A deep learning approach to detecting atmospheric rivers in the Arctic

McGetrick, S., Lu, H. ORCID: https://orcid.org/0000-0001-9485-5082, Muszynski, G., Martínez-Alvarado, O. ORCID: https://orcid.org/0000-0002-5285-0379, Osman, M. ORCID: https://orcid.org/0000-0002-5636-698X, Mattingly, K. and Galea, D. ORCID: https://orcid.org/0000-0001-6180-350X (2026) A deep learning approach to detecting atmospheric rivers in the Arctic. Atmosphere, 17 (1). 61. ISSN 2073-4433 doi: 10.3390/atmos17010061

Abstract/Summary

The Arctic is warming rapidly, with atmospheric rivers (ARs) amplifying ice melt, extreme precipitation, and abrupt temperature shifts. Detecting ARs in the Arctic remains challenging, because AR detection algorithms designed for mid-latitudes perform poorly in polar regions. This study introduces a regional deep learning (DL) image segmentation model for Arctic AR detection, leveraging large-ensemble (LE) climate simulations. We analyse historical simulations from the Climate Change in the Arctic and North Atlantic Region and Impacts on the UK (CANARI) project, which provides a large, internally consistent sample of AR events at 6-hourly resolution and enables a close comparison of AR climatology across model and reanalysis data. A polar-specific, rule-based AR detection algorithm was adapted to label ARs in simulated data using multiple thresholds, providing training data for the segmentation model and supporting sensitivity analyses. U-Net-based models are trained on integrated water vapour transport, total column water vapour, and 850 hPa wind speed fields. We quantify how AR identification depends on threshold choices in the rule-based algorithm and show how these propagate to the U-Net-based models. This study represents the first use of the CANARI-LE for Arctic AR detection and introduces a unified framework combining rule-based and DL methods to evaluate model sensitivity and detection robustness. Our results demonstrate that DL segmentation achieves robust skill and eliminates the need for threshold tuning, providing a consistent and transferable framework for detecting Arctic ARs. This unified approach advances high-latitude moisture transport assessment and supports improved evaluation of Arctic extremes under climate change.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/127892
Identification Number/DOI	10.3390/atmos17010061
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	MDPI
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