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