R-D Seminar: Analyzing weather data to improve prediction of freezing rain events

R-D Seminar: Analyzing weather data to improve prediction of freezing rain events
October 16, 2018 11:00
CRIM (405, avenue Ogilvy, bureau 101, Montréal)


Analyzing weather data to improve prediction of freezing rain events

Freezing rain can cause severe damage, particularly when it persists for many hours. A striking example is the January 1998 Ice Storm, which paralyzed southern Quebec for several weeks. Despite its potentially devastating impacts, forecasting freezing rain remains particularly challenging. Here, we analyze nearly 200,000 observations of freezing rain over Canada and the United States from 1979 to 2016 with Python in order to better understand the conditions favorable for freezing rain event persistence. This allows us to identify key features of long-duration freezing rain events that meteorologists can look for when attempting to determine the severity of a predicted ice storm. In this talk, we will discuss our analysis of these weather observations and how this analysis could be applied to improving weather forecasts. We will also briefly discuss the application of weather observation data to predictions of other variables using open data, for example to predict bicycle traffic on a given day.


Christopher McCray, Ph.D. student in Atmospheric and Oceanic Sciences at McGill University.


Christopher McCray is a Ph.D. student in the Department of Atmospheric & Oceanic Sciences at McGill University, under the supervision of Prof. John Gyakum. His research interests focus on weather prediction and large-scale meteorology, with a focus on winter weather phenomenon. He has completed research internships at several offices of the U.S. National Weather Service as well as at IBM Research in Yorktown Heights, New York. He graduated with a Bachelor of Science degree in Atmospheric Sciences and Mathematics at Lyndon State College in Vermont in 2015.

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