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

Significance Statement Blizzard conditions, defined as visibility 1/4 mile or less in blowing snow or concurrent snow and blowing snow with wind speed > 22 kt (11.3 m s(-1)), are a regular occurrence in the Canadian Arctic. These extreme conditions can last for hours to days, significantly impacting travel and life. Forecasting is challenging due to sparse observations, lack of radar coverage and continuous satellite coverage, and polar night lasting up to six months. We derive recent occurrence statistics from METARs and describe three forecast products developed to address the need for timely automated forecast guidance. They have been well received by forecasters. One is driven by a machine-learning technique. As methods improve we expect the public to benefit in the future from increasingly better prediction. 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 aviation routine weather reports (METARs) from Canadian Arctic stations between October and May 2014-18. Blizzard conditions occur most frequently in open tundra east and north of the boreal forest boundary, with the 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 postprocessed 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. 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.