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. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
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- Kalman Filter Data Assimilation: Targeting Observations and Parameter Estimation
- Bellsky, Thomas (Author)
- Kostelich, Eric (Author)
- Mahalov, Alex (Author)
- College of Liberal Arts and Sciences (Contributor)
- Digital object identifier: 10.1063/1.4871916
- Identifier TypeInternational standard serial numberIdentifier Value1054-1500
- Identifier TypeInternational standard serial numberIdentifier Value1089-7682
- 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