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Session A3: Atmospheric Effects, GNSS Remote Sensing and Scientific Applications

Impacts of High Rate GNSS Satellite Clock Estimation on Radio Occultation Bending Angle Retrievals: Updated Report
Yao-Chun Ko, Colorado State University; Jian Yao, Jan-Peter Weiss, and Teresa VanHove, University Corporation for Atmospheric Research (UCAR)
Location: Seaview Ballroom
Date/Time: Wednesday, Jan. 24, 2:58 p.m.

The GNSS radio occultation (RO) technique is widely used to derive data products used in weather and space weather monitoring and forecasting. Precise estimation of GNSS satellite clock offsets can reduce the uncertainty of neutral atmosphere bending angle estimation in the GNSS RO processing – a crucial figure of merit characterizing the sensitivity of the RO technique. Our previous study [1] indicated that using a 2-second interval GNSS satellite clock product instead of the traditional 30-second interval product, there is a significant reduction in the bending angle uncertainty at high altitudes (e.g., > 50 km), especially for the GLONASS RO processing. Following this study, our team has recently made efforts to set up GNSS RO processing using high-rate GNSS clock products in a post-processing mode. First, we have developed a script that routinely downloads, merges, and/or decimates 1-sec RINEX data from ~50 globally-distributed stations. We then use the GINAN [2] GNSS software to generate the high-rate (1-sec and 2-sec interval) GNSS clock products. The clock products are validated by comparing to the International GNSS Service (IGS) products. We plan to test one month of neutral atmosphere RO processing for the COSMIC-2 mission using 30 sec and high rate GNSS clock products to evaluate the impacts on bending angle retrievals for the GPS and GLONASS constellations.
[1] Jian Yao, Jan-Peter Weiss, and Teresa VanHove, “Impacts of high rate GNSS satellite clock estimation on radio occultation bending angle retrievals: preliminary report,” Proceedings of the 2023 Institute of Navigation International Technical Meeting (ITM), pp. 995 – 1001, 2023.
[2] Rupert Brown, “GINAN project overview,” 2022.

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