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KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de Contributions / input by: Hendrik Reich, Andreas Rhodin, Klaus Stephan, Werner Wergen (DWD) Daniel Leuenberger, Tanja Weusthoff (MeteoSwiss) Marek Lazanowicz (IMGW) Mikhail Tsyrulnikov (HMC) PP Kenda : Status Report christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany status & outlook general issues in the convective scale experiments for assessing importance of km-scale details in IC deterministic analysis Slide 2 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de Task 1:General issues in the convective scale and evaluation of COSMO-DE-EPS Purpose: Guides decision how resources will be spent on/ split betw. LETKF and SIR (COSMO-NWS and universities); part of the learning process main disadvantage of LETKF: assumes Gaussian error distributions Task 1.1.A: investigate non-Gaussianity by means of O B statistics (convective / larger scales, different forecast lead times): provides an upper limit estimate of the non-Gaussianity to deal with talk by Daniel Leuenberger: Statistical characteristics of high-resolution COSMO Ensemble forecasts in view of Data Assimilation Task 1.2: investigate non-Gaussianity by examining perturbations of very-short range (2009) forecasts from COSMO-DE-EPS Slide 3 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de RWWRP / THORPEX Workshop on 4D-Var and EnKF intercomparison we should continue with KENDA (LETKF) as planned (4DVAR / EnKF ok for HR DA: everybody pushes the way further on the current road; ensemble size stable (30 40); synergistic approaches) model errors a (the) key issue for advanced DA important for DA to work on model improvement worthwhile to obtain options for EnKF implementations which also make use of 3DVar (by implementing tasks 2.1, 2.5) talk by Breogan Gomez: Single-column experiments on the vertical localisation in the LETKF difficulty of assimilating non-local satellite data and achieving good resolution in local analysis Task 1.4: M. Tsyrulnikov: Review on Hunt et al. implementation of LETKF Slide 4 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de Task 1.1 D: assess importance of km-scale details versus larger-scale conditions in the IC (do we have to analyse the small scales, or is it sufficient to analyse the large scales, as e.g. incremental 4DVAR (ECMWF) would do ?) Comparison: IEU: IC from interpolated COSMO-EU analysis of ass. cycle (no LHN) IDE: IC from COSMO-DE analysis of ass. cycle (no LHN) LHN: IC from COSMO-DE analysis of ass. cycle (IDE + LHN for use of radar- derived precipitation) Note: IC from assimilation cycle late cut-off, very similar set of observations identical correlation functions (scales) used in nudging for IEU and IDE identical soil moisture, taken from IEU (with variational soil moisture initialisation) model version as operational in summer 2009 Period:31 May 13 June 2007: air mass convection Slide 5 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de 00 UTC runs06 UTC runs ETS FBI LHN IDE (no-LHN) IEU (coarse IC) 31.05. 13.06.07: air-mass convection # radar obs with rain LHN IDE (no-LHN) IEU (coarse IC) time of day 0.1 mm Slide 6 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de 00 UTC runs06 UTC runs ETS FBI LHN IDE (no-LHN) IEU (coarse IC) 31.05. 13.06.07: air-mass convection # radar obs with rain LHN IDE (no-LHN) IEU (coarse IC) time of day 1 mm Slide 7 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de 12 UTC runs18 UTC runs ETS FBI LHN IDE (no-LHN) IEU (coarse IC) 31.05. 13.06.07: air-mass convection 0.1 mm # radar obs with rain LHN IDE (no-LHN) IEU (coarse IC) time of day 1218 Slide 8 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de 12 UTC runs18 UTC runs ETS FBI LHN IDE (no-LHN) IEU (coarse IC) 31.05. 