Date of Award




Document Type

Master's Thesis

Degree Name

Master of Science (MS)


Department of Atmospheric and Environmental Sciences

Content Description

1 online resource (vii, 94 pages) : illustrations (some color), maps (some color)

Dissertation/Thesis Chair

Kristen L Corbosiero

Committee Members

Nicholas P Bassill, Andrea L Lang, Ross A. Lazear


operational meteorology, precipition type, random forest, winter weather, Precipitation forecasting, Precipitation (Meteorology), Meteorology, Weather broadcasting, Machine learning

Subject Categories

Artificial Intelligence and Robotics | Atmospheric Sciences


Operational forecasters face a plethora of challenges when making a forecast; they must consider multiple data sources ranging from radar and satellites to surface and upper air observations, to numerical weather prediction output. Forecasts must be done in a limited window of time, which adds an additional layer of difficulty to the task. These challenges are exacerbated by winter mixed precipitation events where slight differences in thermodynamic profiles or changes in terrain create different precipitation types across small areas. In addition to being difficult to forecast, mixed precipitation events can have large-scale impacts on our society.