A review on statistical postprocessing methods for hydrometeorological ensemble forecasting
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 ap...
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| Published in | Wiley interdisciplinary reviews. Water Vol. 4; no. 6; pp. e1246 - n/a |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Hoboken, USA
John Wiley & Sons, Inc
01.11.2017
Wiley Subscription Services, Inc |
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| 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
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. |
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| AbstractList | 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
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. 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 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/wat2.1246This article is categorized under:Science of Water > MethodsScience of Water > Water Extremes |
| Author | Li, Wentao Ye, Aizhong Duan, Qingyun Miao, Chiyuan Di, Zhenhua Gong, Wei |
| Author_xml | – sequence: 1 givenname: Wentao surname: Li fullname: Li, Wentao organization: Beijing Normal University – sequence: 2 givenname: Qingyun surname: Duan fullname: Duan, Qingyun email: qyduan@bnu.edu.cn organization: Beijing Normal University – sequence: 3 givenname: Chiyuan surname: Miao fullname: Miao, Chiyuan organization: Beijing Normal University – sequence: 4 givenname: Aizhong surname: Ye fullname: Ye, Aizhong organization: Beijing Normal University – sequence: 5 givenname: Wei surname: Gong fullname: Gong, Wei organization: Beijing Normal University – sequence: 6 givenname: Zhenhua surname: Di fullname: Di, Zhenhua organization: Beijing Normal University |
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| Snippet | Computer simulation models have been widely used to generate hydrometeorological forecasts. As the raw forecasts contain uncertainties arising from various... |
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| SubjectTerms | Boundary conditions Computer simulation Hydrologic models Hydrology Hydrometeorology Precipitation Simulation Statistical analysis Statistical methods Statistics Stream discharge Stream flow Weather forecasting |
| Title | A review on statistical postprocessing methods for hydrometeorological ensemble forecasting |
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