Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China
| العنوان: | Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China |
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| المؤلفون: | Huaping Huang, Yue-Ping Xu, Jiliang Xu, Bin Xu, Xiongwei Zheng, Quan J. Wang, Fen Zhou, Yujie Li, Dong Wang |
| المصدر: | Journal of Hydroinformatics, Vol 23, Iss 4, Pp 914-934 (2021) |
| بيانات النشر: | IWA Publishing, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Atmospheric Science, 010504 meteorology & atmospheric sciences, monthly precipitation forecast, post-processing, 0207 environmental engineering, Probabilistic logic, 02 engineering and technology, Information technology, Geotechnical Engineering and Engineering Geology, T58.5-58.64, 01 natural sciences, Environmental technology. Sanitary engineering, general circulation model, Climatology, Environmental science, Precipitation, machine learning model, 020701 environmental engineering, China, bayesian joint probability, TD1-1066, 0105 earth and related environmental sciences, Civil and Structural Engineering, Water Science and Technology |
| الوصف: | 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. HIGHLIGHTS Two advanced Machine Learning Models are employed to generate monthly precipitation forecasts with a resolution of 0.5 degree.; The latest seasonal forecasts of ECMWF are evaluated with the same forecasting grid cells and lead time.; The BJP modeling approach is used to calibrate above four raw forecasts.; A comprehensive comparison is achieved for the raw and post-processing forecasts. |
| اللغة: | English |
| تدمد: | 1465-1734 1464-7141 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6f7c9e87bd69fa221de9a05acfcd533 http://jh.iwaponline.com/content/23/4/914 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e6f7c9e87bd69fa221de9a05acfcd533 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14651734 14647141 |
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