E
Eugenia Kalnay
Researcher at University of Maryland, College Park
Publications - 269
Citations - 56732
Eugenia Kalnay is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Data assimilation & Ensemble Kalman filter. The author has an hindex of 61, co-authored 259 publications receiving 52574 citations. Previous affiliations of Eugenia Kalnay include Goddard Space Flight Center & Eötvös Loránd University.
Papers
More filters
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Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation
TL;DR: In this paper, the local ensemble transform Kalman filter (LETKF) is used to develop a strongly coupled data assimilation (DA) system for an intermediate complexity ocean-atmosphere coupled model.
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Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model
Istvan Szunyogh,Eric J. Kostelich,Gyorgyi Gyarmati,D. J. Patil,Brian R. Hunt,Eugenia Kalnay,Edward Ott,James A. Yorke +7 more
TL;DR: It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy and the importance of testing the ensemble-based Kalman filter data assimilation systems on simulated observations is stressed.
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Accounting for Model Errors in Ensemble Data Assimilation
TL;DR: In this paper, the performance of the local ensemble transform Kalman filter (LETKF) is seriously degraded when compared with the perfect model scenario, and several methods to account for model errors including model bias and system noise are investigated.
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Data Assimilation as Synchronization of Truth and Model: Experiments with the Three-Variable Lorenz System*
Shu Chih Yang,Debra Baker,Hong Li,Katy Cordes,Morgan Huff,Geetika Nagpal,Ena Okereke,Josue Villafañe,Eugenia Kalnay,Gregory S. Duane +9 more
TL;DR: By adding realistic “noise” to observations of the master system, an optimal value of the coupling strength was clearly identifiable and Coupling only the y variable yielded the best results for a wide range of higher coupling strengths.
Journal ArticleDOI
Separating fast and slow modes in coupled chaotic systems
Malaquias Peña,Eugenia Kalnay +1 more
TL;DR: In this paper, a simple technique based on breeding was proposed to separate fast and slow unstable modes in coupled systems with different time scales of evolution and variable amplitudes, taking advantage of the earlier saturation of error growth rate of the fastest mode and of the lower value of the saturation amplitude of perturbation of either the fast or the slow modes.