Stratospheric gravity waves in a post-limb sounder era: can GNSS-RO be used to extend the SABER QBO-driving record?

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Abstract

The quasi-biennial oscillation (QBO) is a slowly repeating cycle of winds which dominates tropical lower-stratospheric dynamics and has been described as the “heartbeat of the stratosphere”. However, it is challenging to represent in weather and climate models because its periodicity and magnitude are controlled by small-scale gravity waves (GWs) that cannot be resolved on model grids. To quantify this GW driving we require high-resolution measurements, ideally from satellites to ensure full spatial coverage. Since 2002, the SABER instrument on the TIMED satellite has provided such data, facilitating long-term studies of QBO GW driving. However, SABER is expected to be decommissioned later this year, and no replacement is planned. Here, we assess the possibility of using GNSS-RO data to extend the 23-year SABER record. GNSS-RO cannot be used as a simple replacement for long-term studies because data volumes are too low before 2006 and for most of the late 2010s, and thus, we ideally wish to supplement rather than replace the SABER record. However, while the two datasets have broadly similar lower stratospheric resolutions when compared to the full Earth observation constellation, GNSS-RO measurements are higher resolution in all three dimensions than SABER and are oriented differently in 3D space. As a result of this, GNSS-RO GW measurements exhibit much larger GW potential energies (GWPE) and shorter vertical wavelengths than those from SABER. To understand these differences, we use a high-resolution run of the GEOS model to produce synthetic GW measurements, then systematically vary the measurement characteristics between those of the two real instruments. This allows us to identify the key drivers of the different GW properties they measure. We demonstrate that the differences between QBO-driving GW properties measured by the two instruments are primarily due to vertical resolution, with horizontal resolution (either along or across line of sight) and orientation angle playing a negligible role. We further demonstrate that, with a simple vertical smoothing of the GNSS-RO data in the vertical before analysis for GWs, the measured GW properties become near-identical, allowing us to use SABER and GNSS-RO data near-equivalently for this use case. Since GNSS-RO data are now a crucial component of the global numerical weather prediction constellation and are hence highly likely to be available in the long term, this allows us to produce a consistent long-term record of QBO GW forcing from 2002 onwards without key gaps which would be otherwise present in the early 2000s and late 2010s.

Original languageEnglish
Pages (from-to)6393-6416
Number of pages24
JournalAtmospheric Measurement Techniques
Volume18
Issue number21
Early online date11 Nov 2025
DOIs
Publication statusPublished - 11 Nov 2025

Data Availability Statement

The repository of GNSS-RO data in the AWS Registry of Open Data can be found at http://registry.opendata.aws/gnss-ro-opendata/ (last access: 1 November 2025). Documentation of the repository, source code for the API, and tutorial demonstrations are published under a digital object identifier (Leroy and McVey, 2023). The database API is published through PyPI; see http://pypi.org/project/awsgnssroutils/ (last access: 1 November 2025). The SABER/TIMED data used in this work can be obtained from https://saber.gats-inc.com/ (last access: 1 November 2025). DYAMONDv2 data can be accessed via the NCCS Dataportal – Datashare (https://portal.nccs.nasa.gov/datashare/G5NR/DYAMONDv2/, last access: 1 November 2025).

Acknowledgements

The ChatGPT LLM was used for minor refinements of textual style at the individual-sentence level, but all scientific results were obtained using non-AI methods. We thank the reviewer for their suggestions, which have added valuable insights to this paper. The lead author would like to dedicate this work to her brother, Abdulrahman Almowafy, who passed away in May 2024.

Funding

This research has been supported by the Natural Environment Research Council, National Centre for Atmospheric Science (grant no. NE/W003201/1), the Natural Environment Research Council, National Centre for Atmospheric Science (grant no. NE/V01837X/1), the Natural Environment Research Council, National Centre for Atmospheric Science (grant no. NE/X017842/1), and the Royal Society (grant nos. RF/ERE/221011 and URF/R/221023). This study was supported by the UK Natural Environment Research Council (NERC) under grant numbers NE/W003201/1 supporting M. Almowafy, C. Wright and N. Hindley, and NE/V01837X/1 supporting C. Wright. N. Hindley was also supported by a NERC Independent Research Fellowship NE/X017842/1, and C. Wright was also supported a Royal Society University Research Fellowship URF/R/221023. M. Almowafy and C. Wright were additionally supported by Royal Society grant no. RF/ERE/221011. DYAMOND data management was provided by the German Climate Computing Center (DKRZ) and supported through the projects ESiWACE and ESiWACE2. The projects ESiWACE and ESiWACE2 have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 675191 and 823988. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project IDs bk1040 and bb1153. This research was supported by the International Space Science Institute (ISSI) in Bern, through ISSI International Team project #567.

ASJC Scopus subject areas

  • Atmospheric Science

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