The Analog Data Assimilation

TLDR

The analog data assimilation (AnDA) addresses the growing need for data‑driven methods in oceanic, atmospheric, and climate sciences by presenting a fully data‑driven framework for state estimation. The study investigates various analog forecasting strategies and develops ensemble Kalman and particle filtering versions of the AnDA approach. The method reconstructs system dynamics from a catalog of trajectories, combining nonparametric analog forecasting with ensemble‑based assimilation (Kalman and particle filters), and is tested on Lorenz‑63 and Lorenz‑96 systems. The performance of the analog data assimilation is discussed relative to classical model‑driven assimilation, and a Matlab toolbox and Python library are provided to facilitate further research.

Abstract

In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA). The proposed framework produces a reconstruction of the system dynamics in a fully data-driven manner where no explicit knowledge of the dynamical model is required. Instead, a representative catalog of trajectories of the system is assumed to be available. Based on this catalog, the analog data assimilation combines the nonparametric sampling of the dynamics using analog forecasting methods with ensemble-based assimilation techniques. This study explores different analog forecasting strategies and derives both ensemble Kalman and particle filtering versions of the proposed analog data assimilation approach. Numerical experiments are examined for two chaotic dynamical systems: the Lorenz-63 and Lorenz-96 systems. The performance of the analog data assimilation is discussed with respect to classical model-driven assimilation. A Matlab toolbox and Python library of the AnDA are provided to help further research building upon the present findings.