assimilation of (satellite) cloud-information at the convective scale in an ensemble kalman filter...
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![Page 1: Assimilation of (satellite) cloud-information at the convective scale in an Ensemble Kalman Filter Annika Schomburg 1, Christoph Schraff 1, Africa Perianez](https://reader035.vdocuments.mx/reader035/viewer/2022062314/56649da05503460f94a8c6f8/html5/thumbnails/1.jpg)
Assimilation of (satellite) cloud-Assimilation of (satellite) cloud-
information at the convective scale in information at the convective scale in
an Ensemble Kalman Filteran Ensemble Kalman Filter
Annika Schomburg1, Christoph Schraff1, Africa Perianez1, Jason Otkin2, Roland Potthast1
1 DWD (German Meteorological Service)2 University of Wisconsin
WWOSC 16-21 August 2014, Montreal
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Motivation: Growing renewable energy sector
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• Weather dependencey of renewable energy: Increasing demands for accurate power predictions for a safe and cost-effective power system Project to improve forecast models
for the grid integration of weather dependent energy sources
Source: energymap.info
solarWindhydroelectricbiomassgasgeothermal
Energy production in Germany for week 30, 2014
Source: Fraunhofer ISE
Development of average renewable energy production in Germany since 2002:
For photovoltaic power a realistic simulation of cloud cover is crucial this talk: exploit cloud information sources for data assimilation
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Motivation
• Difficult weather situations for photovoltaic power predictions:
• Cloud cover after cold front passes
• Convective situations
• Low stratus clouds / fog
• Snow cover on solar panels
Convective scale models needed to capture relief and atmospheric stability locally
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Modelling system
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COSMO-DE :
•Limited-area short-range numerical model weather prediction model•x 2.8 km / 50 vertical layers •Explicit deep convection
•New data assimilation system : Implementation of the Ensemble Kalman Filter: LETKF after Hunt et al. (2007)
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Local Ensemble Transform Kalman Filter
LETKF
Analysis perturbations: linear combination of background
perturbations
First guess ensemble members are weighted according to their departure from the observations
OBS-FG
OBS-FG
OBS-FG
OBS-FG
R
Tbibk
i
bibb
k))((
)1(
1 )(
1
)( xxxxP
Background error covariance
Observation errors
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Outline
• Assimilation of additional data to improve cloud cover:
• Satellite cloud products
• Cloudy infrared satellite radiances
• Photovoltaic power
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Satellite cloud products
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Satellite product: cloud top height
contains information on horizontal and vertical distributions of clouds
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Satellite cloud product information
Geostationary satellite data: Meteosat-SEVIRI (Δx ~ 5km over central Europe, Δt=15 min)
Source: EUMETSAT
Height [km]
Cloud top height
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MODEL EQUIVALENT
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OBSERVATION: Satellite product: cloud top height
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Method
• Extract information if a pixel is observed as cloudy:
Height [km]
Cloud top height
Cloud top height
Relative humidity at cloud top
height
Determine cloud top model equivalent: top of most humid layer k close to observation
100%
see Schomburg et al., QJRMS, 2014
Layer kRH(k)
height(k)
Observation
Model RH profile
Assimilated variables:
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relative humiditycloud covercloud water
cloud iceobserved cloud top
3 lines in one colour indicate ensemble mean and mean +/- spread
• 1 analysis step, 17 Nov. 2011, 6 UTC (wintertime low stratus)
vertical profiles
Example: Single-observation experiments: missed low stratus cloud
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First guess Analysis
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Model equivalent:
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Observation:Satellite product: cloud top height
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Method
• Extract information if a pixel is observed as cloud-free:
Height [km]
Cloud top height
Cloud coverhigh clouds
Cloud covermid-level
clouds
Cloud coverlow clouds
0%
0%
0%
Z [km]
CLC
Maximum cloud cover in
high levels
Maximum cloud cover in
medium levels
Maximum cloud cover in
low levels
see Schomburg et al., QJRMS, 2014
Assimilated variables:
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low clouds mid-level clouds high clouds‘false alarm’ cloud cover
(after 20 hrs cycling)
conventional+ cloud
conventionalobs only
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Comparison “only conventional“ versus “conventional + cloud obs"
[octa]
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conventional only conventional + cloud
Total cloud cover after 12 h free forecast
Observed cloud top height
Comparison of free forecast results
satellite obs
15. Nov 2011, 6:00 UTC
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Cloudy infrared satellite radiances
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Approach: Assimilate water vapour and window channels of Meteosat SEVIRI
• The goal is to obtain information on the cloud cover in the atmosphere
assimilate all-sky radiances from water-vapour channels and window channel(s)
•Challenges: Cloud dependent bias correction?
How to specify observation error
Thinning, localization in LETKF
.. etc..
