# Scalable Data Assimilation with Message Passing

arXiv: https://arxiv.org/abs/2404.12968 · HTML: https://mlsanity.com/paper/2404.12968/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Efficient and Modular Implicit Differentiation](https://mlsanity.com/paper/2105.15183/) (2021)
- [PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX](https://mlsanity.com/paper/2202.04110/) (2022)
- [Message Scheduling for Performant, Many-Core Belief Propagation](https://mlsanity.com/paper/1909.11469/) (2019)
- [Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems](https://mlsanity.com/paper/2402.17036/) (2024)

## Abstract (arXiv)

Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes, yet existing approaches suffer from synchronisation overhead in this setting. In this paper, we exploit the formulation of data assimilation as a Bayesian inference problem and apply a message-passing algorithm to solve the spatial inference problem. Since message passing is inherently based on local computations, this approach lends itself to parallel and distributed computation. In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements.