Interval-Specific AI Weather Forecasting for Lower Compute and Delay
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Solution Overview
Problem
Current weather forecasting methods based on numerical weather prediction consume high computing power and result in significant delays due to complex equation solving processes.
Innovation Solution
A weather forecasting method utilizing a plurality of AI models, each trained for different time intervals, performs iterative inference operations to reduce computing power and forecast delays, while integrating three-dimensional and two-dimensional meteorological data for enhanced precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If numerical weather prediction with complex equation solving is used, then forecast accuracy is maintained, but computing power consumption increases and forecast delay increases
Solution Approach 1:
The patent replaces the traditional mechanical/mathematical system of solving complex hydrodynamic and thermodynamic equations with an artificial intelligence model that performs pattern recognition and prediction through learned representations, significantly reducing computing power requirements while maintaining forecast accuracy
Solution Approach 2:
The AI model is trained in advance on historical weather data to learn complex atmospheric patterns and relationships, so that during actual forecasting, the model can directly make predictions without performing complex real-time calculations, thus reducing computing power consumption and forecast delay
2Reliability
If numerical weather prediction with complex equation solving is used, then forecast accuracy is maintained, but forecast delay increases
Solution Approach 1:
The patent replaces the traditional mechanical/mathematical system of solving complex equations with an AI-based system that uses learned patterns for rapid prediction, dramatically reducing the time required to generate weather forecasts while preserving accuracy
Solution Approach 2:
The AI model performs comprehensive learning and pattern recognition during the offline training phase, enabling it to make accurate predictions during real-time forecasting without requiring complex computational processes, thus minimizing forecast delay
3Reliability
If a single AI model performs multiple iterative inference operations, then forecast accuracy improves, but resource consumption increases
Solution Approach 1:
The patent divides the forecasting task into multiple specialized AI models, each trained for specific time intervals (e.g., short-term, medium-term, long-term forecasts). This segmentation allows each model to be more efficient at its specific task, reducing the need for repeated iterative inferences and lowering overall resource consumption
Solution Approach 2:
The patent creates a suite of AI models that collectively cover different forecasting time intervals and weather conditions. Each model is multi-functional in handling its specific domain, and together they provide comprehensive forecasting coverage, reducing the need for a single model to perform multiple iterative operations across all scenarios
Data Source
AI summary
This application provides a weather forecast method, including: obtaining meteorological data and target time; determining a plurality of first AI models from a model library based on the target time, where different first AI models are used for forecasting weather at different time intervals; and performing inference based on the obtained meteorological data by using the plurality of first AI models, to obtain a first weather forecast result at the target time.