erstellung innovativer wetter- und leistungsprognosemodelle für die netzintegration...
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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 EnergieBerlin, March 18th 2015
On the Improvement of Numerical Weather
Prediction by Assimilation of Wind Power
data
Source: Andrea Streiner, DWD
Who is EWeLiNE?
Agenda
1. Data Assimilation
2. Impact-Study
Agenda
1. Data Assimilation
2. Impact-Study
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?
Weather forecast
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Agenda
1. Data Assimilation
2. Impact-Study
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
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 forecasttruth
artificial obs *
control
create
perturb
assimilate
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* obs: all conventional obs ervations PLUS
wind observations at average park hub height
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
OSSE – Settings
Cycling over N-day evaluation period
Hourly assimilation of artificial wind observations
Hourly free forecast over 21h
UTC time
days
1 2 NN-13
12 18 00 06 12 18
analysis
21h forecast
analysis
21h forecast
analysis
21h forecast
analysis
21h forecast
analysis
21h forecast
analysis
21h forecast
OSSE – Results Test Period
Computational domain
Results for 2013062100 - 2013062918, mean over all 00UTC free forecasts
evaluation region
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)
OSSE – Evaluation 1
Results for 2013062100 - 2013062918, mean over all 00UTC free forecasts
Computational domainevaluation region
OSSE – Evaluation 2
x
x
x
Evaluate locally :
at reference wind park
propagate evaluation point with wind field
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…
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
Thank you for your attention!
Now: Q & A