EnSF-LR combines nonlinear score-based analysis on observed components with EnKF-style linear regression on unobserved components via ensemble covariance, achieving lower full-state RMSE than EnSF and EnKF in nonlinear-observation tests on Lorenz-63 and Lorenz-96.
J., 2009: Particle filtering in geophysical systems
2 Pith papers cite this work, alongside 693 external citations. Polarity classification is still indexing.
2
Pith papers citing it
693
external citations · OpenAlex
years
2026 2verdicts
UNVERDICTED 2representative citing papers
EnSF with score-based diffusion models improves energy consumption state estimation over open-loop propagation and EnKF under nonlinear observations.
citing papers explorer
-
A Two-Step Ensemble Score Filter for Data Assimilation in Partially Observed Systems
EnSF-LR combines nonlinear score-based analysis on observed components with EnKF-style linear regression on unobserved components via ensemble covariance, achieving lower full-state RMSE than EnSF and EnKF in nonlinear-observation tests on Lorenz-63 and Lorenz-96.
-
Diffusion Model-Based Data Assimilation for Real-World Energy Consumption Forecasting
EnSF with score-based diffusion models improves energy consumption state estimation over open-loop propagation and EnKF under nonlinear observations.