Improve short-term precipitation forecasts using numerical weather prediction model output and machine learning

The hydrological forecasting system coupled with precipitation forecasting can bring us a longer forecast period of early warning information, but it is also accompanied by higher uncertainty. With the improvement of hydrological models, the precipitation forecast may be the largest source of uncertainty. Therefore, before incorporating it into the hydrological model, the precipitation forecast needs post-processing to reduce its uncertainty. Meteorological post-processing corrects the bias of future precipitation forecasts by establishing a linear or non-linear relationship between historical observation and simulation. Machine learning (ML) can fit this relationship and process higher-dimensional predictor features, which is a promising method to improve the accuracy of precipitation forecasts. In this study, we selected the Yalong River basin of China as the cast study and compared the performance of 20 different machine learning algorithms (e.g., ridge regression, random forest, and artificial neural network). The daily hindcast data (1985-2018) from NOAA’s Global ensemble forecast system and corresponding observations from the China Meteorological Administration were selected to construct our data set. To improve the accuracy of the precipitation forecasts, we also screened different combinations of predictors to optimize the model configuration of machine learning, including space, time, and ensemble members. Comparative experiments show that all ML models can improve the accuracy of the raw precipitation forecast, but the performance is different. The extra-trees model has the best results, followed by LightGBM. However, linear regression models perform relatively poorly. The predictor combination of 11 ensemble members and a 2-day time window can achieve the best precipitation forecast. The post-processing of precipitation forecasts based on ML can significantly improve the accuracy of the raw forecasts, and it can also help us build a more advanced hydrological forecast system. In addition, the conclusions of this study and experimental design methods can provide references for the same type of research.