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@ARTICLE{Piotrowski:907778,
author = {Piotrowski, Zbigniew P. and Smolarkiewicz, Piotr K.},
title = {{A} suite of {R}ichardson preconditioners for semi-implicit
all-scale atmospheric models},
journal = {Journal of computational physics},
volume = {463},
issn = {0021-9991},
address = {Amsterdam},
publisher = {Elsevier},
reportid = {FZJ-2022-02207},
pages = {111296},
year = {2022},
abstract = {The paper documents a suite of preconditioners for
Krylov-subspace solvers of elliptic boundary-value problems
(BVPs) that underlie semi-implicit integrations of the
nonhydrostatic equations governing the dynamics of all-scale
atmospheric flows. Effective preconditioning of the linear
operators inherent in the semi-implicit models lies at the
heart of the state-of-the-art multiscale-flow simulation.
This is especially evident in simulations of global weather
and climate—posed on a thin spherical shell—where some
form of direct tridiagonal inversion of the operator in the
vertical is crucial to relax the often enormous stiffness of
the problem. The documented preconditioners stem from the
Richardson's (1910) idea of augmenting an elliptic BVP with
a transient diffusion equation. Exploiting this idea for
mixed explicit-implicit pseudo-time-stepping schemes leads
to a broad suite of stationary-iteration solvers, including
the many classical algorithms. Here, the high-performance
all-scale EULAG model (Smolarkiewicz et al. (2014) [58]),
with a flexible three-dimensional decomposition of MPI
tasks, is furnished with the preconditioners akin to the
classical alternating-direction-implicit (ADI) algorithms,
generalized to optional permutations of parallel tridiagonal
inversions. The utility of various options is found to be
problem dependent, in terms of computational accuracy as
well as efficiency. The main thrust of the work is on the
long-range forecasts using large anisotropic grids. The
relative efficiency and/or accuracy gains attainable with
the developed preconditioners are illustrated for idealized
scenarios representative of atmospheric flows from planetary
to a single-cloud and laboratory scales. The key insight
that best encapsulates the significance and novelty of the
present work is that there is no single
“super-preconditioner” that will perform best in all
cases, yet the suite as a whole offers substantial gains in
the model performance.},
cin = {JSC},
ddc = {000},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511)},
pid = {G:(DE-HGF)POF4-5111},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000828339600005},
doi = {10.1016/j.jcp.2022.111296},
url = {https://juser.fz-juelich.de/record/907778},
}