Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China

Yujie Li, Bin Xu, Dong Wang, Quan Jun Wang, Xiongwei Zheng, Jiliang Xu, Fen Zhou, Huaping Huang, Yue‐Ping Xu

Journal of Hydroinformatics · 11 May 2021

In one sentence. Bayesian joint probability calibration of ML and GCM monthly precipitation forecasts over China yields reliable ensembles, though ML outputs lack skill beyond climatology.

Abstract

Abstract Monthly Precipitation Forecasts (MPF) play a critical role in drought monitoring, hydrological forecasting and water resources management. In this study, we applied two advanced Machine Learning Models (MLM) and latest General Circulation Models (GCM) to generate deterministic MPFs with a resolution of 0.5° across China. Then the Bayesian Joint Probability (BJP) modeling approach is employed to calibrate and generate corresponding ensemble MPFs. Raw and post-processing MPFs were put against gridded observations over the period of 1981–2015. The results indicated that: (1) for deterministic evaluation, the forecasting performance of MLMs was more inclined to generate random forecasts around the mean value, while the GCMs could reflect the increasing or decreasing trend of precipitation to some degree; (2) for probabilistic evaluation, the four BJP calibrated ensemble MPFs were unbiased and reliable. Compared to climatology, reliability and sharpness were all significantly improved. However, in terms of overall accuracy metric, the ensemble MPFs generated from MLMs were similar to climatology. In contrast, the ensemble MPFs generated from GCMs achieved better forecasting skill and were not dependent on forecasting regions and months. Moreover, the post-processing method is necessary to achieve not only bias-free but also reliable as well as skillful ensemble MPFs.

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