EGU2020-8724
https://doi.org/10.5194/egusphere-egu2020-8724
EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Will post-processing always improve my forecasts?

Jon Olav Skøien1, Peter Salamon1, and Fredrik Wetterhall2
Jon Olav Skøien et al.
  • 1European Commission, Joint Research Centre, Ispra, Italy (jon.skoien@ec.europa.eu)
  • 2European Centre of Medium-Range Weather Forecasts, Reading, United Kingdom

Different statistical techniques are frequently employed to post-process the outcome of ensemble forecasting models. The main reason is to compensate for biases due to errors in model structure or initial conditions, and as a correction for under- or overdispersed ensembles.

Here we present analyses of the results from one these methods. We use the Ensemble Model Output Statistics method (EMOS; Gneiting et al., 2005) to post-process the ensemble output from a continental scale hydrological model - LISFLOOD (Van Der Knijff et al., 2010; De Roo et al., 2000). The model was calibrated at approximately 700 stations based on long term observations of runoff and meteorological variables. We use the same locations for calibration and verification of the 1-10 days forecasts of the model, based on ensemble and deterministic meteorological forecasts from ECMWF (51 ensemble members + 1 high-resolution), DWD (1 member) and COMSO-LEPS (16 ensemble members).

We calibrated the EMOS-parameters using the Continuous ranked probability score (CRPS). Whereas the post-processing improved the results for the first 1-2 days lead time, the improvement was less for increasing lead times of the verification period. As the post-processing is based on assumptions about the forecast errors, we will here present analyses of the ensemble output that can give some indications of what to expect from the post-processing.

 

Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T.: Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Mon. Weather Rev., 133(5), 1098–1118, doi:10.1175/MWR2904.1, 2005.

Van Der Knijff, J. M., Younis, J. and De Roo, A. P. J.: LISFLOOD: a GIS‐based distributed model for river basin scale water balance and flood simulation, Int. J. Geogr. Inf. Sci., 24(2), 189–212, doi:10.1080/13658810802549154, 2010.

De Roo, A. P. J., Wesseling, C. G. and Van Deursen, W. P. A.: Physically based river basin modelling within a GIS: The LISFLOOD model, in Hydrological Processes, vol. 14, pp. 1981–1992, John Wiley & Sons Ltd. [online] Available from: http://www.scopus.com/inward/record.url?eid=2-s2.0-0034254644&partnerID=tZOtx3y1, 2000.

 

How to cite: Skøien, J. O., Salamon, P., and Wetterhall, F.: Will post-processing always improve my forecasts?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8724, https://doi.org/10.5194/egusphere-egu2020-8724, 2020.

Displays

Display file

Comments on the display

AC: Author Comment | CC: Community Comment | Report abuse

displays version 2 – uploaded on 06 May 2020, no comments
I've added EGU logo, but more important, changing from PP to PDF
displays version 1 – uploaded on 29 Apr 2020, no comments