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Status Report PP KENDA

Status Report PP KENDA. Christoph Schraff Deutscher Wetterdienst, Offenbach, Germany. Contributions / input by: Hendrik Reich, Andreas Rhodin, Annika Schomburg, Ulrich Blahak, Yuefei Zeng, Roland Potthast Yuefei Zeng, Klaus Stephan, Africa Perianez, Michael Bender (DWD)

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Status Report PP KENDA

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  1. Status Report PP KENDA Christoph SchraffDeutscher Wetterdienst, Offenbach, Germany Contributions / input by: Hendrik Reich, Andreas Rhodin, Annika Schomburg, Ulrich Blahak, Yuefei Zeng, Roland Potthast Yuefei Zeng, Klaus Stephan, Africa Perianez, Michael Bender (DWD) Chiara Marsigli, Tiziana Paccagnella (ARPA-SIM) Lucio Torrisi (CNMCA) Daniel Leuenberger, Luca Weber (MeteoSwiss) Mikhail Tsyrulnikov, Igor Mamay (HMC) Amalia Iriza (NMA) • general overview • assimilation of SEVIRI-derived cloud top height in LETKF

  2. LETKF: implementation experiment chain in NUMEX set up • GME LETKF exp. (Nens = 40) • June 2011 for lateral BC: • somewhat too little spread • particularly over Europe, • but LBC spread at least as • large as from COSMO-SREPS (Nens = 12) ana. spread ana. rmse • for COSMO-LETKF: . lateral BC by direct interpolation from 60 km to 2.8 km (moderate noise at model top & surface, acceptable) • MCH / ARPA-SIM: resolution gap IFS-EPS (32 km to 2.8 km) tested: ok

  3. LETKF: implementation experiment chain in NUMEX set up • lateral BC by direct interpolation from 60 km to 2.8 km • KENDA: • 1-hourly cycling, radiosonde, aircraft, wind profiler, synop; 40 ens. members • assimilation only, optimally takes ~ 1 real day for 1 day of assimilation, but in fact: ~ 1 – 4 real months for 1 week of assimilation ! (without forecasts !!)  only 3 experiments so far • Hendrik: new flexible stand-alone scripts to run LETKF experiments • without using NUMEX / archive  very limited disk space •  1 real day for 1 day of LETKF assimilation • to do: implement evaluation / verification tools in script suite • may become very suitable tool for users outside DWD (academia) • offline adaptive estimation of obs errors in observation space • multi-step analysis approach (different localization radii for different sets of obs)

  4. LETKF: ensemble forecasts (20 ens. members) 4 – 12 June 2011 KENDA / COSMO-DE-EPS precipitation max. 10-m wind gusts 2-m temperature RMSE spread  results of LETKF (without explicit surface / soil perturbations) : larger rmse, larger spread larger rmse, initially larger spread larger rmse, equal spread

  5. LETKF at MCH, compare det. LETKF analysis with nudging • COSMO-2, Nens = 40, LETKF as at DWD • lateral BC from IFS (det./EPS); soil moisture from COSMO-2 • LETKF technically works • comparison with surface observations suggest • generally: LETKF better than NO_OBS, but worse than nudging • assimilation of surface pressure appears to work particularly well • LETKF_DET analysis very close to LETKF ensemble mean • SPPT has only small but positive impact

  6. LETKF: implementation of verification • production of ‘full’ NetCDF feedback files done: COSMO observation operators (conventional obs) integrated in 3DVAR package to be done (this autumn !) : extend flow control (read correct Grib files etc.) • ensemble-related diagnostic + verification tool, using feedback files : (Iriza, NMA) • ensemble scores implemented, further testing required

  7. accounting for model error • stochastic perturbation of physics tendencies (SPPT) (Torrisi) • implemented in (private) V4_26 • tests at CNMCA / MCH / ARPA-SIM (WG7) • Pattern Generator (for random fields with prescribed correlation scales) (Tsyrulnikov et al.) • based on a stochastic partial differential equation approach • basic version developed, being revised to make it efficient • being embedded in COSMO code • 2-D version planned for next year

  8. high-resolution obs • radar • obs operators finished, assimilation works technically • radial winds vr in LETKF: Yuefei Zeng (DWD, until summer 2014)  need to test thinning / superobbing strategies  3-hour assimilation with 1-hrly cycle done (different localization radii) • vr + reflectivity Z in LETKF: Theresa Bick (HErZ-I Bonn, until end 2014 at least) • GPS slant path delay • obs operator (incl. TL / adjoint) implemented in 3DVar, approximations tested • implementation in COSMO should start soon

  9. high-resolution obs • (SEVIRI-based, radiosonde-corrected) cloud top height : see next slides (Schomburg) • direct assimilation of SEVIRI radiances (window channels for cloud info) (Perianez) • technically implemented (obs operator (RTTOV), reading / writing) • work on monitoring / assimilation start in Nov. • new task: microwave radiometer & Raman lidar T- , q- profiles (Haefele, MCH)

