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dc.contributor.advisorAnderson, Chuck
dc.contributor.advisorChandrasekar, V.
dc.contributor.authorCuomo, Joaquin M.
dc.contributor.committeememberPallickara, Sangmi Lee
dc.contributor.committeememberSuryanarayanam, Sid
dc.date.accessioned2020-09-07T10:08:40Z
dc.date.available2022-09-02T10:08:40Z
dc.date.issued2020
dc.description2020 Summer.
dc.descriptionIncludes bibliographical references.
dc.description.abstractWeather nowcasting is heavily dependent on the observation and estimation of radar echoes. There are many different types of deployed nowcasting systems, but none of them based on machine learning, even though it has been an active area of research in the last few years. This work sets the basis for considering machine learning models as real alternatives to current methods by proposing different architectures and comparing them against other nowcasting systems, such as DARTS and STEPS. The methods proposed here are based on residual convolutional encoder-decoder architectures, and they reach the state of the art performance and, in certain scenarios, even outperform them. Different experiments are presented on how the model behaves when using recurrent connections, different loss functions, and different prediction lead times.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierCuomo_colostate_0053N_16178.pdf
dc.identifier.urihttps://hdl.handle.net/10217/212034
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020- CSU Theses and Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.rights.accessEmbargo Expires: 09/02/2022
dc.subjectradar echoes prediction
dc.subjectnowcasting
dc.subjectvideo prediction
dc.titleMachine learning models applied to storm nowcasting
dc.typeText
dcterms.embargo.expires2022-09-02
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)


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