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Improvement of Numerical Weather Prediction by Assimilation of Wind Power Data

This study investigates the impact of assimilating wind power data into numerical weather prediction models. It presents the results of an observation system simulation experiment (OSSE) that tests the effect of incorporating artificial wind observations in the data assimilation process. The experiment shows a visible positive impact of the assimilated wind data, especially at the regional and local scales.

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Improvement of Numerical Weather Prediction by Assimilation of Wind Power Data

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

  2. Source: Andrea Streiner, DWD

  3. Who is EWeLiNE?

  4. Agenda • Data Assimilation • Impact-Study

  5. Agenda • Data Assimilation • Impact-Study

  6. Forecast: Can I crossthestreetwithoutgettinghit? Forecast errors due to: • Observation (estimation)errors • Model errors (icystreet) • Case does not matchstatistics Information used: • Observations • Knowledgeaboutcars, street, etc • Experience  statistics

  7. Weather forecast Numericalmodel Data assimilationtool Observations Improved initial conditionsfornextintegrationstep

  8. Agenda • Data Assimilation • 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: • Truthisknownexactly • All generatedathmosphericfieldscanbeusedasobservations • Observation systemcanbealteredeasily • 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 create artificial obs * truth free forecast assimilate control perturb * obs: all conventional obs ervations PLUS wind observations at average park hub height

  11. OSSE – Settings • Artificial wind observations • 68 wind farmsites • Average hub height, farmpointofmass • 15min resolution/10min delay • Observation error: N(0, 2 ms-1) • Control • 2 perturbations @ physics • 2 perturbations @ dynamicalcore

  12. OSSE – Settings • Cycling over N-day evaluation period • Hourly assimilation of artificial wind observations • Hourly free forecast over 21h days 1 2 3 N-1 N 21h forecast 21h forecast 21h forecast 21h forecast 21h forecast 21h forecast analysis analysis analysis analysis analysis analysis UTC time 12 18 00 06 12 18

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

  16. OSSE – Evaluation 2 • Evaluatelocally : • at reference wind park • propagateevaluationpointwith wind field x x x

  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. Conclusion • Data assimilation • NWP is a (boundaryand) initalvalueproblem: youneedaccurate initial fields • Task: create a best-fit atmosphericstateaccordingtofirstguessandobservations • Impact study: OSSE • Visible positive impactofartificial hub height wind speeds • Regional: • Fiercecompetitionwithconventionalobservationnetworks: neutral • Unrivaled: strongly positive over 8 hours • Local: • positive effectformoretha half a dayevenwithconventionalobservationnetworksincluded

  19. Thankyouforyourattention! Now: Q & A

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