Correcting Observation Model Error in Data Assimilation

TL;DR

This paper introduces a novel iterative method to correct observation model errors in data assimilation, improving state estimation accuracy when the true observation function is unknown or inaccurate.

Contribution

It proposes an alternating minimization algorithm for observation model error correction within filtering, leveraging attractor reconstruction techniques.

Findings

Effective correction of observation model errors demonstrated on Lorenz models.

Improved state estimation accuracy shown in radiative transfer model.

Method enhances data assimilation robustness with unknown observation functions.

Abstract

Standard methods of data assimilation assume prior knowledge of a model that describes the system dynamics and an observation function that maps the model state to a predicted output. An accurate mapping from model state to observation space is crucial in filtering schemes when adjusting the estimate of the system state during the filter's analysis step. However, in many applications the true observation function may be unknown and the available observation model may have significant errors, resulting in a suboptimal state estimate. We propose a method for observation model error correction within the filtering framework. The procedure involves an alternating minimization algorithm used to iteratively update a given observation function to increase consistency with the model and prior observations, using ideas from attractor reconstruction. The method is demonstrated on the Lorenz 1963 and Lorenz 1996 models, and on a single-column radiative transfer model with multicloud parameterization.