Continuous and probabilistic Assessment of Long-term Precipitation Forecast of North American Multi Model Ensemble (Case Study: Karkheh Dam Basin)

In recent decades, the ability of meteorological forecasts due to urban development and climate change has become an important issue in human societies. Forecasting these variables in addition to informing different segments of society, plays an effective role in better decision making and planning in different areas, such as water resources management. Nowadays in this field, the use of numerical forecasting models is one of the most common approaches. In this study, the output of seven forecast models from the North American Multi Model Ensemble (NMME) project is used to evaluate the forecast of precipitation in Karkheh Dam basin during the 31 years period (1985-2015). For this purpose, the outputs of precipitation forecast models for 1-3 month lead times were downscaled using Multiple Linear Regression (MLR) method. Then the possibility of improving forecast skill of them has been evaluated using an ensemble model approach. The models were assessed using both continuous and probabilistic methods using Pearson correlation coefficient (ρ) and normalized root mean square error (NRMSE) and reliability diagram respectively. The results indicate that none of the forecast models performed well in all lead times alone and their individual use did not show good performance; while the Multi Model Ensemble (MME) generally shows better performance than the individual models. The results of this study, demonstrate the importance of using an ensemble model based on the outputs of several models for improving the long term precipitation forecast skill.