Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons

Statistical post-processing of ensemble forecasts could effectively improve their accuracy and reliability. In this study, three typical post-processing methods including equal weight (EW), model output statistics (MOS) and Bayesian model averaging (BMA) were applied to the raw multi-model runoff fo...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2022-10, Vol.1087 (1), p.12052
Main Authors: Qu, Bo, Fang, Yuanhao, Li, Lingqi
Format: Article
Language:English
Subjects:
Accuracy
Bayesian analysis
Bias
Flood forecasting
Outliers (statistics)
Regression models
Runoff
Statistical analysis
Statistical methods
Statistics
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Summary:Statistical post-processing of ensemble forecasts could effectively improve their accuracy and reliability. In this study, three typical post-processing methods including equal weight (EW), model output statistics (MOS) and Bayesian model averaging (BMA) were applied to the raw multi-model runoff forecasts during the flood period (from 1 June to 30 September) of 2010-2013, and the processed results were compared and analyzed. It is shown that BMA is a promising post-processing method with highest accuracy, but this becomes completely different at lead times of 126-240 h in 2013. The main problems for the BMA performance instability were found to be outliers and low-correlation, which affect the linear regression model fitting in the bias correction procedure. Following this, a combination model of LR and EW was proposed to improve the bias correction procedure for lead times of 126-240 h. And the test results demonstrate that the combination model is effective and efficient, and it is able to lead to both accurate and reliable multi-model ensemble runoff forecasts for longer lead times.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/1087/1/012052