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- Author or Editor: Emmanuel Roulin x
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Abstract
A hydrological ensemble prediction system, integrating a water balance model with ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS), is evaluated for two Belgian catchments. The skill of streamflow forecast for high flows is analyzed using a 6-yr period of archived EPS forecasts. The probabilistic skill of this hydrological prediction system is much better than the one based on historical precipitation inputs and extends beyond 9 days for both catchments. The skill is larger in winter than in summer. The use of this approach for operational forecasts is briefly discussed.
Abstract
A hydrological ensemble prediction system, integrating a water balance model with ensemble precipitation forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS), is evaluated for two Belgian catchments. The skill of streamflow forecast for high flows is analyzed using a 6-yr period of archived EPS forecasts. The probabilistic skill of this hydrological prediction system is much better than the one based on historical precipitation inputs and extends beyond 9 days for both catchments. The skill is larger in winter than in summer. The use of this approach for operational forecasts is briefly discussed.
Abstract
Extended logistic regression is used to calibrate areal precipitation forecasts over two small catchments in Belgium computed with the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) between 2006 and 2010. The parameters of the postprocessing are estimated from the hindcast database, characterized by a much lower number of members (5) than the EPS (51). Therefore, the parameters have to be corrected for predictor uncertainties. They have been fitted on the 51-member EPS ensembles, on 5-member subensembles drawn from the same EPS, and on the 5-member hindcasts. For small ensembles, a simple “regression calibration” method by which the uncertain predictors are corrected has been applied. The different parameter sets have been compared, and the corresponding extended logistic regressions have been applied to the 51-member EPS. The forecast probabilities have then been validated using rain gauge data and compared with the raw EPS. In addition, the calibrated distributions are also used to modify the ensembles of precipitation traces.
The postprocessing with the extended logistic regression is shown to improve the continuous ranked probability skill score relative to the raw ensemble, and the regression calibration to remove a large portion of the bias in parameter estimation with small ensembles. With a training phase limited to a 5-week moving window, the benefit lasts for the first 2 forecast days in winter and the first 5 or 6 days in summer. In general, substantial improvements of the mean error and of the continuous ranked probability score have been shown.
Abstract
Extended logistic regression is used to calibrate areal precipitation forecasts over two small catchments in Belgium computed with the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) between 2006 and 2010. The parameters of the postprocessing are estimated from the hindcast database, characterized by a much lower number of members (5) than the EPS (51). Therefore, the parameters have to be corrected for predictor uncertainties. They have been fitted on the 51-member EPS ensembles, on 5-member subensembles drawn from the same EPS, and on the 5-member hindcasts. For small ensembles, a simple “regression calibration” method by which the uncertain predictors are corrected has been applied. The different parameter sets have been compared, and the corresponding extended logistic regressions have been applied to the 51-member EPS. The forecast probabilities have then been validated using rain gauge data and compared with the raw EPS. In addition, the calibrated distributions are also used to modify the ensembles of precipitation traces.
The postprocessing with the extended logistic regression is shown to improve the continuous ranked probability skill score relative to the raw ensemble, and the regression calibration to remove a large portion of the bias in parameter estimation with small ensembles. With a training phase limited to a 5-week moving window, the benefit lasts for the first 2 forecast days in winter and the first 5 or 6 days in summer. In general, substantial improvements of the mean error and of the continuous ranked probability score have been shown.
