Remote sensing of clouds

The extraction of information on cloud cover from present-day multispectral satellite images poses a challenge to the remote sensing specialist. When approached one pixel at a time, the derived cloud cover parameters are inherently nonunique. More information is needed than is available in the radiances from each channel of an isolated pixel. The required additional information can be obtained for each scene, however, by analyzing the distribution of pixels in the multi-dimensional space of channel radiances. The cluster patterns in this space yield statistical information that points to the most likely solution for that scene. The geostationary and polar orbiting meteorological satellites all have, at a minimum, a solar reflection channel in the visible spectrum and a thermal infrared channel in the 8–12 micron window. With the information from the cluster patterns and application of the equations of radiative transfer, the measurements in those channels will yield cloud cover fraction, optical thickness, and cloud-top temperature for an assumed microphysical model of the cloud layer. Additional channels, such as the 3.7 micron channel on the AVHRR of the polar orbiting meteorological satellites, will will yield information on the microphysical model—e.g., distinguishing small liquid liquid droplets (typical of low level clouds) from large ice particles (typical of cirrus and the tops of cumulonimbus). New channels to be included in future satellite missions will provide information on cloud height, independent of temperature, and on a particle size and thermodynamic phase, independently of each other. A proposed STS mission using lidar will pave the way for the use of active sensors that will provide more precise information on cloud height and probe the structure of thin cirrus and the top layer of of the thicker cloud.