Error analysis of the projected PO method with additive inflation for the partially observed Lorenz 96 model

2025/07/31 by Kota Takeda, Takeda, Kota
Earth and Planetary Sciences · Economics, Econometrics and Finance · Engineering · #35R30 #62F15 #62M20 #65C05 #93C55 #Dynamical Systems (math.DS) #FOS: Mathematics #Fluid Dynamics and Turbulent Flows #Meteorological Phenomena and Simulations #Numerical Analysis (math.NA) #Stochastic processes and financial applications

paper · pdf · doi:10.48550/arxiv.2507.23199

openalex publication_date 2025/07/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider the filtering problem with the partially observed Lorenz 96 model. Although the accuracy of the 3DVar filter in this problem has been established, the theoretical guarantee for the ensemble Kalman filter (EnKF) remains limited due to the analytical difficulty of handling non-symmetric matrices that emerge in the partial observation setting. This study establishes uniform-in-time error bounds of a stochastic variant of the EnKF, known as the perturbed observation (PO) method. By utilizing additive covariance inflation, we successfully obtain the bounds both with and without projecting the background covariance onto the observation space. Our analysis with the projection complements existing results for the deterministic variant of the EnKF, while our approach without the projection offers an extended mathematical framework to handle the non-symmetric matrix products directly. A numerical example validates the theoretical findings and shows comparable accuracies between the two settings.

Citations

• Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems
• A unified framework for the analysis of accuracy and stability of a class of approximate Gaussian filters for the Navier-Stokes Equations
• Nonlinear stability of the ensemble Kalman filter with adaptive\n covariance inflation
• Data Assimilation: A Mathematical Introduction
• Analysis of the 3DVAR Filter for the Partially Observed Lorenz '63 Model

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