TY - JOUR
T1 - Efficient data assimilation for spatiotemporal chaos
T2 - A local ensemble transform Kalman filter
AU - Hunt, Brian R.
AU - Kostelich, Eric
AU - Szunyogh, Istvan
N1 - Funding Information:
Many colleagues have contributed to this article in various ways. We thank M. Cornick, E. Fertig, J. Harlim, H. Li, J. Liu, T. Miyoshi, and J. Whitaker for sharing their thoughts and experiences implementing and testing the LETKF algorithm in a variety of scenarios. We also thank C. Bishop, T. Hamill, K. Idle, E. Kalnay, E. Ott, D. Patil, T. Sauer, J. Yorke, M. Zupanski, and the anonymous reviewers for their generous input. This feedback resulted in many improvements to the exposition in this article and to our implementation of the algorithm. We thank Y. Song, Z. Toth, and R. Wobus for providing us with the observations and benchmark analyses used in Section 5 , and we thank G. Gyarmati for developing software to read the observations on our computers. This research was supported by grants from NOAA/THORPEX, the J. S. McDonnell Foundation, and the National Science Foundation (grant #ATM034225). The second author gratefully acknowledges support from the NSF Interdisciplinary Grants in the Mathematical Sciences program (grant #DMS0408012).
PY - 2007/6
Y1 - 2007/6
N2 - Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system's time evolution. Rather than solving the problem from scratch each time new observations become available, one uses the model to "forecast" the current state, using a prior state estimate (which incorporates information from past data) as the initial condition, then uses current data to correct the prior forecast to a current state estimate. This Bayesian approach is most effective when the uncertainty in both the observations and in the state estimate, as it evolves over time, are accurately quantified. In this article, we describe a practical method for data assimilation in large, spatiotemporally chaotic systems. The method is a type of "ensemble Kalman filter", in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states. We discuss both the mathematical basis of this approach and its implementation; our primary emphasis is on ease of use and computational speed rather than improving accuracy over previously published approaches to ensemble Kalman filtering. We include some numerical results demonstrating the efficiency and accuracy of our implementation for assimilating real atmospheric data with the global forecast model used by the US National Weather Service.
AB - Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system's time evolution. Rather than solving the problem from scratch each time new observations become available, one uses the model to "forecast" the current state, using a prior state estimate (which incorporates information from past data) as the initial condition, then uses current data to correct the prior forecast to a current state estimate. This Bayesian approach is most effective when the uncertainty in both the observations and in the state estimate, as it evolves over time, are accurately quantified. In this article, we describe a practical method for data assimilation in large, spatiotemporally chaotic systems. The method is a type of "ensemble Kalman filter", in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states. We discuss both the mathematical basis of this approach and its implementation; our primary emphasis is on ease of use and computational speed rather than improving accuracy over previously published approaches to ensemble Kalman filtering. We include some numerical results demonstrating the efficiency and accuracy of our implementation for assimilating real atmospheric data with the global forecast model used by the US National Weather Service.
KW - Data assimilation
KW - Ensemble Kalman filtering
KW - Spatiotemporal chaos
KW - State estimation
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U2 - 10.1016/j.physd.2006.11.008
DO - 10.1016/j.physd.2006.11.008
M3 - Article
AN - SCOPUS:34248675795
SN - 0167-2789
VL - 230
SP - 112
EP - 126
JO - Physica D: Nonlinear Phenomena
JF - Physica D: Nonlinear Phenomena
IS - 1-2
ER -