Leveraging the CYGNSS Spaceborne GNSS‐R Observations to Detect Ionospheric Irregularities Over the Oceans: Method and Verification
Abstract The Global Navigation Satellite System (GNSS) has significantly facilitated the detection of ionospheric irregularities. Nevertheless, the ground‐based GNSS stations are inhomogeneously distributed around the world with large gaps, especially in the vast ocean regions. This adversely affect...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-11-01
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| Series: | Space Weather |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022SW003141 |
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| Summary: | Abstract The Global Navigation Satellite System (GNSS) has significantly facilitated the detection of ionospheric irregularities. Nevertheless, the ground‐based GNSS stations are inhomogeneously distributed around the world with large gaps, especially in the vast ocean regions. This adversely affects the interpretation of the spatio‐temporal variation characteristics of the ionospheric irregularities. In this study, ionospheric irregularities over the regions of the equatorial, and low‐latitude ocean during a G4‐class strong geomagnetic storm were investigated by utilizing the spaceborne GNSS reflectometry (GNSS‐R) technique. We derived and validated the ionospheric regularities index S4R from the signal‐to‐noise ratio measurements of the spaceborne GNSS‐R. Meanwhile, we utilized the ionospheric irregularities data from ground‐based (GNSS and ionosonde) and space‐based (Swarm and FORMOSAT‐3/COSMIC) instruments to evaluate and analyze the CYGNSS‐derived S4R. All comparisons and validations conducted within this study illustrate that the spaceborne GNSS‐R measurements provide complementary data and realize the timely observation as well as confirmation of the ionospheric irregularities at the same location over the oceans. It is expected to benefit the application of spaceborne GNSS‐R in ionospheric irregularities monitoring and applications over the ocean area. |
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| ISSN: | 1542-7390 |