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Bias Estimation

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Data Assimilation

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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Acknowledgments

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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Authors and Affiliations

  1. Air Quality Research Division, Environment Canada, Dorval, Canada

    Richard Ménard

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  1. Richard Ménard
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Correspondence to Richard Ménard .

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Editors and Affiliations

  1. Dept. Meteorology, University of Reading, Earley Gate, Reading, RG66BB, United Kingdom

    William Lahoz

  2. Arapahoe Avenue 373, Boulder, 80302, Colorado, USA

    Boris Khattatov

  3. Atmospheric Science & Technology, Directorate, Environment Canada, Trans-Canada Highway 2121, Dorval, H9P 1J3, Québec, Canada

    Richard Menard

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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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