  The projection of shadows from moving clouds in the troposphere impacts
energy generation in power grids using photovoltaic systems. This investigation
proposes an efficient method of data processing for the statistical
quantification of cloud features using infrared images and global solar
irradiance measurements. The infrared images and the global solar irradiance
measurements are acquired using a sky imager equipped with a commercial
low-cost long-wave infrared radiometric camera and a pyranometer. The enclosure
of the infrared camera is mounted on a solar tracker so that the Sun stays in
the center of images throughout the day. We explain how to remove
cyclostationary biases in global solar irradiance measurements. Seasonal trends
are removed from the global solar irradiance time series, using the theoretical
global solar irradiance to obtain the clear sky index time series. We introduce
an atmospheric model to remove the effect of atmospheric scattering and the
effect of the Sun's direct irradiance from infrared images. Scattering is
produced by water spots and dust particles on the germanium lens of the camera
enclosure. We explain how to remove the scattering effect produced by the
germanium lens attached to the data acquisition system enclosure window of the
infrared camera. An atmospheric condition model classifies the sky conditions
in four different categories: clear sky, cumulus, stratus and nimbus. When an
infrared image is classified in the category of clear sky, it is used to model
the scattering effect produced by the germanium lens.
