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christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany

current DA method: nudging. WG1 Overview. christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany. PP KENDA for km-scale EPS: LETKF. radar-derived rain rates: latent heat nudging. LETKF for HRM (final setup). Long-term impact of LHN assimilation on soil moisture ?.

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christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany

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  1. current DA method: nudging WG1 Overview christoph.schraff@dwd.deDeutscher Wetterdienst, D-63067 Offenbach, Germany PP KENDA for km-scale EPS: LETKF radar-derived rain rates: • latent heat nudging LETKF for HRM (final setup) Long-term impact of LHN assimilation on soil moisture ?

  2. impact of LHN on soil moisture Experiment: 18 months (April 2009 – August 2010) COSMO-DE without LHN compare with operational COSMO-DE with LHN differences in normalised soil moisture (LHN - noLHN) inside radar data domain June 2010

  3. impact of LHN on soil moisture:impact on surface parameters June 2010, 12 UTC runs BIAS LHN noLHN bias reduced for different parameters

  4. impact of LHN on soil moisture:impact on surface parameters monthly relative forecast skill (1 – rmse(LHN) / rmse(noLHN)) dew point depression 30% 35% T_2m model runs Td_2m Summer Winter Summer March 2009 Aug 2010  LHN  higher soil moisture content  improved T-2m , Td-2m forecasts, benefit lasts over whole forecast time

  5. current DA method: nudging WG1 Overview christoph.schraff@dwd.deDeutscher Wetterdienst, D-63067 Offenbach, Germany PP KENDA for km-scale EPS: LETKF radar-derived rain rates: • latent heat nudging • 1DVar (comparison) LETKF for HRM (final setup) Satellite radiances: • 1DVAR: AMSU-A, SEVIRI, IASI (no benefit) • spatial AMSU-A obs errors statistics radar radial wind: • VAD (at DWD: no benefit) • nudg. Vr (being implemented) Vadim Gorin, Mikhail Tsyrulnikov: Estimation of multivariate observation-error statistics for AMSU-A data (MWR, accepted) GPS-IWV (ceased) screen-level: • scatterometer wind (OSCAT, OceanSat2) • improved T2m assimilation, adapt T_soil daytime biases in summer: • Vaisala RS-92 humidity obs dry bias  bias correction operational • model warm bias (low levels) surface (snow …)  PP COLOBOC

  6. Status Overview on PP KENDA Km-scale ENsemble-based Data Assimilation christoph.schraff@dwd.deDeutscher Wetterdienst, D-63067 Offenbach, Germany Contributions / input by: Hendrik Reich, Andreas Rhodin, Roland Potthast, Uli Blahak, Klaus Stephan (DWD) Annika Schomburg (DWD / Eumetat) Yuefei Zeng, Dorit Epperlein (KIT Karlsruhe) Daniel Leuenberger, Manuel Bischof (MeteoSwiss) Mikhail Tsyrulnikov, Vadim Gorin (HMC) Lucio Torrisi (CNMCA) Amalia Iriza (NMA) • Task 1: General issues in the convective scale (e.g. non-Gaussianity) • Task 2: Technical implementation of an ensemble DA framework / LETKF • Task 3: Tackling major scientific issues, tuning, comparison with nudging • Task 4: Inclusion of additional observations in LETKF

  7. Task 2: Technical implementation of an ensemble DA framework / LETKF analysis step (LETKF) outside COSMO code  ensemble of independent COSMO runs up to next analysis time  separate analysis step code, LETKF included in 3DVAR code of DWD collect obs from ]tana–3h , tana] old setup: obs from ]tana–1.5h , tana+1.5h] thinned

  8. Task 2: Technical implementation of an ensemble DA framework / LETKF • modifications in COSMO fully implemented, but not yet in official code (also in order to have a sub-hourly update frequency) • first version LETKF (KENDA) implemented in NUMEX, to be tested  still use scripts to do a few stand-alone cycles with LETKF • deterministic analysis implemented  talk of Hendrik • LBC at analysis time : use (perturbed) analysis ensemble member itself instead of (temporally interpolated) LBC fields from steering ensemble system • next step: LBC from GME LETKF ensemble interpolate GME ensemble perturbations and add to deterministic COSMO-DE LBC

