Assimilation of (satellite) cloud-information at the convective scale in an Ensemble Kalman Filter Annika Schomburg 1, Christoph Schraff 1, Africa Perianez.

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Presentation transcript:

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 WWOSC August 2014, Montreal

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

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

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 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

Outline Assimilation of additional data to improve cloud cover: Satellite cloud products Cloudy infrared satellite radiances Photovoltaic power 6

Satellite cloud products

8 Satellite product: cloud top height  contains information on horizontal and vertical distributions of clouds Satellite cloud product information  Geostationary satellite data: Meteosat-SEVIRI (Δx ~ 5km over central Europe, Δt=15 min) Source: EUMETSAT Height [km] Cloud top height

MODEL EQUIVALENT 9 OBSERVATION: Satellite product: cloud top height 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:

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

Model equivalent: 11 Observation: Satellite product: cloud top height 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:

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 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

Cloudy infrared satellite radiances

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

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

First assimilation results: 12 hours of cycling, assimilation of channel WV Bias RMSE Without radiance assimilation With radiance assimilation

Photovoltaic power

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 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

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

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

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

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 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 :00 UTC  Low stratus cloud cover improved through assimilation of cloud products!

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 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)

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 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]