Improving Met Office Weather and Climate Forecasts with Bespoke Multigrid Solvers

Andrew Malcolm, Eike Hermann Müller, Robert Scheichl

Research output: Chapter or section in a book/report/conference proceedingBook chapter

Abstract

At the heart of the Met Office climate and weather forecasting capabilities lies a sophisticated numerical model which solves the equations of large-scale atmospheric flow. Since this model uses semi-implicit time-stepping, it requires the repeated solution of a large sparse system of linear equations with hundreds of millions of unknowns. This is one of the computational bottlenecks of operational forecasts and efficient numerical algorithms are crucial to ensure optimal performance. We developed and implemented a bespoke multigrid solver to address this challenge. Our solver reduces the time for solving the linear system by a factor two, compared to the previously used BiCGStab method. This leads to significant improvements of overall model performance: global forecasts can be produced 10–15% faster. Multigrid also avoids stagnating convergence of the iterative scheme in single precision. By allowing better utilisation of computational resources, our work has led to estimated annual cost savings of £300k for the Met Office.

Original languageEnglish
Title of host publicationMore UK Success Stories in Industrial Mathematics
EditorsP. J. Ashton
Place of PublicationCham, Switzerland
PublisherSpringer
Pages3-8
Number of pages6
ISBN (Electronic)9783031486838
ISBN (Print)9783031486821
DOIs
Publication statusPublished - 23 Apr 2025

Publication series

NameMathematics in Industry
Volume42
ISSN (Print)1612-3956
ISSN (Electronic)2198-3283

Acknowledgements

The authors thank their collaborators at the Met Office and the members of the GungHo project. In particular, we would like to acknowledge the important contributions to the early stages of this project made by Dr Markus Gross, who tragically passed away in January 2022.

Funding

This work was funded through NERC grants NE/K006754/1 and NE/J005576/1.

FundersFunder number
Natural Environment Research Council

ASJC Scopus subject areas

  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Computational Mathematics
  • Applied Mathematics

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