Performance assessment of radio occultation data from GeoOptics by comparing with COSMIC data

Autor: Hyeyeon Chang, Jiyun Lee, Hyosang Yoon, Y. Jade Morton, Alex Saltman
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Earth, Planets and Space, Vol 74, Iss 1, Pp 1-17 (2022)
Druh dokumentu: article
ISSN: 1880-5981
DOI: 10.1186/s40623-022-01667-6
Popis: Abstract Responding to the ever-growing demand for environmental information, the National Oceanic and Atmospheric Administration (NOAA) seeks to enter into contracts to purchase Global Navigation Satellite System (GNSS) radio occultation (RO) observations produced by commercial vendors at a low-cost. GeoOptics is one commercial vendor awarded a contract with NOAA. GeoOptics operates the Community Initiative for Cellular Earth Remote Observation (CICERO) constellation of low-earth-orbiting (LEO) 6U CubeSats. The 6U-sized CICERO will enable the deployment of GNSS array consisting of RO satellites in the Earth’s atmosphere to obtain many atmospheric observations which can improve weather forecasting. Applying GeoOptics RO data to reliable weather forecasting requires an assessment of its performance. This study analyzes the performance of GeoOptics CubeSats measurements by comparing it with the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) missions (COSMIC-1 and COSMIC-2). The performance analysis was carried on data coverage capabilities and measurement quality. The analysis of data coverage confirmed that GeoOptics can acquire global observational coverage with adequate low-altitude penetration capability, while there should be updated in local time coverage. The analysis of RO measurement quality showed that GeoOptics RO measurements are comparable to those of COSMIC-2, even though GeoOptics exhibited a lower signal-to-noise ratio (SNR). The potential of GeoOptics allows for the development of a GNSS array in the Earth’s atmosphere and a large amount of effective RO measurements to be obtained for reliable weather forecasting. Graphical Abstract
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