Application of deep learning in cloud cover prediction using geostationary satellite images

Application of deep learning in cloud cover prediction using geostationary satellite images

Predicting cloud cover is essential in fields, such as agriculture, climatology, and meteorology, where accurate weather forecasting can significantly impact decision-making. Traditional methods for cloud cover prediction encounter significant limitations in capturing complete spatial and temporal c...

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Bibliographic Details
Main Authors: Yeonjin Lee, Seyun Min, Jihyun Yoon, Jongsung Ha, Seungtaek Jeong, Seonghyun Ryu, Myoung-Hwan Ahn
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:GIScience & Remote Sensing
Subjects:
Cloud cover prediction
deep learning
dynamic learning
Convolutional Long Short-Term Memory (ConvLSTM)
GEO-KOMPSAT-2A (GK2A)
transformer model
Online Access:https://www.tandfonline.com/doi/10.1080/15481603.2024.2440506
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https://www.tandfonline.com/doi/10.1080/15481603.2024.2440506

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