2025/05/16 by Youssef Fahmy, Maria Laura Battagliola, Fahmy, Youssef +3
Earth and Planetary Sciences · Environmental Science · #Applications (stat.AP) #FOS: Computer and information sciences #Meteorological Phenomena and Simulations #Plant Water Relations and Carbon Dynamics #Wind and Air Flow Studies
paper · pdf · doi:10.48550/arxiv.2505.10898
openalex publication_date 2025/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Accurately estimating latent velocity vector fields of atmospheric winds is crucial for understanding weather phenomena. Direct measurement of atmospheric winds is costly, especially in the upper atmosphere, so researchers attempt to estimate atmospheric winds by observing the movement patterns of clouds and other features in satellite images of the atmosphere. These Derived Motion Winds use feature tracking algorithms to search for movement within small windows in space and time. Consequently, these algorithms cannot leverage information from broader-scale features and cannot ensure that the collection of wind vectors over space and time represents a physically realistic velocity field. In this work, we use spatial-temporal Gaussian processes to model the evolution of a scalar quantity transported over time by fluid flow. Our framework simultaneously estimates covariance parameters and latent velocities by maximizing the likelihood. Specifically, flows are represented using time-dependent residual neural networks, and velocities are subsequently derived through closed-form formulas. Performance evaluations using weather model data demonstrate our method's accuracy and efficiency. We apply our method to GOES-16 images, demonstrating computational efficiency and the ability to produce wind estimates where Derived Motion Winds fail.