2021
DOI: 10.1029/2020jc016875
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Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

Abstract: The Ocean State Ocean Model (OSOM) is an application of the Regional Ocean Modeling System spanning the Rhode Island waterways, including Narragansett Bay, Mt. Hope Bay, larger rivers, and the Block Island Shelf circulation from Long Island to Nantucket. This study discusses the physical aspects of the estuary (Narragansett and Mount Hope Bays and larger rivers) to evaluate physical circulation predictability. This estimate is intended to help decide if a forecast and prediction system is warranted, to prepare… Show more

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Cited by 4 publications
(14 citation statements)
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“…The unstructured mesh after optimization may fail to achieve the optimal effect in the same field, and there is room for further improvement. The reasons are as follows: (1) In order to fully verify the validity of the proposed optimization algorithm of an unstructured mesh, the unstructured mesh is deliberately unordered at the time of generation. Before the generation of the unstructured mesh, the existing stationing method is not applied.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The unstructured mesh after optimization may fail to achieve the optimal effect in the same field, and there is room for further improvement. The reasons are as follows: (1) In order to fully verify the validity of the proposed optimization algorithm of an unstructured mesh, the unstructured mesh is deliberately unordered at the time of generation. Before the generation of the unstructured mesh, the existing stationing method is not applied.…”
Section: Discussionmentioning
confidence: 99%
“…In marine science and engineering, a physical oceanographic model generally consists of a set of partial differential equations, with its performance largely dependent on a mesh discretization scheme [1][2][3][4]. Currently, there are two kinds of mesh discretization schemes widely adopted in physical oceanographic models: structured mesh and unstructured mesh.…”
Section: Introductionmentioning
confidence: 99%
“…A natural question is how much of the variability observed in GCM simulations is covered in the training data. We can estimate this using Shannon entropy (Shannon, 1948) which measures the amount of uncertainty and variability in a variable (Carcassi et al, 2021;Sane et al, 2020Sane et al, , 2021.…”
Section: Appendix B: Quantifying Uncertainty Range Covered In the For...mentioning
confidence: 99%
“…The choice of this specific SMC parameterization is made to be consistent with Reichl and Hallberg (2018). Vertical mixing parameterizations remain an active research topic, and currently used schemes, including SMC, can exhibit biases in different forcing regimes and regions (Damerell et al., 2020; Li et al., 2019; Peters & Baumert, 2007; Sane et al., 2021; Tirodkar et al., 2022). Any biases in the training data are inherited by the neural networks.…”
Section: Artificial Neural Network and Training Proceduresmentioning
confidence: 99%
“…Information theory metrics are not new to climate sciences. They have been introduced in predictability studies, evaluating the skill of statistical models, as well as uncertainty studies (DelSole & Tippett, 2007; Kleeman, 2002; Leung & North, 1990; Majda & Gershgorin, 2010; Schneider & Griffies, 1999; Stevenson et al., 2013), and recently in studying variability (Gomez, 2020), coastal predictability (Sane et al., 2021) and drivers of drought (Shin et al., 2023).…”
Section: Introductionmentioning
confidence: 99%