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

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...

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Autores principales:	Yujie Li, Bin Xu, Dong Wang, QJ Wang, Xiongwei Zheng, Jiliang Xu, Fen Zhou, Huaping Huang, Yueping Xu
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	bayesian joint probability general circulation model machine learning model monthly precipitation forecast post-processing Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/35432b20f4ff41c39aa6be768780e224
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spelling	oai:doaj.org-article:35432b20f4ff41c39aa6be768780e2242021-11-05T17:49:12ZDeterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China1464-71411465-173410.2166/hydro.2021.176https://doaj.org/article/35432b20f4ff41c39aa6be768780e2242021-07-01T00:00:00Zhttp://jh.iwaponline.com/content/23/4/914https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Monthly 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.;Yujie LiBin XuDong WangQJ WangXiongwei ZhengJiliang XuFen ZhouHuaping HuangYueping XuIWA Publishingarticlebayesian joint probabilitygeneral circulation modelmachine learning modelmonthly precipitation forecastpost-processingInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 4, Pp 914-934 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bayesian joint probability general circulation model machine learning model monthly precipitation forecast post-processing Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	bayesian joint probability general circulation model machine learning model monthly precipitation forecast post-processing Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Yujie Li Bin Xu Dong Wang QJ Wang Xiongwei Zheng Jiliang Xu Fen Zhou Huaping Huang Yueping Xu Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China
description	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.;
format	article
author	Yujie Li Bin Xu Dong Wang QJ Wang Xiongwei Zheng Jiliang Xu Fen Zhou Huaping Huang Yueping Xu
author_facet	Yujie Li Bin Xu Dong Wang QJ Wang Xiongwei Zheng Jiliang Xu Fen Zhou Huaping Huang Yueping Xu
author_sort	Yujie Li
title	Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China
title_short	Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China
title_full	Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China
title_fullStr	Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China
title_full_unstemmed	Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China
title_sort	deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of china
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/35432b20f4ff41c39aa6be768780e224
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Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China

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