A combined physical/statistical approach for the downscaling of model wind speed

1. A combined physical/statistical approach for the downscaling of model wind speed Submitted to Weather & Forecasting Wim de Rooy and Kees Kok Royal Netherlands Meteorological Institute

2. Structure • Error decomposition • definitions • model results • Combined approach • Explanation • validation • example of a wind speed field • Conclusions and discussions

3. Decomposition of errors Total error(t) = Model output(t)-observation(t) Total error(t)Model error(t) + Representation Mismatch(t) Where: Model error(t)  Model output(t) – “true” gridbox mean(t) Representation Mismatch(t)  “true” gridbox mean(t) – observation(t) Model error Representation Mismatch large scale small scale

4. The Netherlands with the locations of the synop stations

5. Verification of model 10m wind speed against synop stations

6. How can we use this for downscaling? • We want to minimize the Total error • Normal statistical technique: Regression between model output and observations • Let regression handle only the large scale Model error • Remove Representation Mismatch before the derivation of the regression equation • Use physical method to remove the Representation Mismatch (roughness) • Physical method is based on surface-layer theory and a blending height concept x

7. The combined approach

8. Validation results of the combined approach

9. Example of a downscaled wind speed field Hirlam 6h forecast valid for 27 October 1998, 12 UTC Standard 10m model wind (interpolated) Downscaled 10m wind (1x1 km2 resolution) (local roughness according to Job Verkaik)

10. Model verification results fit well in concept of small-scale Representation mismatch and large-scale Model error. • Concept of decomposed errors is valuable. • Combined approach results in an almost perfect reduction of the wind speed bias. • Regression equation is also effective on locations without observations. Conclusions & Discussion

11. Conclusions & Discussion • Advantages of the statistical component • Not only the Representation Mismatch but also the Model error is handled • Combined approach can be optimized for a particular use (e.g. high wind speeds) • Further improvement with additional large-scale predictors • Combined approach might be valuable for other parameters (e.g. temperature)

14. Surface layer theory • Blending height at 140m • Stability correction Hirlam = Local stability correction

15. (Strongly) reduce Representation Mismatch with a physical method • Reduce Model error with a statistical method • Local estimate of the 10m wind with a physical method based on surface-layer theory and a blending height concept. • Statistical method is a linear regression between the local estimate of the 10m wind speed and the observation