1. Главная
  2. Ресурсы
  3. Библиотечный поиск
Harnessing the Powerof GraphRepresentation inClimate Forecasting:Predicting GlobalMonthly Mean SeaSurface Temperaturesand… : научноеиздание Ning DingVetrova VarvaraBryan Karin R.2024 год

Harnessing the Power of Graph Representation in Climate Forecasting: Predicting Global Monthly Mean Sea Surface Temperatures and Anomalies : научное издание
статья из журнала

Дополнительные ссылки: eLibrary
База данных: Каталог библиотеки СФУ
Библиографическое описание: Harnessing the Power of Graph Representation in Climate Forecasting: Predicting Global Monthly Mean Sea Surface Temperatures and Anomalies : научное издание / D. Ning, V. Vetrova, Karin R. Bryan, Y. S. Koh. - Текст : непосредственный // Earth and Space Science. - 2024. - Т. 11, № 3. - ISSN 2333-5084, DOI 10.1029/2023ea003455.
Аннотация: <jats:title>Abstract</jats:title><jats:p>The variability of sea surface temperatures (SSTs) is crucial in climate dynamics, influencing marine ecosystems and human activities. This study leverages graph neural networks (GNNs), specifically a GraphSAGE model, to forecast SSTs and their anomalies (SSTAs), focusing on the global scale structure of climatological data. We introduce an improved graph construction technique for SST teleconnection representation. Our results highlight the GraphSAGE model's capability in 1-month-ahead global SST and SSTA forecasting, with SST predictions spanning up to 2 years with a recursive approach. Notably, regions with persistent currents exhibited enhanced SSTA predictability, contrasting with equatorial and Antarctic areas. Our GNN outperformed both the persistence model and traditional methods. Additionally, we offer an SST and SSTA graph data set based on ERA5 and a graph generation tool. This GNN case study has shown how the GraphSAGE can be used in SST and SSTA forecasting, and will provide a foundation for further refinements such as adjusting graph construction, optimizing imbalanced regression techniques for extreme SSTAs, and integrating GNNs with other temporal pattern learning methods to improve long-term predictions.</jats:p>
Год издания: 2024
Авторы: Ning Ding , Vetrova Varvara , Bryan Karin R. , Koh Yun Sing
Источник: Earth and Space Science
Выпуск: Т. 11, № 3
Держатель оригинала документа: School of Computer Science University of Auckland Auckland New Zealand, School of Mathematics and Statistics University of Canterbury Christchurch New Zealand, School of Science University of Waikato Hamilton New Zealand
Количество экземпляров:
Твёрдая копия издания отсутствует в фонде библиотеки

Похожие публикации

The MOSAiCDistributed Network:Observing the coupledArctic system withmultidisciplinary,coordinated platforms Rabe BenjaminCox Christopher J.Fang Ying-Chih2024 год

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary,… : статья из журнала

  • Rabe Benjamin и др.
  • 2024 год
Assessing RadiativeFeedbacks and TheirContribution to theArctic AmplificationMeasured by VariousMetrics Huo YilingWang HailongLu Jian2024 год

Assessing Radiative Feedbacks and Their Contribution to the Arctic Amplification Measured by… : статья из журнала

  • Huo Yiling и др.
  • 2024 год
A staircase signal inthe warming of themid-20th century Belolipetsky P.V.Bartsev S.I.Saltykov M.Y.2017 год

A staircase signal in the warming of the mid-20th century : доклад, тезисы доклада

  • Belolipetsky P.V. и др.
  • 2017 год
Future permafrostdegradation underclimate change in aheadwater catchmentof central Siberia:quantitative… Xavier ThibaultOrgogozo LaurentProkushkin Anatoly S.2024 год

Future permafrost degradation under climate change in a headwater catchment of central Siberia:… : статья из журнала

  • Xavier Thibault и др.
  • 2024 год