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dc.contributor.advisorHammerling, Dorit
dc.contributor.authorDaniels, William S.
dc.date.accessioned2021-09-13T10:17:30Z
dc.date.accessioned2022-02-03T13:24:47Z
dc.date.available2021-09-13T10:17:30Z
dc.date.available2022-02-03T13:24:47Z
dc.date.issued2021
dc.identifierDaniels_mines_0052N_12195.pdf
dc.identifierT 9155
dc.identifier.urihttps://hdl.handle.net/11124/176467
dc.descriptionIncludes bibliographical references.
dc.description2021 Summer.
dc.description.abstractWe present two statistical modeling efforts that seek to address pressing environmental issues through the use of remotely sensed data. The first study is motivated by the extreme fire seasons now commonly experienced across the globe (e.g. the 2015 Indonesian forest fires and the 2019/2020 Australian bush fires). We develop interpretable models for remotely sensed carbon monoxide, a proxy for fire intensity in the Southern Hemisphere, fit using a flexible regularization framework. These models are parsimonious by design, allowing for scientific insight into the primary climate drivers of fire season intensity in different regions. The models have good predictive skill at considerable lead times, making them a useful tool for predicting upcoming fire season intensity. The second study is motivated by a growing dependence on natural gas for energy in the United States. Methane (the primary component of natural gas) burns cleaner than coal and oil but is a potent greenhouse gas. Therefore, limiting emissions during natural gas production is essential if it is to be considered a cleaner alternative to other fossil fuels. With the goal of localizing small-scale emissions, we develop a hierarchical spatial model for estimating methane concentrations on a fine grid given coarsely pixelated satellite observations. We apply our model to a satellite overpass of the Denver-Julesburg (DJ) Basin (located in northeast Colorado) to demonstrate its effectiveness. We use conditional simulation for uncertainty quantification and inferences related to emissions monitoring.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2021 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectfire season intensity
dc.subjectmethane
dc.subjectspatial statistics
dc.subjectlinear regression
dc.subjectcarbon monoxide
dc.subjectsatellite data
dc.titleStatistical methods for the interpretation, prediction, and localization of remotely sensed atmospheric pollutants
dc.typeText
dc.contributor.committeememberNychka, Douglas
dc.contributor.committeememberBuchholz, Rebecca
dc.contributor.committeememberBazilian, Morgan
thesis.degree.nameMaster of Science (M.S.)
thesis.degree.levelMasters
thesis.degree.disciplineApplied Mathematics and Statistics
thesis.degree.grantorColorado School of Mines


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