  9. ensemble deterministic • deterministic run must use same set of observations as the ensemble system ! • Kalman gain / analysis increments not optimal, if deterministic background xB (strongly) deviates from ensemble mean background Analysis for a deterministic forecast run :use Kalman Gain K of analysis mean deterministic analysis recently implemented L : interpolation of analysis increments from grid of LETKF ensemble to (possibly finer) grid of deterministic run

  10. Task 2: Technical implementation: verification for KENDA ‘stat’-utility: compute model (forecast) – obs for complete NEFF (NetCDF feedback files) :  adapt verification mode of 3DVar/LETKF package  need to implement COSMO observation operators with QC in 3DVar/LETKF package (  hybrid 3DVar–EnKF approaches in principle applicable to COSMO )  build COSMO modules with clean interfaces & introduce them in LETKF • no further progress • still plan to first implement upper-air obs (needed as input for VERSUS)

  11. Task 2: Technical implementation: verification for KENDA NEFFprove (Amalia Iriza, NMA) : tool to compute and plot verification scores based on NetCDF feedback files (NEFF) • computes statistical scores for different runs (‘experiments’), focus: use exactly the same observation set in each experiment ! •  select obs according to namelist values (area, quality + status of obs, … ) • compute scores (upper-air obs, screen-level obs, RR, cloud, etc.) • (for each experiment, variable, forecast time, vertical level, …) • - deterministic continuous: BIAS, RMSE, MSE • - dichotomous: accuracy, Heidke Skill Score, Hanssen and Kuiper discriminant • - ensemble scores (to be done): reliability, ROC, Brier Skill Score, • (continuous) Ranked Probability Score • plot the scores, using GNUplot (work is in progress)

  12. Task 3: scientific issues & refinement of LETKF • lack of spread: (partly ?) due to model error and limited ensemble size which is not accounted for so far to account for it: covariance inflation, what is needed ?  multiplicative (tuning, or adaptive (y – H(x) ~ R + HTPbH))  additive ((e.g. statistical 3DVAR-B), stochastic physics (Torrisi)) • localisation (multi-scale data assimilation, 2 successive LETKF steps with different obs / localisation ?)  talk by Hendrik Reich • model bias • update frequency : up to now only 3-hourly • non-linear aspects, convection initiation (latent heat nudging ?) • technical aspects: efficiency, system robustness (Nov. 2012: regular ‘pre-operational’ LETKF suite)

  13. LETKF scientific issues / refinements: investigate idealised convection • investigate LETKF in Observing System Simulation Experiments (OSSE) • apply LETKF to idealized convective weather systems, tune LETKF settings (localization, covariance inflation) • quantify + reduce spin-up time to assimilate convective storm by LETKF MeteoSwiss: plan for 2-year project not accepted • however master thesis (5 months): Manuel Bischof, MeteoSwiss perturbations of the storm environment for ensemble generation: • wind speed, temperature (low-level stability), and (low-level) humidity all suitable to perturb • meaningful perturbation amplitudes: 2 m/s, 0.25 K, 2 %

  14. LETKF scientific issues / refinements: model error estimation • stochastic physics (Palmer et al., 2009) : by Lucio Torrisi, available Oct. 2011

  15. LETKF scientific issues / refinements: model error estimation • objective estimation and modelling of model (tendency) errors (not to be confused with forecast errors) M. Tsyrulnikov, V. Gorin (HMC Russia) 1. set up a (revised) stochastic model (parameterisation) for model error (ME) MEM : e= *Fphys(x) + eadd • involves stochastic physics ( * Fphys(x) ) and additive components eadd andincludes multi-variate and spatio-temporal aspects • model error model parameters: (E(), D(), D(eadd)) 2. develop MEM Estimator : estimate parameters by fitting to statistics from real forecast (COSMO-RU-14) and observation (radiosonde) tendency data, using a revised (maximum likelihood based) method (tested using bootstrap) 3. develop ME Generator embedded in COSMO code 4. develop MEM Validator: exactly known ME in OSSE set-up, retrieve with MEM Estimator  The reproducibility of the MEM parameters is not yet established !

