Transcript Folie 1 - uni

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
Source: Andrea Streiner, DWD
Who is EWeLiNE?
Agenda
1. Data Assimilation
2. Impact-Study
Agenda
1. Data Assimilation
2. Impact-Study
Forecast: Can I cross the street without getting hit?
Information used:
Forecast errors due to:
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Observations
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Observation (estimation) errors
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Knowledge about cars, street, etc
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Model errors (icy street)
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Experience  statistics
•
Case does not match statistics
Weather forecast
Numerical model

Data assimilation tool
Observations

 
  
 ( x , t )  F  ( x , t ), x , t

  (u, v, w, p' , T , q,...)
Improved initial conditions for next integration step
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
create
artificial obs *
truth
assimilate
perturb
* obs: all conventional obs ervations
PLUS
wind observations at average park hub height
free forecast
control
 ( x, t )
 F ( , x, t )  G Wk  k ,obs  ( xk , t )
t
k( obs )
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
days
1
21h forecast
analysis
2
21h forecast
analysis
3
21h forecast
analysis
N-1
21h forecast
analysis
21h forecast
analysis
N
21h forecast
analysis
UTC time
12
18
00
06
12
18
OSSE – Results Test Period
 Results for 2013062100 - 2013062918, mean over all 00UTC free forecasts
Computational domain
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 domain
evaluation region
OSSE – Evaluation 2
 Evaluate locally :
 at reference wind park
 propagate evaluation point with wind field
x
x
x
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…
Conclusion
 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
 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