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current status long-term strategy mid-term strategy some ongoing or planned activities

CH , D , GR , I , PL , RO ( cosmo-model.cscs.ch ). Overview and Strategy on Data Assimilation for LM christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany Jürgen Steppeler. current status long-term strategy mid-term strategy some ongoing or planned activities.

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current status long-term strategy mid-term strategy some ongoing or planned activities

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  1. CH , D , GR , I , PL , RO ( cosmo-model.cscs.ch ) Overview and Strategy on Data Assimilation for LMchristoph.schraff@dwd.deDeutscher Wetterdienst, D-63067 Offenbach, GermanyJürgen Steppeler • current status • long-term strategy • mid-term strategy • some ongoing or planned activities

  2. Data Assimilation for LM: Current Status: Scheme based on Nudging Approach • operational continuous DA cycles at x = 7 km at DWD, MeteoSwiss, ARPA-EMR Meteo Swiss COSMO- LEPS • data assimilation scheme based on nudging technique • observations used operationally: radiosonde, aircraft, wind profiler synop, ship, buoy • adjusted variables: horizontal wind, temperature, relative humidity, ‘near-surface’ pressure • analysis of upper-air observations on horizontal surfaces (i.e. not on model levels) • explicit balancing: • temperature correction for surface pressure analysis increments • wind increments by weak geostrophic balancing • hydrostatic balancing of total analysis increments • robust • in most cases of investigated forecast failures: LM test runs from GME-OI analysis even worse • easily applicable to other model domains

  3. Data Assimilation for LM: Current Status on the Convective Scale LM on the convective scale: • deep convection explicit, shallow convection parameterised • prognostic precipitation (rain, snow, graupel) Model Domain of LM-K (DWD) LM-K DWD: - x = 2.8 km (421 x 461 grid pts.), 50 layers - 18-h forecasts every 3 hours - pre-operational (operational 2Q 2007) : LM-K MeteoSwiss: - x = 2.2 km , Alpine domain - (pre-)operational (2007) 2008 ARPA-SMR (Bologna), IMGW (PL) : similar plans Data Assimilation: • conventional observations: Nudging scheme as for x = 7 km LM version • in addition: use of radar-derived precipitation by latent heat nudging (→ talk by D. Leuenberger)

  4. Data Assimilation for LM: Long-term Vision & Strategy Generalized global + regional FC + DA: ICON (DWD + MPI) • global non-hydrostatic model with regional grid refinement for - global and regional modelling - NWP and climate • will replace GME and LM-E in 2010 & provide lateral boundaries for convective-scale LM-K • 3DVAR with Ensemble Transform Kalman Filter Long-term vision (for NWP) • PDFs: deliver not only deterministic forecasts, but a representation of the PDF (ensemble members with probabilities), particularly for the convective scale • use of indirect observations at high frequency even more important Long-term strategy • 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

  5. Data Assimilation for LM: Long-term Strategy for Convective Scale → Sequential Importance Re-Sampling (SIR) filter (Monte Carlo method) Ensemble members Observation (of quantity h) PDF 1. take an ensemble with a prior PDF Prior PDF 2. find the distance of each member to the obs (using any norm / H) Obs. PDF 3. combine prior PDF with distance to obs to obtain posterior PDF Members after re-sampling Posterior PDF 4. construct new ensemble reflecting posterior PDF 5. integrate to next observation time Forecast from re-sampled members weighting of ensemble members by observations and redistribution according to posterior PDF no modification of forecast fields h • COSMO should focus more and more on the convective scale (LM-K), & Ensemble DA should play a major role

  6. Data Assimilation for LM: Long-term Strategy for Convective Scale SIR method can handle the major challenges on the convective scale: • Non Gaussian PDF • Highly nonlinear processes • Model errors • Balance (unknown and flow-dependent) • Direct and indirect observations with highly nonlinear observation operators and norms • COSMO: gets lateral b.c. from LM-SREPS, provides initial conditions for LM-K EPS Potential problems: Ensemble size, filter can potential drift away from reality, but it cannot be brought back to right track without fresh blood, dense observations may not be used optimally However: • for LM-K: Strong forcing from lower and lateral boundaries expected to avoid drift into unrealistic states • if method does not work well the pure way: Fallback positions: • combine with nudging: (some) members be (weakly) influenced by nudging • approaches for localising the filter

