Blizzard Conditions in the Canadian Arctic: Observations and Automated Products for Forecasting

• Published in: Weather and forecasting Vol. 36; no. 3; p. 1113
• Main Authors: Burrows, William R., Mooney, Curtis J.
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01.06.2021
• Subjects: Algorithms; Arctic observations; Automation; Aviation; Blizzard forecasting; Blizzards; Blowing; Blowing snow; Boreal forests; Economic forecasting; Names; Polar environments; Probability theory; Remote observing; Remote sensing; Satellites; Snow; Statistical analysis; Statistical methods; Tundra; Visibility; Weather forecasting; Wind; Arctic region
• ISSN: 0882-8156; 1520-0434
• DOI: 10.1175/WAF-D-20-0077.1

Abstract Blizzard conditions occur regularly in the Canadian Arctic, with high impact on travel and life there. These extreme conditions are challenging to forecast for this vast domain because the observation network is sparse and remote sensing coverage is limited. To establish occurrence statistics we analyzed METeorological Aerodrome Reports (METARs) from Canadian Arctic stations between October and May 2014-2018. Blizzard conditions occur most frequently in open tundra east and north of the boreal forest boundary, with highest frequency found on the northwest side of Hudson Bay and over flat terrain in central Baffin Island. Except in sheltered locations, the reported cause of reduced visibility is blowing snow without precipitating snow in about one-half to two-thirds of METARs made by a human observer, even higher at some stations. We produce three products that forecast blizzard conditions from post-processed NWP model output. The blizzard potential (BP), generated from expert’s rules, is intended for warning well in advance of areas where blizzard conditions may develop. A second product (BH) stems from regression equations for the probability of visibility ≤ 1 km in blowing snow and/or concurrent snow derived by Baggaley and Hanesiak (2005). A third product (RF), generated with the Random Forest ensemble classification algorithm, makes a consensus YES/NO forecast for blizzard conditions. We describe the products, provide verification, and show forecasts for a significant blizzard event. Receiver Operator Characteristic curves and critical success index scores show RF forecasts have greater accuracy than BP and BH forecasts at all lead times.