18th Conference on Weather and Forecasting, 14th Conference on Numerical Weather Prediction, and Ninth Conference on Mesoscale Processes

Monday, 30 July 2001
Estimation of model and data biases using the variational data assimilation method
Chungu Lu, NOAA/ERL/FSL, Boulder, CO; and O. Talagrand
Forecast biases result from a combination of accumulative model bias and data bias in the initial condition. When a biased model and/or biased data are used in a data assimilation system, it is conjectured that these biases will compulsorily be ingested into the system. Incidentally, the adjoint solution at its minimum from a variational data assimilation procedure, in particular, should contain information about model and data biases. It turns out that a variational data assimilation procedure can provide an approach to estimating model and/or data biases in forecasts, and the difference between a variational assimilated initial field and an observed initial field gives an estimation of forecast errors.

Because a data assimilation procedure allows for continuous data ingest into model integration, the retrieved forecast errors using this method may contain crucial time- and flow-variation information, thus may possess better error representativeness. One of the applications of the proposed method is the specification of the forecast (background) error-covariance matrix in a data assimilation system. This would suggest a two-step assimilation process: first, to conduct a simple (with no background term) and strong model-constraint assimilation to generate ``analysis-consistent" background forecast errors; second, to complete the full data assimilation (either 3D or 4D) by implementing in the developed forecast error covariances.

Supplementary URL: