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Istvan Szunyogh
Researcher at Texas A&M University
Publications - 96
Citations - 6297
Istvan Szunyogh is an academic researcher from Texas A&M University. The author has contributed to research in topics: Data assimilation & Ensemble Kalman filter. The author has an hindex of 28, co-authored 94 publications receiving 5712 citations. Previous affiliations of Istvan Szunyogh include Eötvös Loránd University & National Oceanic and Atmospheric Administration.
Papers
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Journal ArticleDOI
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter
TL;DR: A practical method for data assimilation in large, spatiotemporally chaotic systems, 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.
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Efficient Data Assimilation for Spatiotemporal Chaos: a Local Ensemble Transform Kalman Filter
TL;DR: In this article, the authors describe a method for data assimilation in large, spatio-temporally chaotic systems, in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states.
Journal ArticleDOI
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.
Posted Content
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.
Journal ArticleDOI
Four-dimensional ensemble Kalman filtering
Brian R. Hunt,Eugenia Kalnay,Eric J. Kostelich,Edward Ott,D. J. Patil,Timothy Sauer,Istvan Szunyogh,James A. Yorke,Aleksey V. Zimin +8 more
TL;DR: This paper shows that the ensemble approach makes possible an additional benefit: the timing of observations, whether they occur at the assimilation time or at some earlier or later time, can be effectively accounted for at low computational expense.