A local ensemble Kalman filter for atmospheric data assimilation
Edward Ott,Brian R. Hunt,Istvan Szunyogh,Aleksey V. Zimin,Eric J. Kostelich,M. Corazza,Eugenia Kalnay,D. J. Patil,James A. Yorke +8 more
TL;DR: A new, local formulation of the ensemble Kalman filter approach for atmospheric data assimilation based on the hypothesis that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region.
read more
Abstract: In this paper, we introduce a new, local formulation of the ensemble Kalman filter approach for atmospheric data assimilation. Our scheme is based on the hypothesis that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region. Ensemble Kalman filters, in general, take the analysis resulting from the data assimilation to lie in the same subspace as the expected forecast error. Under our hypothesis the dimension of the subspace corresponding to local regions is low. This is used in our scheme to allow operations only on relatively low-dimensional matrices. The data assimilation analysis is performed locally in a manner allowing massively parallel computation to be exploited. The local analyses are then used to construct global states for advancement to the next forecast time. One advantage, which may take on more importance as ever-increasing amounts of remotely-sensed satellite data become available, is the favorable scaling of the computational cost of our method with increasing data size, as compared to other methods that assimilate data sequentially. The method, its potential advantages, properties, and implementation requirements are illustrated by numerical experiments on the Lorenz-96 model. It is found that accurate analysis can be achieved at a cost which is very modest compared to that of a full global ensemble Kalman filter.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
The Data Assimilation Research Testbed: A Community Facility
Jeffrey L. Anderson,Timothy J. Hoar,Kevin Raeder,Hui Liu,N. Collins,Ryan D. Torn,Avelino Avellano +6 more
- 01 Sep 2009
TL;DR: DART is an open-source community facility for data assimilation research and education, providing tools and algorithms for building state-of-the-art data assimilation systems.
171
Asynchronous data assimilation with the EnKF
TL;DR: In this paper, the authors revisited the problem of assimilation of asynchronous observations, or four-dimensional data assimilation, with the ensemble Kalman filter (EnKF), and showed that for a system with perfect model and linear dynamics, the EnKS provides a simple and efficient solution for the problem: one just needs to use the ensemble observations (that is, the forecast observations for each ensemble member) from the time of observation during the update, for each assimilated observation.
Simultaneous assimilation of satellite NO2, O3, CO, and HNO3 data for the analysis of tropospheric chemical composition and emissions
Kazuyuki Miyazaki,Kazuyuki Miyazaki,Henk Eskes,Kengo Sudo,Kengo Sudo,Masayuki Takigawa,M. van Weele,K. F. Boersma,K. F. Boersma +8 more
TL;DR: In this article, the authors developed an advanced chemical data assimilation system to combine observations of chemical compounds from multiple satellites, which simultaneously optimized the chemical species, as well as the emissions of O3 precursors, while taking their chemical feedbacks into account.
Sampling the posterior: An approach to non-Gaussian data assimilation
TL;DR: In this article, a range of techniques for probing the posterior distribution, based around the Langevin equation, are proposed and compared with existing methods, and the relationship between the Bayesian approach outlined here and the commonly used Kalman filter based techniques, prevalent in practice, is discussed.
167
Impacts of localisation in the EnKF and EnOI: experiments with a small model
TL;DR: EnOI may provide a practical and cost-effective alternative to the EnKF for some applications where computational cost is a limiting factor and localisation can significantly compromise the model’s dynamical balances.
165
References
A New Approach to Linear Filtering and Prediction Problems
Tamer Basar
- 01 Jan 2001
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
22.7K
New Results in Linear Filtering and Prediction Theory
R. E. Kalman,R. S. Bucy +1 more
TL;DR: The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results and properties of the variance equation are of great interest in the theory of adaptive systems.
6.9K
Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
TL;DR: In this article, a new sequential data assimilation method is proposed based on Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter.
Chaos in dynamical systems
Edward Ott
- 01 Jan 1993
TL;DR: In the new edition of this classic textbook, the most important change is the addition of a completely new chapter on control and synchronization of chaos as discussed by the authors, which will be of interest to advanced undergraduates and graduate students in science, engineering and mathematics taking courses in chaotic dynamics, as well as to researchers in the subject.
3.4K