JOURNAL ARTICLE

A Global C-Staggered Composite Model for Shallow Water Equations with Latitude–Longitude Grid and Reductions in the Polar Regions.

  • Published In: International Journal of Computational Methods, 2024, v. 21, n. 9. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Lima, Genilson S. 3 of 3

Abstract

To develop a numerical method for global geophysical fluids, we usually need to choose a spherical grid and numerical approximations to represent the partial derivative equations. Some alternatives include the use of finite differences or finite volumes with latitude–longitude or reduced grids. Each of these cases has some advantages and also some limitations. This paper presents a comparison between two methods and describes a composite model using them side by side. The first is a well-known method for latitude–longitude grids and was used from 75 ∘ S until 75 ∘ N. The second is a recently developed scheme for reduced grids and was used only in the polar regions. The similarity between the two methods allows the use of small adaptations in their approximations to obtain consistency and mass conservation also in the transition between the two regions. The composite model combines advantages of the other two schemes and has a smaller computational cost. Numerical tests indicated order 2 of convergence, prevention of grid-imprinting errors, and avoidance of nonlinear instability. This model has numerical properties that may lead to efficient implementations with massive parallel computation. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Computational Methods. 2024/11, Vol. 21, Issue 9, p1
  • Document Type:Article
  • Subject Area:Astronomy and Astrophysics
  • Publication Date:2024
  • ISSN:02198762
  • DOI:10.1142/S0219876224500294
  • Accession Number:180681565
  • Copyright Statement:Copyright of International Journal of Computational Methods is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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