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1 results

  • Scalable data assimilation with message passing

  • Oscar Key, So Takao, Daniel Giles, Marc Peter Deisenroth
  • Journal:
    Environmental Data Science / Volume 4 / 2025
    Published online by Cambridge University Press:
    08 January 2025, e1
    • Article
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  • 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 synchronization overhead in this setting. In this article, 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.