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

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