Aviation Wind Forecast Uncertainty Prediction via Historical Deviation Analysis
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Solution Overview
Problem
Conventional wind forecasting systems using ensemble approaches are computationally expensive and fail to accurately gauge explicit uncertainty in wind forecasts, leading to inefficient fuel calculations and unreliable flight plans due to wide deviations in predictions.
Innovation Solution
A wind forecasting system that utilizes historical wind forecast patterns and actual wind data to generate a predicted wind forecast by determining deviation magnitude and probability of occurrence, combining these with current wind data to improve forecast accuracy and reduce fuel contingencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ensemble forecasting systems are used to generate multiple wind forecasts, then forecast coverage and contingency planning are improved, but computational cost and complexity increase significantly
Solution Approach 1:
The patent introduces a single most likely forecast as an intermediary that mediates between multiple ensemble forecasts and the final prediction. Instead of directly processing multiple complex ensemble forecasts, the system first identifies one representative forecast, then applies deviation analysis to it. This intermediary step simplifies the overall system while maintaining reliability.
Solution Approach 2:
The patent extracts the essential uncertainty information from multiple ensemble forecasts by identifying only the single most likely forecast and its deviation characteristics. Rather than using all ensemble members, it extracts the key forecast and applies historical deviation patterns to it, reducing computational complexity while preserving forecast reliability.
2Reliability
If multiple ensemble forecasts are processed to determine fuel contingencies, then fuel safety is improved, but computational expense increases
Solution Approach 1:
The patent extracts only the essential information needed for fuel contingency calculation from multiple ensemble forecasts - specifically, the single most likely forecast and its historical deviation patterns. This extraction approach maintains fuel safety by preserving the key uncertainty information while dramatically reducing computational energy requirements.
Solution Approach 2:
The patent performs preliminary analysis by pre-calculating deviation magnitudes and probability of occurrence for historical forecast patterns. This preliminary action allows the system to quickly assess fuel contingencies without re-processing multiple ensemble forecasts in real-time, reducing computational energy while maintaining safety.
3Reliability
If conventional forecasting systems calculate multiple flight plans, then contingency planning is improved, but calculation time and productivity decrease
Solution Approach 1:
The patent extracts the essential contingency information from multiple flight plans by focusing on the single most likely flight plan and its deviation characteristics. This allows the system to maintain contingency planning capability while generating flight plans more quickly, as it doesn't need to fully process multiple complete flight plans.
Solution Approach 2:
The patent performs preliminary identification of the single most likely forecast and pre-calculates deviation patterns. This preliminary action enables faster flight plan generation because the system has already determined the key forecast parameters before actual flight plan creation begins.
4Loss of information
If ensemble forecasts are used to gauge spread of contingencies, then uncertainty assessment is improved, but explicit uncertainty measurement in most likely forecast is lost
Solution Approach 1:
The patent uses historical forecast patterns and their actual deviations as feedback to improve the single most likely forecast. By feeding back historical deviation magnitudes and probability of occurrence, the system achieves explicit uncertainty measurement for the most likely forecast, converting the loss of information into a precision improvement.
Solution Approach 2:
The patent performs preliminary analysis of historical forecast patterns to establish deviation magnitudes and probability of occurrence before applying them to the current most likely forecast. This preliminary action enables explicit uncertainty measurement by pre-establishing the statistical relationship between forecasts and actual outcomes.
Data Source
AI summary
A wind forecast prediction apparatus includes an historical forecast patterns module configured to generate at least one historical wind forecast pattern and determine a deviation magnitude and a probability of occurrence of the at least one historical wind forecast pattern based on at least one actual wind value; a current wind forecast module; and a wind forecast prediction controller connected to the historical forecast patterns module and current wind forecast module, the wind forecast prediction controller is configured to generate a current wind forecast pattern based on current wind data from the current wind forecast module; and determine a matching historical wind forecast pattern from the at least one historical wind forecast pattern that matches the current wind forecast pattern, where the deviation magnitude and the probability of occurrence of the matching historical wind forecast pattern are combined with the current wind forecast pattern to generate a predicted wind forecast.