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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. 1.1.1 Top Latent Heat Nudging Stefan Klink (DWD), Klaus Stephan (DWD), Daniel Leuenberger (MCH) Christoph Schraff (DWD), Andrea Rossa (ARPAV).

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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. 1.1.1 Top Latent Heat NudgingStefan Klink (DWD), Klaus Stephan (DWD), Daniel Leuenberger (MCH)Christoph Schraff (DWD), Andrea Rossa (ARPAV) • Ongoing at DWD, MCH, coordination meetings in January, April, and September • talk by Leuenberger, MCH (diagnostic precipitation, x=7km, 2.2km) • talk by Schraff, DWD (prognostic precipitation, x=2.8km) • main problems related to prognostic precipitation solved to a large degree (overestimation of precipitation strongly reduced) • reasonable forecast impact on precipitation • progress ok, although still problems to solve (LHN forcing too strong, insufficient quality of radar data, data exchange) LHN ‘survived’ ! 1.1 Top Use of Radar Data (Radar Workshop)Christoph Schraff (DWD), Werner Wergen (DWD) • Idea of Radar Workshop due to problems in LHN / prognostic precipitation. • Plan of Radar Workshop dismissed since these problems mitigated, and due to info from SRNWP Workshop on VAR DA (Exeter, 11/04) and WMO Symposium on DA (Prag, 04/05): nobody has the solution for assimilation of radar reflectivity. Instead: • WG1 Workshop about Strategy on DA in COSMO (Nudging / 3DVAR / Ensemble Techniques ; focus on convective scale)

  3. Legionowo (Warsaw) Radar 26-07-2003 [km] Doppler radial wind at 2000 m , 13:04 UTC horizontal wind retrieval 1.1.2 Top 3D Simple Adjoint Wind RetrievalJerzy Achimowicz (IMGW) • Ongoing, but progress slower than expected due to data quality problems • 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) • real data from Polish radars: much work on quality control / filtering • delay of OSSE to evaluate realism of retrievals and tune weights in cost functions

  4. 1.1.3 VAD winds (monitoring)Michael Buchhold (DWD); Oliver Marchand (MCH) • DWD: Monitoring done. • Quality of VAD winds from DWD network insufficient (better data hopefully in 1Q/06 after revision of VAD pre-processing) • MCH: CN-MET accepted, since 05/05 Oliver Marchand: evaluate opr. assimilation of wind profilers and (at least) Swiss VAD. So far, technical local implementation work. 1.1.4 Nudging of Profiles derived from LAPS analysesFabrizio Nerozzi, Davide Cesari, Pier-Paolo Alberoni, Tiziana Paccagnella (ARPA-EMR) • regular production of hourly profiles ready. • some delay for impact study (technical problems due to high density of profiles) • results available soon

  5. 1.2 Top Multi-Sensor Humidity Analysis (incl. GPS-obs)N.N. (Jean-Marie Bettems) • GPS tomography • no resources at MCH (project Stormnet not accepted) • Swisstopo: operational production of hourly humidity profiles over Switzerland • further work on tomography e.g. by ETHZ (Troller) and GFZ Potsdam / Uni Leipzig (tomography: pre-processor to integrate different sources of humidity information, – all weather, high spatial and temporal resolution, humidity profiles over land – retrieved profiles can easily be assimilated by nudging – need dense GPS networks (costs!) ) project (50 % FTE) submitted • ZTD-derived integrated water vapour: no resources at MCH 1.3 Production and Use of Cloud AnalysisChristoph Schraff (DWD) • No resources (due to e.g. 1.4 and 1.6)

  6. GPS-tomography – Voxel model • 3x6 mesh elements, open elements at the boundaries • 15 levels between 0 and 8000 meters; 1 more level aloft • Hourly profiles

  7. GPS Tomography: Comparison of Refractivity Profiles FALE ZIMM PAYE - comparison to aLMo: r m s e 1 mm per voxel (10000 profiles) - tendency to smooth include aLMo first guess C: Inter-voxel constraintsP: Screen-level observations T: Time constraintsR: Radiosondes

  8. 1.2 Top Multi-Sensor Humidity Analysis (incl. GPS-obs)N.N. (Jean-Marie Bettems) • GPS tomography • no resources at MCH (project Stormnet not accepted) • Swisstopo: operational production of hourly humidity profiles over Switzerland • further work on tomography e.g. by ETHZ (Troller) and GFZ Potsdam / Uni Leipzig (tomography: pre-processor to integrate different sources of humidity information, e.g. GPS occultation (transverse data), cloud cover, lidar, etc. – all weather, high spatial and temporal resolution, humidity profiles over land – retrieved profiles can easily be assimilated by nudging – need dense GPS networks (costs!) ) project (50 % FTE) submitted • ZTD-derived integrated water vapour: no resources at MCH 1.3 Production and Use of Cloud AnalysisChristoph Schraff (DWD) • No resources (due to e.g. 1.4 and 1.6)

