Introduces power-law, logistic, and discrepancy-based tapers for correlation-based localization that suppress spurious correlations and often preserve more posterior ensemble variance than distance-based methods in synthetic reservoir assimilation tests.
C.: The potential of the ensemble Kalman filter for NWP—A comparison with 4D-Var, Q
2 Pith papers cite this work. Polarity classification is still indexing.
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A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
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Statistical Tapers for Correlation-Based Localization in Ensemble Data Assimilation
Introduces power-law, logistic, and discrepancy-based tapers for correlation-based localization that suppress spurious correlations and often preserve more posterior ensemble variance than distance-based methods in synthetic reservoir assimilation tests.
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A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.