書誌事項
- 公開日
- 2016-10
- 資源種別
- journal article
- 権利情報
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- https://www.elsevier.com/tdm/userlicense/1.0/
- DOI
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- 10.1016/j.jcp.2016.06.048
- 公開者
- Elsevier BV
この論文をさがす
説明
The advance of massively parallel computing in the nineteen nineties and beyond encouraged finer grid intervals in numerical weather-prediction models. This has improved resolution of weather systems and enhanced the accuracy of forecasts, while setting the trend for development of unified all-scale atmospheric models. This paper first outlines the historical background to a wide range of numerical methods advanced in the process. Next, the trend is illustrated with a technical review of a versatile nonoscillatory forward-in-time finite-volume (NFTFV) approach, proven effective in simulations of atmospheric flows from small-scale dynamics to global circulations and climate. The outlined approach exploits the synergy of two specific ingredients: the MPDATA methods for the simulation of fluid flows based on the sign-preserving properties of upstream differencing; and the flexible finite-volume median-dual unstructured-mesh discretisation of the spatial differential operators comprising PDEs of atmospheric dynamics. The paper consolidates the concepts leading to a family of generalised nonhydrostatic NFTFV flow solvers that include soundproof PDEs of incompressible Boussinesq, anelastic and pseudo-incompressible systems, common in large-eddy simulation of small- and meso-scale dynamics, as well as all-scale compressible Euler equations. Such a framework naturally extends predictive skills of large-eddy simulation to the global atmosphere, providing a bottom-up alternative to the reverse approach pursued in the weather-prediction models. Theoretical considerations are substantiated by calculations attesting to the versatility and efficacy of the NFTFV approach. Some prospective developments are also discussed.
収録刊行物
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- Journal of Computational Physics
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Journal of Computational Physics 322 267-287, 2016-10
Elsevier BV
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詳細情報 詳細情報について
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- CRID
- 1360002216001510656
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- ISSN
- 00219991
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- 資料種別
- journal article
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- データソース種別
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- Crossref
- KAKEN
- OpenAIRE
- IRDB
