Nicole Loeb

Date of Award

January 2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Aaron Kennedy

Abstract

Blowing snow is an impactful process in cold climates that affects regional thermodynamics, radiation properties, and the surface mass balance of snow. Though it has significant climatic impacts, the process is still poorly understood and not widely included in weather and climate models. In 2016, the AWARE Field Campaign saw the deployment of a large suite of in situ and remote sensing instruments to McMurdo Station, Antarctica allowing for investigation of blowing snow. A ceilometer-based blowing snow detection algorithm used elsewhere in Antarctica is applied to data from AWARE, yielding a blowing snow frequency of 14.1% compared to 8.2% as detected by human observers. To increase confidence in detections, the algorithm is updated to have shorter temporal averaging and to include a variety of meteorological thresholds to limit false detections due to fog. The revised algorithm detected a blowing snow frequency of 7.8%, which is in closer agreement with human observations. A multi-instrument probabilistic depth algorithm is developed to increase confidence in the depth estimations given for detected blowing snow. This algorithm is applied to 41 blowing snow case days and found an average depth of 218.3 m and a mean absolute difference of 97.6 m when compared to the results of the ceilometer-based algorithm. The largest differences between the two algorithms were found during intense events occurring with precipitation. The results of this study help to aid the modelling community in reproducing the process and its impact on the regional climate.

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