| 05 Nov 2026
Automatic detection of laminated clouds in lidar data
Abstract. In the Arctic, days with clouds containing laminations (layers), as identified by visual inspection of lidar data (McCullough, 2019), are strongly correlated with ground-level precipitation (McCullough, 2021). This provides insight into the cloud conditions and processes involved in the production of snow. We now present a computationally-inexpensive computer algorithm that (1) automatically identifies laminated cloud features in time-altitude arrays of lidar data and (2) classifies the sky scene according to the presence of laminated clouds. A discretized residual method is used.
Algorithm validation uses the 2016-2019 dataset from McCullough (2019): 532 nm backscatter, up to 5 km altitude, measured by the Canadian Network for the Detection of Atmospheric Composition Change (CANDAC) Rayleigh-Mie-Raman lidar (CRL; 1 min, 7.5 m resolution) at Eureka, Nunavut (79.6 N, 85.6 W). Sky scene classification at daily resolution is replicated with accuracy = 0.86. Correlations with snow are nearly identical: r = 0.62 (algorithm) and r = 0.63 (visual).
The computer algorithm is then employed at hourly resolution (r = 0.50), and for regions below and above 1 km separately. Correlations with snow are largest when laminations occur simultaneously above and below 1 km (r = 0.44), followed by laminations exclusively above 1 km (r = 0.28); there is only a very small correlation for laminations exclusively below 1 km (r = 0.09). Therefore, cloud laminations above 1 km altitude are key lidar indicators of the production of precipitating snow detected at ground level.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
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