1. EUMETSAT fellow day, 17 March 2014, Darmstadt
Assimilating satellite cloud information with an Ensemble Kalman Filter at the convective scale
Annika Schomburg, Christoph Schraff
EUMETSAT fellow day, 17 March 2014, Darmstadt

2. Motivation
12 UTC synoptic situation: stable high pressure system over central Europe

3. Motivation: Verification for 23 October 2012
12 UTC synoptic situation: low stratus clouds over Germany
Satellite cloud type classification

4. Motivation: Verification for 23 October 2012
12 hour forecast from 0:00 UTC
T2m: COSMO-DE minus synop
Total cloud cover: COSMO-DE versus synop
Low cloud cover: COSMO-DE versus satellite
Green: hits; black: misses
red: false alarms, blue: no obs
Courtesy of K. Stephan

5. Motivation
 Main motivation: improve cloud cover simulation of low stratus clouds in stable wintertime high-pressure systems
May also be useful for frontal system or convective situations
If convective clouds are captured better while developing, convective precipitation may be improved
Transmission network operator

6. The COSMO model COSMO-DE :
Limited-area short-range numerical model weather prediction model
x  2.8 km / 50 vertical layers
Explicit deep convection
Implementation of the Ensemble Kalman Filter: COSMO priority project KENDA (Km-scale ensemble-based data assimilation)

7. Local Ensemble Transform Kalman Filter
Obs
Observation errors R
Analysis perturbations: linear combination of background perturbations
LETKF
OBS-FG
OBS-FG
OBS-FG
OBS-FG
Background error covariance
First guess ensemble members are weighted according to their departure from the observations.

8. Local Ensemble Transform Kalman Filter
Obs
Observation errors R
Analysis perturbations: linear combination of background perturbations
LETKF
OBS-FG
OBS-FG
Localisation: the global analysis is not confined to the k-dimensionsal subspace, but explores a much higher-dimensional space
OBS-FG
OBS-FG
Background error covariance
Local: the linear combination is fitted in a local region
- observation have a spatially limited influence region
Transform: most computations are carried out in ensemble space  computationally efficient
Implementation after Hunt et al., 2007 8

9. Local Ensemble Transform Kalman Filter
Obs
Observation errors R
Analysis perturbations: linear combination of background perturbations
LETKF
OBS-FG
OBS-FG
OBS-FG
OBS-FG
Background error covariance
Additional: one deterministic run:
Kalman gain matrix from LETKF

10. Which observations?
For shortrange limited-area models: geostationary satellite data: Meteosat-SEVIRI (Δx ~ 5km over central Europe, Δt=15 min)
Here: Assimilation of NWC-SAF cloud top height
Source: EUMETSAT
Height [km]
Cloud top height
Retrieval algorithm needs temperature and humidity profile information from a NWP model  cloud top height might be at wrong height if temperature-profile in NWP model is not simulated correctly!  use also radiosonde information where available

11. Determine the model equivalent cloud top
Z [km]
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 for
a ‘best fitting’ model level k, i.e. with minimum ‘distance’ d:
model profile k1 k2 k3 k4 k5
relative humidity
height of
model level k
= 1
Cloud top
CTHobs
(make sure to choose the top of the detected cloud)
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
RH [%]

12. Determine model equivalent: cloudfree pixels
Z [km]
What information can we assimilate for pixels which are observed to be cloudfree? 12
„no high cloud“
assimilate cloud fraction CLC = 0 separately
for high, medium, low clouds
model equivalent:
maximum CLC within vertical range 9
„no mid-level cloud“ 6 3
„no low cloud“
CLC

13. Schematic illustration of approach
cloudy obs
= 1
= 0
cloud-free obs

14. „Single observation“ experiment
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

15. 3 lines in one colour indicate ensemble mean and mean +/- spread
Single-observation experiments: missed cloud event
1 analysis step, 17 Nov. 2011, 6 UTC (wintertime low stratus)
vertical profiles
relative humidity
cloud cover
cloud water
cloud ice
observed cloud top
3 lines in one colour indicate ensemble mean and mean +/- spread

16. Cross section of analysis increments for ensemble mean
Single-observation experiments: missed cloud event
Cross section of analysis increments for ensemble mean
specific water content [g/kg]
relative humidity [%]
observed cloud top
observation location

17. Deterministic run First guess Analysis
Relative humidity
Cloud cover
Cloud water
Cloud ice
Observed cloud top
Humidity at cloud layer is increased in deterministic run

