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AI enhances sea surface temperature data for better climate and weather forecasts

Every summer, typhoons threatening the Korean Peninsula draw their energy from the warm waters of the Northwest Pacific Ocean. In recent years, the frequency and intensity of extreme weather events—such as heat waves, droughts, and heavy rains—have been increasingly linked to rising sea surface temperatures (SST).

Accurate prediction of SSTs has thus become a vital component of climate and weather forecasting. However, satellite observations, which provide broad and continuous monitoring, often suffer from data gaps caused by clouds, precipitation, and other observational limitations, hampering long-term, high-resolution climate analysis.

Responding to this challenge, a team of researchers at UNIST has developed a pioneering artificial intelligence (AI) model capable of restoring missing satellite data and generating continuous, high-resolution SST datasets with unprecedented accuracy.

were published in Remote Sensing of Environment.

Led by Professor Jungho Im from the Department of Civil, Urban, Earth, and Environmental Engineering, the team announced that they have created an innovative AI-based reconstruction system that fills in observational gaps, producing SST data at a 2-kilometer spatial resolution and on an hourly basis.

This development promises to significantly enhance our understanding of oceanic conditions that directly influence regional weather and climate patterns.

The ocean retains approximately 90% of Earth's surface energy, with SST serving as a critical boundary where heat exchange between the ocean and atmosphere occurs. Elevated SSTs can transfer heat upward, fueling powerful typhoons, intensifying heat waves, and increasing the risk of heavy rainfall events. Yet, despite its importance, continuous, high-resolution monitoring of SST remains challenging due to data gaps in satellite imagery.

To overcome this obstacle, the researchers employed a Generative Adversarial Network (GAN)—a sophisticated AI architecture originally designed for image synthesis—and trained it using high-frequency satellite data combined with thermodynamic insights from numerical weather prediction (NWP) models.

Unlike conventional models, this approach integrates physical oceanic principles, enabling the AI to produce SST data that aligns closely with real-world physical conditions, even in the presence of missing observations.

"Traditional methods like linear interpolation or statistical models often struggle to preserve the fine details of SST, especially during rapid temperature changes," explained Sihun Jung, the study's first author. "Our AI model not only surpasses these methods in accuracy but also maintains high fidelity even in challenging conditions, making it a powerful tool for climate monitoring."

Professor Im emphasized the broader impact, saying "This advanced reconstruction technology is particularly crucial for the Northwest Pacific, a region prone to frequent typhoons and climate variability."

He further noted, "By providing high-resolution SST data, we can significantly improve weather forecasts and climate models. In the long run, this technology could also be instrumental in early warning systems for marine disasters, such as marine heat waves, helping to safeguard communities and ecosystems."