Intelligent Long Endurance Observing System

Existing satellites provide coarse-grained data on column concentrations of atmospheric trace gases. While these data can be supplemented by fine-pointing satellites and aircraft, the spatio-temporal resolutions available are not sufficient to observe stochastic, ephemeral events that take place between observations. Emerging High Altitude Long Endurance Uncrewed Aerial Systems (HALE UAS) can operate for months at a time and loiter over targets to provide continuous daylight geostationary-like observations. In this presentation, we will describe the Intelligent Long Endurance Observing System (ILEOS). ILEOS helps scientists build optimized flight plans for HALE UAS flights. Optimization of HALE UAS spatio-temporal placement will improve sampling of atmospheric constituents by fusing coarse-grained sensor data from satellites, in situ datasets, and other sources (e.g., terrain, wind and weather forecasts) to produce an overall spatio-temporal resolution increase. ILEOS enables longer observational periods and allows observations of environments currently not accessible through neither in-situ observations nor field campaigns. ILEOS optimizes flight plans in order to maximize science return in light of environmental factors and UAS constraints. ILEOS consists of 3 components: The Targeter generates a prioritized list of targets to be observed given a set of data sources. Using the Targeter output together with a set of constraints (instruments, HALE UAS performance, etc.), the Planner produces a strategic observation plan. The Reporter explains to scientists the reasons for Targeter-assigned target priority, the reasons for Planner- generated decisions, and allows scientists to iterate over Targeter and Planner generation steps. To illustrate the potential of ILEOS, we present two potential case studies of a HALE UAS fitted with a nitrogen dioxide remote sensor. The first case study concerns observing air pollution from oil and natural gas operations; the second case study focuses on air pollution in a city at high spatial resolution for environmental justice studies.