Statistical Assessment and Augmentation of European Centre for Medium-Range Weather Forecasts Monthly Precipitation Forecast (SEASonal Prediction of Precipitation)

Abstract

Accurate prediction of precipitation is of paramount importance for effective planning of future water resources. In this study, we focused on the improvement and evaluation of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation ensemble-based seasonal precipitation prediction product, designated (SEASonal prediction of precipitation (SEAS5)). Three selected linear regression methods, namely ordinary least squares (OLS), flexible least squares (FLS) and the quantile-quantile (Q-Q) methods, were used to develop a correction procedure. The watershed of Lake Urmia was selected as a case study. The application of these augmentation methods has yielded encouraging results, demonstrating an improvement in the statistical metrics of SEAS5 precipitation forecasts for the first and second-coming months. However, all linear projection methods improve the performance of the SEAS5 products. The Q-Q method has shown the highest efficiency among the methods, playing a significant role in improving the accuracy of the hindcast precipitation. A variety of statistics (deterministic, forecast skill and uncertainty scores) were used to evaluate the effectiveness of both the raw and enhanced SEAS5 products. These analyses provide a comprehensive understanding of the performance of the SEAS5 product in its original form and after augmentation. The results highlight the potential of the linear projection method (specifically Q-Q method) to improve the accuracy of hindcast precipitation and provide valuable insights for water resource planning in the study area.