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WG1 Overview christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany

WG1 Overview christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany. Priority Project 1 SIR filter project leader ad interim: Christoph Schraff (DWD) scientific guidance: Werner Wergen (DWD) task 0: Cosmin Barbu, Victor Pescaru (INM).

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WG1 Overview christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany

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  1. WG1 Overviewchristoph.schraff@dwd.deDeutscher Wetterdienst, D-63067 Offenbach, Germany

  2. Priority Project 1 SIR filterproject leader ad interim:Christoph Schraff (DWD)scientific guidance: Werner Wergen (DWD)task 0: Cosmin Barbu, Victor Pescaru (INM) Task 0.2 (implement & test SIR filter for KdV equation): stopped The number of accounted FTE is subject to discussion in the STC: • Rumania: either 0 FTE (if FTE means: trained FTE) or 0.3 FTE (any FTE) • Germany: 0.1 FTE Task 1 Meeting 14 June 07: Schüttemeyer (Uni Bonn, 2-y PostDoc) evaluate COSMO-DE-EPS for SIR (ensemble spread in first 1 – 3 hrs, ensemble size / drift, indication of non-Gaussianity) initial evaluation should be finished by end of 07 Other news • SAC/STC decision: long-term strategy of COSMO for DA to be re-discussed (2 meetings with external experts, 5 Sept (P.J. van Leeuwen), 18 Sept (Chris Snyder)) • SIR project is being revised / replaced

  3. Workshop on strategy for convective-scale data assimilationOffenbach, 5. Sept. 2007Participants: COSMO SAC, STC, SPM, WG1 (Tsyrulnikov, Bonavita), DWD (Wergen, Rhodin), Uni Bonn (Prof. Simmer), van Leeuwen Recommendations (from discussion after van Leeuwen’s talk on SIR) • we want EPS, therefore we need EnDA (Ensemble-based Data Assimilation) (this, and having too little resources rules (for it) out 4DVAR) • keep the methodology / algorithm open (SIR, LETKF, …) • set up a modular system / framework for ensemble DA • modular means that others (universities ..) can use it • components of the system can be replaced by alternatives • Nudging play a role e.g. in the SIR approach • look into how non-Gaussian (prior pdf & obs pdf) and non-linear the system is, how important convective scale details in the initial state are (in the work plan: exploit ensembles) • put also sufficient resources into improving the model physics at the convective scale

  4. New PP: Km-scale Ensemble-based Data Assimilation (KENDA) Strategy: 2 approaches • Sequential Importance Resampling (SIR) filter (van Leeuwen, 2003) • can handle major challenges on convective scale (non-Gaussian distributions, highly non-linear processes & obs operators, flow-dep & unknown balance, (model errors ?) ) • not yet applied to NWP, practical challenges: • ensemble size, ens drift, ens spread, compression of obs information • localisation approach alleviates these challenges, introduces new ones (need to glue members together) • more basic research required, should rely mainly on resources from co-operating universities and research institutions • Local Ensemble Transform Kalman Filter (LETKF, Hunt et al., 2007) • Gaussian approximation • applied successively to NWP, less problems expected to get it working • devote resources from weather services in COSMO

  5. New PP: Km-scale Ensemble-based Data Assimilation (KENDA) Discussion with input from Chris Snyder 18 Sept 2007 on EnKF • no new obstacles seen for the EnKF • to get a system to evaluate, need 2 people (with good background) for 2 years • do EnKF first without radar data (quality control problems), gain experiences, detect bugs / flaws in the scheme, later include radar data

  6. Priority Project 2 Use of 1dVar Satellite RetrievalsProject leader: Reinhold Hess (DWD)SEVIRI (MSG)Francesca di Giuseppe, Elementi, Marsigli (ARPA-EMR)ATOVS (NOAA1x) (Blazej Krzeminski) (IMGW), Hess, Schraff (DWD)Model / Nudging issues: Christoph Schraff (DWD) talk by Reinhold Hess

  7. Nothing done on: • Assimilation of dual Doppler wind • GPS-ZTD-derived integrated water vapour (IWV) • Production and use of cloud analysis • Further tuning of nudging • Use of lake temperature analysis

  8. 1.1.1 Latent Heat NudgingKlaus Stephan (DWD), Daniel Leuenberger (MCH)Christoph Schraff, Stefan Klink (DWD) • DWD: – LHN switched off in pre-op. trials during winter due to bright band problem • COSMO-DE with LHN operational since April 2007 • too strong LHN forcing due to microphysics changes  grid point search revised • benefit slightly enhanced with new version of LHN (throughout forecast) • assessed benefit from revisions done in 2005 / 2006 to cope with prognostic precip

