Intelligent early warning method for severe convective weather based on phased array weather radar cooperative networking

By combining multiple phased array weather radars in a coordinated network with the ViT-Large neural network model, the problems of blind spots and data uniformity in traditional radars have been solved, achieving high spatiotemporal resolution and high precision in severe convective weather warnings, thus improving the warning effect.

CN121559518BActive Publication Date: 2026-07-24CHENGDU RUNLIAN TECH DEV +2
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Patent Information

Application Number
CN202511802910.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-03
Publication Date
2026-07-24
Estimated Expiration
2045-12-03

AI Technical Summary

Technical Problem

Traditional single-weather radar detection methods in existing technologies suffer from limited data types, low temporal resolution, and blind spots in the boundary layer at medium and long distances, making it difficult to fully capture the formation, development, and evolution characteristics of severe convective weather. Furthermore, existing deep learning models suffer from limited training sample data, poor global feature capture capabilities, uneven feature representation, and low computational efficiency, resulting in poor intelligent early warning effects for severe convective weather.

Method used

Multiple phased array (dual polarization) weather radars are networked collaboratively to acquire high spatiotemporal resolution observation data. Through data quality control, data fusion, and feature sample extraction, a smart early warning model for severe convective weather is constructed using the ViT-Large neural network architecture. The model is trained using a multi-channel input-single-channel output mechanism to output early warning results.

Benefits of technology

It significantly improves the timeliness and accuracy of early warnings for severe convective weather under complex climate and terrain conditions, reduces the rate of missed and false reports, provides more comprehensive information on the characteristics of severe convective weather, and enhances the effectiveness of early warnings.

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Abstract

The application discloses a severe convective weather intelligent early warning method based on phased array weather radar cooperative networking, and belongs to the field of weather warning. The method comprises the following steps: S1, multiple phased array (dual-polarization) weather radars are used for cooperative networking observation to obtain high space-time resolution observation data; S2, quality control is performed on the obtained dual-polarization radar observation base data, including ground object clutter suppression, electromagnetic wave interference suppression, radial velocity deblurring and missing data filling; S3, the observation data of the multiple phased array weather radars are projected into three-dimensional grid point data in a Cartesian coordinate system, and data fusion processing is performed. The application has the beneficial effects that the severe convective weather intelligent early warning method based on phased array weather radar cooperative networking is provided, dual-polarization detection data and characteristic quantities thereof are added, and the recognition accuracy and suppression rate of ground object clutter are significantly improved.
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