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A review on statistical postprocessing methods for hydrometeorological ensemble forecasting
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DOI：10.1002/wat2.1246.png)
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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. (C) 2017 Wiley Periodicals, Inc.
Keyword:
QUANTITATIVE PRECIPITATION FORECAST
HYDROLOGIC UNCERTAINTY PROCESSOR
EXTENDED LOGISTIC-REGRESSION
MODEL CONDITIONAL PROCESSOR
PROBABILISTIC FORECASTS
PREDICTIVE UNCERTAINTY
TEMPERATURE FORECASTS
QUANTILE REGRESSION
MULTIMODEL ENSEMBLE
BIAS CORRECTION
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725
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