Machine Learning Weather Data Generation for Observation Gaps
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Solution Overview
Problem
Current weather observation systems face limitations in generating high-resolution data for observation gap regions due to the concentration of weather observation instruments in urban areas and the occurrence of missing data in certain observation areas.
Innovation Solution
A computing device equipped with a processor and memory that utilizes a machine learning module, specifically a neural network, to recognize initial ground weather observation data and generate weather data for gap regions by integrating satellite observation data and land surface characteristics, thereby filling in missing data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weather observation instruments are continuously increased to obtain high-resolution ground-observation meteorological factor values, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates virtual copies of observation data by training a neural network model on existing ground observation data and satellite data. The model generates synthetic weather data for unobserved regions, effectively copying and extrapolating patterns from observed areas without requiring physical replication of observation instruments.
Solution Approach 2:
The patent introduces satellite observation data as an intermediary between ground observation instruments and the final weather data product. The neural network model acts as another intermediary, processing both ground and satellite data to fill observation gaps, thereby reducing the need for direct ground instrument coverage in all areas.
2Ease of operation
If observation instruments are concentrated in urban areas for easier deployment, then ease of operation is improved, but measurement precision deteriorates due to observation gaps in rural areas
Solution Approach 1:
The patent transitions from a two-dimensional ground-based observation network to a three-dimensional data fusion approach by incorporating satellite data that provides top-down observation coverage. This dimensional addition allows the system to infer ground conditions in rural areas without deploying ground instruments there, maintaining ease of deployment while improving coverage.
Solution Approach 2:
The neural network model serves multiple functions: it processes data from urban observation instruments, integrates satellite data, fills gaps in rural areas, and provides a unified weather data product that works across all regions. This multi-functionality allows the system to maintain concentrated urban instrumentation while achieving comprehensive coverage.
3Loss of information
If machine learning models are trained on artificial missing data to fill observation gaps, then data coverage is improved, but manufacturing precision of training data decreases
Solution Approach 1:
The patent performs preliminary actions by first training the neural network model on complete, non-missing ground observation data and satellite data to learn the relationships between them. Only after the model is trained does it proceed to fill the artificially created missing data, ensuring that the filling process is based on learned patterns rather than speculation.
Solution Approach 2:
The patent implements feedback mechanisms by comparing the generated weather data for gap regions against the actual observed data in validation scenarios. This feedback loop allows the model to learn from its errors and continuously improve its ability to fill observation gaps accurately, refining the training process over multiple iterations.
Data Source
AI summary
Disclosed is a computing device for generating weather observation data for solving the problem. The computing device includes: a memory including computer executable components; and a processor executing following computer executable components stored in the memory, and the computer executable components may include an initial ground weather observation data recognition component recognizing observed initial ground weather observation data, and a weather data generation component trained to generate weather data of a gap region on the initial ground weather observation data by using a machine learning module.