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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 experiment for June 2011 • lateral BC by direct interpolation from 60 km to 2.8 km • KENDA: • 1-hourly cycling, radiosonde, aircraft, wind profiler, synop • 40 ensemble members, basic set-up (only adaptive cov. infl.) • 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

  3. LETKF: implementation • 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) • other work: • adaptive estimation of observation errors in observation space • multi-step analysis implemented • compare KENDA ensemble forecasts with COSMO-DE-EPS (8 days) • at MCH / ARPA-SIM : studies on influence of different lateral BC (resolution gap) • at MCH: compare determinstic LETKF analysis with nudging ana. (7 days)

  4. Impact of lateral BC resolution Marsigli, Paccagnella, Montani: Test impact of lateral BC resolution for convection-permitting EPS • 8 cases from Hymex SOP: 6 Sept. – 5 Nov. 2012 • COSMO-H2-EPS set-up (‘H2-CLEPS’): • 2.8 km, 50 levels • 10 members • IC + BC COSMO-LEPS, • res. 7 km • run at 12 UTC • 36h forecast range • exp. ‘H2-IFS’: direct nesting • IC and BC from IFS-EPS • res: 0.25 °

  5. Impact of lateral BC resolution mean difference between pairs of ensemble members < spread < rmse (i.e. e.g. betw. Member 1 of H2-CLEPS and Member 1 of H2-IFS) 500 hPa geopotential • 1 exceptional case • spin-up noise dies • within 30 min • impact on precip • scores negligible

  6. LETKF: implementation of verification • production of ‘full’ NetCDF feedback files done: make clean interfaces to observation operators in COSMO … … integrate them into 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. testing KENDA at MCH, ARPA-SIM • OSSE • temperature inversion in Po Valley: assimilate synthetic TEMP • (semi-)idealised: horizontally homogeneous case; daily cycle of Alpine pumping  develop OSSE capability  COSMO reading feedback files implemented • no PhD project on idealised OSSE to investigate convective-scale issues

  8. accounting for model error • stochastic perturbation of physics tendencies (SPPT) (Torrisi) • perturbation of moist turbulence tendencies implemented • implemented in (private) V4_26 • tests (LETKF for COSMO-ME): drying effect on precip • further tests in MCH / ARPA-SIM • Pattern Generator (for random fields with prescribed correlation scales) (Tsyrulnikov et al.)

  9. high-resolution obs • radar • obs operators finished • radial winds vr in LETKF: Yuefei Zeng (DWD, until summer 2014)  monitoring of 3d volumn data (statistics of observation increments) : large scatter, data quality problems  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

  10. 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: Raman lidar and microwave radiometer T- , q- profiles (Haefele, MCH)

  11. 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] Cloud top CTHobs (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 [%]

  12. Z [km] 12 „no cloud“ 9 model profile Cloud top 6 - no data - 3 use of (SEVIRI-based) cloud top height (CTH) ‘observations’ in LETKF: method Z [km] type of obs increment , if no cloud observed ? 12 • assimilate cloud fraction CLC = 0 separately for high, medium, low clouds • model equivalent: maximum CLC within vertical range „no cloud“ 9 „no cloud“ 6 3 „no cloud“ CLC

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. cycled assimilation of dense CTH obs time series of errors for deterministic run, averaged over cloudy obs locations ‘CTH’  CTHobs  heightk ‘RH’  1 - RHk (k : ‘best fitting model level’) ‘RH at obslev’ (i.e. at observed CTH) RMSE FG ANA bias  analysis error smaller than first guess (FG) errors

  22. cycled assimilation of dense CTH obs time series of 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

  23. cycled assimilation of dense CTH obs time series of first guess errors (det. run), averaged over cloudy obs locations ‘RH at obslev’ RMSE no assimilation with cloud assimilation bias • CTH assimilation : hardly reduces height error of selected level k • reduces RH (1-hour forecast) errors

  24. cycled assimilation of dense CTH obs time series of first guess errors, averaged over cloudy obs locations RH at obslev no assimilation with cloud assimilation RMSE bias • CTH assimilation : reduces RH errors related to low + mid-level cloud

  25. 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]

  26. cycled assimilation of dense CTH obs ‘false alarm’ cloud cover (after 6 hrs cycling) high clouds mid-level clouds low clouds with cloud assimilation noassimilation

  27. cycled assimilation of dense CTH obs assimilation of conventional obs only assimilation of conventional + cloud obs localization scale: adaptive / 20 km time series of first guess errors, averaged over cloudy obs locations RMSE bias • CTH assimilation : reduces RH (1-hour forecast) errors • even slightly reduces height error of selected level k

  28. cycled assimilation of dense CTH obs cloud assimilation no assimilation first guess fields after 20 hours of cycling (cloudy obs locations) RH at obslev conventional only conventional + cloud CTH obs

  29. 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 PBPV – 03/2013

  30. 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

  31. 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

  32. 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

  33. cycled assimilation of dense CTH obs first guess fields after 20 hours of cycling (cloudy obs locations) with cloud assimilation RH at obslev ‘CTH‘ OBS-FG RH no assimilation CTH obs

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

  35. RMSE and bias for different heights separately: Cloud assim. vs. no assim. RH@oblsev

  36. LETKF: validation of forecasts from KENDA COSMO-DE EPS intermediate EPS on KENDA combined

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