Integrating Deep Neural Network with Numerical Models to Have Better Weather and Air Quality Forecast Both Spatially and Temporally

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dc.contributor.advisorChoi, Yunsoo
dc.contributor.committeeMemberJiang, Xun
dc.contributor.committeeMemberRappenglueck, Bernhard
dc.contributor.committeeMemberLee, Kyo
dc.creatorSayeed, Alqamah
dc.date.accessioned2022-06-18T23:40:27Z
dc.date.createdAugust 2021
dc.date.issued2021-08
dc.date.submittedAugust 2021
dc.date.updated2022-06-18T23:40:28Z
dc.description.abstractNumerical models are excellent tools for forecasting future weather events or air quality. The scientific and computational advancements in numerical models provided us with more accurate forecasts while promoting an understanding of various physical and chemical processes in the atmosphere. However, due to the simplified implementation of such processes, the understanding of modeling uncertainties was limited. In addition to this, these numerical models require significant computational resources and time to have a quality forecast. This thesis tries to mitigate the uncertainties which lead to large biases, using advanced deep neural network (DNN) models and integrate them into the existing numerical model to have better and faster weather and air quality forecasts. In this study, a long-term forecasting system based on a deep Convolutional Neural Network (CNN) was developed for the air pollutants such as ozone, NO2, PM2.5, and PM10 for up to two weeks. An optimized deep learning algorithm was used to develop species-specific and location-neutral models. These models used the simulation outputs of the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model at the first step, and then train a deep neural network (DNN) model for each air pollutant in the second step. Once trained, these models forecasted the next 24-hour in advance for all species across the geographical domain for up to two weeks. In the final task, a deep CNN system was developed to bias-correct and reduce systematic uncertainties in the simulation of meteorology, such as wind speed and direction, surface pressure, temperature, humidity, etc., in the WRF model.
dc.description.departmentEarth and Atmospheric Sciences, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Sayeed, A., Choi, Y., Eslami, E., Lops, Y., Roy, A., Jung, J., 2020b. Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance. Neural Networks. https://doi.org/10.1016/j.neunet.2019.09.033; and in: Sayeed, A., Choi, Y., Eslami, E., Jung, J., Lops, Y., Salman, A.K., Lee, J.-B., Park, H.-J., Choi, M.-H., 2021a. A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance. Sci Rep 11, 1–8. https://doi.org/10.1038/s41598-021-90446-6; and in: Sayeed, A., Lops, Y., Choi, Y., Jung, J., Salman, A.K., 2021b. Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks. Atmospheric Environment 253, 118376. https://doi.org/10.1016/j.atmosenv.2021.118376
dc.identifier.urihttps://hdl.handle.net/10657/9383
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.subjectDeep Learning, Forecasting, Air pollution, CNN, Ozone, Wind Speed, Temperature
dc.titleIntegrating Deep Neural Network with Numerical Models to Have Better Weather and Air Quality Forecast Both Spatially and Temporally
dc.type.dcmiText
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2023-08-01
local.embargo.terms2023-08-01
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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