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Local Probabilistic Weather Predictions for Switzerland

This paper discusses the motivation for local probabilistic forecasts and the OptiControl project, which focuses on stochastic model predictive control of indoor climate and wind power prediction for the electricity market. It also examines the calibration potential and error models for temperature and wind speed predictions using the COSMO-LEPS model. The results show that bias and spread calibration using a combined Kalman Filter system can significantly improve forecast skill.

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Local Probabilistic Weather Predictions for Switzerland

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  1. Local Probabilistic Weather Predictions for Switzerland COSMO General Meeting Vanessa Stauch Offenbach, September 2009

  2. motivation for local prob. forecasts OptiControl project: stochastic model predictive control of indoor climate wind power prediction for electricity market

  3. COSMO-LEPS spatial spread COSMO-LEPS mean spread, lead time 24h, May 2008 COSMO-LEPS mean spread, lead time 123h, May 2008

  4. Swiss met. measurement network 72 stations

  5. COSMO-LEPS calibration potential T2M for Swiss stations (03.2008-02.2009) bias correction potential spread correction potential 5th & 95th spatial percentile

  6. COSMO-LEPS calibration potential FF10M for Swiss stations (03.2008-02.2009) bias correction potential spread correction potential 5th & 95th spatial percentile wanted: bias and spread calibration

  7. error : ^ error model: with with states evolution: ^ prediction: bias calibration with Kalman Filter apply this to ensemble median and correct all member equally

  8. calibration with COSMO-LEPS median no beneficial spread calibration

  9. local forecast error calibration I systematic error

  10. local forecast error calibration II σ

  11. spread error : ^ error model: with with states evolution: ^ prediction: bias calibration with Kalman Filter

  12. spread calibration with Kalman Filter error : ^ error model: with with states evolution: ^ prediction:

  13. forecast distributions expectation value standard deviation » simulate normally distributed ensemble

  14. CRPS verification x < observed x≥ observed skill score:

  15. calibration results for T2M bias correction spread correction

  16. calibration results for T2M ref: COSMO-LEPS DMO

  17. calibration results for FF10M bias correction spread correction

  18. calibration results for FF10M ref: COSMO-LEPS DMO

  19. conclusions » local observations are needed for useful uncertainty prediction » 2m temperature and 10m wind speed of COSMO-LEPS are suitable candidates for bias & spread calibration with a combined Kalman Filter system » forecast skill increases by 30-60 % » potential for spread calibration highest for the first 3 days and in summer higher than in winter » different probabilistic verification scores should be additionally used to quantify the effect of the spread calibration

  20. T(2m) spread-skill of COSMO-LEPS monthly variability (Ø55 stations) station-based variability (Ø1 year)

  21. uncertainty forecast with COSMO-7 ref: COSMO-7 KF & COSMO-LEPS spread

  22. COSMO models COSMO-LEPS COSMO-LEPS 10km, +132 hours COSMO-7 6.6km, +72 hours COSMO-2 2.2km, +24 hours COSMO-2 COSMO-7

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