Comparison of digital and manual methods of snow particle size estimation
2012 (English)In: Hydrology Research, ISSN 0029-1277, Vol. 43, no 3, 192-202 p.Article in journal (Refereed) Published
Maintaining long time series of observations of the Cryosphere is a key issue in climate research. Long observational time series involve problems due to change in methodology or observers. In order to extend time series and introduce new methods, careful comparisons must be made to ensure homogeneity in the observational data. We have compared an established method for snow grain-size observations used by the Abisko Scientific Research Station (ASRS) in northern Sweden, based on visual interpretation, with a newly developed method for Digital Snow Particle Properties (DSPP) analysis. Transition from subjective visual method into digital reproducible analysis creates less subjective and more comparable results. The ASRS method generates size classifications excluding quantitative analysis size ranges. By determining the sizes of the classified snow using the DSPP method, actual size ranges for classified snow can be established. By performing a digital analysis of the reference samples and the snow samples classified, we can compare the ASRS classification system to existing official classification systems. The results indicate underestimation of the visual particle size in comparison to the reference samples. Our results show how to quantify the historical data set, which enables us to perform quantitative analysis on the historical data set.
Place, publisher, year, edition, pages
2012. Vol. 43, no 3, 192-202 p.
Abisko Research Station, classification, particle size, methods, snow
Physical Geography Meteorology and Atmospheric Sciences
Research subject Earth Science with specialization in Physical Geography; Meteorology
IdentifiersURN: urn:nbn:se:uu:diva-146048DOI: 10.2166/nh.2012.078ISI: 000300635000003OAI: oai:DiVA.org:uu-146048DiVA: diva2:397456
ProjectsInterpretation and evaluation of snow and ice from remote sensing using indigenous and scientific expertise, ISIS