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.
This research topic is a component of other research topics
and research projects
- Ensemble data assimilation system based on control theory
- Impact of fundamental assumptions of probabilistic data assimilation/ensemble forecasting: conditional mode vs. conditional mean
- Weak constraint approach to ensemble data assimilation: Application to microwave precipitation observations
- Research and development for GOES-R risk reduction
- Ensemble Kalman filtering for Army-scale meteorology
This theme is addressed in the following projects:
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