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Strategy defined in WG1 Workshop: sequential importance resampling (SIR) filter

WG1 Projects and New Activities christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany. Strategy defined in WG1 Workshop: sequential importance resampling (SIR) filter continue to develop nudging, in particular retrieval methods COSMO Projects:

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Strategy defined in WG1 Workshop: sequential importance resampling (SIR) filter

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  1. WG1 Projects and New Activitieschristoph.schraff@dwd.deDeutscher Wetterdienst, D-63067 Offenbach, Germany

  2. Strategy defined in WG1 Workshop: • sequential importance resampling (SIR) filter • continue to develop nudging, in particular retrieval methods COSMO Projects: • Sequential importance resampling (SIR) filter • Retrievals for nudging: 1dVar for satellite radiances (in practice, was already a running project) 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 • humidity profiles from GPS tomography (submitted, but not accepted as project) • latent heat nudging • Doppler radial velocity (SAR method)

  3. Project: Sequential Importance Resampling (SIR) Filter Aim: Development of a prototype for a new data assimilation system for the convective scale by means of a SIR filter Motivation: Method avoids problems related to strong non-linearity, unknown and flow-dependent balance, and non-Gaussian probability densities present in the convective scale. Approach provides the initial conditions for the convective-scale ensemble forecasting. Prerequisites: Availability of a realistic larger scale ensemble with very short-range spread (LM SREPS) for providing the lateral boundary conditions. Tasks: - develop a very short-range convective-scale (LMK) ensemble system - define norm (estimate most likely members by measuring ‘distance’ obs.) - develop resampling strategy for constructing a new ensemble reflecting posterior pdf - evaluate performance Required Resources: - required this year: 2.5 FTE - able to contribute: 1.0 FTE, plus possibly 0.5 (project DAQUA) -required in total: 15 FTE (up to 2009) - Project Leader: not known (DWD)

  4. Project: Retrievals for Nudging: 1D-Var for Satellite Radiances Aim: Operational assimilation of ATOVS and SEVIRI data for LM Motivation: Use of satellite radiances especially over data-sparse areas over sea has the potential to provide essential information to improve forecasts. Note: Memory management for 1D-Var and flow of processing facilitates inclusion of further satellite instruments in the future. Tasks: - finalise prototype that provides simulated satellite radiances and performs minimisation of cost function for generating the retrievals - evaluation of cloud detection and computation of bias correction - assess usability over land - tuning 1D-Var retrievals, horizontal thinning, background errors, etc. - assimilation of retrievals, trial experiments, tuning nudging Required Resources: - required this year: 2 - 2.5 FTE - able to contribute: 2.2 FTE ( 0.6 Di Giuseppe ARPA-EMR, 1.2 Krzeminski + Eumetsat Fellowship (for AIRS/ IASI data) IMGW, (0.4 Hess, Schraff DWD)) - required in total: 4 FTE, within next 2 years - Project Leader: Reinhold Hess (DWD)

  5. 1.1 High Use of Radar Data 1.1.1 High Latent Heat NudgingStefan Klink (DWD), Klaus Stephan (DWD), Daniel Leuenberger (MCH)Christoph Schraff (DWD) • DWD: Reduce (negative effects of) too strong forcing of LHN, and at the same time retain positive impact on precipitation forecasts. • Further diagnosis to understand problems (0-UTC runs, limited forecast impact, etc.), improve / tune scheme, consider e.g.: • role of gravity waves • vertical structure of precipitating cells (e.g. wind field) • environment of precipitation cells (moisture convergence) • vertical distribution of LHN increments • horizontal filtering • introduction of PI-data (international composite) outside the German DX-area • further use of cloud type product of Nowcasting-SAF within LHN (humidity adjustment) • LMK test suites (periods up to three months), with comprehensive verification • MCH: idealised experiments with LHN using prognostic precipitation, run test suite with LHN, apply LHN to dynamical ensemble from DLR (VERTICATOR case)

  6. 1.1.2 High 3D Simple Adjoint Wind RetrievalJerzy Achimowicz (IMGW) • Ongoing, continue to work on quality control / de-aliasing / filtering / gap filling etc. • OSSE to evaluate realism of retrievals and tune weights in cost functions 1.1.3 VAD winds (monitoring)Michael Buchhold (DWD); Oliver Marchand (MCH) • DWD: another round of monitoring after hopefully better data available from DWD network in 1Q/06 after revision of VAD pre-processing (local effort) • MCH: evaluate operational assimilation of wind profilers and (at least) Swiss VAD. (wind profiler: mainly technical local implementation work required) 1.1.4 Nudging of Profiles derived from LAPS analysesFabrizio Nerozzi, Davide Cesari, Pier-Paolo Alberoni, Tiziana Paccagnella (ARPA-EMR) • impact study with assimilating dense arrays of hourly profiles, results available soon

