Our article on statistically post-processing ECMWF weather re-forecasts for use in operational streamflow forecasting

Recently, James Brown, Albrecht Weerts, Paolo Reggiani and myself published a research article on statistically post-processing ECMWF-EPS weather re-forecasts for use in streamflow forecasting.

Graphical abstract

Graphical abstract

The article’s highlights can be summarized as follows:

  • 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 muted.

The manuscript has been published in Elsevier’s Journal of Hydrology. In due time, it will constitute one of the chapters of my PhD dissertation. An author copy can be downloaded from here. The paper’s full reference is:

Verkade, J. S., Brown, J. D., Reggiani, P. and Weerts, A. H.: Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales, Journal of Hydrology, 501, 73–91, doi:10.1016/j.jhydrol.2013.07.039, 2013.

Note added (December 3, 2014): I recently presented the results of this work at the November 2014 “H-SAF and HEPEX workshops on coupled hydrology” (link). The slides I used can be downloaded from here.

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