一种基于ENSO和MJO的极端正IOD事件预测方法
By employing a statistical modeling method based on ENSO and MJO collaborative signals, the accuracy and stability issues in predicting extreme positive IOD events are resolved, enabling efficient prediction of extreme events and making it suitable for climate prediction operational systems.
CN122087774BActive Publication Date: 2026-07-17JIANGSU METEOROLOGICAL OBSERVATORY
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIANGSU METEOROLOGICAL OBSERVATORY
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing technologies lack accuracy and stability in predicting extreme positive IOD events. Dynamic models underestimate amplitude, and statistical methods based on local sea surface temperature signals are unable to reflect the large-scale tropical circulation modulation effect.
Method used
We used ENSO and MJO co-signals, combined with model forecast field information, to perform statistical modeling, extract key influencing fields, and reconstruct the prediction model for extreme positive IOD events using the least squares linear regression method.
Benefits of technology
It improves the accuracy and stability of predictions for extreme positive IOD events, reduces the underestimation problem of dynamic patterns, and is suitable for operational climate prediction systems.
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Figure CN122087774B_ABST
Abstract
本发明公开了一种基于ENSO和MJO的极端正IOD事件预测方法，本发明采用ENSO与MJO协同信号并提取了ENSO和MJO影响IOD的关键信号，同时结合模式预报场信息进行统计建模的极端正IOD事件预测方法，避免了动力模式对较强IOD振幅的系统性低估问题的同时，能提高对极端正IOD事件的预测提前期和预测准确性。
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