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Data assimilation and observing systems strategies

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.

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Data assimilation and observing systems strategies

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  1. Data assimilation and observing systems strategies Pierre GauthierData Assimilation and Satellite Meteorology DivisionMeteorological Service of CanadaDorval, Québec CANADA Co-chair of the THORPEX working group on DAOS

  2. 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

  3. 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

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

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

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

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

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

  9. 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

  10. ´ ´ ´ ´ ´ ´ ´ ´ 3D-Var ´ -3h 0-h +3h ´ ´ ´ ´ ´ ´ ´ ´ 4D-Var ´ -3h 0-h +3h 3D and 4D data screening

  11. Data assimilated 4D-Var vs3D-Var(12Z 16 February 2005)

  12. 4D-Var Anomaly correlation: winter period 3D-Var

  13. Impact of the various components of 4D-Var

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

  15. 3D-Var 16% FGAT (thinning 3D) 18% FGAT (thinning 4D) (TL/AD dynamics) 55% 4D-Var (thinning 3D) 35% (thinning 4D) 4D-Var (simpler,1 loop) 3% (Updated trajectory) 4D-Var (simpler) 7% (better simplified physics) 4D-Var Impact of the various components of 4D-Var

  16. Total influence (%) of satellite and in-situ observations when assimilated by ECMWF 4DVar System. From Cardinali et al. 2004.

  17. 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

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

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

  20. Subjective Evaluation (Winter 2004-2005)% in favor of 3D-Var or 4D-Var

  21. 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: 2007-2008 • 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

  22. 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

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

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

  25. The End

  26. Computed with dry physics • ATReC 026 • 48-h Singular vector SV1 at initial time • (Zadra and Buehner) • Valid time: 5 Dec. 2003 12 UTC • MSC-GEM • Simplified physics • Vertical diffusion • orographic blocking and GWD • stratiform condensation • convection Computed with moist physics

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