Date of Award
12-1-2022
Language
English
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College/School/Department
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
Keywords
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
Abstract
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.
Recommended Citation
Filipiak, Brian Chandler, "Probabilistic forecasting of winter mixed precipitation types in New York State utilizing a random forest" (2022). Legacy Theses & Dissertations (2009 - 2024). 2906.
https://scholarsarchive.library.albany.edu/legacy-etd/2906