Probability of Precipitation Forecasting Algorithm Segmentation
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Solution Overview
Problem
Current probability of precipitation forecasting methods, such as ensemble and quantitative precipitation forecasting algorithms, have limitations in accuracy and reliability, particularly in predicting high spatial and temporal resolution events like thunderstorms and low-probability high-amount precipitation.
Innovation Solution
A method and system that associate a spatiotemporal subregion with multiple probability of precipitation forecasting algorithms based on a data segment definition, selecting and calibrating climatological data to determine skill and reliability values, optimizing the forecasting algorithm for that region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If QPF-based POP forecasting algorithm is used, then spatial and temporal resolution discrimination is improved, but reliability for low-probability high-amount precipitation deteriorates
Solution Approach 1:
The patent segments the forecasting approach by applying different algorithms to different spatiotemporal subregions based on precipitation characteristics. QPF-based algorithms are applied to regions requiring high spatial/temporal resolution discrimination, while ensemble-based algorithms are applied to regions requiring reliable low-probability high-amount precipitation prediction. This segmentation resolves the contradiction by allowing each algorithm to excel in its appropriate domain.
Solution Approach 2:
The patent implements local quality by calibrating and applying different forecasting algorithms to different spatiotemporal subregions based on their specific precipitation characteristics. Each subregion receives a customized forecasting approach optimized for its local conditions, whether that emphasizes spatial/temporal resolution or reliability for extreme events.
2Reliability
If ensemble-based POP forecasting algorithm is used, then low-probability high-amount precipitation prediction is improved, but spatial and temporal resolution discrimination deteriorates
Solution Approach 1:
The patent segments the forecasting domain into different spatiotemporal subregions where ensemble-based algorithms are specifically applied to regions requiring reliable low-probability high-amount precipitation prediction, while other regions use QPF-based algorithms for high resolution discrimination.
Solution Approach 2:
The patent applies ensemble-based algorithms with local calibration to specific spatiotemporal subregions where reliable prediction of low-probability high-amount precipitation is the priority, allowing each region to have forecasting quality optimized for its specific needs.
3Measurement precision
If multiple probability of precipitation forecasting algorithms are applied to different spatiotemporal subregions, then forecasting accuracy is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-calibrating multiple forecasting algorithms for different spatiotemporal subregions and storing the calibrated parameters. During operational forecasting, the system simply selects and applies the pre-calibrated algorithm appropriate for the given subregion, avoiding the complexity of real-time calibration while maintaining high accuracy.
Solution Approach 2:
The patent introduces an intermediary calibration system that manages multiple algorithms and their parameters. This intermediary layer handles the complexity of algorithm selection, calibration, and coordination, allowing the core forecasting function to remain simple while achieving high accuracy through multiple specialized algorithms.
Data Source
AI summary
A method of optimizing a probability of precipitation forecast is provided. The method includes uniquely associating, based on a data segment definition, a spatiotemporal subregion with a probability of precipitation forecasting algorithm of two or more probability of precipitation forecasting algorithms. The method further includes selecting climatological data meeting the data segment definition and calibrating, using the selected climatological data meeting the data segment definition, the probability of precipitation forecasting algorithm associated with the spatiotemporal subregion.