Wendy Brazenec, M.S.

RESEARCH:
Evaluation of Two Ultrasonic Snow Depth Sensors To Predict Six Hour Snowfall at National Weather Service Automated Surface Observing Sites

EDUCATION:
M.S. (Watershed Science) 2005 Colorado State University, Fort Collins, CO, USA 80523-1472
B.S. (Env. Res. Mgmt.) 2002 Pennsylvania State University, University Park, PA 16802


Brazenec, W.A., 2005. Evaluation of Two Ultrasonic Snow Depth Sensors To Predict Six Hour Snowfall at National Weather Service Automated Surface Observing Sites. Unpublished M.S. thesis, Watershed Science, Colorado State University, Fort Collins, Colorado, USA, 85pp + 3 appendices.

Abstract

The deployment of ASOS in the early 1990's compromised snowfall and snow depth measurements at hundreds of airport locations across the country. This study explored the solution of using ultrasonic snow depth sensors to automate snow measurements at 9 sites across the coterminous U.S. This study aimed to answer four questions: 1.) Can a reliable method for smoothing/processing the sensor data be developed? 2.) Have any factors affecting sensor performance been identified? 3.) Do the Judd and Campbell snow depth sensors agree with manual measurements of snow depth? 4.) Can an algorithm be written to derive 6 hour snowfall from the sensor snow depth? A reliable data smoothing/processing technique was achieved using filtering of large variability and smoothing with a moving average to smooth small variations in snow depth. Factors found to affect sensor performance were: snow crystal type, wind speed, blowing/drifting snow, uneven snow surface, extremely low temperatures, and intense snowfall. The Judd and Campbell sensors both did a satisfactory job measuring snow beneath the sensor within +/-0.4 inches. Two separate algorithms were created due to differing degrees of variation between the two sensors. It was found that the Campbell did a better job at estimating six hour snowfall than the Judd using an algorithm that calculated snowfall over 5 minute periods and applying a temperature based compaction model to the estimated snowfall. The Judd has higher variability in the measurements than the Campbell which is why the Judd needed more smoothing than the Campbell sensor.

Committee:
Advisor: Steven Fassnacht
Co-Advisor: Nolan Doesken
John Stednick (Watershed Science)
Gene Kelly (Soil and Crop Science)

Last update: SRF, 2016.06.15