An Independent School • Grades 5-12

by Simon K. ’21

Junior Simon K. reflects on his experience with the Lakeside Summer Research Institute (LSRI). For more information on the LSRI, see teacher Michael Town’s introductory blog.

My project is centered around analyzing the U.S. Forest Service’s Northwest Avalanche Center’s (NWAC) past forecasts and automating a process that will allow this analysis to be efficient and user-friendly. Every day in operational seasons, typically October to May, NWAC posts an avalanche forecast using a danger rating scale, separated by region and elevation, for the next day, and an outlook for the day after that. For example, on Friday, they may state that above the treeline, the Olympics have a danger rating of ‘low’ on Saturday and ‘moderate’ on Sunday as part of the forecast. 

Because of limited funding and the demands of a daily operation cycle, NWAC’s focus is on generating and posting their forecasts, rather than on analyzing their past forecast data. NWAC enters their data on a spreadsheet that is formatted for efficient data entry, so a large part of my project was automating the process of downloading and collating all of this data into a format readable by a python program made last year and another I made for this year’s data. These programs generate visuals such as bar graphs that detail danger ratings for different locations, elevations, and problem types, and discrepancies between day one and day two forecasts. Two such bar graphs are shown below.

The first graph shows the individual danger ratings for elevations below the tree level. The blue bars show the ratings for day one forecasts, and the grey bars show day two forecasts. The second figure shows the “escalations,” or times when a forecast moved up one or two danger ratings, for elevations below the tree line. From these graphs, we can see, for example, that the most common escalations below the tree line are from “Low” to “Moderate.” My ultimate goal is to develop a program that will make it easy for NWAC or future students to quickly download and analyze the forecast data, which can lead to similar conclusions about different regions, elevations, or dangerous problems and improved forecasts of avalanche danger across the Pacific Northwest.