  9. free forecasts (00 + 12UTC runs) forecast hour LHN and prognostic precipitation scores for hourly precipitation 15 – 30 August 2006 (16 days) threshold = 0.5 mm/h (conventional) LHN , diagnostic precip. conventional LHN , prognostic precip. revised LHN , prognostic precip. FBI Frequency Bias assimilation ETS Equitable Threat Score [%] time [UTC]

  10. new microphysics changesnew revision of grid point search reduced snow fall velocity, modified snow geometry & size distribution, modif. auto conversion rate more drifting of precipitation (snow), less drizzle, less orographic precipitation  stronger violation of basic assumption of LHN (vertically integr. latent heating  precip rate) due to larger temporal delay + horizontal drift Problem 1: unrealistic pressure perturbations : greatly reduced radar LHN, old grid point search LHN, new grid point search mm / 24h mm / 1h

  11. 15 – 30 August 2006: radar old search new search new revision of grid point search Problem 2: overestimation of precipitation (amount + area) reduced by ≥ 50 % radar LHN, old grid point search LHN, new grid point search Mean = 7.2 Mean = 4.0 Mean = 2.8 mm / 24h

  12. free forecast free forecast ASS ASS scores with latest version of microphysics & LHN 15 – 30 August , 00 and 12 UTC runs (32 forecasts) threshold : 0.1 mm / h LHN noLHN LHN noLHN ETS FBI thr. : 0.1 mm / h threshold : 0.1 mm / h LHN noLHN LHN noLHN ETS ETS threshold : 1.0 mm / h threshold : 5.0 mm / h

  13. 1.1.1 Latent Heat NudgingKlaus Stephan (DWD), Daniel Leuenberger (MCH)Christoph Schraff, Stefan Klink (DWD) • DWD: – LHN switched off in pre-operational trials during winter due to bright band problem • COSMO-DE with LHN operational since April 2007 • too strong LHN forcing due to microphysics changes  grid point search revised • benefit slightly enhanced with new version of LHN (throughout forecast) • assessed benefit from revisions done in 2005 / 2006 to cope with prognostic precip • MetCH: – LHN real-time test suite for June – Aug 07 with COSMO-2 using Swiss radar data – verification in comparison to pre-opr. COSMO-2 without LHN – preliminary results (!), evaluate only 18-UTC forecast runs (not affected by exp. set-up error) – positive impact of LHN on surface parameters throughout forecast, particularly for 2-m temperature and cloudiness – very clear positive impact on precipitation in some cases • ARPA-SMR: started work on 1DVAR retrievals from rain rates

  14. Examples of forecast improvement 0 - 6h precipitation forecast (12.06.2007, 18 - 24UTC) Radar assimilation with LHN at MeteoSwiss Radar Radar Ass. No Radar Ass. 6 - 12h precipitation forecast (19.06.2007, 00 - 06UTC) Radar Radar Ass. No Radar Ass.

  15. 1.1.2 3D Simple Adjoint Wind RetrievalJerzy Achimowicz (IMGW) the problem: the resolution of the radar data has been increased and the SAR method is very sensitive to errors in radial velocity input data  1 year ago: good progress reported, the SAR method worked for single radar data, but now, most of the data pre-processing for the SAR has to be re-tuned or refined • introduce additional control procedures for unfolding the radial velocities (e.g. based on calc. gradient of consecutive radial velocity samples & comparison with Nyquist velocity, or referencing radar data to background wind field if available from a local sounding or analysis cycles etc.) • refine interpolation of radar wind + reflectivity from radar coordinates to cartesian grid • input data: 3 consecutive scans of 3-d reflectivity and radial velocity at 10’-intervals • sensitivity of 3-d wind retrieval (particularly of vertical wind) to errors in input fields: very strong even to low levels of noise in radial velocity (less to reflectivity errors) applying the method to the composite (1x1 km resolution) covering the whole of Poland is not straightforward because of data gaps due to limited doppler range (100 km radius); filling these gaps resulted in the necessity of running the analysis cycle with 1-km resolution. This is done with the ARPS model.

  16. 1.1.4 Wind ProfilesMichael Buchhold (DWD); Oliver Marchand, Christophe Hug (MCH) • DWD: VAD radar wind profiles: • monitoring performed, 8 out of 16 DWD radars are ok, quality variable • height assignment error will be corrected • neutral to slightly positive impact in assimilation experiments • MCH: – Wind Profiler, VAD, SODAR, radiometers: new monitoring tool based on observation increments allows to detect anomalies & potential problems in the way observations are assimilated • VAD soon used passively for regular monitoring