  9. 1.4 Top Use of 1dVar Satellite Retrievals Reinhold Hess, Christoph Schraff (DWD) 1.4.1 Top MSG Francesca di Giuseppe (ARPA-EMR) 1.4.2 Top ATOVS Blazej Krzeminski (IMGW) • work on stand-alone procedures related to 1dVar • implementation of quality control (cloud / rain detection), bias correction software, calculation of bias correction coefficients, etc. • MSG: stand-alone 1dVar package running in pre-operational mode, validation of retrievals against radiosonde observations • working visit to DWD in 06/05 to define interface of 1dVar with LM / nudging • define data interfaces and organise code (e.g. modular) and internal data structures in a such way that code can be easily adapted to accommodate observations from other instruments (e.g. AIRS, IASI) • since then: develop interface of 1dVar routines into LM / nudging (completed: reading of 1dVar input files) • work well coordinated and focused, additional Eumetsat Fellowship at IMGW for AIRS / IASI • poster (MSG)

  10. 1.4.3 ATOVS retrievals for HRMMassimo Bonavita, Antonio Vocino (CNMCA) • Implementation of a new air-mass bias correction scheme (using model first guess instead of AMSU-A 4, 9 as predictors); improves HIRS water vapour channel data • full suite of ATOVS retrievals implemented, but abandoned due to only neutral impact  decide to work on assimilation of radiances directly into 3DVAR 1.5 Assimilation of Scatterometer WindHeinz-Werner Bitzer (MetBw), Christina Köpken, Alexander Cress (DWD) • Work on GME continued (bias correction, quality control, thinning), regular monitoring • delay due to additional quality control needs, operational use 1Q/06 • first tests with LM postponed to 12/05

  11. new QC ref QC obs > 100 mm Stuttgart 30 July 23 UTC 1.6 Evaluation / Monitoring / Tuning of Nudging Christoph Schraff (DWD) • revision of quality control for radiosonde humidity • smaller, stability-dependent thresholds in ‘first-guess’ check • revised multi-level check • spatial consistency check for integrated water vapour (IWV) data derived for radiosonde humidity profiles or GPS-ZTD using model fields as background (without neighbouring observations: equivalent to first guess check for IWV) • goes into next LM version to become operational 24-h precipitation 30 – 31 July 2004, 6 UTC humidity profile rejected by spatial consistency check of IWV

  12. 1.6.1 Temporal WeightsJean-Marie Bettems (MCH) • Experiments show negative impact on precipitation, cloud, and humidity, and small positive impact on wind and pressure • delayed: further tests to selectively reduce time window for radiosonde wind only 1.6.2 Vertical CorrelationsAntonio Vocino (CNMCA) • Programs tested, cleaned; documentation to be finished 1.5 Assimilation of Surface-level wind and humidityDavide Cesari (ARPA-EMR) • nothing done

  13. 1.7.1 Soil Moisture InitialisationMartin Lange (DWD) • ELDAS project finished, summary report for ELDAS available • resources found to continue work : • soil moisture initialisation for multi-layer soil model implemented in LME. Results ok. • Impact studies • 2-m humidityincluded in cost function (in addition to 2-m temperature) • model precipitation replaced by observed precipitation for the updating of soil moisture from one day to next

  14. T2m forecasts for 12 and 15 UTC (ELDAS domain and time average for 00-UTC LM runs) time series of running monthly mean bias r m s e SMA reduces bias in warm season SMA reduces rmse by ≥10% in warm season, use of RH2m slightly beneficial use of observed precip slightly beneficial

  15. RH2m forecasts for 12 and 15 UTC (ELDAS domain and time average for 00-UTC LM runs) time series of running monthly mean bias r m s e use of RH2m reduces bias in May - July SMA reduces rmse by 10 – 30 %, use of RH2m beneficial use of observed precip beneficial

  16. 6- to 30-hour precipitation forecasts(ELDAS domain average for 00-UTC LM runs) time series of running monthly mean frequency bias 5 mm threshold TSS SMA strongly reduced bias, increases TSS by about 5 – 10 % use of RH2m : neutral impact use of observed precip : beneficial

  17. 1.7.2 Snow Cover AnalysisMichael Buchhold (DWD), Jean-Marie Bettems (MCH) • Coordination meeting in 02/05 (with Uni Bern and Eumetsat Fellowship). Decisions • MCH (SLF Davos) and DWD exchange local in-situ data (DWD sends data) • DWD and MCH will exchange components of their snow mask analysis schemes (MCH: Meteosat 8; DWD: NOAA AVHRR) depending on validation results • DWD developed a version to incorporate snow mask into current snow analysis scheme (snow mask corrects result of snow analysis). Cloud mask product and its impact needs to evaluated for longer periods. 1.7.3 Use of Lake Temperature AnalysisDimitri Mironov (DWD) • nothing done

  18. Snow cover mask from Meteosat8 : problem : discrimination between snow and ice cloud Spectral image classification (10-3-2004,12:12 UTC) ice cloud ice cloud snow snow r0.81 r1.6 ice cloud ice cloud snow snow new: (BT3.9 - BT10.8) / (BT3.9 - BT13.4 ) BT3.9 - BT10.8

  19. clouds snow more ice more water Temporal classification First principal component of the temporal standard deviation of the 9 channels used (10-3-2004, 12:12 UTC): Second and third components are also useful for detecting clouds.

