Increasing accuracy and resolution of weather forecasts using deep generative models
US12205029B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 16, 2024 |
| Grant date | Jan 21, 2025 |
| Priority date | — |
| Expiry date | Jan 16, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present invention provide the use of a conditional Generative Adversarial Network (GAN) to simultaneously correct and downscale (super-resolve) global ensemble weather or climate forecasts. Specifically, a generator deep neural network (G-DNN) in the cGAN comprises a corrector DNN (C-DNN) followed by a super-resolver DNN (SR-DNN). The C-DNN bias-corrects coarse, global meteorological forecasts, taking into account other relevant contextual meteorological fields. The SR-DNN downscales bias-corrected C-DNN output into G-DNN output at a higher target spatial resolution. The GAN is trained in three stages: C-DNN training, SR-DNN training, and overall GAN training, each using separate loss functions. Embodiments of the present invention significantly outperform an interpolation baseline, and approach the performance of operational regional high-resolution forecast models across an array of established probabilistic metrics. Crucially, embodiments of the present invention, once trained, produce high-resolution predictions in seconds on a single machine.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.