  17. 1.2 Multi-Sensor Humidity Analysis (incl. GPS-obs)Daniel Leuenberger (MCH) GPS tomography: • comprehensive monitoring (14 months) of quasi-operational tomography profiles (at CSCS) against Payerne radiosonde and COSMO fields done • results: tomographic refractivity profiles have rather large errors unlessCOSMO forecasts are included as background info • start working on assimilating humidity profiles derived from tomography retrievals • new PhD (Perler) at ETH started working on tomography method itself • BIAS – wet bias below 1500 m, large dry bias around 2000 m • Summer: 10 – 15 ppm (~1.5-2.5 g/kg) or ~35% • Winter: 5 ppm (~ 0.75 g/kg) or 20% • (much) larger than NWP model (+12h / +24h fc) • STD – Summer: up to 12 ppm (~1.8 g/kg) or 10% – 30% in PBL • Winter: up to 7 ppm (~1 g/kg) or 20% – 40% in PBL • slightly smaller than NWP model (+12h / +24h fc)

  18. 0 UTC July + Aug 12 UTC 0 UTC Nov + Dec 12 UTC verification against Payerne radiosonde BIAS larger than in model forecasts dry bias around 2000m improved with model c. wet bias at 1000m COSMO-7, + 12-h fc COSMO-7, + 24-h fc GPS tomo GPS tomo, with model constraint STD slightly smaller than in model forecasts improved with model constraint improve tomography algorithm or do bias correction

  19. 1.4 Assimilation of Screen-level Observations PBL AnalysisJean-Marie Bettems, Oliver Marchand, Andre Walser (MCH), Andrea Rossa + collaborators (ARPA-Veneto), Antonella Sanna (ARPA-Piemonte) • main objects: data selection, extrapolation to 10 m, vertical + horizontal structure functions • up to now: only case studies done • Diploma work at MCH (Lilian Blaser): 9 case studies, standard assimilation parameters • 10-m wind ass.: analysis impact: positive at surface, also for upper-air wind speed forecast impact: neutral, except 1 positive case (+8 h) • 2-m temperature & humidity additionally (1 convective case): clear positive impact on analysis of surface parameters, negative for upper-air wind speed • surface pressure (1 winter case): slight negative impact on 10-m wind, not due to geostrophic correction • need to select representative stations, need appropriate vertical structure functions (impact of screen-level obs reaching high)

  20. 5 June 2002, 12 UTC - 18-h precipitation sum 5 June 2002, 12 UTC diurnal cycle T2m 1.4 Assimilation of Screen-level Observations PBL AnalysisAntonella Sanna, M. Milelli, D. Cane, D. Rabuffetti (ARPA-Piemonte) • Sensitivity study (1 case with floodings, 2.8 km resolution) on assimilation of non-GTS data and soil moisture initialisation (PREVIEW framework): • clear positive impact from ass of high-res 10-m wind and 2-m temperature data and with nudging parameters adjusted to fit denser obs network • no benefit from replacing IC soil moisture by FEST-WB (hydrological model for floods) CTRL: interpol. ana SET2: standard nudging SET3: + nudge T2m, v10m adjusted parameters SET4: + init. soil moisture analysis 12-h forecast T -profiles

  21. Opr (no QSCAT) – Exp (QSCAT) PMSL 19 June 2007, 9 UTC hPa QSCAT 19 June 2007, 6 – 9 UTC 15 W 50N 48N 1.5 Assimilation of Scatterometer WindHeinz-Werner Bitzer (MetBw), Alexander Cress, Christoph Schraff (DWD) • nudging of scatterometer wind data as buoy observations technically implemented, taking into account all quality control / bias correction steps developed for use in GME • idealised case studies: model rejects largest part of 10-m wind info unless mass field is explicitly balanced derive surface pressure analysis correction in geostrophic balance with 10-m wind analysis increments (implies need to solve Poisson equation): implemented, model now accepts data • first real case study computed

  22. 1.5 Assimilation of Scatterometer WindHeinz-Werner Bitzer (MetBw), Alexander Cress, Christoph Schraff (DWD) analysis (21 June 2007, 12 UTC) + 48-h , no QSCAT + 48-h , with QSCAT m/s 10-m wind gusts minor impact, central pressure error reduced from – 5 hPa to – 3 hPa

  23. 1.7 3DVAR / EnKF for HRMMassimo Bonavita, Lucio Torrisi, Antonio Vocino (CNMCA) • 3D-VAR for HRM: • FGAT innovations (for better use of asynoptic data) • Increase of DA cycle: 6 -> 3-hourly • Increase of DA spatial resolution: 28 -> 14 Km NMC covariances from 6-month T+24 – T+48 forecasts for T, u, v, q, ps • little done on EnKF for hybrid 3DVAR-ETKF system, but developing LETKF for testing • Use & verification of interpolated 3D-VAR analyses as initial condition for COSMO model implementations: • 7-km COSMO-MED: works well, slightly better than ARPA-SMR version with nudging (insufficient explicit large-scale balance in nudging ?) • 2.8-km COSMO-ITA: interpolation from 14 Km analysis provides unbalanced I.C. (evident in ps verification, spin-up problems in precipitation) (with nudging, implicit balance by model seems effective)  use nudging for 2.8-km COSMO-ITA

