1. Assimilation of (satellite) cloud-information at the convective scale in an Ensemble Kalman Filter Annika Schomburg 1, Christoph Schraff 1, Africa Perianez 1, Jason Otkin 2, Roland Potthast 1 1 DWD (German Meteorological Service) 2 University of Wisconsin annika.schomburg@dwd.de WWOSC 16-21 August 2014, Montreal

2. annika.schomburg@dwd.de Motivation: Growing renewable energy sector 2 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 solar Wind hydroelectric biomass gas geothermal 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

3. annika.schomburg@dwd.de 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 3

4. annika.schomburg@dwd.de Modelling system 4 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)

5. annika.schomburg@dwd.de 5 Local Ensemble Transform Kalman Filter LETKF Analysis perturbations: linear combination of background perturbations Obs First guess ensemble members are weighted according to their departure from the observations OBS-FG R Background error covariance Observation errors

6. annika.schomburg@dwd.de Outline Assimilation of additional data to improve cloud cover: Satellite cloud products Cloudy infrared satellite radiances Photovoltaic power 6

7. Satellite cloud products

8. annika.schomburg@dwd.de 8 Satellite product: cloud top height  contains information on horizontal and vertical distributions of clouds 1 2 3 4 5 6 7 8 9 10 11 12 13 Satellite cloud product information  Geostationary satellite data: Meteosat-SEVIRI (Δx ~ 5km over central Europe, Δt=15 min) Source: EUMETSAT Height [km] Cloud top height

9. annika.schomburg@dwd.de MODEL EQUIVALENT 9 OBSERVATION: Satellite product: cloud top height 1 2 3 4 5 6 7 8 9 10 11 12 13 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 k RH(k) height(k) Observation Model RH profile Assimilated variables:

10. annika.schomburg@dwd.de relative humidity cloud cover cloud water cloud ice observed 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 10 First guess Analysis

11. annika.schomburg@dwd.de Model equivalent: 11 Observation: Satellite product: cloud top height 1 2 3 4 5 6 7 8 9 10 11 12 13 Method Extract information if a pixel is observed as cloud-free: Height [km] Cloud top height Cloud cover high clouds Cloud cover mid-level clouds Cloud cover low clouds 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:

12. annika.schomburg@dwd.de low clouds mid-level clouds high clouds ‘false alarm’ cloud cover (after 20 hrs cycling) conventional + cloud conventional obs only 12 Comparison “only conventional“ versus “conventional + cloud obs" [octa]

13. annika.schomburg@dwd.de 13 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

14. Cloudy infrared satellite radiances

15. annika.schomburg@dwd.de 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.. 15 Source: Schmetz et al, BAMS, 2002 SEVIRI channels

16. annika.schomburg@dwd.de First step: Monitoring 16 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

17. annika.schomburg@dwd.de First assimilation results: 12 hours of cycling, assimilation of channel WV7.3 17 Bias RMSE Without radiance assimilation With radiance assimilation

18. Photovoltaic power

19. annika.schomburg@dwd.de 19 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 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)

20. annika.schomburg@dwd.de 20 Example of simulated and observed photovoltaic power Model forecast solar insolation at surface Observed photovoltaic power Simulated photovoltaic power (based on model forecast radiation) Normalized power [W/Wpeak] 20

21. annika.schomburg@dwd.de 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 21 Thank you for your attention Thank you for your attention!

22. annika.schomburg@dwd.de 22

23. annika.schomburg@dwd.de Determine the model equivalent cloud top  Avoid strong penalizing of members which are dry at CTH obs but have a cloud or even only high humidity close to CTH obs  search in a vertical range  h max around CTH obs for a ‘best fitting’ model level k, i.e. with minimum ‘distance’ d: relative humidity height of model level k = 1  use y= CTH obs H(x)=h k and y= RH obs =1 H(x)=RH k ( relative humidity over water/ice depending on temperature) as 2 separate variables assimilated by LETKF  use y= CTH obs H(x)=h k and y= RH obs =1 H(x)=RH k ( relative humidity over water/ice depending on temperature) as 2 separate variables assimilated by LETKF 23 Z [km] RH [%] CTH obs k1k1 k2k2 k3k3 k4k4 k5k5 Cloud top model profile (make sure to choose the top of the detected cloud)

24. annika.schomburg@dwd.de 24 Example: 17 Nov 2011, 6:00 UTC Observations and model equivalents RH model level k Observation Model „Cloud top height“

25. annika.schomburg@dwd.de 25 COSMO cloud cover where observations “cloudfree” Example: 17 Nov 2011, 6:00 UTC High clouds (oktas)Mid-level clouds (oktas)Low clouds (oktas)

26. annika.schomburg@dwd.de Example: Missed cloud case: Effect on temperature profile temperature profile [K] (mean +/- spread) first guess analysis  LETKF introduces inversion due to RH  T cross correlations in first guess ensemble perturbations  LETKF introduces inversion due to RH  T cross correlations in first guess ensemble perturbations observed cloud top 26

27. annika.schomburg@dwd.de 27 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!

28. annika.schomburg@dwd.de 28 Results III: Forecast impact 24 h free forecast starting after 21h cycling 14 Nov. 2011, 18 UTC – 15 Nov. 18 UTC Wintertime low stratus

29. annika.schomburg@dwd.de 29 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)

30. annika.schomburg@dwd.de The forecast of cloud characteristics can be improved through the assimilation of the cloud information 30 Results: Comparison of free forecast: time series of errors Conventional + cloud data Only conventional data RMSE Bias (Obs-Model) Low clouds Mid-level clouds High clouds Mean squared error averaged over all cloud-free observations RH (relative humidity) at observed cloud top averaged over all cloudy observations

31. annika.schomburg@dwd.de 31 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 Forward operator for PV module Sensitivities: Direct solar irradiance [W/m²] Diffuse part of solar irradiance [W/m²] Ambient air temperature [K]