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Bias Estimation

  • Richard MénardEmail author
Chapter

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.

Keywords

Data Assimilation Bias Correction Model Bias Assimilation System Bias Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Air Quality Research DivisionEnvironment CanadaDorvalCanada

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