1. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 1 current status long-term strategy mid-term strategy some ongoing or planned activities Overview and Strategy on Data Assimilation for LM christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany Jürgen Steppeler CH, D, GR, I, PL, RO ( cosmo-model.cscs.ch )

2. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 2 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 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

3. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 3 LM on the convective scale: deep convection explicit, shallow convection parameterised prognostic precipitation (rain, snow, graupel) 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) Data Assimilation for LM: Current Status on the Convective Scale 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 Model Domain of LM-K (DWD) LM-K

4. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 4 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 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 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 Data Assimilation for LM: Long-term Vision & Strategy

5. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 5 Sequential Importance Re-Sampling (SIR) filter (Monte Carlo method) h Ensemble members Observation (of quantity h) PDF Prior PDF1. take an ensemble with a prior PDF Obs. PDF2. find the distance of each member to the obs (using any norm / H) Posterior PDF3. combine prior PDF with distance to obs to obtain posterior PDF Members after re-sampling4. construct new ensemble reflecting posterior PDF Forecast from re-sampled members 5. integrate to next observation time weighting of ensemble members by observations and redistribution according to posterior PDF no modification of forecast fields COSMO should focus more and more on the convective scale (LM-K), & Ensemble DA should play a major role Data Assimilation for LM: Long-term Strategy for Convective Scale

6. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 6 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 Data Assimilation for LM: Long-term Strategy for Convective Scale 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. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 7 Mid-term strategy start development of SIR (for the longer-term, with option to include nudging) Data Assimilation for LM: Mid-term Strategy 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. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 8 derive 3-dim. wind fieldfrom 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 : 1.for radial velocity:in a standard way 2.for a tracer (reflectivity):reflectivity from 1 st scan advected with the retrieved velocity and compared to reflectivity observations from 2 nd and 3 rd scan horizontal wind retrievalDoppler radial wind at 2000 m, 13:04 UTC [km] Legionowo (Warsaw) Radar 26-07-2003 Data Assimilation for LM: Radar Data: Simple Adjoint 3-D Wind Retrieval (PL) recently: noise problems for real data from Polish radars much reduced, method works now for single doppler radar

9. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 9 scaling of models humidity profiles (modified by layer representativeness weights) positive impact on upper-air humidity and temperature forecasts occasionally with significant positive impact on precipitiation 0-h to 6-h LM forecast of precipitation valid for 20 June 2002, 6 UTC LM CNTL radar LM GPS precipitation: positive cases outnumber negative cases only slightly problem: vertical distribution of vertically integrated humidity information better: vertical profiles GPS Tomography Data Assimilation for LM: Ground-based GPS: ZTD / Integrated Water Vapour (D, CH)

10. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 10 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 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 Data Assimilation for LM: Ground-based GPS: Tomography (CH) 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. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 11 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 Data Assimilation for LM: Cloud Analysis – Outline of Planned Method (D) adjustment of specific humidity (optionally cloud water / ice, vertical velocity) dynamic balance ? work not started yet 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 cloud type (2 Feb 2006, 14 UTC ) MSG1 (channels 1,2,9 )

12. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 12 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) Data Assimilation for LM: Short- & Mid-term Work

13. 13 th SRNWP / 28 th EWGLAM Meeting Zürich, 9 – 12 Oct 2006 christoph.schraff@dwd.de 13