China brings AI into advanced ocean forecasting systems
The system combines physical models with AI reasoning to deliver faster and more adaptable forecasting tools for maritime decision-making.
China has unveiled LangYa 2.0, an upgraded AI-powered ocean forecasting system designed to predict complex marine phenomena with greater precision and detail. The model was unveiled at the Fourth China Digital Earth Conference in Qingdao and represents a step forward from earlier ocean monitoring tools.
Developed by the Institute of Oceanology at the Chinese Academy of Sciences, the system goes beyond monitoring variables such as temperature and salinity to forecast high-impact events, including typhoons, storm surges, extreme rainfall, internal waves, mesoscale eddies, and sea ice.
The platform combines specialised AI sub-models trained on diverse datasets and informed by physical ocean processes.
LangYa 2.0 is designed to provide decision-support information for applications including disaster preparedness, maritime safety, polar navigation and climate adaptation. The system can simulate rapid typhoon intensification and sudden track shifts, while also forecasting hidden ocean dynamics that may impact offshore infrastructure.
According to researchers, the model ranked first in a 2025 international Arctic sea ice forecasting evaluation, highlighting its potential for polar forecasting applications. Researchers are exploring ways to expand the system into broader climate and ecological modelling, with the aim of supporting future marine intelligence and environmental monitoring platforms.
Why does it matter?
Accurate ocean forecasting plays a critical role in disaster preparedness, maritime safety, climate adaptation and the protection of coastal infrastructure. AI-based systems can process large volumes of environmental data more quickly and identify complex patterns that may be difficult to capture using traditional forecasting methods alone.
LangYa 2.0 also reflects a broader trend towards using AI in Earth system science. As climate-related risks become more frequent and complex, governments and researchers are increasingly investing in AI-driven tools to improve environmental monitoring, risk assessment and decision-making.
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