in view, multi-faced verification (binary and continuous) of quantitative precipitation forecasts for consecutive 3 days by a Regional Meso-scale Weather Simulation Model (MM5 Model) has been carried out to get complete insight into its performance. The MM5 model forecasts at 10-km resolution for 792 days of six winters (winter 2003/2004 to winter 2008/2009) are compared with the observational data of six stations in the complex topography of Northwest Himalaya (NWH) in India. The model forecasts are verified using binary categorical forecast verification measures such as Probability of Detection, False Alarm Rate, Miss Rate, Correct Non-occurrence, Critical Success Index and Percent correct, and continuous forecast verification measures such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). BIAS is computed to know over-forecast/under-forecast tendency of a precipitation day (PT day) by the MM5 model. MAE (RMSE) of the MM5 model is computed separately for all days, PT days and no precipitation days (NPT days). MAE (RMSE) of PT days is found to be relatively larger as compared to NPT days and all days. These findings indicate that MAE (RMSE) computed separately for all days, PT days and NPT days provides better insight into the performance of the MM5 model. Results also suggest that the MM5 model shows reasonably good performance for binary forecasts (PT days/NPT days) for day 1 (0–24 h), day 2 (24–48 h) and day 3 (48–72 h). However, large errors are seen in predicting the observed precipitation amount of PT days over NWH.

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