  24. 1.8.1 Soil Moisture InitialisationMartin Lange (DWD) • aim: replace additional model runs by parameterized regressions to the determine the gradient of the cost function in the variational scheme (absolutely required for GME (long term dry drift), welcome for COSMO model) Cost function penalizes deviations from observations and initial soil moisture content Analysed soil moisture depends on T2m forecast error and sensitivity T2m/w

  25. 1.8.1 Soil Moisture InitialisationMartin Lange (DWD) Sensitivity of 2m temperature on soil moisture (with plants) Assumption: Root density constant with soil depth ! rx : resistances dzk,root: depth of the part of layer k that contains roots model quantities: known tuning parameter  needs to be determined by fitting to training data sets Evaluation with Terra-2L • Variation of initial soil moisture at selected grid points in the whole range between plant wilting point and field capacity (for days with radiative conditions) • Compare sensitivity of T2m to soil moisture with parameterisation

  26. 1.8.1 Soil Moisture InitialisationMartin Lange (DWD) Preliminary studies show good correlation between parameterisation and variational method for radiation conditions in the full range between plant wilting point and field capacity tune  so that highest correlation is along diagonal, validate against other data sets (other days) Date: 20050525 All grid points Lhfl (11:00-12:00) > 200 W/m2 dT2m / dw2 (12:00) (param.) dT2m / dwb (param.) dT2m (12:00) / dw2 (0:00) (variational) dT2m / dw2 (variational) No further need for additional model runs !

  27. 1.8.1 Soil Moisture InitialisationMartin Lange (DWD) Model experiment May-June 2006 • Parameterisation implemented for multi-layer soil model Terra-ml (L1–3 , L4–5 are aggregated to top and bottom layer) bias T2m, LM domain avg 12:00 15:00 rmse T2m, LM domain avg 12:00 15:00 noSMA opr (var SMA) new: param SMA even better than operational COSMO-EU slightly better than opr. COSMO-EU • parameterisation of soil moisture analysis developed and successfully tested • results are comparable or even better than current expensive opr. scheme, differences in soil moisture increments need to be investigated and understood • method: efficient and appropriate for all operational models (at DWD)

  28. 1.8.2 Snow Cover AnalysisJean-Marie Bettems (MCH), M. de Ruyter (ETH) • refinement on scheme for snow mask derived from MSG • validation of MSG snow mask at high resolution (HRV channel) • reduction of time scale to reflect aging of info in / from snow mask (7d < 1d) • extraction of snow albedo • fractional snow cover derived from MSG, including quality flags, in near-real time at 2 km resolution • snow analysis in production for COSMO-7 and for COSMO-2 • improved interpolation scheme implemented, introducing a local dependency of snow depth with height (derived from in-situ observations where their density is high enough) • technical and scientific documentation have been written

  29. Case study 24.05.2007: Alps New MCH snow ana ECMWF snow ana DWD snow ana Case study 24.05.2007: Alps SLF snow ana (avalanche research) snow mask quality flag (white is high), dep on: time of last update (cloud-free pixels)

  30. Thank you for your attention

  31. Ensemble Transform Kalman Filter (ETKF)Slide by Neil Bowler, UK MetOffice 0.9 Pert 1 -0.1 Pert 2 -0.1 Pert 3 -0.1 Pert 4 -0.1 Pert 5 ( - ) + = ( - ) + = ( - ) + = ( - ) + = ( - ) + = T+12 perturbed forecast T+12 ensemble mean forecast Transform matrix Control analysis Perturbed analysis

  32. COSMO–EU opr: SMA, no LHN COSMO–DE (pre-)op: no SMA, LHN except Jan-Mar COSMO–DE test: LHN Jan – Mar normalised soil moisture (radar composite area) 0,5 cm 0,5 cm 2 cm and 6 cm soil layers: very similar to 0.5 cm layer 18 cm 18 cm Aug 15 2006 Dec 15 2006 2007 2007

  33. COSMO–EU opr: SMA, no LHN COSMO–DE (pre-)op: no SMA, LHN except Jan-Mar COSMO–DE test: LHN Jan – Mar normalised soil moisture(radar composite area) 54 cm 54 cm 162 cm 162 cm Aug 15 2006 Dec 15 2006 2007 2007

  34. validation of soil moisture at Lindenberg COSMO–DE Lindenberg Obs remarkably small errors

  35. Running monthly mean precipitation and evaporation Daily soil moisture top and bottom layers COSMO-7 exceeds COSMO-EU precipitation due to soil moisture initialisation

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