Articles | Volume 19, issue 14
https://doi.org/10.5194/amt-19-4833-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-4833-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How does assimilating a large commercial GNSS RO dataset impact HAFS hurricane forecasts? An evaluation in support of the ROMEX experiment
William J. Miller
CORRESPONDING AUTHOR
Cooperative Institute for Satellite Earth System Studies (CISESS), Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20740, USA
Yong Chen
NOAA National Environmental Satellite, Data, and Information Service, Center for Satellite Applications and Research, College Park, MD, 20740, USA
Shu-Peng Ho
NOAA National Environmental Satellite, Data, and Information Service, Center for Satellite Applications and Research, College Park, MD, 20740, USA
Xi Shao
Cooperative Institute for Satellite Earth System Studies (CISESS), Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, 20740, USA
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Yong Chen, Xinjia Zhou, Xin Jing, Shu-Peng Ho, Xi Shao, and Tung-Chang Liu
Atmos. Meas. Tech., 19, 3781–3800, https://doi.org/10.5194/amt-19-3781-2026, https://doi.org/10.5194/amt-19-3781-2026, 2026
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We developed a Full Spectrum Inversion algorithm to process Global Navigation Satellite System Radio Occultation (RO) data, a key source for weather forecasting. Comparing it with standard systems, we found strong agreement mid-atmosphere but larger differences near the surface and upper levels. This clarifies processing uncertainties and supports improved use of RO data in numerical weather prediction and atmospheric research.
Tim Trent, Marc Schröder, Shu-Peng Ho, Steffen Beirle, Ralf Bennartz, Eva Borbas, Christian Borger, Helene Brogniez, Xavier Calbet, Elisa Castelli, Gilbert P. Compo, Wesley Ebisuzaki, Ulrike Falk, Frank Fell, John Forsythe, Hans Hersbach, Misako Kachi, Shinya Kobayashi, Robert E. Kursinski, Diego Loyola, Zhengzao Luo, Johannes K. Nielsen, Enzo Papandrea, Laurence Picon, Rene Preusker, Anthony Reale, Lei Shi, Laura Slivinski, Joao Teixeira, Tom Vonder Haar, and Thomas Wagner
Atmos. Chem. Phys., 24, 9667–9695, https://doi.org/10.5194/acp-24-9667-2024, https://doi.org/10.5194/acp-24-9667-2024, 2024
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In a warmer future, water vapour will spend more time in the atmosphere, changing global rainfall patterns. In this study, we analysed the performance of 28 water vapour records between 1988 and 2014. We find sensitivity to surface warming generally outside expected ranges, attributed to breakpoints in individual record trends and differing representations of climate variability. The implication is that longer records are required for high confidence in assessing climate trends.
Xi Shao, Shu-Peng Ho, Xin Jing, Xinjia Zhou, Yong Chen, Tung-Chang Liu, Bin Zhang, and Jun Dong
Atmos. Chem. Phys., 23, 14187–14218, https://doi.org/10.5194/acp-23-14187-2023, https://doi.org/10.5194/acp-23-14187-2023, 2023
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Atmospheric water vapor plays an essential role in the global energy balance, hydrological cycle, and climate system. This paper characterizes and compares the global, latitudinal, and regional variabilities of COSMIC and ERA5 water vapor distribution, as well as the seasonality and long-term trends at selected pressure levels from 2007 to 2018. Evaluation of spatiotemporal variabilities of atmospheric water vapor ensures the qualities of COSMIC and reanalysis water vapor for climate studies.
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Short summary
This study evaluates the impacts of assimilating commercial Global Navigation Satellite System (GNSS) radio occultation (RO) observations on HAFS-A regional model forecasts of four 2022 Atlantic hurricanes. Containing about 20 000 daily profiles, the commercial dataset roughly quadruples the volume of GNSS RO observations assimilated operationally. Findings show that the temperature and water information provided when assimilating the commercial RO dataset reduces an over-intensification bias.
This study evaluates the impacts of assimilating commercial Global Navigation Satellite System...