1. Challenges and practical applications of data assimilation in numerical weather prediction Data Assimilation Education Forum Part I: Overview of Data Assimilation January 21, 2008 presented by Stephen Lord Director, Environmental Modeling Center NCEP/NWS/NOAA

2. WHY Data Assimilation Data assimilation brings together all available information to make the best possible estimate of: –The atmospheric state –The initial conditions to a model which will produce the best forecast.

4. Data Assimilation Information Sources –Observations –Background (forecast) –Dynamics (e.g., balances between variables) –Physical constraints (e.g., q > 0) –Statistics –Climatology

7. Atmospheric analysis problem (theoretical) J = J b + J o + J c J = (x-x b ) T B x -1 (x-x b ) + (K(x)-O) T (E+F) -1 (K(x)-O) + J C J = Fit to background + Fit to observations + constraints x= Analysis x b = Background B x = Background error covariance K= Forward model (nonlinear) O= Observations E+F= R = Instrument error + Representativeness error J C = Constraint term

8. Data Assimilation Techniques

9. Data Assimilation Development Strategy (1) Three closely related efforts –Develop Situation-Dependent Background Errors (SDBE) and Simplified 4D-Var (S4DV) –“Classical” 4D-Var (C4DV) –Ensemble Data Assimilation (EnsDA) Partners –NCEP/EMC –NASA/GSFC/GMAO –THORPEX consortium (TC) NOAA/ESRL CIRES U. Maryland U. Washington NCAR

10. Data Assimilation Development Strategy (2) DescriptionLead Org. EncouragingRisk Factors All: cost (computer+human) increase ~3-10x SDBE+ S4DV Extension of GSI NCEP/EMCEvolutionary development path Experience through RTMA GSI operational 2007:Q3 Definition of appropriate covariance uncertain Multiple approaches (incl. ensembles) C4DVStrong constraint Model Adjoint + Tangent Linear (ATL) NASA/ GMAO Positive impact at other WX centers (ECMWF, UKMO, CMC, JMA) Various approximations Cost + (3x code) Which forecast model will be used? EnsDASeveral algorithms proposed Supported by THORPEX THORPEX CONSORT IUM Good results at low res & low data volumes No ATL Relatively simple algorithms Ens. Degrees Of Freedom may not be sufficient (esp. at hires) Data handling for large data volumes challenging Obs & model bias correction Covariance inflation, area averaging are questionable but required

13. NCEP Data Assimilation (1) 3d-var system: Gridpoint Statistical Interpolation (GSI) 19 million gridpoints (768x386x64) 7 analysis variables [T, Q, Ps, wind (2), ozone, cloud water] 28 minutes 160 IBM Power 5 processors

14. NCEP Data Assimilation (2)

15. NCEP Data Assimilation (3) Plans –Implement FOTO – Spring 2008 –Collaborate with GMAO to work on 4d-var system (if resources available) –Add new observations – Summer 2008 (or earlier) ASCAT – surface winds NOAA-18 ozone SSM/IS – microwave sounding radiances IASI – European advanced IR sounder