Regional Precipitation Nowcasting with Dual Cycle-GAN Feedback
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Solution Overview
Problem
Conventional precipitation nowcasting systems face challenges in accurately predicting localized heavy rain and providing real-time weather information for specific local areas, with limitations in short-term forecasting and local area coverage.
Innovation Solution
A regional precipitation nowcasting system based on cycle-generative adversarial network (GAN) extension, utilizing a first and second cycle-GAN for forward and backward image mapping, with generators and discriminators trained using specific loss functions to generate precise future precipitation images from current data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If conventional NWP models are used for precipitation prediction, then wide range time period coverage is achieved, but accuracy for first few hours deteriorates due to spin-up problem
Solution Approach 1:
The prediction task is segmented into two distinct phases: nowcasting for short-term (0-2 hours) using radar-based AI models to avoid spin-up problems, and NWP for medium-term (2+ hours) prediction. This segmentation allows each method to operate in its optimal performance range, with the AI model handling the critical short-term period where NWP fails.
Solution Approach 2:
A dual-model system acts as an intermediary between radar observations and NWP models. The cycle-GAN-based AI model processes radar data to generate accurate short-term predictions, which can then be integrated with or transition into NWP models for extended forecasting, bridging the gap between observation and long-term prediction.
2Area of stationary object
If conventional nowcasting methods are used, then general area coverage is achieved, but localized heavy rain prediction capability deteriorates
Solution Approach 1:
The system applies local quality by processing radar data at multiple resolution levels. The cycle-GAN model learns to preserve local features and fine-scale structures in precipitation patterns while operating on regional data, enabling accurate prediction of localized heavy rain events within broader coverage areas.
Solution Approach 2:
The patent extends cycle-GAN from standard image-to-image translation to 4D spatio-temporal domain by incorporating time as an additional dimension. This allows the model to capture temporal evolution of precipitation patterns while maintaining spatial resolution necessary for localized prediction accuracy.
3Measurement precision
If cycle-GAN is extended to 4D spatio-temporal domain with complementary cycles, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges forward and backward cycle-GANs into a unified 4D spatio-temporal model. By combining these complementary cycles, the system leverages both temporal directions (past-to-future and future-to-past) to constrain and improve prediction accuracy, while sharing computational resources and parameters to manage complexity.
Solution Approach 2:
The backward cycle-GAN provides feedback to the forward prediction by learning the reverse mapping from future to past states. This feedback mechanism constrains the forward model's predictions to be physically consistent with observed patterns, improving accuracy without requiring a completely separate model for each direction.
Data Source
AI summary
A regional precipitation nowcasting system based on cycle-generative adversarial network (GAN) extension includes an input unit configured to receive an input composite hybrid surface rainfall (HSR) image including precipitation information of a region of interest corresponding to a first time, a cycle-GAN configured to generate a resultant composite HSR image including precipitation information of the region of interest corresponding to a second time which comes later than the first time on the basis of the input composite HSR image using a first cycle-GAN and a second cycle-GAN which is complementary to the first cycle-GAN, and an output unit configured to output the resultant composite HSR image as a nowcasting image of the region of interest. The regional precipitation nowcasting system and method based on cycle-GAN extension can ensure robust temporal causality by applying pixel losses to a cycle-GAN.