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

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

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

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

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

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)

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.

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

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

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 1 2 3 4 5 6 7 8 9 10 11 12 13 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

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 [%]

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

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

„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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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!

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

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

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…