Method for monitoring aircraft icing risk based on microwave radiometer and cloud radar data

By integrating microwave radiometer and millimeter-wave cloud radar data, and employing fuzzy logic algorithms and piecewise functions, the problem of low resolution in aircraft icing monitoring was solved, achieving efficient and accurate icing risk early warning.

CN122354780APending Publication Date: 2026-07-10BEIJING AIERDA ELECTRONIC EQUIP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-10
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing technologies for monitoring aircraft icing suffer from low spatial and temporal resolution. While meteorological satellite data provides accurate cloud top information, the microphysical parameters within clouds are unreliable. Direct aircraft monitoring has a limited range and low temporal resolution.

Method used

By integrating microwave radiometer and millimeter-wave cloud radar data, and employing fuzzy logic algorithms for detailed processing, and by introducing piecewise functions and icing intensity matrices, a hierarchical mapping strategy of first subdividing and then summarizing is adopted to monitor aircraft icing risk.

Benefits of technology

It significantly improves the real-time computational efficiency and accuracy of icing risk early warning, enhances the monitoring accuracy and resolution of aircraft icing areas, reduces the risk of cloud phase misjudgment, and strengthens the model's robustness and anti-interference ability.

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Abstract

This invention provides a method for monitoring aircraft icing risk based on microwave radiometer and cloud radar data, relating to the field of aviation safety monitoring. The method includes: using microwave radiometer data and millimeter-wave cloud radar data from grid points within a target flight area, calculating the probability of multiple refined particle phase types corresponding to each grid point using a fuzzy logic algorithm; then, determining the target cloud phase at each grid point based on the probability from the multiple refined particle phase types; identifying the category of the target cloud phase; calculating the particle radius and cloud water content according to the category; combining the determined piecewise temperature function value, piecewise particle radius function value, piecewise cloud water content function value, and a constructed icing intensity matrix to obtain the icing risk intensity (ICE) value; and outputting the aircraft icing risk monitoring results for each grid point based on the icing intensity indicated by the target cloud phase or the ICE value. This invention can effectively improve the accuracy and resolution of icing monitoring and identification.
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