Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images

TL;DR

This paper introduces a novel algorithm that leverages fluid dynamics principles, specifically the Navier-Stokes equations, to forecast cloud motion from satellite images, enhancing solar energy prediction accuracy.

Contribution

It proposes a new physics-inspired method that models cloud dynamics using fluid flow equations, integrating optical flow estimation with Navier-Stokes fitting for improved forecasting.

Findings

Effective cloud motion prediction demonstrated on NOAA satellite data

The method outperforms traditional optical flow approaches

Provides a physics-based framework for satellite image analysis

Abstract

This paper describes a new algorithm for solar energy forecasting from a sequence of Cloud Optical Depth (COD) images. The algorithm is based on the following simple observation: the dynamics of clouds represented by COD images resembles the motion (transport) of a density in a fluid flow. This suggests that, to forecast the motion of COD images, it is sufficient to forecast the flow. The latter, in turn, can be accomplished by fitting a parametric model of the fluid flow to the COD images observed in the past. Namely, the learning phase of the algorithm is composed of the following steps: (i) given a sequence of COD images, the snapshots of the optical flow are estimated from two consecutive COD images; (ii) these snapshots are then assimilated into a Navier-Stokes Equation (NSE), i.e. an initial velocity field for NSE is selected so that the corresponding NSE' solution is as close as possible to the optical flow snapshots. The prediction phase consists of utilizing a linear transport equation, which describes the propagation of COD images in the fluid flow predicted by NSE, to estimate the future motion of the COD images. The algorithm has been tested on COD images provided by two geostationary operational environmental satellites from NOAA serving the west-hemisphere.