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Great Lakes wave forecast system on high-resolution unstructured meshes.

Authors :
Abdolali, Ali
Banihashemi, Saeideh
Alves, Jose Henrique
Roland, Aron
Hesser, Tyler J.
Anderson Bryant, Mary
McKee Smith, Jane
Source :
Geoscientific Model Development; 2024, Vol. 17 Issue 3, p1023-1039, 17p
Publication Year :
2024

Abstract

Wind-wave forecasts play a crucial role in the North American Great Lakes region towards ensuring the safety of communities, enhancement of the economy, and protection of property. Modeling wind waves in closed and relatively shallow basins with complex bathymetry like the Great Lakes is a challenge that is successfully tackled in part by using variable-resolution triangular unstructured meshes with no limits in terms of computational scalability and maximum resolution in the coastal areas. In this paper, we discuss recent advances in developing unstructured mesh capabilities as part of the spectral wave model WAVEWATCH III, in the context of National Oceanic and Atmospheric Administration (NOAA) operational requirements such as model robustness, efficiency, and accuracy. We revisit the history of developments leading to the transition from rectilinear to curvilinear grids and finally to an unstructured mesh version of NOAA's operational Great Lakes wave modeling system (GLWUv2.0). The article describes the development of the operational GLWUv2.0, from mesh design and scalability analysis to validation and verification for hindcast of storm cases and re-forecast using 4 months of retrospective simulations. In closed Great Lakes basins untouched by swell from distant sources, the atmospheric model's direct impact on wave behavior stands apart, showing reduced forecast accuracy over time, while maintaining consistent precision in accurately wind-hindcasted stormy conditions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1991959X
Volume :
17
Issue :
3
Database :
Complementary Index
Journal :
Geoscientific Model Development
Publication Type :
Academic Journal
Accession number :
175630745
Full Text :
https://doi.org/10.5194/gmd-17-1023-2024