18. Effect on temperature profile
Missed cloud case:
Effect on temperature profile
temperature profile [K] (mean +/- spread)
first guess
analysis
observed cloud top
LETKF introduces inversion due to RH  T cross correlations
in first guess ensemble perturbations

19. 3 lines on one colour indicate ensemble mean and mean +/- spread
Single-observation experiments: False alarm cloud
 assimilated quantity: cloud fraction (= 0)
vertical profiles
relative humidity
cloud cover
cloud water
cloud ice
observed cloud top
3 lines on one colour indicate ensemble mean and mean +/- spread

20. Comparison cycling experiment: conventional + cloud data
only conventional vs
conventional + cloud data
1-hourly cycling over 21 hours with 40 members
13 Nov., 21UTC – 14 Nov. 2011, 18UTC
Wintertime low stratus
Thinning: 14 km

21. RH (relative humidity) at observed cloud top
Comparison “only conventional“ versus “conventional + cloud obs"
Time series of first guess errors, averaged over cloudy obs locations
RH (relative humidity) at observed cloud top
assimilation of conventional obs only
assimilation of conventional + cloud obs
RMSE
Bias (OBS-FG)
Cloud assimilation reduces RH (1-hour forecast) errors

22. Satellite cloud top height
Comparison of cycled experiments
Total cloud cover of first guess fields after 20 hours of cycling
satellite obs
conventional only
conventional + cloud
Satellite cloud top height
12 Nov 2011
17:00 UTC

23. Cycled assimilation of dense observations
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]
low clouds
High clouds
False alarm clouds reduced through cloud data assimilation

24. Comparison “only conventional“ versus “conventional + cloud obs"
‘false alarm’
cloud cover
(after 20 hrs cycling)
low clouds
mid-level clouds
high clouds
conventional
obs only
[octa]
conventional
+ cloud

25. Comparison forecast experiment: conventional + cloud data
only conventional vs
conventional + cloud data
24 deterministic forecast based on analysis of two experiments (after 12 hours of cycling)
14 Nov., 9UTC – 15 Nov. 2011, 9UTC
Wintertime low stratus

26. Comparison of free forecast: time series of errors
RH (relative humidity) at observed cloud top averaged over all cloudy observations
Mean squared error averaged over all cloud-free observations
RMSE
Bias (Obs-Model)
Low clouds
Mid-level clouds
High clouds
Conventional + cloud data
Only conventional data
The forecast of cloud characteristics can be improved through the assimilation of the cloud information

27. Verification against independent measurements
Errors for SEVIRI infrared brightness temperatures (model values computed with RTTOV)
Conventional + cloud data
Only conventional data
RMSE
Bias (Obs-Model)
 RMSE is smaller for first 16 hours of forecast for cloud experiment, bias varies

28. Verification against independent measurements: SEVIRI brightness temperature errors
Only CONV
experiment
CONV+CLOUD
experiment
14 Nov 2011,
18 UTC
Cloud top height
Also the high clouds are simulated better in the cloud experiment

29. Renewable energy project
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
Joint project of DWD and Fraunhofer-Institut for Wind Energy & Energy System Technology in Kassel and three transmission network operators EWeLiNE: Erstellung innovativer Wetter- und Leistungsprognosemodelle für die Netzintegration wetterabhängiger Energieträger
Objective: improve weather and power forecasts for wind and phovoltaic power and develop new prognostic products
4 year project, 13 researchers at DWD

30. 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
Here cloud data assimilation from satellite cloud information / satellite radiances can be helpful!

31. Example: problematic weather situation for PV power prediction: low stratus clouds
Low stratus clouds not predicted
Low stratus clouds observed in reality
Power from PV modules
Hochrechnung
Projection
Day-Ahead
Intra-Day
time
Error Day-Ahead: 4800 MW
Courtesy by TENNET

32. Conclusions Use of (SEVIRI-based) cloud observations in LETKF:
Tends to introduce humidity / cloud where it should (+ temperature inversion)
Tends to reduce ‘false-alarm’ clouds
Despite non-Gaussian pdf’s
Promising results for free forecast impact for a stable wintertime high pressure system

33. Next steps Evaluate impact on other variables (temperature, wind)
Other cases (e.g. convective)
Also work on direct SEVIRI radiance assimilation
Revision on QJRMS article on single observation experiments, write second article on full cycling and forecasts results
Application in renewable energy project EWeLiNE…