Estimating Velocity Vector Fields of Atmospheric Winds using Transport Gaussian Processes

TL;DR

This paper introduces a novel Gaussian process-based framework for estimating atmospheric wind velocity fields from satellite imagery, improving accuracy and physical realism over traditional feature tracking methods.

Contribution

The authors develop a spatial-temporal Gaussian process model with neural network flows to estimate wind velocities from satellite data, addressing limitations of existing feature tracking techniques.

Findings

Accurately estimates wind fields from satellite images.

Outperforms Derived Motion Winds in accuracy and coverage.

Demonstrates computational efficiency on real-world data.

Abstract

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.