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) is uniquely important to the Earth's atmospheresince its dynamics are a major force in shaping local and global climate andprofoundly affect our ecosystems. Accurate forecasting of SST bringssignificant economic and social implications, for example, better preparationfor extreme weather such as severe droughts or tropical cyclones months ahead.However, such a task faces unique challenges due to the intrinsic complexityand uncertainty of ocean systems. Recently, deep learning techniques, such asgraphical neural networks (GNN), have been applied to address this task. Eventhough these methods have some success, they frequently have serious drawbackswhen it comes to investigating dynamic spatiotemporal dependencies betweensignals. To solve this problem, this paper proposes a novel static and dynamiclearnable personalized graph convolution network (SD-LPGC). Specifically, twograph learning layers are first constructed to respectively model the stablelong-term and short-term evolutionary patterns hidden in the multivariate SSTsignals. Then, a learnable personalized convolution layer is designed to fusethis information. Our experiments on real SST datasets demonstrate thestate-of-the-art performances of the proposed approach on the forecasting task.

Towards Spatio-temporal Sea Surface Temperature Forecasting via Static and Dynamic Learnable Personalized Graph Convolution Network