English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Sea surface temperature (SST) forecasts help with managing the marineecosystem and the aquaculture impacted by anthropogenic climate change.Numerical dynamical models are resource intensive for SST forecasts; machinelearning (ML) models could reduce high computational requirements and have beenin the focus of the research community recently. ML models normally require alarge amount of data for training. Environmental data are collected onregularly-spaced grids, so early work mainly used grid-based deep learning (DL)for prediction. However, both grid data and the corresponding DL approacheshave inherent problems. As geometric DL has emerged, graphs as a moregeneralized data structure and graph neural networks (GNNs) have beenintroduced to the spatiotemporal domains. In this work, we preliminarilyexplored graph re-sampling and GNNs for global SST forecasts, and GNNs showbetter one month ahead SST prediction than the persistence model in most oceansin terms of root mean square errors.

Graph-Based Deep Learning for Sea Surface Temperature Forecasts