Bias and data assimilation

All data assimilation systems are affected by biases, caused by problems with the data, by approximations in the observation operators used to simulate the data, by limitations of the assimilating model, or by the assimilation methodology itself. A clear symptom of bias in the assimilation is the presence of systematic features in the analysis increments, such as large persistent mean values or regularly recurring spatial structures. Bias can also be detected by monitoring statistics of observed-minus-background residuals for different instruments. Bias-aware assimilation methods are designed to estimate and correct systematic errors jointly with the model state variables. Such methods require attribution of a bias to a particular source, and its characterization in terms of some well-defined set of parameters. They can be formulated either in a variational or sequential estimation framework by augmenting the system state with the bias parameters.

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