Combined GNSS reflectometry–refractometry for automated and continuous in situ surface mass balance estimation on an Antarctic ice shelf

• Published in: The Cryosphere
• Main Authors: Steiner, Ladina, Schmithüsen, Holger, Wickert, Jens, Eisen, Olaf
• Format: Text
• Language: English
• Published: 2023
• Subjects: Antarctic; Dronning Maud Land; Ekström Ice Shelf; Antarc*; Antarctica; Ice Shelf
• Online Access: https://doi.org/10.5194/tc-17-4903-2023; https://tc.copernicus.org/articles/17/4903/2023/

Reliable in situ surface mass balance (SMB) estimates in polar regions are scarce due to limited spatial and temporal data availability. This study aims at deriving automated and continuous specific SMB time series for fast-moving parts of ice sheets and shelves (flow velocity > 10 m a −1 ) by developing a combined global navigation satellite system (GNSS) reflectometry and refractometry (GNSS-RR) method. In situ snow density, snow water equivalent (SWE), and snow deposition or erosion are estimated simultaneously as an average over an area of several square meters and independently on weather conditions. The combined GNSS-RR method is validated and investigated regarding its applicability to a moving, high-latitude ice shelf. A combined GNSS-RR system was therefore installed in November 2021 on the Ekström ice shelf (flow velocity ≈ 150 m a −1 ) in Dronning Maud Land, Antarctica. The reflected and refracted GNSS observations from the site are post-processed to obtain snow accumulation (deposition and erosion), SWE, and snow density estimates with a 15 min temporal resolution. The results of the first 16 months of data show a high level of agreement with manual and automated reference observations from the same site. Snow accumulation, SWE, and density are derived with uncertainties of around 9 cm, 40 kg m −2 a −1 , and 72 kg m −3 , respectively. This pilot study forms the basis for extending observational networks with GNSS-RR capabilities, particularly in polar regions. Regional climate models, local snow modeling, and extensive remote sensing data products will profit from calibration and validation based on such in situ time series, especially if many such sensors will be deployed over larger regional scales.