  10. avoid too strong penalizing of members with high humidity but no cloud 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 hmaxaround CTHobs for a ‘best fitting’ model level k, i.e. with minimum ‘distance’ d: height of model level k function of relative humidity = 1 use of (SEVIRI-based) cloud top height (CTH) ‘observations’ in LETKF: method if cloud observed with cloud top height CTHobs , what is the appropriate type of obs increment ? Z [km] model profile k1 k2 Cloud top CTHobs k3 k4 k5 (if above a layer with cloud fraction > 70 %, then choose top of that layer) • use f (RHobs=1)– f (RHk) and CTHobs– hk as 2 separate obs increments in LETKF RH [%]

  11. use of (SEVIRI-based) cloud top height (CTH) ‘observations’ in LETKF: method Z [km] type of obs increment , if no cloud observed ? 9 • assimilate cloud fraction CLCobs = 0 separately for high, medium, low clouds • model equivalent: maximum CLC within vertical range „no high cloud“ 6 model profile „no medium cloud“ 3 „no low cloud“ CLC

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

  13. CTH single-observation experiments • example: missed cloud event cross section of analysis increments for ensemble mean specific water content [g/kg] observation location relative humidity [%] observed cloud top

  14. CTH single-observation experiments • example: missed cloud event temperature profile (mean +/- spread) 3000 m first guess analysis 2000 m observed cloud top 1000 m 270 K 280 K 290 K 270 K 280 K 290 K • LETKF introduces inversion due to RH(CTH)  T cross correlations • in first guess ensemble perturbations

  15. CTH single-observation experiments • example: 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

  16. FG ANA FG ANA FG ANA CTH single-observation experiments • example: false alarm cloud  assimilated quantity: cloud fraction (= 0) observation increments - histogram over ensemble members low cloud cover [octas] mid-level cloud cover [octas] high cloud cover [octas]  LETKF decreases erroneous cloud cover despite very non-Gaussian distributions cover

  17. cycled assimilation of dense CTH obs 1-hourly cycle over 21 hours, 13 Nov., 21 UTC – 14 Nov. 2011, 18 UTC (wintertime low stratus) observed cloud top height (CTH) 6:00 UTC 17:00 UTC 0:00 UTC 12:00 UTC

  18. cycled assimilation of dense CTH obs : LETKF setup • thinning: use obs at every 5th grid pt. • adaptive covariance inflation, adaptive localisation scale (  ~ 35 km) • Observation error variances : relative humidity = 10 % • cloud cover = 3.2 octa • cloud top height [m] :  6:00 UTC 17:00 UTC 0:00 UTC 12:00 UTC

  19. cycled assimilation of dense CTH obs time series of first guess errors of ensemble mean / spread of ensemble averaged over cloudy obs locations RMSE • underdispersive, • but no trend • for reduction • of spread spread averaged over cloud-free obs locations

  20. cycled assimilation of dense CTH obs time series of first guess errors of RH at observed CTH (det. run), averaged over cloudy obs locations no assimilation with cloud assimilation RMSE bias • CTH assimilation : reduces RH (1-hour forecast) errors

  21. cycled assimilation of dense CTH obs time series of first guess errors of RH at observed CTH (det. run), averaged over cloudy obs locations no assimilation with cloud assimilation assimilation of conventional obs only assimilation of conventional + cloud obs localization scale: adaptive / 20 km RMSE bias • CTH assimilation : reduces RH (1-hour forecast) errors

  22. cycled assimilation of dense CTH obs time series of first guess errors, averaged over cloud-free obs locations (errors are due to false alarm cloud) mean square error of cloud fraction [octas] • error reduced • (almost) everywhere

  23. cycled assimilation of dense CTH obs satellite obs cloud assimilation no assimilation conventional only conventional + cloud CTH obs No assim total cloud cover of first guess fields after 20 hours of cycling

  24. use of (SEVIRI-based) cloud top height (CTH) ‘observations’ in LETKF • Summary • assimilation of CTH by LETKF reduces errors of first guess (1-h forecast) • tends to introduce humidity / cloud where it should (+ temperature inversion) • tends to reduce ‘false-alarm’ clouds • despite non-Gaussian pdf’s • no sign of filter collapse (decrease of spread) • next: evaluate forecast impact

  25. Status of PP KENDA Thank you for your attention Questions ?

  26. LETKF: implementation

  27. LETKF: implementation • multistep analysis (batch assimilation) implemented  motivation: • local / nonlocal observations (e.g. radiances) • different observation errors  better use different localization scales • in view of adaptive localization: different obs densities (conventional / radar) • (Perianez et al.: work on paper with theoretical concept + toy model/ idealised experiments) • next step: test with radar / SEVIRI CTH data • Hendrik: new flexible stand-alone scripts to run LETKF experiments • without using NUMEX / archive  very limited disk space •  1 real day for 1 day of LETKF assimilation • to do: implement evaluation / verification tools in script suite • may become very suitable tool for users outside DWD (academia)

  28. cycled assimilation of dense CTH obs ‘false alarm’ cloud cover (after 20 hrs cycling) high clouds mid-level clouds low clouds conventional + cloud conventional obs only

  29. Low cloud cover (COSMO) 17:00 UTC Cloud assim No assim Cloud +conv conv PBPV – 03/2013

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