Detection and Characteristics of Blowing Snow at ARM Sites

Abstract

High-latitude regions of the globe are subject to adverse conditions during sub-freezing temperatures including snowfall and high winds. When winds act upon fallen or falling snow, blowing snow can result and this phenomenon can have significant societal and economic impacts. Besides these immediate impacts, blowing snow plays an important role in the climate system. This process is responsible for large hydrological mass balance changes in Antarctica, and has been shown to impact the surface radiation budget. The lofting of snow is also tied to the process of sublimation that alters temperatures within the lowest layer of the atmosphere. This can in turn impact the evolution of weather systems. This is problematic because the process of blowing snow is not widely considered within both weather and climate models. This leads to additional uncertainty within these simulations. Finally, blowing snow can contaminate measurements of the atmosphere, and it is unclear to what extent this impacts other studies. As a result, blowing snow been identified as a challenge to the Atmospheric Radiation Measurement (ARM) program.
Observations taken at high latitude ARM sites present an unprecedented opportunity to investigate blowing snow events. A combination of radar, lidar, and surface instruments will be used to detect blowing snow events at ARM sites including locations deployed during the ARM West Antarctic Radiation Experiment (AWARE) field campaign, the permanent North Slope Alaska (NSA) site, and the ARM Mobile Facility (AMF) at Oliktok Point, Alaska. Observations will be used to force a blowing snow and radiative transfer model and compared to reanalysis data to investigate the following three objectives:

• Objective 1: What are the macrophysical properties of blowing snow events at ARM sites?
• Objective 2: What is the radiative impact of these events?
• Objective 3: What are the causes for blowing snow occurrence at ARM sites?

The methodology of Objective 1 will determine the gross properties (plume height, frequency, duration, etc.) of blowing snow. This will require separating blowing snow events from other surface based events such as falling snow. The cornerstone for this methodology includes adaptation of a ceilometer based blowing snow detection algorithm. Other radar and lidar instruments will be used to refine this algorithm, and shed light on sky conditions above blowing snow plumes. Combined with a radiative transfer model, this activity will determine how blowing snow impacts the radiation budget (Objective 2). The result of this procedure will be a hydrometeor mask with identified blowing snow events, and this information will be used to a) identify the macrophysical properties of blowing snow and b) produce data for dissemination to the ARM community.
The last objective will explore the meteorological drivers for blowing snow events to understand whether they are generated locally or forced externally from ARM sites. Weather patterns from the European Centre for Medium Range Weather Forecasting (ECMWF) Interim Reanalysis (ERAI) will be objectively classified using a machine learning algorithm known as a Self-Organizing Map. Blowing snow properties will be treated as independent variables to understand how these characteristics vary by pattern. The PIEKTUK-D blowing snow model will be driven by ARM surface observations and ERAI reanalysis data to shed additional light on the source of blowing snow events. Insights gained will not only improve understanding of blowing snow events, but also pave the way for this process to be included in weather and climate models.