  7. Data Assimilation for LM: Mid-term Strategy Mid-term strategy • start development of SIR (for the longer-term, with option to include nudging) • Nudging at moment: • robust and efficient • requires retrievals for use of indirect observations • no severe drawbacks (for short term, convective scale) if we can make retrievals available • further develop nudging, in particular retrieval techniques (for mid-term + fallback) • few examples outlined here

  8. Data Assimilation for LM: Radar Data: Simple Adjoint 3-D Wind Retrieval (PL) Legionowo (Warsaw) Radar 26-07-2003 [km] Doppler radial wind at 2000 m , 13:04 UTC horizontal wind retrieval • derive 3-dim. wind field from 3 consecutive scans of 3-d reflectivity and radial velocity at 10’-intervals, by means of a simple adjoint (SA) method (ARPS, Gao et. al. 2001) • Cost function with 2 observation terms : • for radial velocity: in a standard way • for a tracer (reflectivity): reflectivity from 1st scan advected with the retrieved velocity and compared to reflectivity observations from 2nd and 3rd scan • recently: noise problems for real data from Polish radars much reduced, method works now for single doppler radar

  9. Data Assimilation for LM: Ground-based GPS: ZTD / Integrated Water Vapour (D, CH) 0-h to 6-h LM forecast of precipitation valid for 20 June 2002, 6 UTC radar LM CNTL LM GPS • precipitation: positive cases outnumber negative cases only slightly • problem: vertical distribution of vertically integrated humidity information • better: vertical profiles  GPS Tomography • scaling of model’s humidity profiles (modified by layer representativeness weights) • positive impact on upper-air humidity and temperature forecasts • occasionally with significant positive impact on precipitiation

  10. Data Assimilation for LM: Ground-based GPS: Tomography (CH) • provides profiles, uses zenith and slant path delay (and 2-m humidity obs in Swiss study) • quasi-operationally produced: grid of 18 hourly humidity profiles over Switzerland • tomography can be supplemented with additional data to produce consistently high-quality profiles, e.g. • microwave radiances / WV channels • GPS occultation (transverse data) • satellite-derived cloud cover (or cloud analysis) • model fields possibly used as first guess GPS w. inter-voxel constraints GPS incl. screen-level obs + time constraints LM-aLMo analysis Radiosonde  provides all weather humidity profiles over land, at high spatial and temporal resolution • easily assimilated by nudging (at full temporal resolution) • need dense GPS networks

  11. Data Assimilation for LM: Cloud Analysis – Outline of Planned Method (D) cloud type(2 Feb 2006, 14 UTC) • Cloud Type product of MSG Nowcasting SAF used as cluster analysis to spread horizontally the vertical profiles • a class is assigned to each cloud profile, at several time levels • profiles spread only to pixels with same class (weighting depending on spatial and temporal distance) • cloud-top height adjusted for certain cloud types (model fields as background) • cloud analysis adjusted by radar information MSG1 (channels 1,2,9 ) • derivation of vertical profiles of cloudiness • from radiosonde humidity • from surface synoptic reports and ceilometers, using MSG IR brightness temperature and model fields as background • adjustment of specific humidity (optionally cloud water / ice , vertical velocity) • dynamic balance ? • work not started yet

  12. Data Assimilation for LM: Short- & Mid-term Work Short- & mid-term work • start development of SIR (for the longer-term, with option to include nudging) • further develop nudging, in particular retrieval techniques (for mid-term + fallback) , e.g. • precipitation derived from radar reflectivity: Latent Heat Nudging ( → talk by D. Leuenberger) • radar wind (+ reflectivity): simple adjoint 3-d wind retrieval / VAD profiles • ground-based GPS: (scaling of humidity profile, or) GPS tomography • cloud analysis • satellite radiances (ATOVS, SEVIRI, AIRS, IASI): 1DVAR • improve use of screen-level data and initialisation of PBL, include scatterometer wind over water • improve lower boundary (snow analysis, soil moisture analysis)

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