Enabling Space-Based Computed Cloud Tomography with a Mixed Integer Linear Programming Scheduler

Cumuliform clouds in Earth’s atmosphere scatter outgoing longwave radiation which interacts with aerosols to generate new clouds. These new clouds scatter additional outgoing radiation creating a feedback loop. Uncertainties in the strength of this feedback is the primary source of uncertainty in transient and equilibrium climate model predictions. Conventional remote cloud observation methods are inadequate for inferring the internal structures of these clouds. Cloud tomography operates by imaging a single cloud target from multiple locations with a large angular range. Many low convective clouds only have a lifetime of 15-25 minutes necessitating autonomous scheduling of observation targets as they appear. On-board autonomous scheduling is formulated as a mixed integer optimization problem (MILP) with a finite time horizon and a reward scheme designed to maximize the angular range of the observations. A finite time horizon MILP scheduler is well suited to this mission because the short lifetime of low convective clouds creates a natural time horizon. This MILP can solve for an optimal observation schedule in a maximum time of 15 ms on a conventional desktop CPU. The MILP scheduler is able to observe 59.4% more targets than a conventional push broom camera configuration. This initial result is promising and demonstrates the need for continued research efforts in this area.