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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
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Source: Andrea Streiner, DWD
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Who is EWeLiNE?
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Agenda 1. Data Assimilation 2. Impact-Study
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Agenda 1. Data Assimilation 2. Impact-Study
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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?
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Weather forecast Numerical model Observations Improved initial conditions for next integration step Data assimilation tool
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Agenda 1. Data Assimilation 2. Impact-Study
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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
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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
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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
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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
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OSSE – Results Test Period Computational domain Results for 2013062100 - 2013062918, mean over all 00UTC free forecasts evaluation region
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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)
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OSSE – Evaluation 1 Results for 2013062100 - 2013062918, mean over all 00UTC free forecasts Computational domain evaluation region
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OSSE – Evaluation 2 x x x Evaluate locally : at reference wind park propagate evaluation point with wind field
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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…
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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
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Thank you for your attention! Now: Q & A
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