ArticleScience advances2020
Purely satellite data-driven deep learning forecast of complicated tropical instability waves.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Global distribution and amplitudes of internal solitary waves revealed by SWOT.Science advances · 2026Article
- Blockchain Technology for Big-data Sharing in Material Genome Engineering.Scientific data · 2025Article
- Advancing forecasting capabilities: A contrastive learning model for forecasting tropical cyclone rapid intensification.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Deep learning-based fishing ground prediction with multiple environmental factors.Marine life science & technology · 2024Article
- A self-attention-based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions.Science advances · 2023Article
- The AI gambit: leveraging artificial intelligence to combat climate change-opportunities, challenges, and recommendations.AI & society · 2023Article
- Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations.National science review · 2022Article
- Tropical instability wave modulation of chlorophyll-a in the Equatorial Pacific.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forecasting fields of oceanic phenomena has long been dependent on physical equation-based numerical models. The challenge is that many natural processes need to be considered for understanding complicated phenomena. In contrast, rules of the processes are already embedded in the time-series observation itself. Thus, inspired by largely available satellite remote sensing data and the advance of deep learning technology, we developed a purely satellite data-driven deep learning model for forecasting the sea surface temperature evolution associated with a typical phenomenon: a tropical instability wave. During the testing period of 9 years (2010-2019), our model accurately and efficiently forecasts the sea surface temperature field. This study demonstrates the strong potential of the satellite data-driven deep learning model as an alternative to traditional numerical models for forecasting oceanic phenomena.
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Registered trials
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