Variational Autoencoder for Extreme Weather Scenario Synthesis
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Solution Overview
Problem
Conventional weather generators struggle to generate realistic extreme weather scenarios, such as severe rainfall, windstorms, and droughts, due to their reliance on historical data and inability to explicitly learn the training data distribution, which limits their ability to predict rare and intense weather events effectively.
Innovation Solution
A computer-implemented method using a variational autoencoder (VAE) to generate synthetic weather data by processing historical climate data, where the extremeness of weather events is defined by their rarity and intensity, allowing for stochastic synthesis and regularization of the latent space to a known distribution, enabling the prediction of extreme weather events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional weather generators use historical data to extrapolate future climate data, then they can generate plausible climatic scenarios, but they struggle to synthesize data with complex trends and extreme weather events
Solution Approach 1:
The patent transforms the approach by changing from direct historical data extrapolation to a two-stage process: first encoding historical data into a latent space representation, then sampling from this latent space to generate synthetic weather data. This parameter transformation enables the model to capture complex dependencies while generating extreme weather scenarios that go beyond simple historical repetition.
Solution Approach 2:
The patent introduces a latent space as an intermediary between historical weather data and generated scenarios. The variational autoencoder compresses historical data into this intermediate representation, which captures the essential statistical properties and dependencies. This intermediary layer allows the system to generate realistic extreme weather events without being constrained by direct historical patterns.
2Measurement precision
If weather generators rely on historical data distribution, then they can maintain consistency with observed patterns, but they cannot effectively predict rare and intense weather events
Solution Approach 1:
The patent performs preliminary encoding of historical weather data into a latent space representation before generation. This pre-processing step captures the statistical properties and dependencies of historical data, including rare events, in a compressed form. When generating new scenarios, the model can sample from this pre-processed latent space to produce extreme weather events with appropriate rarity and intensity.
Solution Approach 2:
The patent moves the problem from the original weather data dimension to a latent space dimension through encoding. This dimensional transformation allows the model to represent complex weather patterns and rare events in a lower-dimensional space where they can be more effectively sampled and generated, improving the ability to predict extreme events while maintaining consistency with historical patterns.
3Reliability
If GANs are used for weather synthesis, then realistic weather patterns can be generated, but they do not explicitly learn the training data distribution and depend on auxiliary variables for conditioning
Solution Approach 1:
The patent extracts the essential statistical properties and dependencies of weather data by encoding them into a latent space representation. This extraction process separates the core climatic patterns from the need for complex auxiliary conditioning variables. The generated scenarios are produced by sampling from this extracted latent representation, reducing dependence on auxiliary variables while maintaining realism.
Solution Approach 2:
The patent creates a compressed copy of the historical weather data distribution in the latent space. Instead of directly manipulating the original high-dimensional weather data with complex conditioning, the model works with this lower-dimensional copy that captures the essential characteristics. This copied representation enables realistic generation with simpler conditioning requirements.
Data Source
AI summary
A computer implemented method of predictive weather occurrences includes generating, by a computer processor, a training model through artificial intelligence. The training model is based on climate data processed by a variational autoencoder. A geographic location is selected for climate study. Historical weather measurements associated with the selected geographic location are retrieved from a knowledge climate database. The retrieved historical weather measurements are processed using the training model. The training model receives threshold parameters defining extremeness of weather. Extremeness is based on a weather intensity data point being farther from a norm than closer to the norm. Synthetic weather data is generated for the selected location, wherein the synthetic weather data predicts weather events satisfying the extremeness threshold parameters.