Campuses:

Covariance Matching; A Method for Estimating Model and Data Errors A Priori

Wednesday, May 1, 2002 - 3:00pm - 3:20pm
Keller 3-180
Ichiro Fukumori (National Aeronautics and Space Administration (NASA))
The a priori covariance matching provides an effective means of estimating model and data errors based on comparisons of observations and model simulation (non-assimilated free run). Data error employed in data assimilation is best regarded as data constraint error, because it is the sum of instrumental error of the observing system and errors of the model failing to resolve certain aspects of reality (model representation error). Model error concerns errors of what the models resolve. Adaptive methods have been advanced to estimate data and model errors as part of data assimilation. The a priori covariance matching provides an alternate method of estimating these errors prior to assimilation.

References:

Fu, L.-L., I. Fukumori and R. N. Miller, 1993. Fitting dynamic models to the Geosat sea level observations in the Tropical Pacific Ocean. Part II: A linear, wind-driven model, J. Phys. Oceanogr., 23, 2162-2181.

Fukumori, I., R. Raghunath, L. Fu, and Y. Chao, 1999. Assimilation of TOPEX/POSEIDON data into a global ocean circulation model: How good are the results?, J. Geophys. Res., 104, 25,647-25,665.