Assimilating satellite cloud information with an Ensemble Kalman Filter at the convective scale Annika Schomburg, Christoph Schraff, Hendrik Reich, Roland Potthast EnKF workshop May 2014, Buffalo

Motivation: Weather situation 23 October UTC synoptic situation: stable high pressure system over central Europe

Motivation: Weather situation 23 October UTC synoptic situation: low stratus clouds over Germany Satellite cloud type classification

Motivation: Verification for 23 October 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: misses red: 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 Projection Day-Ahead Intra-Day time Power from PV modules courtesy by TENNET 5

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 6

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 7

The COSMO model 8 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

9 NWCSAF satellite product: cloud top height 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 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 10 Z [km] RH [%] CTH obs k1k1 k2k2 k3k3 k4k4 k5k5 Cloud top model profile (make sure to choose the top of the detected cloud )

11 Example: 17 Nov 2011, 6:00 UTC Observations and model equivalents RH model level k Observation Model „Cloud top height“

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 12

13  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 14

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 Single-observation experiments: missed cloud event 15

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 16

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 17

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) 18 Comparison “only conventional“ versus “conventional + cloud obs"  Cloud assimilation reduces RH (1-hour forecast) errors RH (relative humidity) at observed cloud top

19 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 :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 reduced through cloud data assimilation Cycled assimilation of dense observations 20 low clouds High clouds Mid-level clouds Solid: conv only Dashed: conv + clouds

low clouds mid-level clouds high clouds ‘false alarm’ cloud cover (after 20 hrs cycling) conventional + cloud conventional obs only 21 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 22

 The forecast of cloud characteristics can be improved through the assimilation of the cloud information 23 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 Cloudy pixels Cloudfree pixels Solid: conv only Dashed: conv + clouds

Verification: fit against independent measurements Fit to SEVIRI infrared brightness temperatures (model values computed with RTTOV) 24  RMSE is smaller for first 16 hours of forecast for cloud experiment, bias varies RMSE Bias (Obs-Model) Conventional + cloud data Only 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 25 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 26  Moistening of the layer where cloud is observed.