Generating Unseen Nonlinear Evolution in Sea Surface Temperature Using a Deep Learning-Based Latent Space Data Assimilation Framework

TL;DR

This paper introduces a deep learning-based latent space data assimilation framework called DeepDA, which effectively captures and generates nonlinear sea surface temperature evolution, even with limited observational data, enhancing Earth system prediction accuracy.

Contribution

The paper presents a novel purely data-driven latent space DA framework using generative AI to model nonlinear ocean temperature dynamics, demonstrating robustness and explainability.

Findings

DeepDA remains stable with large missing data.

Error increase is limited to 40% with only 10% observations.

DeepDA effectively fuses real observations and simulations.

Abstract

Advances in data assimilation (DA) methods have greatly improved the accuracy of Earth system predictions. To fuse multi-source data and reconstruct the nonlinear evolution missing from observations, geoscientists are developing future-oriented DA methods. In this paper, we redesign a purely data-driven latent space DA framework (DeepDA) that employs a generative artificial intelligence model to capture the nonlinear evolution in sea surface temperature. Under variational constraints, DeepDA embedded with nonlinear features can effectively fuse heterogeneous data. The results show that DeepDA remains highly stable in capturing and generating nonlinear evolutions even when a large amount of observational information is missing. It can be found that when only 10% of the observation information is available, the error increase of DeepDA does not exceed 40%. Furthermore, DeepDA has been shown to be robust in the fusion of real observations and ensemble simulations. In particular, this paper provides a mechanism analysis of the nonlinear evolution generated by DeepDA from the perspective of physical patterns, which reveals the inherent explainability of our DL model in capturing multi-scale ocean signals.