Abstract: Apparatuses, systems, and techniques for meteorological prediction. In at least one embodiment, a neural network model includes at least one regression model and a diffusion model. The at least one regression model is configured to obtain first observation corresponding to a geographical region, and predict a first image representing a second observation corresponding to the geographical region. The diffusion model is configured to predict, based on the first image, a second image representing a refined second observation corresponding to the geographical region.

Abstract: Apparatuses, systems, and techniques for probabilistic forecasting. In at least one embodiment, a neural network is configured to receive an input frame indicating a current status, and obtain, based on the input, a sequence of first frames to form a first processing window. The first frames in the sequence are corrupted with one or more predefined noise schedules. The neural network is configured to further denoise the sequence of first frames within the first processing window simultaneously to produce a sequence of second frames, output one or more second frames from the first processing window, and form a second processing window by appending one or more additional frames to the remaining one or more second frames from the first processing window. As such, the neural network produces output frames based on a rolling processing window.

Abstract: Apparatuses, systems, and techniques for adaptive flow matching. In at least one embodiment, input is received, which includes one or more first variables at first scale. An encoder is used to encode the input to provide a base distribution at the first scale. The base distribution is associated with one or more second variables, and the one or more second variables include one or more variables absent from the one or more first variables. A perturbed base distribution is obtained based on the base distribution and an adaptive noise. A diffusion model is used to generate a target distribution at a second scale. The target distribution is associated with the one or more second variables. The second scale is finer than the first scale.