Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales

• Published in: Journal of hydrology (Amsterdam) Vol. 501; pp. 73 - 91
• Main Authors: Verkade, J.S., Brown, J.D., Reggiani, P., Weerts, A.H.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 25.09.2013; Elsevier
• Subjects: Bias; Bias-correction; Decomposition; Earth sciences; Earth, ocean, space; Ensemble forecasting; Exact sciences and technology; Hydrology; Hydrology. Hydrogeology; logistic-regression; Mathematical models; model output; nonparametric postprocessor; Post-processing; Precipitation; prediction system; probabilistic forecasts; Regression; Rhine; score; Skills; Transforms; uncertainty; Uncertainty estimation; united-states; Verification; Rhine River; Europe; Western Europe; Europe, Rhine R; Verification; Bias-correction; Uncertainty estimation; Post-processing; Ensemble forecasting; Rhine; rivers; tributaries; models; probability; water storage; corrections; temperature; uncertainties; streamflow
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2013.07.039

[Display omitted] •ECMWF ensemble reforecasts of precipitation and temperature were tested for biases.•An attempt was made to reduce these biases through statistical post-processing.•This resulted in modest improvements in the quality of the forcing ensembles.•The effect on streamflow ensembles was explored by verifying against simulated flow.•At all spatial scales considered, the improvements in streamflow quality were modest. The ECMWF temperature and precipitation ensemble reforecasts are evaluated for biases in the mean, spread and forecast probabilities, and how these biases propagate to streamflow ensemble forecasts. The forcing ensembles are subsequently post-processed to reduce bias and increase skill, and to investigate whether this leads to improved streamflow ensemble forecasts. Multiple post-processing techniques are used: quantile-to-quantile transform, linear regression with an assumption of bivariate normality and logistic regression. Both the raw and post-processed ensembles are run through a hydrologic model of the river Rhine to create streamflow ensembles. The results are compared using multiple verification metrics and skill scores: relative mean error, Brier skill score and its decompositions, mean continuous ranked probability skill score and its decomposition, and the ROC score. Verification of the streamflow ensembles is performed at multiple spatial scales: relatively small headwater basins, large tributaries and the Rhine outlet at Lobith. The streamflow ensembles are verified against simulated streamflow, in order to isolate the effects of biases in the forcing ensembles and any improvements therein. The results indicate that the forcing ensembles contain significant biases, and that these cascade to the streamflow ensembles. Some of the bias in the forcing ensembles is unconditional in nature; this was resolved by a simple quantile-to-quantile transform. Improvements in conditional bias and skill of the forcing ensembles vary with forecast lead time, amount, and spatial scale, but are generally moderate. The translation to streamflow forecast skill is further muted, and several explanations are considered, including limitations in the modelling of the space–time covariability of the forcing ensembles and the presence of storages.