Treating Model Error in 3-D and 4-D Data Assimilation
The aim of a data assimilation scheme is to use measured observations in combination with a dynamical system model to derive accurate estimates of the current and future states of the system. In operational schemes for atmosphere and ocean forecasting, the model equations are generally assumed to be a ‘perfect’ representation of the true dynamical system and are treated as strong constraints in the assimilation process. The model equations do not, in practice, represent the system behaviour exactly, however, and model errors arise due to lack of resolution and inaccuracies in physical parameters, boundary conditions and forcing terms. Errors also occur due to discrete approximations and random disturbances.
KeywordsModel Error Data Assimilation Extend Kalman Filter Bias Error Augmented System
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