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A review on statistical postprocessing methods for hydrometeorological ensemble forecasting
Author(s) -
Li Wentao,
Duan Qingyun,
Miao Chiyuan,
Ye Aizhong,
Gong Wei,
Di Zhenhua
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.413
H-Index - 24
ISSN - 2049-1948
DOI - 10.1002/wat2.1246
Subject(s) - hydrometeorology , computer science , ensemble forecasting , hydrological modelling , statistical model , streamflow , meteorology , precipitation , environmental science , data mining , machine learning , climatology , geography , geology , drainage basin , cartography
Computer simulation models have been widely used to generate hydrometeorological forecasts. As the raw forecasts contain uncertainties arising from various sources, including model inputs and outputs, model initial and boundary conditions, model structure, and model parameters, it is necessary to apply statistical postprocessing methods to quantify and reduce those uncertainties. Different postprocessing methods have been developed for meteorological forecasts (e.g., precipitation) and for hydrological forecasts (e.g., streamflow) due to their different statistical properties. In this paper, we conduct a comprehensive review of the commonly used statistical postprocessing methods for both meteorological and hydrological forecasts. Moreover, methods to generate ensemble members that maintain the observed spatiotemporal and intervariable dependency are reviewed. Finally, some perspectives on the further development of statistical postprocessing methods for hydrometeorological ensemble forecasting are provided. WIREs Water 2017, 4:e1246. doi: 10.1002/wat2.1246 This article is categorized under: Science of Water > Methods Science of Water > Water Extremes