Exploring Remote Sensing Payload Hosting on Alternative Near-Space and Space-Based Platforms

In the past decade, the global observing system (GOS) has seen unprecedented growth in alternative sources of environmental observation from remote sensing platforms. The expansion consists of both near-space and space-based platforms, from commercial data sources, ride shares, missions of opportunity, and payload hosting opportunities, all primarily in support of more cost effective, agile observing systems enabled by parallel improvements in sensor technology. A study at the NOAA/NESDIS Center for Satellite Applications and Research (STAR), supported by the NESDIS Office of Projects, Planning, and Analysis (OPPA) Technology Maturation Program (TMP), was initiated to explore benefit to NOAA (and NESDIS) products and services from hosting remote sensing payloads on emerging near-space and space-based platforms. Specifically, for a near-space architecture, hosting instruments on superpressure stratospheric balloons was evaluated; similarly for a space-based architecture, hosting instruments on the SpaceX Starlink constellation was evaluated. In each case, a feasibility assessment was performed to 1) align the characteristics of each observing system, e.g. size, weight and power (SWaP) capacity, orbital/flight configuration, etc., to emerging remote sensing technology, and 2) fill gaps in the global observing system and in particular observables which would be of benefit to NOAA's products, services and overall mission. The outcome of the feasibility assessment for near-space balloon platforms focused on hosting of Global Positioning System (GPS) receivers for Radio Occultation (RO) observations. To assess the impact on NOAA numerical weather prediction (NWP), Observing System Simulation Experiments (OSSEs) were performed to compare the forecast impact of a balloon-based GPS-RO constellation to the impact of current satellite-based GPS-RO (prior to the launch of COSMIC-2), each with roughly the same number of occultations available per data assimilation cycle. For the space-based Starlink constellation, we explore the utility of hosting a midwave/shortwave infrared hyperspectral sounder to derive atmospheric temperature and water vapor profiles. To fill the observation gap, machine learning techniques are applied to derive 3D-winds from water vapor retrievals globally, which is well suited for the orbital configuration of Starlink. The design and simulation of each constellation will be demonstrated, and the approach to the OSSEs and retrieval, along with their results, will be presented.