The Role of Satellite-derived Cloud Climatologies in Deep-space to Ground Laser Communications

Future deep-space communications will require the collection and transmission of data from high-bandwidth links. NASA's Jet Propulsion Laboratory (JPL) is investigating the utility of laser communications for future missions to the Moon, Mars, and other deep space locations. Among the many obstacles to successful laser communications is signal interruption by clouds. To mitigate the effects of clouds on optical communications, a geographically diverse network of ground communication stations is required. Selecting the number and location of stations for a network requires an optimization algorithm that can distinguish and rank site availability based on multi-year cloud climatologies for many locations around the globe. The optimization algorithm must also consider the movement and location of a space-borne probe. In this study funded by JPL, the TASC Lasercom Network Optimization Tool (LNOT) is used to determine optimal networks of receiving stations by analyzing cloud mask data from the continental United States, Hawaii, South America, Europe, northern and southern Africa, the Middle East, central and eastern Asia, and Australia. To generate cloud masks, raw visible and infrared radiance data from GOES, Meteosat, and MTSAT-1R satellites are compared to predicted clear sky background values. Several threshold tests in the TASC Cloud Mask Generator (CMG) involving radiance-derived cloud identification products (e.g., fog product, albedo) are used to estimate the probability of cloud cover for a given pixel of a satellite image. To achieve a network availability of 90 % or better, six ground stations from a list of sites of interest as selected by JPL are needed.