A015-11
Science Planning for the MethaneSAT Mission

Monday, 7 December 2020: 06:00
Virtual
Joshua Simon Benmergui1, Reuben Rohrschneider2, Mark Omara3, Ritesh Gautam4, Christopher Chan Miller5, Kang Sun6, Jonathan E Franklin1, Steve Hamburg7, Bill Preetz8 and Steven C Wofsy9, (1)Harvard University, Cambridge, MA, United States, (2)Ball Aerospace, Boulder, CO, United States, (3)Environmental Defense Fund New York, Austin, TX, United States, (4)Environmental Defense Fund DC, Washington, DC, United States, (5)Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, United States, (6)Harvard-Smithsonian Center for Astrophysics, Cambridge, United States, (7)Environmental Defense Fund New York, New York, NY, United States, (8)Ubiquity Robotics, Cupertino, United States, (9)Harvard University, John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, United States
Abstract:
MethaneSAT is a greenhouse gas imaging satellite under development by MethaneSAT LLC, an affiliate of the Environmental Defense Fund. Its goal is to characterize changes in anthropogenic methane emissions with an initial focus on the global oil and gas production sector. MethaneSAT will retrieve images of column-averaged dry-air mole fractions of methane (XCH4) over 200 X 140 km2 target areas with precision of at least 3 ppb over approximately 1 km2 pixels. In view of the mission objectives, MethaneSAT is designed to have global access and the ability to quantify emissions regionally and attribute them locally. Due to the voluminous data collection and processing requirements, systematic science planning is required to maximize MethaneSAT’s application and effectively guide the mission to achieve its goal of robust methane emission quantification and source attribution. In this work, we describe MethaneSAT’s science data planning system and its implementation. We present an implementation of the MethaneSAT Learning Orbit Scheduling and Simulation Tool (MSAT-LOSST). MSAT-LOSST performs optimal on-orbit scheduling of MethaneSAT’s tasks, including target scans, data downlinks, vicarious calibrations, and orbit maintenance. It uses a tunable target selection criterion that considers the scientific value of targets, and also the observing conditions. We simulated a year in the life of MethaneSAT, implementing a 3-phased science plan. In Phase 1: Demonstration, we repeatedly observe a small number of highly accessible and valuable targets. Phase 1 provides data for the MethaneSAT Science Team to fine tune retrievals and analytics and yields valuable early results. In Phase 2: Baseline, we take an even-handed approach to target selection. Phase 2 develops a broad understanding of the statistical properties of methane emissions. In Phase 3: Evaluation, we select targets based on statistical leverage and knowledge gained during Phases 1 and 2. By developing and simulating an effective science mission and observational strategy, MethaneSAT is minimizing uncertainties and maximizing science value of the overall mission and data products.
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