### Abstract

Due to the wide separation of time scales in geophysical fluid dynamics, semi-implicit time integrators are commonly used in operational atmospheric forecast models. They guarantee the stable treatment of fast (acoustic and gravity) waves, while not suffering from severe restrictions on the timestep size. To propagate the state of the atmosphere forward in time, a non-linear equation for the prognostic variables has to be solved at every timestep. Since the nonlinearity is typically weak, this is done with a small number of Newton- or Picard- iterations, which in turn require the efficient solution of a large system on linear equations with O(10^6−10^9) unknowns. This linear solve is often the computationally most costly part of the model. While the solution of the linear system is relatively straightforward for simple finite-difference- and finite-volume- discretisations, it is much more challenging for advanced finite element discretisations, which have recently been explored in atmospheric modelling.

In this paper an efficient linear solver for the LFRic next-generation model, currently developed by the Met Office, is described. The model uses an advanced mimetic finite element discretisation which makes the construction of efficient solvers challenging. The linear solver hinges on a bespoke multigrid preconditioner of the Schur-complement system for the pressure correction.

By comparing to Krylov-subspace methods, the superior performance and robustness of the multigrid algorithm is demonstrated for standard test cases and realistic model setups. In production mode, the model will have to run in parallel on 100,000s of processing elements. As confirmed by numerical experiments, one particular advantage of the multigrid solver is its excellent parallel scalability due to avoiding expensive global reduction operations.

In this paper an efficient linear solver for the LFRic next-generation model, currently developed by the Met Office, is described. The model uses an advanced mimetic finite element discretisation which makes the construction of efficient solvers challenging. The linear solver hinges on a bespoke multigrid preconditioner of the Schur-complement system for the pressure correction.

By comparing to Krylov-subspace methods, the superior performance and robustness of the multigrid algorithm is demonstrated for standard test cases and realistic model setups. In production mode, the model will have to run in parallel on 100,000s of processing elements. As confirmed by numerical experiments, one particular advantage of the multigrid solver is its excellent parallel scalability due to avoiding expensive global reduction operations.

Original language | English |
---|---|

Number of pages | 13 |

Journal | Quarterly Journal of the Royal Meteorological Society |

Publication status | Acceptance date - 20 Jul 2020 |