Data assimilation and observing systems strategies Pierre Gauthier Data Assimilation and Satellite Meteorology Division Meteorological Service of Canada Dorval, Québec CANADA Co-chair of the THORPEX working group on DAOS

Data assimilation and observing strategies Optimal use of observations –Adaptive observations (targeted observations) Deploy observations over regions where small changes lead to substantial changes in the forecasts –Better use of existing observations, particularly satellite data Satellite data Data assimilation methodology A few scientific objectives

Satellite data Relatively low proportion of received data makes its way to the assimilation (<20%) Observation error –Biases: assimilation is bias blind and innovations cannot distinguish between model and observation bias –Observation error correlation Characterization of surface emissivity to assimilate many satellite data types

Distribution of ATOVS satellite data received over a 6-h window

Distribution of ATOVS satellite data assimilated over a 6-h window

Channel selection of IASI radiances in meteorologically sensitive areas (Fourrié and Rabier, 2003)

Current and Planned Satellites (1/2) Current and Planned Satellites (1/2) Source: JCSDA (Joint Center for Satellite Data Assimilation) 13 th AMS Conf. 2004

Current and Planned Satellites (2/2) Source: JCSDA

Data assimilation methods Several NWP centres have now implemented 4D-Var –Significant impact on the forecasts –Better usage of satellite and asynoptic data –Issues on specific aspects of the implementation, particularly when it comes to humidity analysis Assimilation with a numerical model –Leads to model improvements and assimilation methodology –Attention needs to be paid to the details of the implementation

3D and 4D data screening 4D-Var 0-h-3h+3h          3D-Var 0-h-3h+3h         

Type4D-Var3D-VarDifference Aircrafts % Radiosonde ~0% Satwind % ATOVS % GOES % Profilers % Data assimilated 4D-Var vs 3D-Var (12Z 16 February 2005)

Anomaly correlation: winter period 4D-Var 3D-Var

Impact of the various components of 4D-Var TypeOuter loopsSimplified physics Observation thinning 3D-Var1-3D 3D-Var (FGAT) 1-3D 3D-Var (FGAT) 1-4D 4D-Var1 (simpler) 4D 4D-Var2 (simpler,simpler) 4D 4D-Var2 (simpler, better) 3D 4D-Var2 (simpler, better) 4D

August 2004 RMS error GZ 500 hPa Southern Hemisphere Impact of the various components of 4D-Var

4D-Var 3D-Var 4D-Var (simpler) 4D-Var (simpler,1 loop) 4D-Var (thinning 3D) 7% (better simplified physics) 3% (Updated trajectory) 35% (thinning 4D) (TL/AD dynamics) 55% FGAT (thinning 3D) FGAT (thinning 4D) 16% 18% Impact of the various components of 4D-Var

Total influence (%) of satellite and in-situ observations when assimilated by ECMWF 4DVar System. From Cardinali et al

Spectre d’erreur GZ 500 hPa (été 2003) 24h 48h 120h 3D-Var 24h 4D-Var 24h 3D-Var 48h 4D-Var 48h 3D-Var 120h 4D-Var 120h

Further developments in data assimilation methods Background term –Up to now: little (but positive) impact –Requirements for the assimilation of fine scale structures, particularly in the humidity field –Hybrid methods (EnKF +4D-Var?) Nonlinearities –Observation and physical parameterizations Weak-constraint 4D-Var –Extending the assimilation window (Fisher, 2004) –Dealing with model error Surface analyses, high-resolution analysis for mesoscale models

Verification of 48-h forecasts against radiosondes observations over North America Regional forecast issued directly from the 4D-Var global analysis 12-h regional assimilation cycle initiated from the 4D-Var global analysis Impact of 4D-Var analysis on regional (15 km) forecasts (24 winter case)

Impact of 4D-Var global analysis on regional 3D- Var cycle 1 case : 48 hr forecast valid on November 16 th 2004, at 12z

Subjective Evaluation (Winter ) % in favor of 3D-Var or 4D-Var

A few scientific objectives (1) THORPEX regional campaigns –Storm Winter Reconnaissance Program (US) over the North Pacific since 1998 –Fall of 2003 in the North Atlantic (A-TReC 2003) –Pacific campaign: Seattle meeting 6-7 June 2005 What needs to be observed to improve the large scale forecasts –Design of TReCs by learning from previous ones –Recommendations for future campaigns

A few scientific objectives (2) Improving the assimilation of existing satellite data –What is not currently well observed (e.g., winds) –Estimation of observation error characteristics –Targeting methods Impact of large-scale improvements on local short- term forecasts (downscaling) –Relevant weather elements for socio-economic studies often need the magnifying glass of a higher resolution model Ensemble prediction –Impact of changes in the observation network on the estimated variability in ensemble prediction systems

Error variance estimated with a Kalman filter (Radiosonde coverage only) (Gauthier et al., 1993)

Error variance estimated with a Kalman filter (Radiosonde and satellite coverage)

The End

ATReC h Singular vector SV1 at initial time (Zadra and Buehner) Valid time: 5 Dec UTC MSC-GEM Simplified physics Vertical diffusion orographic blocking and GWD stratiform condensation convection Computed with dry physics Computed with moist physics