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