{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:24:24Z","timestamp":1769559864240,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Digital"],"abstract":"<jats:p>Climate change in Antarctica has profound global implications, influencing sea level rise, atmospheric circulation, and the Earth\u2019s energy balance. This study presents a data-driven baseline analysis of meteorological observations from a British Antarctic Survey automatic weather station (2020\u20132024). Temporal and seasonal analyses reveal strong insolation-driven variability in temperature, snow depth, and solar radiation, reflecting the extreme polar day\u2013night cycle. Correlation analysis highlights solar radiation, upwelling longwave flux, and snow depth as the most reliable predictors of near-surface temperature, while humidity, pressure, and wind speed contribute minimally. A linear regression baseline and a Random Forest model are evaluated for temperature prediction, with the ensemble approach demonstrating superior accuracy. Although the short data span limits long-term trend attribution, the findings underscore the potential of lightweight, reproducible pipelines for site-specific climate monitoring. All analysis codes are openly available in github, enabling transparency and future methodological extensions to advanced, non-linear models and multi-site datasets.<\/jats:p>","DOI":"10.3390\/digital5040050","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T15:07:48Z","timestamp":1759417668000},"page":"50","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Data-Driven Baseline Analysis of Climate Variability at an Antarctic AWS (2020\u20132024)"],"prefix":"10.3390","volume":"5","author":[{"given":"Arpitha Javali","family":"Ashok","sequence":"first","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, Think Campus, Konrad-Zuse-Ring 11, 14469 Potsdam, Germany"}]},{"given":"Shan","family":"Faiz","sequence":"additional","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, Think Campus, Konrad-Zuse-Ring 11, 14469 Potsdam, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9068-2337","authenticated-orcid":false,"given":"Raja Hashim","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, Think Campus, Konrad-Zuse-Ring 11, 14469 Potsdam, Germany"},{"name":"Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan"}]},{"given":"Talha Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Business, University of Europe for Applied Sciences, Think Campus, Konrad-Zuse-Ring 11, 14469 Potsdam, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shehzadi, M., Ali, R.H., Abideen, Z.u., Ijaz, A.Z., and Khan, T.A. 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