13.06.07: air-mass convection 1 mm # radar obs with rain LHN IDE (no-LHN) IEU (coarse IC) time of day Slide 9 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de 12- UTC runs, 6 18 h fcsts. (nighttime precipitation) radar (12-h sum)LHN (fine-scale IC)IEU (coarse IC) 10 June 18 UTC 11 June 06 UTC 2007 4 June 18 UTC 5 June 06 UTC 2007 Examples Slide 10 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de Task 1.1.D: assess importance of km-scale details Results of comparison to coarse-scale IEU : IDE better than IEU for 12- and 18-UTC runs up to +15h (similar FBI, higher ETS) similar for 0- and 6-UTC runs improvement by LHN during first 6 hours LHN has better precipitation patterns (6 18 h forecast) than IEU in some cases Radar With LHN Without LHN (dashed: determininistic) Past experiments (Leuenberger): environment affects impact of fine-scale details in analysis from LHN Slide 11 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de Task 2:Technical implementation of an ensemble data assimilation 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 Slide 12 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de perturbed forecasts ensemble mean forecast (local, inflated) transform matrix W a analysis meanperturbed analyses LOCAL Ensemble Transform Kalman Filter (LETKF) (chart based on a slide of Neil Bowler, UK MetOffice) = background perturbations X b flow-dep. backgr. error covar. analysis error covariance in the (5-dimensional) sub-space S spanned by background perturbations : (linearise obs operator around the mean of ensemble of simulations of an obs) explicit solution for minimisation of cost function (Hunt et al., 2007) ( - ) + = 0.9 Pert 1 -0.1 Pert 2 -0.1 Pert 3 -0.1 Pert 4 -0.1 Pert 5 analysis ensemble member : and in model space : analysis (mean) and analysis error covariance : columns of Slide 13 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de Analysis for a deterministic forecast run : use Kalman Gain K of analysis mean K is already computed in the (L)ETKF ; if the resolution of the deterministic run is higher than of ensemble, the analysis increments have to be interpolated to the fine grid determ. Kalman gain / analysis increments not optimal, if deterministic run (strongly) deviates from analysis mean of ensemble Slide 14 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de LETKF COSMO read obs (NetCDF) + Grib analysis of ensemble member compute obsfg (obs. increm.) + QC (contains obs operator) write NetCDF feedback files (obs + obsfg + QC flags) + Grib files (model) read ensemble of NetCDF feedback files + ensemble of COSMO S-R forecast Grib files perform LETKF (based on obsfg values around each grid pt., calc. transformation matrices and analysis (mean & pert.)) (adapt: C-grid, specific variables (w), efficiency) write ensemble of COSMO S-R analysis Grib files + NetCDF feedback files with additional QC flags ( verif.) exp. system Task 2.3: finished Task 2.2: almost finished Task 2.4: not yet done However: scripts written to do a few stand-alone cycles with LETKF preliminary tests can start soon Slide 15 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de y: observations x: model state d = y H(x): innovation H: observation operator qf: quality flag QC: quality control Verification / diagnostics: new stat tool : compute model (forecast) obs, as input for verification GRIB new tool (included in 3DVar code) NetCDF observation y qf read obs write feed- back x H(x) QC H read feedback y qf H(x) read grib NetCDF feedback y, qf, d want have capability of computing distance of model (ensemble member) to observations that have not been used previously in a COSMO run ( SIRF) need to include NetCDF observation QC H & in stat module common codes in COSMO & stat tool Slide 16 KENDA (Km-Scale Ensemble-based Data Assimilation) COSMO General Meeting, Offenbach, 7 11 Sept. 2009 KENDA christoph.schraff@dwd.de Task 2.1: extract from COSMO: - library 2 : in progress - library 1 : finished Task 2.5:- in progress: include above libraries in 3DVar environment, (translate COSMO data structure into 3DVar data structure and vice versa) - not yet started: extend flow control (e.g. reading several Grib files and temporal interpolat.) Task 2:Technical implementation of an ensemble data assimilation framework / LETKF for verification: stat-module: compute model (forecast) obs : adapt verification mode of 3DVar/LETKF package Advantages: COSMO obs operators available in 3DVAR/LETKF environment 3DVar/ EnKF approaches requiring 3DVar in principle applicable to COSMO LETKF for ICON will require COSMO obs operators in the future 1 common code for GME/ICON and COSMO to produce input for diagnostics / verif.. Disadvantages: more complex code for this diagnostic task possibly additional transformation from COSMO data structure into 3DVAR data structure a

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