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Data assimilation and observing systems strategies

Explore optimal use of satellite data, assimilation methods, and the impact on forecasts. Learn about observational strategies and advancements in NWP centers. Enhance your knowledge in assimilation techniques to improve forecast models.

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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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