Cyclone strength prediction result correction method and device based on diffusion model, equipment and medium
By correcting the tropical cyclone intensity prediction results using a neural network-based denoised diffusion probability model, the problems of high computational resource consumption and insufficient forecast accuracy in existing technologies are solved, enabling rapid and accurate cyclone intensity forecasting.
CN121388765APending Publication Date: 2026-01-23SHANGHAI INSTITUTE OF SCIENCE & INTELLIGENCE
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Patent Information
- Application Number
- CN202511509618.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-22
- Publication Date
- 2026-01-23
AI Technical Summary
Technical Problem
Existing technologies consume high computational resources in tropical cyclone intensity forecasting and cannot achieve real-time or rapid updates, resulting in insufficient forecast accuracy.
Method used
The initial prediction results were corrected using a denoised diffusion probability model based on neural networks, and the meteorological prediction model with ERA5 reanalysis data as boundary conditions was used for data processing and correction.
Benefits of technology
It enables rapid and accurate correction of tropical cyclone intensity, improves forecast accuracy and significantly increases computational efficiency, from hourly to second-level forecasts, solving the problems of insufficient forecast accuracy and low computational efficiency.
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Abstract
The invention discloses a cyclone strength prediction result correction method and device based on a diffusion model, equipment and a medium, and relates to the technical field of meteorological prediction, and the method comprises the steps: obtaining target meteorological data in a target time range; inputting the target meteorological data into a preset meteorological prediction server, so that a meteorological prediction model in the preset meteorological prediction server analyzes the target meteorological data by taking a preset ERA5 reanalysis data set as a boundary condition to obtain a tropical cyclone strength prediction result; and obtaining a tropical cyclone strength prediction result from a preset weather server, and correcting the tropical cyclone strength prediction result by using the target cyclone strength correction model. The model constructed based on the neural network is adopted to transform the initial prediction result, rapid and accurate correction of the tropical cyclone strength is realized, and the problems of insufficient prediction precision and low calculation efficiency are solved.
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