  7. 1.2 High Multi-Sensor Humidity Analysis (incl. GPS-obs)Jean-Marie Bettems, Daniel Leuenberger (MeteoSwiss) • GPS tomography • Long-term assessment of profile quality (monitoring) • include aLMo first guess in retrieval algorithm (and possibly further obs) • Impact on analysis and forecast quality, tuning of structure functions etc. • further work on tomography e.g. by ETHZ (Troller) and GFZ Potsdam / Uni Leipzig • 50 % project submitted (DAQUA), otherwise resources unclear because topic does not count for FTE • ZTD-derived integrated water vapour: some resources may be available 1.3 Production and Use of Cloud AnalysisChristoph Schraff (DWD) • Some resources hopefully at DWD (up to 0.3 FTE) • Produce profiles from radiosonde, synop, ceilometers, etc. • use MSG-NWCSAF cloud type product as cluster analysis • (result might be ingested into GPS tomography)

  8. 1.4.3 ATOVS retrievals for HRMMassimo Bonavita, Antonio Vocino (CNMCA) • assimilation of radiances directly into 3DVAR 1.5 Assimilation of Scatterometer WindHeinz-Werner Bitzer (MetBw), Christina Köpken, Alexander Cress (DWD) • first tests with LM postponed to 12/05

  9. 1.6 Evaluation / Monitoring / Tuning of Nudging Christoph Schraff (DWD) • Subject to upcoming problems in nudging, possibly tuning for LMK required 1.6.1 Temporal WeightsJean-Marie Bettems (MCH) • further tests to selectively reduce time window for radiosonde wind only 1.6.2 Assimilation of Screen-level ObservationsOliver Marchand (MCH), Andrea Rossa (ARPA-Veneto), Antonella Sanna (ARPA-Piemonte • MCH, ARPAV: 10-m wind: data selection, vertical (and horizontal) spreading, impact studies • MCH, ARPAP: 2-m temperature, 2-m humidity: data selection, vertical (and horizontal) spreading, impact studies

  10. 1.7 (Sub-) Surface Analyses 1.7.1 Soil Moisture InitialisationMartin Lange (DWD) • Investigation to replace the variationally derived relationship between 2-m temperature and soil moisture by a parameterised regression as a gradient used to minimise the cost function in the variational scheme. This would render obsolete the extra model forecast integrations required in the current SMI scheme. 1.7.2 Snow Cover AnalysisJean-Marie Bettems (MCH) • MCH: implement new snow analysis pre-operationally in aLMo/7 cycle for next winter. • Further refinement of snow mask algorithm, e.g. by including 2-m temperature data ARPAP: exchange in-situ data, and validate new product • high-resolution cloud mask by additional use of HRV channel • refinement of algorithm to combine cloud mask and in-situ observation in a snow analysis • Cloud mask product and cloud analysis: evaluate impact for longer periods 1.7.3 Use of Lake Temperature AnalysisDimitri Mironov (DWD) • To be checked (at DWD)

  11. Final Remarks nudging needs retrievals techniques for indirect observations: • radar reflectivity : latent heat nudging • radar wind : variational simple-adjoint retrieval • GPS, humidity : tomography • cloud info : cluster analysis • sat radiances : 1dVar • need not be optimal in practice to put everything in one big pot ‘3DVAR’, also retrieval approaches for some data (reflectivity, cloudy radiances,...) • in convective scale, balances not well known, flow-dependent • Ensemble approaches, nudging can be combined e.g. with SIR filter • nudging still an option on convective scale

  12. 1.4 High Use of 1dVar Satellite Retrievals Reinhold Hess, Christoph Schraff (DWD) 1.4.1 High MSG Francesca di Giuseppe (ARPA-EMR) 1.4.2 High ATOVS Blazej Krzeminski (IMGW) • since then: develop interface of 1dVar routines into LM / nudging (completed: reading of 1dVar input files) • work well coordinated and focused • Perspective: Éxtension of methodology to cloudy radiances by retrieving profiles of cloud cover, liquid water content and ice content, and converting them to temperature and humidity profiles by employing the diagnostic cloud scheme to LM and its adjoint.

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