  20. Spectral and temporal classification UTC:200403101212 white : snow dark gray : clouds light gray : snow-free land black : sea temporal cloudmask is ‘liberal’, only used to check snowy pixels for misclassifications: UTC:200403101212 UTC:200403101212 spectral UTC:200403101212 spectral/temporal • using temporal info, most clouds detected • temporal classification classifies snow in a conservative way (somewhat too little snow detected, but with high certainty) : temporal

  21. Composite snow map, March 10th, 2004, 07:00 - 12:00 UTC Composite snow maps March 10th, 2004, 12:12 UTC UTC:200403101212 spectral/temporal Composite snow map, March 8th - March 10th spectral/temporal • high frequency strongly reduces cloud obscurance • snow mapping also possible in hrv channel • start of implementation at MCH this winter spectral/temporal white: snow dark gray: clouds light gray: snow-free land black:sea

  22. WG1 Workshop for Strategy, 19 Sept 2005 Long-term vision : how will / shall the NWP system look like in 2015 ? • PDFs: deliver not only deterministic forecasts, but a representation of the PDF (ensemble members with probabilities), particularly for the convective scale • resolution: global: 10 km , fine-mesh:x = 1 km • use of indirect observations at high frequency even more important • high-frequency update (DA + FC) • emphasis on ensemble techniques (FC + DA) • due to special conditions in convective scale (non-Gaussian pdf, balance flow-dependent and not well known, high non-linearity), DA split up into: • generalised DA for global + regional scale modelling ( variational DA) • separate DA for convective scale

  23. WG1 Workshop for Strategy, 19 Sept 2005 ICON (DWD + MPI): 2008 • Project bringing together - global and regional modelling - NWP and climate • global non-hydrostatic model with grid refinement • 3DVAR with Ensemble Transform Kalman Filter • will replace GME and LM(E) • (subject to approval of strategy in DWD) • COSMO should concentrate on the convective scale (LMK)

  24. WG1 Workshop for Strategy, 19 Sept 2005 Convective scale: Discussed data assimilation methodfor the longer term: Sequential importance resampling filter (Monte Carlo DA) Weighting of ensemble members by observations and redistribution according to posterior PDF • Take an ensemble together with a prior pdf • Find the distance of each member to the observations (using any norm or forward operator) • Combine the prior pdf with the distance to the observations to obtain a posterior pdf • Construct a new ensemble reflecting the posterior pdf • Integrate to the next observation time No modification of forecast fields Reference: Van Leeuwen, 2003: A variance minimizing filter for large scale applications. MWR, 131, 2071 – 2084.

  25. WG1 Workshop for Strategy, 19 Sept 2005 SIR method can handle the major challenges on the convective scale: • Non Gaussian PDF • Highly nonlinear processes • Model errors • Balance • Direct and indirect observations with highly nonlinear observation operators and norms • COSMO: gets lateral b.c. from LM-SREPS, provides initial conditions for LMK-EPS Potential problem: Ensemble size, filter can potential drift away from reality, but it cannot be brought back to right track without fresh blood However: • for LMK: Strong forcing from lower and lateral boundaries avoids drift into unrealistic states • if method does not work well the pure way: Fallback position (or 2nd option): combine with nudging: (some) members be (weakly) influenced by nudging

  26. WG1 Workshop for Strategy, 19 Sept 2005 Consensus : • Ensemble DA should play a major role • Nudging at moment: • robust and efficient • requires retrievals for use of indirect observations, but no severe drawbacks if we can make them available • not foreseeable when / whether nudging will cease, but we keep on reviewing the situation • can use it for fallback in SIR Strategy: • Start development of SIR (for the longer-term, with option to include nudging) • further develop nudging, in particular retrieval techniques (for mid-term + fallback)

  27. WG1 Workshop for Strategy, 19 Sept 2005 COSMO Projects: • SIR: • defining norm • resampling • Retrieval techniques: continue 1dVar for satellite radiances as a pilot project (in practice, it is already a running project, but does not yet have official status) other retrieval techniques for nudging (success of nudging depends on success of retrieval schemes), with a focus on convective scale, will be continued to be worked in COSMO activities • GPS tomography • latent heat nudging • Doppler radial velocity (SAR method) ?

  28. WG1 Workshop for Strategy, 19 Sept 2005 Technical issues: • ODB (as standard interface for COSMO tools ?, will replace AOF) • recommendations (also for WG6 resp. WG5): • exchange of data (radar reflectivity and wind, GPS, snow, ... ): should have standard procedure (instead of bilateral agreements)  WG6 • to facilitate use of observations, in particular satellite data: should use common formats (e.g. NetCDF), common software (e.g. IDL) • Ninjo: no good capabilities to handle ensemble forecasts

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