Airborne Weather Data Compression via Geometric Shape Approximation
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Solution Overview
Problem
Current systems for sharing and storing airborne weather data are cumbersome, error-prone, and inefficient, particularly due to the large size of radar data which increases communication costs and computational requirements, and lacks a clear visual representation of weather conditions.
Innovation Solution
A method that de-clusters radar data into geometric shapes defined by parameters, reducing data size and bandwidth requirements by approximating weather cells with geometric shapes, which are then transmitted and stored using fewer parameters, thereby optimizing data exchange and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If radar data is shared and stored in its original form, then complete weather information is available, but communication costs and data storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential boundary information of weather cells from the complete radar data. Instead of transmitting all radar returns, the system identifies and transmits only the boundary points that define the weather cell geometry, significantly reducing data volume while preserving the essential weather information needed for situational awareness.
Solution Approach 2:
The patent inverts the traditional approach by not transmitting the weather cell interior points but only its boundary. This inversion allows the receiving system to reconstruct the weather cell shape from the boundary points, achieving data compression while maintaining the ability to represent complete weather information.
2Measurement precision
If detailed radar data is transmitted, then accurate weather representation is achieved, but bandwidth requirements and communication costs increase
Solution Approach 1:
The system extracts only the boundary points that define weather cell geometry from the complete radar dataset. By transmitting only these essential boundary coordinates rather than all radar returns, the system maintains accurate weather representation while dramatically reducing bandwidth consumption and communication costs.
3Reliability
If complete radar data is stored, then full weather analysis capability is maintained, but memory requirements and computational power increase
Solution Approach 1:
The patent extracts and stores only the boundary point coordinates that define weather cell geometry instead of storing complete radar datasets. This extraction approach maintains the reliability of weather analysis capabilities while significantly reducing memory requirements and computational power needed for data processing and storage.
4Loss of information
If manual PIREP reporting is used, then weather information is captured, but pilot workload increases and reporting becomes irregular
Solution Approach 1:
The system implements automated weather data capture and transmission that operates without requiring pilot intervention. The onboard weather radar automatically identifies weather cells, extracts their boundary information, and transmits the data to ground stations, eliminating the need for manual PIREP reporting while ensuring consistent and regular data capture.
Solution Approach 2:
The patent replaces the manual mechanical process of pilot reporting with an automated electronic system. The automated system performs weather cell detection, boundary extraction, and data transmission, substituting the manual PIREP process with an autonomous electronic workflow that reduces pilot workload while maintaining comprehensive weather information capture.
Data Source
Figure 1A~1B
Figure 2A
Figure 2B
AI summary
A method approximates outlines of weather cells within the weather radar data using geometric shapes. The weather cells are filtered and clustered into clusters. The edges of the clusters are detected through an edge detection algorithm. Geometric shapes are fit to the edges of the clusters. The geometric shapes require significantly less bandwidth when transmitting over a communication channel, as compared to the weather radar data. The method saves the radar data with very few parameters and thus reduces the required memory for storage and required throughput for exchange.