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.

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.

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.

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.

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.