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
15
Embed
Challenges and practical applications of data assimilation in numerical weather prediction Data Assimilation Education Forum Part I: Overview of Data Assimilation.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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
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