A shadowing-based inflation scheme for ensemble data assimilation

TL;DR

This paper introduces a novel shadowing-based inflation scheme for ensemble data assimilation, which enhances robustness and performance over traditional methods, especially in observation-sparse scenarios.

Contribution

It proposes a new covariance inflation method based on shadowing concepts from dynamical systems, improving robustness and forecast accuracy in ensemble data assimilation.

Findings

Shadowing inflation outperforms standard inflation in robustness.

It extends forecast shadowing times in chaotic systems.

Better performance in observation-sparse conditions.

Abstract

Artificial ensemble inflation is a common technique in ensemble data assimilation, whereby the ensemble covariance is periodically increased in order to prevent deviation of the ensemble from the observations and possible ensemble collapse. This manuscript introduces a new form of covariance inflation for ensemble data assimilation based upon shadowing ideas from dynamical systems theory. We present results from a low order nonlinear chaotic system that supports using shadowing inflation, demonstrating that shadowing inflation is more robust to parameter tuning than standard multiplicative covariance inflation, outperforming in observation-sparse scenarios and often leading to longer forecast shadowing times.