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Evaluation of ultrasonic snow depth sensors for automated surface observing systems (ASOS)

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dc.contributor.authorBrazenec, Wendy Ann, author
dc.contributor.authorFassnacht, Steven R., advisor
dc.contributor.authorDoesken, Nolan, committee member
dc.contributor.authorKelly, Gene, committee member
dc.contributor.authorStednick, John, committee member
dc.date.accessioned2021-08-24T16:23:33Z
dc.date.available2021-08-24T16:23:33Z
dc.date.issued2005
dc.description.abstractIn the 1990's the National Weather Service deployed automated surface observing systems at hundreds of airport locations across the country. Prior to the automation, human observers made snow observations every six hours. Once the automated systems were deployed, snow measurements ceased due to the lack of an automated sensor to measure snow. This study explored how well ultrasonic snow depth sensors compared to manual snow observations at nine sites across the country. This study had four objectives: 1.) Develop a method of quality assurance and quality control 2.) Identify factors which affect sensor performance 3.) Compare automated sensors to manual observations of snow depth 4.) Derive an algorithm to estimate six hour snowfall from automated 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 included: 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 precision between the two sensors. It was found that the Campbell sensor 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 Campbell agreed with the manual data with an average mean absolute error between measurements of 0.23 inches. The Judd sensor results improved by using an algorithm which calculated snowfall using the change in snow depth over sixty minutes, however, the Campbell results were better using the five minute snowfall algorithm. Overall, both sensors accurately depicted the snow depth on the ground, however the Campbell sensor was more accurate at predicting six hour snowfall using the algorithms presented in this research.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifier.urihttps://hdl.handle.net/10217/233654
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relationCatalog record number (MMS ID): 991023068379703361
dc.relationQC929.S7B739 2005
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleEvaluation of ultrasonic snow depth sensors for automated surface observing systems (ASOS)
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineForest, Rangeland, and Watershed Stewardship
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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