Abstract
One of the standard assumptions in data assimilation is that observation and model errors are purely random, i.e., they do not contain systematic errors (see chapter Mathematical Concepts of Data Assimilation, Nichols). In reality, the distinction between random errors and systematic errors is somewhat academic.
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The author wishes to thank Stephen Cohn for the careful review of the manuscript, and Olivier Talagrand and Dick Dee for their thoughtful review which helped clarify the assumptions and limitations built in these algorithms.
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Ménard, R. (2010). Bias Estimation. In: Lahoz, W., Khattatov, B., Menard, R. (eds) Data Assimilation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74703-1_6
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