Webinar on ‘pre-processing’ in hydrologic forecasting

Wednesday, March 27th, I’ll be giving a webinar on pre-processing in hydrologic forecasting. The webinar is part of a new series of online presentations that are part of the HEPEX initiative. It is hosted by ECMWF and starts at 4pm CET. Full details of how to join the webinar are found here.

graphical-abstract

The webinar’s full title is “Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales”. The accompanying abstract reads as follows:

The ECMWF temperature and precipitation ensemble reforecasts are evaluated for biases in the mean and spread, and how these biases propagate to streamflow ensemble forecasts. The forcing ensembles are subsequently post-processed to reduce bias and increase skill, and it is investigated whether this leads to improved streamflow ensemble forecasts. Multiple post-processing techniques are used: quantile-to-quantile transform, linear regression with an assumption of joint normality and logistic regression. Both 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 show 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.

The presentation is based on research that James Brown, Albrecht Weerts, Paolo Reggiani and myself have conducted. A manuscript describing this research has been accepted for publication in Journal of Hydrology, pending ‘moderate’ revisions.

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