Development of a Z-R Relationship with Uniform Sampling to Mitigate Sampling Variability

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dc.contributor.advisorLarsen, Michael L
dc.contributor.authorO'Dell, Katelyn Ashley
dc.date.accessioned2022-03-29T19:01:32Z
dc.date.available2022-03-29T19:01:32Z
dc.date.created2016-05
dc.date.submittedMay 2016
dc.description.abstractA new method for sampling precipitation events was developed and tested on raw data and with data-based Monte-Carlo simulations. This new method separates rain events into samples of the same number of drops, unlike conventional sampling methods where each sample is taken over a fixed temporal duration. By containing a uniform number of drops per sample, the new method was expected to mitigate sampling variability and weight drops equally in determining relationships between two bulk quantities: rain rate (R) and radar reflectivity factor (Z). Using data from a two dimensional video disdrometer, this hypothesis was tested on six separate rain events. From investiga- tions with raw data, uniform sampling did not conclusively mitigate sampling variability in practical application. Subsequent investigations with data based Monte-Carlo simulations revealed very large sample sizes, on the order of 10,000 or more drops, might be necessary to mitigate sampling variability in precipitation measurements.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://repository.library.cofc.edu/handle/123456789/5348
dc.language.isoen_US
dc.subjectRain, Statistics, Sampling, Physics
dc.titleDevelopment of a Z-R Relationship with Uniform Sampling to Mitigate Sampling Variability
dc.type.genrethesis
dc.type.materialtext
local.embargo.lift2017-05-01
local.embargo.terms2017-05-01
thesis.degree.departmentPhysics and Astronomy
thesis.degree.disciplinePhysics
thesis.degree.grantorCollege of Charleston
thesis.degree.nameBachelor of Science
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