Work

Using Diffusion Models to Do Data Assimilation

Year: 2025

Type: article

Abstract: The recent surge in machine learning (ML) methods for geophysical modeling has raised the question of how these methods might be applied to data assimilation (DA). We focus on diffusion modeling (a form of generative artificial intelligence) for systems that can perform the entire DA process, rather than on ML-based tools used within a conventional DA system. We identify at least three distinct types of diffusion-based DA systems and show that they differ in the posterior distribution they target for sampling. These posterior distributions correspond to different priors and/or likelihoods, which in turn result in unique training datasets, computational requirements, and state estimate qualities. Our analysis further shows that a diffusion DA system designed to target the same posterior distribution as current ensemble DA algorithms requires retraining at each DA cycle. We discuss the implications of these findings for the use of diffusion modeling in DA.

Source: Monthly Weather Review

DOI: 10.1175/mwr-d-25-0125.1

Authors: Daniel Hodyss, Matthias Morzfeld

Institutions: United States Naval Research Laboratory, Naval Research Laboratory Remote Sensing Division, Scripps Institution of Oceanography, University of California San Diego

Language: English

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: green

Funder: Office of Naval Research

Awards: G5585658262, G914202962