CIRA » Home » Research » Theme » Model Error and Bias
Imperfections of model equations and numerical schemes cause errors of the model. One of the most important components of this imperfection is model bias. We are developing methodologies for model bias and model error correction in ensemble data assimilation. In particular, we focus on estimation of uncertainty of model errors, which have important implications on the model error covariance learning process.
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