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Deep neural network based rain/no-rain classification and rain rate estimation

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dc.contributor.authorPotnis, Jay U., author
dc.contributor.authorChandrasekar, V., advisor
dc.contributor.authorCheney, Margaret, committee member
dc.contributor.authorSiller, Thomas, committee member
dc.date.accessioned2022-08-29T10:15:53Z
dc.date.available2024-08-22T10:15:53Z
dc.date.issued2022
dc.description.abstractQuantitative Precipitation Estimation is the process of computing rainfall rate or rainfall accumulation based on the state of the atmosphere. Atmospheric conditions can be described by using observations from meteorological instruments. Extreme weather events caused due to high rainfall can be dangerous in terms of loss of property and life. To prevent such disasters, accurate QPE algorithms that analyze and estimate the amount of rainfall observed in a region are critical. Moreover, rain rate estimates are crucial products in making management decisions in water, energy, construction infrastructure, and many other institutions. Researching state-of-the-art rainfall estimation techniques that make use of reliable remote sensing equipment such as satellites and radars is important as deploying rain gauges everywhere is not possible and is not a viable option. As rain precipitation is a complicated phenomenon, depending on multiple factors in the atmosphere, research is being done in this domain for many decades and the goal is to improve the accuracy of estimation by using new state-of-the-art methods. Weather radars are reliable remote sensing instruments that are used to capture the different properties of weather in form of products called moments. The goal of this work is to use weather radars in conjunction with Deep Neural Networks to provide solutions to multiple tasks in the QPE domain. Neural networks can be used for precipitation flagging such as classifying rain and no rain events. They can also be used for estimating the rain rates at specific coordinates or along regions. Though multiple empirical relationships between radar moments and rain rate already exist, this work provides good state-of-the-art alternatives to these equations and can even achieve comparable accuracy.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierPotnis_colostate_0053N_17261.pdf
dc.identifier.urihttps://hdl.handle.net/10217/235579
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
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.rights.accessEmbargo Expires: 08/22/2024
dc.subjectprecipitation estimation
dc.subjectrain classification
dc.subjectrainfall estimation
dc.subjectquantitative precipitation estimation
dc.subjectdeep neural networks
dc.subjectrain rate estimation
dc.titleDeep neural network based rain/no-rain classification and rain rate estimation
dc.typeText
dcterms.embargo.expires2024-08-22
dcterms.embargo.terms2024-08-22
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.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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