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13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 1christoph.schraff@dwd.de
• 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 )
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 2christoph.schraff@dwd.de
• 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
MeteoSwiss
COSMO-LEPS
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 3christoph.schraff@dwd.de
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
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 4christoph.schraff@dwd.de
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
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 5christoph.schraff@dwd.de
→ Sequential Importance Re-Sampling (SIR) filter (Monte Carlo method)
h
Ensemble members
Observation (of quantity h)
PDF Prior PDF 1. take an ensemble with a prior PDF
Obs. PDF 2. find the distance of each member to the obs (using any norm / H)
Posterior PDF 3. combine prior PDF with distance to obs to obtain posterior PDF
Members after re-sampling 4. 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
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 6christoph.schraff@dwd.de
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 avoiddrift 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
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 7christoph.schraff@dwd.de
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
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 8christoph.schraff@dwd.de
• 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 :
1. for radial velocity: in a standard way
2. for a tracer (reflectivity): reflectivity from 1st scan advected with the retrieved velocity and compared to reflectivity observations from 2nd and 3rd 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
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 9christoph.schraff@dwd.de
• 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
0-h to 6-h LM forecast of precipitationvalid for 20 June 2002, 6 UTC
LM CNTLradar 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)
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 10christoph.schraff@dwd.de
• 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 constraintsLM-aLMo analysisRadiosonde
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
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 11christoph.schraff@dwd.de
• 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 )
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 12christoph.schraff@dwd.de
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
13th SRNWP / 28th EWGLAM MeetingZürich, 9 – 12 Oct 2006 13christoph.schraff@dwd.de
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