Applications of Deep Learning in Atmospheric Sciences: Air Quality Forecasting, Post-Processing, and Hurricane Tracking

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dc.contributor.advisorChoi, Yunsoo
dc.contributor.committeeMemberTalbot, Robert W.
dc.contributor.committeeMemberJiang, Xun
dc.contributor.committeeMemberEick, Christoph F.
dc.creatorEslami, Ebrahim 1985-
dc.creator.orcid0000-0002-7721-6534
dc.date.accessioned2020-01-04T00:35:52Z
dc.date.createdMay 2020
dc.date.issued2020-05
dc.date.submittedMay 2020
dc.date.updated2020-01-04T00:35:53Z
dc.description.abstractThis study employs deep learning-based models for developing: fast, real-time air quality forecasting systems; a post-processing tool for bias-correcting the chemical transport model; and a reliable hybrid hurricane tracking model. A deep convolutional neural network (CNN) algorithm, which is an advanced deep learning algorithm, was employed to predict the hourly ozone concentrations each day (24 hours in advance) for the entire year using several meteorological variables and air pollution concentrations from the previous day. The CNN model showed a reasonable performance with an average index of agreement (IOA) of 0.84-0.89 and a Pearson correlation coefficient of 0.74-0.81. Although the CNN model successfully captured daily trends of the ozone concentrations, it notably underpredictd high ozone peaks during the summer. To address this issue, six generalized machine leaning ensemble models were developed to regularize low- and high-ozone episodes. By resampling the training dataset based on the daily peaks, the ‘best’ ensemble model reduced the ozone peak prediction error by 5 to 30 ppb during summer. Another deep CNN model was developed to post-process the results of the Community Multiscale Air Quality (CMAQ) model. The CNN model significantly improved the performance of the CMAQ model by improving its absolute correlation coefficient by 0.16 and reducing its prediction bias by more than 20 ppb on average. To improve the prediction of hurricane models, a novel, hybrid approach was proposed using a CNN and an ensemble Kalman filter (EnKF). First, a three-step CNN ensemble model was developed to predict direction, distance traveled, and intensity of a hurricane. Then, an EnKF was applied as a post-processing step. The results of the hybrid model for 17 tropical storms in 2017 showed statistical advantages over official National Hurricane Center (NHC) 24-hour-ahead forecasts (i.e., ~13% and ~34% improvement in track and intensity forecast biases, respectively).
dc.description.departmentEarth and Atmospheric Sciences, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Eslami, Ebrahim, Yunsoo Choi, Yannic Lops, and Alqamah Sayeed. "A real-time hourly ozone prediction system using deep convolutional neural network." Neural Computing and Applications (2019): 1-15. And in: Eslami, Ebrahim, Ahmed Khan Salman, Yunsoo Choi, Alqamah Sayeed, and Yannic Lops. "A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks." Neural Computing and Applications (2019): 1-17.
dc.identifier.urihttps://hdl.handle.net/10657/5714
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMachine learning
dc.subjectAtmospheric sciences
dc.subjectDeep neural networks
dc.subjectModel post-processing
dc.subjectTime-series forecasting
dc.subjectHurricane tracking
dc.subjectHurricanes
dc.titleApplications of Deep Learning in Atmospheric Sciences: Air Quality Forecasting, Post-Processing, and Hurricane Tracking
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2022-05-01
local.embargo.terms2022-05-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentEarth and Atmospheric Sciences, Department of
thesis.degree.disciplineAtmospheric Sciences
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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