Variability in wet and dry snow radar zones in the North of the Antarctic Peninsula using a cloud computing environment

• Published in: Anais da Academia Brasileira de Ciências Vol. 96; no. suppl 2; p. e20230704
• Main Authors: Idalino, Filipe D, Rosa, Kátia K DA, Hillebrand, Fernando L, Arigony-Neto, Jorge, Mendes, Jr, Claudio Wilson, Simões, Jefferson C
• Format: Journal Article
• Language: English
• Published: Brazil Academia Brasileira de Ciências 01-01-2024
• Subjects: Antarctic Peninsula Ice-sheet; Antarctic Regions; Cloud Computing; cloud computing environment; CryoSat2; Environmental Monitoring - methods; MULTIDISCIPLINARY SCIENCES; Radar; radar glacier zones; Seasons; Sentinel-1; Snow; snowmelt; Temperature; Antarctic Regions; cloud computing environment; Antarctic Peninsula Ice-sheet; CryoSat2; radar glacier zones; snowmelt; Sentinel-1
• ISSN: 0001-3765; 1678-2690; 1678-2690
• DOI: 10.1590/0001-3765202420230704

This work investigated the annual variations in dry snow (DSRZ) and wet snow radar zones (WSRZ) in the north of the Antarctic Peninsula between 2015-2023. A specific code for snow zone detection on Sentinel-1 images was created on Google Earth Engine by combining the CryoSat-2 digital elevation model and air temperature data from ERA5. Regions with backscatter coefficients (σ⁰) values exceeding -6.5 dB were considered the extent of surface melt occurrence, and the dry snow line was considered to coincide with the -11 °C isotherm of the average annual air temperature. The annual variation in WSRZ exhibited moderate correlations with annual average air temperature, total precipitation, and the sum of annual degree-days. However, statistical tests indicated low determination coefficients and no significant trend values in DSRZ behavior with atmospheric variables. The results of reducing DSRZ area for 2019/2020 and 2020/2021 compared to 2018/2018 indicated the upward in dry zone line in this AP region. The methodology demonstrated its efficacy for both quantitative and qualitative analyses of data obtained in digital processing environments, allowing for the large-scale spatial and temporal variations monitoring and for the understanding changes in glacier mass loss.