000907778 001__ 907778
000907778 005__ 20230123110621.0
000907778 0247_ $$2doi$$a10.1016/j.jcp.2022.111296
000907778 0247_ $$2ISSN$$a0021-9991
000907778 0247_ $$2ISSN$$a1090-2716
000907778 0247_ $$2Handle$$a2128/31233
000907778 0247_ $$2WOS$$aWOS:000828339600005
000907778 037__ $$aFZJ-2022-02207
000907778 041__ $$aEnglish
000907778 082__ $$a000
000907778 1001_ $$0P:(DE-Juel1)186793$$aPiotrowski, Zbigniew P.$$b0$$eCorresponding author$$ufzj
000907778 245__ $$aA suite of Richardson preconditioners for semi-implicit all-scale atmospheric models
000907778 260__ $$aAmsterdam$$bElsevier$$c2022
000907778 3367_ $$2DRIVER$$aarticle
000907778 3367_ $$2DataCite$$aOutput Types/Journal article
000907778 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1654058925_21247
000907778 3367_ $$2BibTeX$$aARTICLE
000907778 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000907778 3367_ $$00$$2EndNote$$aJournal Article
000907778 520__ $$aThe 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.
000907778 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000907778 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000907778 7001_ $$00000-0001-7077-3285$$aSmolarkiewicz, Piotr K.$$b1
000907778 773__ $$0PERI:(DE-600)1469164-4$$a10.1016/j.jcp.2022.111296$$gVol. 463, p. 111296 -$$p111296$$tJournal of computational physics$$v463$$x0021-9991$$y2022
000907778 8564_ $$uhttps://juser.fz-juelich.de/record/907778/files/PrecLFRrevised2.pdf$$yOpenAccess
000907778 909CO $$ooai:juser.fz-juelich.de:907778$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000907778 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186793$$aForschungszentrum Jülich$$b0$$kFZJ
000907778 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000907778 9141_ $$y2022
000907778 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-27
000907778 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000907778 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-27
000907778 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2022-11-30$$wger
000907778 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ COMPUT PHYS : 2021$$d2022-11-30
000907778 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-30
000907778 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2022-11-30
000907778 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2022-11-30
000907778 915__ $$0StatID:(DE-HGF)0020$$2StatID$$aNo Peer Review$$bASC$$d2022-11-30
000907778 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2022-11-30
000907778 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2022-11-30
000907778 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2022-11-30
000907778 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2022-11-30
000907778 920__ $$lyes
000907778 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000907778 980__ $$ajournal
000907778 980__ $$aVDB
000907778 980__ $$aUNRESTRICTED
000907778 980__ $$aI:(DE-Juel1)JSC-20090406
000907778 9801_ $$aFullTexts