Probabilistic Weather Severity Estimation via Dynamic Aircraft Data Collection
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Solution Overview
Problem
Current weather data gathering systems for flight safety rely on periodic communication methods, which may not efficiently cover large areas with the highest number of nodes, leading to suboptimal data collection and processing, especially in dynamic weather conditions.
Innovation Solution
A probabilistic weather severity estimation system using a non-periodic, iteratively optimized communication method that creates a four-dimensional volumetric airspace grid, where each cell is associated with a node in a status matrix, allowing for dynamic selection and optimization of aircraft for data collection based on need, using integer linear programming and heuristic blending algorithms to predict future weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If periodic communication methods are used for weather data collection, then system simplicity is maintained, but data collection efficiency and coverage are suboptimal
Solution Approach 1:
The patent implements dynamic, non-periodic communication scheduling where the ground station adaptively determines when to request weather data from aircraft based on current weather conditions, aircraft positions, and data needs. This replaces static periodic communication with a dynamic system that optimizes data collection timing and targets specific aircraft, thereby improving productivity without requiring fundamentally complex communication infrastructure
Solution Approach 2:
The system changes the parameter of communication timing from fixed periodic intervals to variable intervals based on multiple factors including weather severity, aircraft proximity to weather cells, and data freshness requirements. This parameter change enables efficient data collection during critical weather events while reducing unnecessary communications during stable conditions, improving overall system productivity
2Measurement precision
If comprehensive weather data is collected from multiple sources, then measurement precision improves, but data processing complexity increases
Solution Approach 1:
The patent segments the volumetric airspace into discrete cells with associated nodes, where each cell's weather conditions are independently tracked and estimated. This segmentation allows the system to process weather data in manageable units rather than as a monolithic dataset, reducing processing complexity while maintaining comprehensive coverage through the collective analysis of multiple segmented cells
Solution Approach 2:
The system introduces an intermediary probabilistic estimation layer that processes raw weather data from multiple aircraft and sources. This intermediary processing layer uses status matrices and probabilistic models to synthesize comprehensive weather information, providing accurate measurements while abstracting away the complexity of handling multiple data sources through a unified probabilistic framework
3Loss of information
If high-resolution weather forecasts are provided, then situational awareness improves, but data transmission requirements increase
Solution Approach 1:
The patent provides high-resolution weather information locally to each aircraft based on its specific position, heading, and operational context. Rather than transmitting complete high-resolution weather grids to all aircraft, the system calculates and transmits only the relevant weather data for each aircraft's local area and flight path, maintaining high situational awareness quality while minimizing data transmission volumes through localized information delivery
Data Source
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AI summary
A method implementing a probabilistic weather severity estimation system is provided. The method includes gathering meteorological information from a plurality of vehicles based on a non-periodic iteratively optimized communication method that at least in part takes into consideration an vehicle's position and heading in relation to a weather event. A probabilistic weather severity estimation for future weather conditions is calculated based at least in part on the gathered meteorological information and an assigning of computed probabilistic weather severity values to nodes in a matrix. Each node is associated with a respective part of space volume in which the plurality of vehicles are gathering the meteorological information.