assimilating satellite cloud information with an ensemble kalman filter at the convective scale
DESCRIPTION
Annika Schomburg , Christoph Schraff, Hendrik Reich, Roland Potthast. Assimilating satellite cloud information with an Ensemble Kalman Filter at the convective scale. EnKF workshop 18-22 May 2014, Buffalo. Motivation: Weather situation 23 October 2012. - PowerPoint PPT PresentationTRANSCRIPT
Assimilating satellite cloud information with an Ensemble
Kalman Filter at the convective scale
Annika Schomburg, Christoph Schraff, Hendrik Reich, Roland Potthast
EnKF workshop 18-22 May 2014, Buffalo
Motivation: Weather situation 23 October 2012
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12 UTC synoptic situation: stable high pressure system over central Europe
Motivation: Weather situation 23 October 2012
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12 UTC synoptic situation: low stratus clouds over Germany
Satellite cloud type classification
Motivation: Verification for 23 October 2012
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12 hour forecast from 0:00 UTC
Low cloud cover: COSMO-DE versus satellite
Total cloud cover: COSMO-DE versus synop
T2m: COSMO-DE minus synop
Green: hits; black: missesred: false alarms, blue: no obs
Courtesy of K. Stephan
Hochrechnung
Problematic weather situation for photovoltaic power production: low stratus clouds
Error Day-Ahead: 4800 MW
Low stratus clouds not predicted
Low stratus clouds observed in reality
ProjectionDay-AheadIntra-Day
time
Power from PV modules
courtesy by TENNET
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Problematic weather situations for photovoltaic power prediction
Cloud cover after cold front pass
Convective situations
Low stratus / fog weather situations
Snow coverage of photovoltaic modules
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Motivation
• Photovoltaic power production forecasts: Germany plans to increase the percentage of renewable energy to 35% in 2020
Increasing demands for accurate power predictions for a safe and cost-effective power system
• Project EWeLiNE: Objective: improve weather and power forecasts for wind and photovoltaic power
• Main motivation: improve cloud cover simulation of low stratus clouds in stable wintertime high-pressure systems
• Should also prove useful for frontal system or convective situations
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The COSMO model
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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)
• See also posters on • Observation impact in a convective-scale
LETKF by Martin Weissmann• Usage of convective-scale LETKF to
provide initial conditions for ensemble forecasts by Florian Harnisch
NWCSAF satellite product: cloud top height
1 2 3 4 5 6 7 8 9 10 11 12 13
Observation systems
Geostationary satellite data: Meteosat-SEVIRI (Δx ~ 5km over central Europe, Δt=15 min)
Source: EUMETSAT
Height [km]
Cloud top height Cloud top
height
Relative humidity at cloud
top height
Cloud cover
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)
Example: 17 Nov 2011, 6:00 UTCObservations and model equivalents
RH model level kObservation Model
„Cloud top height“
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6
9
12
Z [km]
„no high cloud“
„no mid-level cloud“
„no low cloud“
CLC
• assimilate cloud fraction CLC = 0 separately
for high, medium, low clouds
• model equivalent:
maximum CLC within vertical range
What information can we assimilate for pixels which are observed to be cloudfree?
Determine model equivalent: cloudfree pixels
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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)
• Analysis for 17 November 2011, 6:00 UTC (no cycling)
• Each column is affected by only one satellite observation
• Objective:– Understand in detail what the filter does with such special
observation types– Does it work at all?– Detailed evaluation of effect on atmospheric profiles– Sensitivity to settings
“Single observation“ experiment
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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
Single-observation experiments: missed cloud event
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Missed cloud case:Effect on temperature profile
temperature profile [K] (mean +/- spread)
first guess analysis
observed cloud top
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• 1-hourly cycling over 20 hours with 40 members• 13 Nov., 21UTC – 14 Nov. 2011, 18UTC• Wintertime low stratus
• Thinning: 14 km• Results from additional “deterministic“ simulation based
on LETKF Kalman gain matrix:
Comparison cycling experiment: only conventional
vs conventional + cloud data
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111 ])1[( RYYRYIXK T
bb
T
bb k
))(( detdetdetboba H xyKxx
Time series of first guess errors, averaged over cloudy obs locations
assimilation of conventional obs only assimilation of conventional + cloud obs
RMSE
Bias (OBS-FG)
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Comparison “only conventional“ versus “conventional + cloud obs"
RH (relative humidity) at observed cloud top
conventional only conventional + cloud
Total cloud cover of first guess fields after 20 hours of cycling
Satellite cloud top height
Comparison of cycled experiments
satellite obs
12 Nov 2011 17:00 UTC
Time series of first guess errors, averaged over cloud-free obs locations(errors are due to false alarm clouds)
mean square error of cloud fraction [octa]
False alarm clouds False alarm clouds reduced through cloud reduced through cloud data assimilationdata assimilation
Cycled assimilation of dense observations
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low clouds
High clouds
Mid-level clouds
Solid: conv onlyDashed: conv + clouds
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]
• 24h deterministic forecast based on analysis of two experiments (after 12 hours of cycling)
• 14 Nov., 9UTC – 15 Nov. 2011, 9UTC• Wintertime low stratus
Comparison forecast experiment: only conventional
vs conventional + cloud data
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The forecast of cloud characteristics can be improved through the assimilation of the cloud information
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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
Cloudy pixelsCloudfree
pixels
Solid: conv onlyDashed: conv + clouds
Verification: fit against independent measurements
Fit to SEVIRI infrared brightness temperatures
(model values computed with RTTOV)
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RMSE is smaller for first 16 hours of forecast for cloud experiment, bias variesRMSE is smaller for first 16 hours of forecast for cloud experiment, bias varies
RMSE
Bias (Obs-Model)
Conventional + cloud dataOnly conventional data
Use of (SEVIRI-based) cloud observations in LETKF:
• Increases humidity / cloud where it should and reduces ‘false-alarm’ clouds
• Long-lasting free forecast impact for a stable wintertime high pressure system
• Current work: Evaluate impact on other variables (temperature, wind) and other weather situations
• Also work on cloudy infrared SEVIRI radiance assimilation (see poster by Africa Perianez)
• Application in renewable energy project EWeLiNE to improve photovoltaic power predictions
• Also planned to assimilate the PV power itself...
Conclusion / Outlook
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Thank you for your attention!Thank you for your attention!
observed cloud top
observation location
specific water content [g/kg] relative humidity [%]
Cross section of analysis increments for ensemble mean
Single-observation experiments: missed cloud event
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