A review on statistical postprocessing methods for hydrometeorological ensemble forecasting. (14th September 2017)

• Record Type: Journal Article
• Authors: Li, Wentao; Duan, Qingyun; Miao, Chiyuan; Ye, Aizhong; Gong, Wei; Di, Zhenhua
• Is Part Of: Wiley interdisciplinary reviews. Volume 4:Number 6(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-09-14
• Subjects: Hydrology -- Periodicals; 553.705
• DOI: 10.1002/wat2.1246
• Languages: English
• ISSNs: 2049-1948

Abstract

Abstract : 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 Abstract : Statistical postprocessors are statistical models constructed from historical observations and reforecasts. They can be applied to generate calibrated hydrometeorological ensemble forecasts for any given real‐time raw forecasts.