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Evaluation of Seasonal Precipitation Forecasts over the Upper Tigris-Euphrates Basin
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DOI：10.1175/JHM-D-24-0112.1.png)
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Abstract
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This study aims to assess the performance of seasonal precipitation forecasts for the wet season (December-April) in the upper Tigris-Euphrates (T-E) basin, focusing on dynamic models from the North American Multi-Model Ensemble (NMME) and newly developed statistical models based on atmospheric-oceanic indices. The satellite-based Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product is used as the reference dataset for evaluation. The analysis reveals that the correlation between NMME forecasts and IMERG observations ranges from 0.44 to 0.70, with CanCM4i-initialization case 3 (CanCM4i-IC3) exhibiting the highest correlation. Although all NMME models show some bias, with varying tendencies to either underestimate or overestimate precipitation, models such as CFSv2 and CanCM4i-IC3 show skill in predicting dry conditions, while CFSv2 and GFDL-Seamless System for Prediction and Earth System Research (GFDL-SPEAR) demonstrate skill in forecasting wet conditions. However, all models tend to underestimate extreme precipitation events. Furthermore, the study highlights the significant influence of atmospheric-oceanic indices-the North Atlantic Oscillation (NAO), El Ni & ntilde;o-Southern Oscillation (ENSO), and dipole mode index (DMI)-on wet-season precipitation variability. These indices contribute to precipitation variability, with NAO showing the strongest association. Additionally, the comparison of NMME models with a simple linear regression model based on atmospheric-oceanic indices demonstrates that the regression model outperforms NMME models, suggesting that statistical models incorporating atmospheric-oceanic indices may offer a computationally efficient and reliable alternative to complex NMME models for seasonal precipitation forecasting. However, a hybrid statistical model that incorporates CanCM4i-IC3 outputs along with atmospheric-oceanic indices outperforms both stand-alone statistical and NMME models.
Keywords:
Regression analysis
Forecast verification/skill
Seasonal forecasting
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