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This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations.

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Date Created
  • 2014-06-01
Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.1063/1.4871916
    • Identifier Type
      International standard serial number
      Identifier Value
      1054-1500
    • Identifier Type
      International standard serial number
      Identifier Value
      1089-7682
    Note
    • Copyright 2014 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. along with the following message: The following article appeared in 24, 2 (2014) and may be found at http://dx.doi.org/10.1063/1.4871916, opens in a new window

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    Bellsky, Thomas, Kostelich, Eric J., & Mahalov, Alex (2014). Kalman filter data assimilation: Targeting observations and parameter estimation. CHAOS, 24(2),024406. http://dx.doi.org/10.1063/1.4871916

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