  16. Task 4: use of additional observationsTasks 4.1, 4.2: use of radar obs • radar : assimilate 3-D radial velocity and 3-D reflectivity directly Uli Blahak (DWD), Yuefei Zeng, Dorit Epperlein (KIT Karlsruhe) 1. Implement full, sophisticated observation operators (by end 2011) • compute reflectivity from model values using Mie- or Rayleigh-scattering

  17. sub-domain 1 sub-domain 2 MPP parallelisation: beam in an azimuthal slice has to be collected on 1 PE Tasks 4.1, 4.2: use of radar obs : radar observation operator propagation of radar beam : ‘online’ depend. on refractive index, or approximate standard atmosphere averaging over beam weighting function: weighted spatial mean over measuring volumn (cross-beam vertically / horizontally)

  18. Tasks 4.1, 4.2: use of radar obs : radar observation operator without attenuation with attenuation of radar reflectivity by atmospheric gases + hydrometeors (using the extinction coefficients)

  19. Task 4: use of additional observationsTasks 4.1, 4.2: use of radar obs • radar : assimilate 3-D radial velocity and 3-D reflectivity directly Uli Blahak (DWD), Yuefei Zeng, Dorit Epperlein (KIT Karlsruhe) 1. implement full, sophisticated observation operators (by end 2011) 2. apply / test sufficiently accurate and efficient approximations • by looking at the simulated obs (by March 2012) • doing assimilation experiments : OSSE setup

  20. Task 4.3: use of GNSS slant path delay • ground-based GNSS slant path delay(early 2012, N.N.) • implement non-local obs operator in parallel model environment • test and possibly compare with using GNSS data in the form of IWV or of tomographic refractivity profiles Particular issue: localisation for (vertically and horizontally) non-local obs

  21. Task 4.4: use of cloud info • cloud information based on satellite and conventional data Annika Schomburg (DWD / Eumetsat) • derive incomplete analysis of cloud top + cloud base, using conventional obs (synop, radiosonde, ceilometer) and NWC-SAF cloud products from SEVIRI • use obs increments of cloud or cloud top / base height or derived humidity • use SEVIRI brightness temperature directly in LETKF in cloudy (+ cloud-free) conditions, in view of improving the horizontal distribution of cloud and the height of its top • compare approaches Particular issues: non-linear observation operators, non-Gaussian distribution of observation increments

  22. Task 4.4: use of cloud info • make observations available • aggregation / interpolation to COSMO-DE / COSMO-EU grids • regular archiving: • NWC-SAF cloud products available since May 2011: (CT: cloud type, CTT: cloud top temperature, CTH: cloud top height) • BT available since June 2011: all 12 SEVIRI channels • assumption in LETKF: no bias  look at systematic differences in BT / cloud products

  23. Task 4.4: use of cloud info BT high clouds: cold bias of model-BT, (partly ?) due to known bias in RTTOV-9

  24. Task 4.4: use of cloud info NWC-SAF SEVIRI cloud products: example cloud type CT cloud top height CTH fractional water clds high semitransparent very high clouds high clouds medium clouds low clouds very low clouds cloud-free water cloud-free land undefined COSMO: cloud water qc > 0 , or cloud ice qi > 5 .10-5 kg/kg clc = 100 % subgrid-scale clouds  clc = f(RH; shallow convection; qi ,qi,sgs) < 100 %

  25. Task 4.4: use of cloud info cloud top height CTH distribution May – July 2011 clc > 1% clc > 20% NWC-SAF CTH ‘obs’ COSMO CTH for clc > x % clc > 50% clc > 70% clc > 90%

  26. Task 4.4: use of cloud info cloud top height CTH distribution May – July 2011 NWC-SAF CTH ‘obs’ COSMO CTH with optimal threshold: for clc > 70 % for levels above 5000 m , for clc > 40 % for levels below 5000 m

  27. cloud cover [%] , shading: model isoline: CTH MSG Task 4.4: use of cloud info CTH COSMO 6-h forecast, for 18 May 2011, 18 UTC clc > 0.1% clc > 30% clc > 50% clc > 70% clc > 90% optimal clc height [km]

  28. Technical implementation ofverification for KENDA thank you for your attention

  29. 0.9 Pert 1 -0.1 Pert 2 -0.1 Pert 3 -0.1 Pert 4 +( - ) +( - ) +( - ) +( - ) +( - ) +( - ) +( - ) +( - ) local transform matrix w analysis mean analysis perturbations Wa(i) analysis error covariance (computed only in ensemble space) perturbed analyses in the (k-1) -dimensional (!) sub-space S spanned by background perturbations : set up cost function J(w) in ensemble space, explicit solution for minimisation (Hunt et al., 2007) Local Ensemble Transform Kalman FilterLETKF (Hunt et al., 2007) ensemble mean forecast k perturbed forecasts ensemble mean fcst. forecast perturbations Xb flow-dep background error cov. Pb = (k – 1) Xb (Xb)T

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