1. Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Accounting for Change: Local wind forecasts from the high- resolution model COSMO Vanessa Stauch (MeteoSwiss) ECAC & EMS, September, 14 th 2010 COSMO-GM, September 2011, Roma

2. 2 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Spatial verification of wind speed Model topography fairly complex Model performance pretty good

3. 3 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Wind speed SYNOP verification @CH COSMO-2, all 8 runs, May-July 2011, averaged over ~85 stations in Switzerland Daytime (UTC) Mean error (m/s) M e a n o b s/ fc st ( m /s )

4. 4 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Spatial verification of wind speed Model topography fairly complex Model performance pretty good Model performance at some stations rather poor

5. 5 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Aim of this work Make use of the strength of COSMO-2 wind forecasts Use observations to statistically correct for local forecast errors Derive probabilistic information from deterministic forecasts to provide comprehensive forecast information to the user >> Augment the forecasts of COSMO-2

6. 6 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch September 04, 2009 ***************** COSMO News No. 39 ***************** Dear internal COSMO Clients With the 09UTC run of Thursday, 10.9.2009, we will introduce the new version 4.7.4 of the COSMO model with a parametrization of the sub-grid scale orographic (SSO) drag both in COSMO-2 and COSMO-7. What is this SSO scheme: The model orography is a smoothed version of the real orography containing much less details, i.e. less deep valleys and lower mountain peaks. This leads to an underestimation of the drag exerted by the orography on the atmosphere. The SSO-scheme (implemented following Lott and Miller, 1997, QJRMS) improves this situation with a parametrization of both the form drag and gravity wave drag components. The verification of the new version during two 3 week periods (in winter and summer) shows that the main impact is on the wind: 10m-wind, gusts and wind in the boundary layer. The overestimation of the 10m-wind on the Swiss Middleland is reduced on many stations as well also the overestimation in the boundary layer. Over the full domain of COSMO-7 the bias of 10m-wind is reduced substantially. The gusts are reduced at some stations over the Swiss Middleland and over the Alps, i.e. leading to a bit higher underestimation (except in COSMO-2 the stations near lakes with a reduced overestimation). Detailed verification results are available here… The postprocessors’ dilemma we will introduce the new version… the main impact is on the wind forecasts…

7. 7 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Accounting for change Length of database ~ complexity of statistical correction temporal flexibility (e.g. when model error changes) “global MOS” “KF” “UMOS” “COSMO- MOS” „global MOS “: e.g. MOSMIX at DWD, multiple linear regression based on global NWP models (GME and IFS) “UMOS”: ‘updateable’ MOS of Canadians, weighting when model chsnges “KF”: Kalman Filter based estimation, online update + Sampling for many cases, good discrimination - A bit inert when model changes + insensitive to model changes - simple error model, poor discrimination of weather condition Need for models with few parameters “MOS with reforecasts”

8. 8 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch COSMO-2 @ MeteoSwiss +24h +48h +24h Some uncertainty information?

9. 9 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Logistic regression: forecast probabilities for one threshold Sampleclimatology Wind speed threshold Obs Fcst p(obs)!=p(fcst)

10. 10 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch probability Wind speed 1 0. … … ….... … ….... Obs Fcst (p) Logistic regression: forecast probabilities for one threshold ln(p/(1-p)) 1 0 Obs Fcst (p) Wind speed ° ° ° ° ° ° °

11. 11 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Extended logistic regression Wilks 2009 Sampleclimatology Wind speed threshold Obs Fcst Add thresholds as predictor, estimate one additional parameter

12. 12 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Extended logistic regression Wilks 2009 Wind speed Obs Fcst Results in full probability distributions for each forecast

13. 13 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Set up for estimation & correction Forecasts and observations for all Swiss stations for wind speed and wind gusts for two power stations Length of training period: 3 months Estimation of parameters daytime dependent, once each day Predictors: wind forecast and 4 thresholds (0.2, 0.4, 0.6, 0.8) Evaluate deterministically and probabilistically

14. 14 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Results: bias correction wind speed

15. 15 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Results: probabilistic verification

16. 16 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Results: evaluation of distribution

17. 17 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Results: bias correction for vmax

18. 18 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Results: variability of parameters

19. 19 Local wind forecasts | ECAC/EMS 2011, Berlin Vanessa Stauch, vanessa.stauch@meteoswiss.ch Conclusions and outlook In case no EPS is available, extended logistic regression can help augment a deterministic model Ext. log. regression helps removing the model bias and decreases the standard deviation of the error Ext. log. regression is a promising candidate for model output statistics for many parameters (independent from their distribution) To do: further investigation on model selection, length of training period, number of thresholds for estimation, …