Scheduling space-to-ground optical communication under cloud cover uncertainty

Any reliable model for scheduling optical space-to-ground communication must factor in cloud cover conditions due to attenuation of the laser beam by water droplets in the clouds. In this article, we provide two alternative models of uncertainty for cloud cover predictions: a robust optimization model with a polyhedral uncertainty set and a distributionally robust optimization model with a moment-based ambiguity set. We computationally analyze their performance over a realistic communication system with one satellite and a network of ground stations located in the U.K. The models are solved to schedule satellite operations for six months utilizing cloud cover predictions from official weather forecasts. We found that the presented formulations with the treatment of uncertainty outperform in the long-term models, in which uncertainty is ignored. Both treatments of uncertainty exhibit similar performance. Nonetheless, the novel variant with the polyhedral uncertainty set is considerably faster to solve.