Challenges and practical applications of data assimilation in numerical weather prediction

Data Assimilation Education ForumPart I: Overview of Data Assimilation

January 21, 2008

presented byStephen Lord

Director, Environmental Modeling Center

NCEP/NWS/NOAA

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.

Data Assimilation Information Sources

– Observations– Background (forecast)– Dynamics (e.g., balances between

variables)– Physical constraints (e.g., q > 0)– Statistics– Climatology

Atmospheric analysis problem (theoretical)

J = Jb + Jo + Jc

J = (x-xb)TBx-1(x-xb) + (K(x)-O)T(E+F)-1(K(x)-O) + JC

J = Fit to background + Fit to observations + constraints

x = Analysisxb = BackgroundBx = Background error covarianceK = Forward model (nonlinear)O = ObservationsE+F = R = Instrument error + Representativeness

errorJC = Constraint term

Data Assimilation Techniques

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

Data Assimilation Development Strategy (2)

Description Lead Org.

Encouraging Risk FactorsAll: cost (computer+human) increase ~3-10x

SDBE+S4DV

Extension of GSI

NCEP/EMC

Evolutionary development pathExperience through RTMAGSI operational 2007:Q3

Definition of appropriate covariance uncertainMultiple approaches (incl. ensembles)

C4DV Strong constraintModel 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?

EnsDA Several algorithms proposedSupported by THORPEX

THORPEXCONSORTIUM

Good results at low res & low data volumesNo ATLRelatively simple algorithms

Ens. Degrees Of Freedom may not be sufficient (esp. at hires)Data handling for large data volumes challengingObs & model bias correctionCovariance inflation, area averaging are questionable but required

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

NCEP Data Assimilation (2)

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