Three-dimensional sea temperature space-time prediction method and El Nino event prediction method

By combining reanalysis data and numerical model forecast data, and utilizing a pre-trained three-dimensional intelligent sea surface temperature prediction model, the uncertainties and errors in ENSO predictions have been resolved, achieving high-precision predictions of sea surface temperature and El Niño events, thus enhancing the development of climate prediction technology.

CN121480862APending Publication Date: 2026-02-06NAT MARINE ENVIRONMENTAL FORECASTING CENT
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Patent Information

Application Number
CN202511655422.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-12
Publication Date
2026-02-06

AI Technical Summary

Technical Problem

Existing technologies have uncertainties and errors in ENSO prediction, especially in predicting extreme events. Deep learning methods lack a complete theoretical framework and cannot fully reflect the complex evolution of ENSO.

Method used

By combining reanalysis data and numerical model forecast data, and utilizing a pre-trained three-dimensional intelligent sea surface temperature prediction model, including a data fusion input module and a spatiotemporal recurrent neural network module, the model is trained through a target pre-training algorithm and a transfer learning algorithm. By combining the upper ocean temperature anomaly field and the average sea surface temperature anomaly index of the target ocean area, the prediction accuracy and generalization ability are improved.

Benefits of technology

It has improved the accuracy of sea surface temperature forecasting and El Niño event prediction, provided reliable data support, offered a scientific basis for climate monitoring and disaster early warning, and reduced the risks of climate anomalies to the ecological environment and economic activities.

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Abstract

The invention is suitable for the field of marine meteorology, and provides a three-dimensional sea temperature space-time prediction method, which comprises the following steps: obtaining reanalysis data and numerical mode prediction data; and inputting the reanalysis data and the numerical mode prediction data into a pre-trained three-dimensional sea temperature intelligent prediction model to obtain a three-dimensional sea temperature space-time prediction result. According to the method, the spatial-temporal characteristics of the sea temperature change can be effectively captured, so that the prediction precision is improved, a high-precision sea temperature prediction result can be provided, good interpretability is achieved, and more powerful support is provided for scientific research and climate change response.
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