1. Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger - Eine Kooperation von Meteorologie und Energiewirtschaft - Stefan Declair*, Klaus Stephan, Roland Potthast 79. DPG-Jahrestagung, Arbeitskreis Energie Berlin, March 18 th 2015 On the Improvement of Numerical Weather Prediction by Assimilation of Wind Power data

2. Source: Andrea Streiner, DWD

3. Who is EWeLiNE?

4. Agenda 1. Data Assimilation 2. Impact-Study

5. Agenda 1. Data Assimilation 2. Impact-Study

6. Information used: Observations Knowledge about cars, street, etc Experience  statistics Forecast errors due to: Observation (estimation) errors Model errors (icy street) Case does not match statistics Forecast: Can I cross the street without getting hit?

7. Weather forecast Numerical model Observations Improved initial conditions for next integration step Data assimilation tool

8. Agenda 1. Data Assimilation 2. Impact-Study

9. OSSE  What: Observation System Simulation Experiment  Goal: Test the impact of newly available observations in the data assimilation  Method: assimilate artificial observations in slightly perturbed truth  Advantages:  Truth is known exactly  All generated athmospheric fields can be used as observations  Observation system can be altered easily  Observation errors  Observation densities  Temporal resolution/delay

10. OSSE  What: Observation System Simulation Experiment  Goal: Test the impact of newly available observations in the data assimilation  Method: assimilate artificial observations in slightly perturbed truth free forecast truth artificial obs * control create perturb assimilate * obs: all conventional obs ervations PLUS wind observations at average park hub height

11. OSSE – Settings  Artificial wind observations  68 wind farm sites  Average hub height, farm point of mass  15min resolution/10min delay  Observation error: N(0, 2 ms -1 )  Control  2 perturbations @ physics  2 perturbations @ dynamical core

12. OSSE – Settings  Cycling over N-day evaluation period  Hourly assimilation of artificial wind observations  Hourly free forecast over 21h UTC time days 12NN-13 121800061218 analysis 21h forecast analysis 21h forecast analysis 21h forecast analysis 21h forecast analysis 21h forecast analysis 21h forecast

13. OSSE – Results Test Period Computational domain  Results for 2013062100 - 2013062918, mean over all 00UTC free forecasts evaluation region

14. OSSE – Results Test Period  Results for 2013062100 – 2013062918  How many observations have been assimilated?  Conventional observations (AIREP,TEMP,etc):~4000-5000 / h  Artificial wind information: <300 / h  New observations have small weight compared to conventional obs!  3 possibilities:  Reduce amount of conventional observations  Evaluate locally around station / along wind path  Rerun with higher artificial wind observation density (work in progress)

15. OSSE – Evaluation 1  Results for 2013062100 - 2013062918, mean over all 00UTC free forecasts Computational domain evaluation region

16. OSSE – Evaluation 2 x x x  Evaluate locally :  at reference wind park  propagate evaluation point with wind field

17. OSSE – Evaluation 2  Results for 2013062100 - 2013062918, mean over all 00UTC free forecasts  RMSE between NTR analysis and ctl (marks) / exp  68 stations  Positive local impact  Horizon:  Stat: up to 12h  Dyn: up to 17h  Diurnal error: slightly…

18.  Data assimilation  NWP is a (boundary and) inital value problem: you need accurate initial fields  Task: create a best-fit atmospheric state according to first guess and observations Conclusion  Impact study: OSSE  Visible positive impact of artificial hub height wind speeds  Regional:  Fierce competition with conventional observation networks: neutral  Unrivaled: strongly positive over 8 hours  Local:  positive effect for more tha half a day even with conventional observation networks included

19. Thank you for your attention! Now: Q & A