Machine Learning for Precipitation Forecasts Postprocessing: Multimodel Comparison and Experimental Investigation - Zhang, Yuhang; Ye, Aizhong (Journal of hydrometeorology, 2021)
Obtaining high-quality quantitative precipitation forecasts is a key precondition for hydrological forecast systems. Due to multisource uncertainties (e.g., initial conditions, model structures, and parameters), raw forecasts are subject to systematic biases; hence, statistical postprocessing is often required to reduce these errors before the forecasts can proceed to hydrological applications. Machine learning (ML) algorithms are canonical statistical models, and they are diverse in type and variation. It is important to verify and compare their performance in the same scenario (e.g., precipitation postprocessing). In this paper, we conduct a large-scale comparison study for the major ML models with diverse model structures and regularization strategies as postprocessors for improving the quality of precipitation forecasts. Specifically, we compare the efficiency and effectiveness of 21ML algorithms on solving this task. Daily reforecast precipitation with lead times up to 8 days from the Global Ensemble Forecast System and corresponding observations are employed to determine the usability of different models in the Yalong River basin in China. The performance of each model is validated by a group of carefully designed experiments and statistical metrics. The results reveal that improvements in model structures are more effective than regularization strategies. Among these algorithms, the optimized extra-trees regressor exhibits the best performance, effectively reducing overestimation and achieving the best skill in forecasting precipitation. Eleven ensemble members and a 3-day forward-rolling time window can be used as predictors to obtain the best model performance. The systematic experiments and findings also offer useful guidelines for other related studies.