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
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Journal ArticleDOI
Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions.
Yan Li,Yan Li,Damien Sulla-Menashe,Safa Motesharrei,Xiao-Peng Song,Eugenia Kalnay,Qing Ying,Shuangcheng Li,Zongwen Ma +8 more
TL;DR: Although several MODIS datasets support an overall forest increase in China, the direction and magnitude of net forest change is still unknown due to the large uncertainties in satellite-derived estimates.
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Lyapunov, singular and bred vectors in a multi-scale system: an empirical exploration of vectors related to instabilities
TL;DR: In this paper, the authors compared three types of Lyapunov vectors (LVs), singular vectors (SVs) and bred vectors (BVs) to predict regime changes and the duration of new regime based on their growth rates in the last orbit of the old regime.
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Using Singular Value Decomposition to Parameterize State-Dependent Model Errors
TL;DR: In this paper, the authors used the singular value decomposition (SVD) to generate a basis of model errors and states and showed that the SVD method explains a significant component of the effect that the model's unresolved state has on the resolved state.
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The Effects of the RAW Filter on the Climatology and Forecast Skill of the SPEEDY Model
TL;DR: In this article, the authors evaluated the effect of the RAW filter on the performance of the simplified Parameterizations, Primitive Equation Dynamics (SPEEDY) atmospheric general circulation model.
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Estimating the Impact of Real Observations in Regional Numerical Weather Prediction Using an Ensemble Kalman Filter
TL;DR: In this article, the ensemble sensitivity method is implemented with the local ensemble transform Kalman filter (LETKF) and the Weather Research and Forecasting (WRF) model with real observations.