Precipitation Downscaling with Spatiotemporal Video Diffusion
This provides more reliable precipitation forecasts for climate science and meteorology applications, though it is an incremental improvement over existing diffusion model approaches.
The paper tackles the problem of generating high-resolution local precipitation predictions from low-resolution climate model outputs by extending video diffusion models to precipitation super-resolution, achieving state-of-the-art performance on metrics like CRPS and MSE compared to six baselines.
In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statistical downscaling, or super-resolution, is a common workaround where a low-resolution prediction is improved using statistical approaches. Unlike traditional computer vision tasks, weather and climate applications require capturing the accurate conditional distribution of high-resolution given low-resolution patterns to assure reliable ensemble averages and unbiased estimates of extreme events, such as heavy rain. This work extends recent video diffusion models to precipitation super-resolution, employing a deterministic downscaler followed by a temporally-conditioned diffusion model to capture noise characteristics and high-frequency patterns. We test our approach on FV3GFS output, an established large-scale global atmosphere model, and compare it against six state-of-the-art baselines. Our analysis, capturing CRPS, MSE, precipitation distributions, and qualitative aspects using California and the Himalayas as examples, establishes our method as a new standard for data-driven precipitation downscaling.