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 Mars and for future communication stations on the moon. Cloud cover impacts the availability of space to ground optical communications. Mitigating these impacts requires a geographically diverse network of ground communication. 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 JPL-funded study, 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 (Geostationary Operational Environmental Satellite) and Meteosat satellites are compared to predicted clear sky background values. Several threshold tests in the Cloud Mask Generator (CMG) involving radiance-derived cloud identification tools (e.g., fog product, albedo product) are used to estimate the probability of cloud cover for a given pixel of a satellite image. When stations are chosen from a list of sites of interest, six stations are needed to achieve a network availability of 90 % or better.
NASA Jet Propulsion Laboratory (JPL) is interested in adding optical communications to its deep space communications network. Clouds adversely affect the transmission of optical communications; in order to mitigate the effects of clouds and achieve reliable communications, a geographically diverse set of ground receiver stations is needed. To study cloud effects on optical communications we have developed a high-resolution cloud climatology based on NOAA Geostationary Environmental Operational Satellite (GOES) imager data. The GOES imager includes multi-spectral channels, one visible and four infrared, at 4-km spatial resolution and 15-minute time resolution. Cloud detection is accomplished by modeling the radiance of the ground in the absence of clouds and comparing the actual radiance values from the imagery. A composite cloud decision is formed by objectively combining the results of the tests from the individual channels. Ground site selection studies are accomplished using the Lasercom Network Optimization Tool (LNOT). LNOT applies a discrete optimization algorithm to the cloud climatology dataset to find the optimal number and locations of ground stations for a given concept of operations. Applying LNOT to the JPL problem we find that 90% availability could be achieved with 4-5 ground stations in the continental US and Hawaii. We also present the results of a pilot study that includes 6 months of cloud data over South America.