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Source: Schmetz et al, BAMS, 2002
SEVIRI channels
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First step: Monitoring
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Example for water vapour band WV7.3, sensitive to mid-level moisture (and clouds)
• Obs minus model statistics look promising with the exception of high-level clouds, semitransparent clouds should be excluded here
• Bias correction is needed: the model is 2-4 K too warm
Observation minus Background
RMSE
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First assimilation results: 12 hours of cycling, assimilation of channel WV7.3
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Bia
sR
MS
E
Without radiance assimilation With radiance assimilation
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Photovoltaic power
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Assimilation of photovoltaic power
Model variables:- surface irradiance- 2m temperature
- albedo
Model variables:- surface irradiance- 2m temperature
- albedo
Source: Yves-Marie Saint-Drenan, IWES
Forward operator:
Compute radiation on tilted
plane
Compute radiation on tilted
plane
Forward operator for PV module
Challenge: Data availability - Up to now only data for a few solar parks available for DWD
Synthetic PV power (clouds
main forcing factor)
Synthetic PV power (clouds
main forcing factor)
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Example of simulated and observed photovoltaic power
Model forecast solar insolation at surfaceObserved photovoltaic powerSimulated photovoltaic power (based on model forecast radiation)
No
rmal
ized
po
wer
[W
/Wp
eak]
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Conclusions
• Work on assimilating cloud information from various sources at the convective scale (∆x~3km) in a LETKF system ongoing:
• Satellite products, satellite radiances, PV power
• Challenges:
• Nonlinearity
• Presentation of clouds in the model
• Forward modelling
• PV data and meta-data availability and quality• Shading by trees, string failures, soiling…
• Improved cloud cover simulations is also expected to lead to a better onset of convection and temperature predictions
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Thank you for your attentionThank you for your attention!
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Determine the model equivalent cloud top
Avoid strong penalizing of members which are dry at CTHobs but have a cloud or even only high humidity close to CTHobs
search in a vertical range hmax around CTHobs fora ‘best fitting’ model level k, i.e. with minimum ‘distance’ d:
2
max
2 )(1
)(min obskobskk
CTHhh
RHRHd
relative humidity height ofmodel level
k
= 1
use y=CTHobs H(x)=hk
and y=RHobs=1 H(x)=RHk (relative humidity over
water/ice depending on temperature)as 2 separate variables assimilated by LETKF
use y=CTHobs H(x)=hk
and y=RHobs=1 H(x)=RHk (relative humidity over
water/ice depending on temperature)as 2 separate variables assimilated by LETKF
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Z [km]
RH [%]
CTHobs
k1
k2
k3
k4
k5
Cloud top
model profile
•(make sure to choose the top of the detected cloud)
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Example: 17 Nov 2011, 6:00 UTCObservations and model equivalents
RH model level kObservation Model
„Cloud top height“
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• COSMO cloud cover where observations “cloudfree”
Example: 17 Nov 2011, 6:00 UTC
High clouds (oktas)Mid-level clouds (oktas)Low clouds (oktas)
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Example: Missed cloud case:Effect on temperature profile
temperature profile [K] (mean +/- spread)
first guess analysis
observed cloud top
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conventional only conventional + cloud
Total cloud cover of first guess fields after 20 hours of cycling
Satellite cloud top height
Results: Comparison of cycled experiments
satellite obs
12 Nov 2011 17:00 UTC
Low stratus cloud cover improved through assimilation of cloud products!Low stratus cloud cover improved through assimilation of cloud products!
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Results III:Forecast impact
• 24 h free forecast starting after 21h cycling• 14 Nov. 2011, 18 UTC – 15 Nov. 18 UTC• Wintertime low stratus
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conventional only conventional + cloud
Observed cloud top height
Results after 12 hours of free forecast
satellite obs
Total cloud cover after 12h forecast (15. Nov 2011, 6:00 UTC)
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• The forecast of cloud characteristics can be improved through the assimilation of the cloud information
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Results: Comparison of free forecast: time series of
errors
Conventional + cloud dataOnly conventional data
RMSE
Bias (Obs-Model)
Low cloudsMid-level cloudsHigh clouds
Mean squared error averaged over all cloud-free observations
RH (relative humidity) at observed cloud top averaged over all cloudy
observations
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Assimilation of photovoltaic power
Model variables:- surface irradiance- 2m temperature
- albedo
Model variables:- surface irradiance- 2m temperature
- albedo
Source: Yves-Marie Saint-Drenan, IWES
Forward operator:
Compute radiation on tilted
plane
Compute radiation on tilted
plane
Forward operator for PV module
Sensitivities:
Direct solar irradiance [W/m²]
Diffuse part of solar irradiance
[W/m²]
Ambient air temperature [K]