Snowfall Probability Distribution Forecasting System
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Solution Overview
Problem
Current snowfall accumulation forecasts provide deterministic predictions without conveying probability distributions or the forecaster's confidence level, failing to account for the range of potential outcomes and uncertainties in snowstorms.
Innovation Solution
A rules-based process leveraging third-party weather forecasts to generate snowfall probability distributions, including the most likely accumulation range and probabilities within and outside that range, ensuring consistency with deterministic forecasts and normalizing data for accurate representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If deterministic forecasts are used, then forecast simplicity and ease of operation are improved, but information completeness and reliability deteriorate
Solution Approach 1:
The forecast is segmented into multiple discrete outcomes with associated probabilities. Instead of a single deterministic value, the system divides the forecast into distinct snowfall accumulation ranges (e.g., 0-1 inch, 1-2 inches, 2-3 inches) and assigns probability weights to each range, allowing users to understand both the most likely outcome and the full spectrum of possible outcomes.
Solution Approach 2:
The forecast transitions from a one-dimensional deterministic value to a two-dimensional probability distribution. By adding the probability dimension to the snowfall accumulation forecast, the system provides both the predicted value and the uncertainty associated with that prediction, enriching the information without complicating the basic forecast structure.
2Reliability
If probability distributions are generated from ensemble forecasts, then reliability and information completeness are improved, but device complexity and computational requirements worsen
Solution Approach 1:
The system extracts and processes only the relevant snowfall accumulation data from the ensemble forecasts, filtering out unnecessary meteorological variables and model details. By focusing solely on the snowfall accumulation values and their probability distributions, the system reduces processing complexity while maintaining forecast reliability.
Solution Approach 2:
The system transforms the raw ensemble forecast data into standardized probability distribution parameters (mean, standard deviation, percentile values) that can be directly used for forecasting. This parameter transformation simplifies the complex raw data into actionable statistical summaries, reducing computational requirements while preserving forecast accuracy.
3Productivity
If third-party weather forecasts are leveraged, then productivity and forecasting speed are improved, but measurement precision and data reliability worsen
Solution Approach 1:
The system merges multiple third-party weather forecasts into a unified probability distribution. By combining the predictions from various independent sources and using statistical methods to aggregate their results, the system achieves both speed (by processing multiple forecasts simultaneously) and precision (through statistical aggregation that accounts for individual forecast uncertainties).
Data Source
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AI summary
Currently available weather forecasts, which include a specific snowfall accumulation or range, do not convey the probability that snowfall will be within the forecasted snowfall accumulation range, probabilities of other snowfall accumulation amounts, or a forecaster's level of confidence. A snowfall probability distribution forecasting system is disclosed that uses a rules-based process to leverage third party weather forecasts, including members of ensemble forecasts, to generate snowfall probability distributions forecasting the most likely snowfall accumulation range, the probability that snowfall accumulation will be within the most likely snowfall accumulation range, and probabilities that snowfall accumulation will be outside of the most likely snowfall accumulation range. To ensure consistency with the deterministic forecast, the snowfall probability distribution may be shifted based on a deterministic forecast. Because third party weather forecasts can produce a non-normal distribution of snowfall accumulation forecasts, the snowfall probability distribution may be normalized.