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Rainfall estimation from spaceborne and ground based radars using neural networks

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dc.contributor.authorAlqudah, Amin, author
dc.contributor.authorChandra, Chandrasekar V., advisor
dc.date.accessioned2024-03-13T18:14:54Z
dc.date.available2024-03-13T18:14:54Z
dc.date.issued2009
dc.description.abstractRainfall observed on the ground is dependent on the four dimensional radar observations. However it is difficult to express this in a simple form. A simple Z-R relation is not sufficient and has large uncertainty and it needs to be adaptively adjusted. Prior research has shown that neural networks can be used to estimate ground rainfall from radar measurements. Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) is the first space borne observation platform for mapping precipitation over the tropics. TRMM measured rainfall is important in order to study the precipitation distribution all over the globe in the tropics. TRMM ground validation is a critical important component to ensure the measurement accuracy. However, this ground validation has quite different characteristics from TRMM in terms of resolution, scale, viewing aspect, and uncertainties. This makes the use of ground radar rainfall information to correct TRMM rainfall estimates a very challenging task. In this dissertation, rainfall estimation using neural networks is investigated in order to improve rainfall estimation based on measurements taken by ground radars and TRMM-PR. Ground Radar measurements will be used to estimate rainfall using adaptive neural networks. Improvements are also suggested and performed including the use of Principal Components Analysis, ensemble average neural network, and the use of Bayesian Neural Networks. For TRMM-PR purposes a single neural network is not efficient to extract the relation between TRMM-PR measurements and the rain gauges; this is because of the resolution differences between TRMM-PR profile and the rain gauges and the low number of TRMM overpasses over these gauges which will make the training data set to have less number of profiles and not be able to generalize. Therefore, a novel hybrid Neural Network model is presented to train ground radars for rainfall estimate using rain gauge data and subsequently the trained ground radar rain estimates to train TRMM-PR based Neural Networks for rainfall estimation. This hybrid neural network model will derive the relation between rain gauges and ground radar measurements, and transfer this relation to adaptive rainfall estimation for TRMM-PR in order to estimate rainfall and generate global rainfall maps.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierETDF_Alqudah_2009_3385187.pdf
dc.identifier.urihttps://hdl.handle.net/10217/237552
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
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.licensePer the terms of a contractual agreement, all use of this item is limited to the non-commercial use of Colorado State University and its authorized users.
dc.subjectprecipitation
dc.subjectrainfall estimation
dc.subjecttropical rainfall
dc.subjectelectrical engineering
dc.titleRainfall estimation from spaceborne and ground based radars using neural networks
dc.typeText
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.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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