Abstract: Machine learning systems and methods for extreme weather event modeling using generative diffusion models are provided. The system includes a weather modeling processor and a weather modeling engine executed by the processor. The weather modeling engine causes the processor to: receive a dataset including a plurality of vorticity samples; process the dataset using a deterministic mean model having a temporal attention unit to model spatial, cross-channel, and temporal dependencies using dynamical attention units; and process output of the deterministic mean model using a reverse diffusion model to capture stochastic fine scale features and to generate a denoised output. A downscaling pipeline can also be executed by the weather modeling engine to downscale outputs of the system.

Abstract: Machine learning systems and methods for continuous latent representations for modeling precipitation using deep learning are provided. The system includes aa precipitation modeling processor and a precipitation modeling engine executed by the processor. The precipitation modeling engine causing the processor to receive a first dataset including vertically-integrated moisture divergence (VIMD) data, receive a second dataset including total precipitation (TP) data, process the VIMD data and the TP data to blend the VIMD data and the TP data into a pseudo-precipitation (PP) field using a machine learning encoder, and process the PP field using a machine learning decoder to reconstruct the TP data from the PP field.