Winter 2020 Research Projects

Developing an Objective Method to Measure Snow Depth
Developing an Objective Method to Measure Snow Depth 

Snow Depth: Caleb Buchler – University of Delaware, Meteorology

Observer Guide: Ian Bailey

Mentor: Eric Kelsey, PhD   Mount Washington Observatory/ Plymouth State University

The strong winds the summit experiences on a near-daily basis throughout the 8-month snow season drifts and scours the snow across the summit in a complex pattern that is challenging to average no matter the method. For over 85 years, MWO Observers have visually estimated snow depth at the summit of Mount Washington. A visual estimate of average snow depth inherently comes with significant subjectivity and uncertainty. An objective measure of snow depth is needed to meet National Weather Service and World Meteorological Organization guidelines.

The purpose of this project is to develop an objective method for measuring snow depth at the summit. The method developed must be subjective, be close to the true average snow depth, and also be logistically simple and robust enough that an Observer can take the measurement in extreme winds, frigid temperatures, and near-zero visibility in a short amount of time so that they can complete the other weather observations in time to submit to the National Weather Service.

Caleb Buchler continued the work done by previous intern, Chloe Boehm, in which she developed a snow depth measurement method that meets these requirements. Chloe’s method involves taking multiple measurements across the area where Observers make their visual estimate (the area between the precipitation can and the Sherman Adams Building), calculating an average snow depth, and then using statistics to determine the one point that is best representative of the average. Caleb, and the intern on the other shift Eve Cinquino, developed a dataset during the winter of 2019-2020 by measuring snow depth with a gradated avalanche probe at the nine points.

Together, Caleb and Eve measured snow depth at the same time that the Observers visually estimated snow depth 60 times. They recorded the snow depth for each of the nine points and calculated the mean for each measurement time. Using data from the 60 measurement times, Caleb did a statistical analysis to determine which site is most representative of the average. Caleb determined the one site that was closest to the overall average, had the lowest variance from the overall average, and was closest to the average most frequently. Caleb also discovered that the average snow depth was lower in the measured area than the visually-estimated average.

For future research, Caleb proposed putting stakes with measurement ticks in the ground at the nine locations before the winter season begins. This would reduce human error with measurements in the exact same location each time, allow the person measuring to go outside in high winds without having to carry an instrument, and allow for measurements that include ice layers that are difficult to penetrate and often form throughout the season.

Caleb taking measurements on a calm day. This was taken just south of the Sherman Adams Summit Building. For reference, the precipitation can is visible to his left while he is facing in the direction of the summit sign.
A Rise in Mid-Winter Thaws on the Summit
A Rise in Mid-Winter Thaws on the Summit


Thaw Events: Eve Cinquino – Bates College (graduated), B.S. Computer Science

Observer Guide: AJ Grimes

Mentor: Eric Kelsey, PhD Mount Washington Observatory/ Plymouth State University 

Commonly referred to as a “January Thaw” for its common occurrence in January, periods of unusually warm weather in the middle of the winter season can spell trouble for winter snowpacks. These episodes can create unpleasant and dangerous skiing conditions as well as increase avalanche likelihood, leading to a decline in skier turnout and an increased risk for those who still choose to ski. The mountain ecosystem on Mount Washington also relies on a thick, insulating layer of snowpack to protect vegetation from harsh conditions. The most important thaw events for snow melt occur when both temperature and dewpoint are above freezing, which leads to warm, wet weather that can rapidly melt the snowpack. Under these conditions, water vapor condenses onto the snowpack in the same way that water vapor condenses onto a cold soda can on a humid summer day. In addition to this added liquid water to the snowpack, heat is released during condensation, which effectively warms and melts the snowpack. This project examines how both temperature and dewpoint related thaw events are changing at the summit and their effect on snow depth during meteorological winter (December-February) for 1939-2020, building off of the work of Ethan Rogers and Laura Kee, previous summit interns.

Rates of event frequency (number of events/winter), length (hours/winter), and intensity (thawing degree hours/winter; TDH/winter) were found to be significantly increasing (p < 0.05) for both temperature and dewpoint over the Observatory’s history. These results indicate that mid-winter thaws are becoming stronger and more common on the summit. The most significant trends were in intensity, with dewpoint increasing by 2.59 TDH/year and temperature increasing by 3.16 TDH/year. Temperature thaw events where dewpoint remained below freezing were calculated as well, but were not found to be increasing significantly. Average snow loss during the three types of thaw events (all temperature events, dewpoint events, and temperature events with dewpoint below freezing) was calculated as well, finding that during dewpoint events, snow melted at a rate of 0.41 cm/hr. This rate is almost double that of temperature events (0.28 cm/hr) and quadruple the rate of melt during temperature events where dewpoint remained below freezing (0.10 cm/hr), confirming that dewpoint thaw events are indeed the most detrimental for snow melt on Mount Washington.


Figure 1: Trends in winter thaw occurrences based on summit temperature, where (a) is the total events that occurred per season (DJF), (b) is the number of hours when temperature was above freezing each season, and (c) is the total temperature thawing degree hours per season. The blue dashed lines are the Sen’s slopes, or linear trends in the datasets. Error bars account for missing observations where a thaw likely occurred.
Figure 2: Trends in winter thaw occurrences based on dewpoint, where (a) is the total events that occurred per season (DJF), (b) is the number of hours when dewpoint was above freezing each season, and (c) is the total dewpoint thawing degree hours per season. The blue dashed lines are the Sen’s slopes, or linear trends in the datasets.


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