Choi, Yunsoo2022-06-17December 22021-12December 2Portions of this document appear in: Lops, Y., Choi, Y., Eslami, E. et al. Real-time 7-day forecast of pollen counts using a deep convolutional neural network. Neural Comput & Applic 32, 11827–11836 (2020). https://doi.org/10.1007/s00521-019-04665-0; and in: Lops, Y., Pouyaei, A., Choi, Y., Jung, J., Salman, A. K., & Sayeed, A. (2021). Application of a partial convolutional neural network for estimating geostationary aerosol optical depth data. Geophysical Research Letters, 48, e2021GL093096. https://doi.org/10.1029/2021GL093096https://hdl.handle.net/10657/9265The advancement and development of new technology provide atmospheric scientists and modelers to acquire an overwhelming amount of data on meteorology and air quality from space, numerical simulations, and in-situ monitoring sites. Integrating these data sources provides unique opportunities to enhance understanding of atmospheric processes to better simulate and forecast these processes. While Global Climate Models and Chemical Transport Models have undergone significant optimizations and improvements over the past decades, they are still unable to provide fully reliable biogenic air quality predictions or long-term climate forecasting. These limitations can be alleviated and addressed by incorporating in-situ measurements and remote sensing products into data assimilation or reanalysis techniques. While ground-based remote sensing measurements provide detailed point observations, they lack the spatial coverage of remote sensing-derived measurements. Unfortunately, these remote sensing measurements experience issues caused by outside factors such as cloud cover contamination and false reflectance. Internal issues involve sensor errors that corrupt or lead to failed measurements of the data. This study utilizes the advanced capability of several deep learning models for the forecasting of pollen concentrations by up to 7 days; the imputation of remote sensing measurements spatially with partial convolutional neural networks and subsequent revision to incorporate spatio-temporal imputation; and long-term forecasting system of the climate index Nino3.4 by up to 36 months.application/pdfengThe 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).Deep LearningAir QualityClimateForecastRemote SensingConvolution Neural NetworkAerosolsImputation2022-06-17Thesisborn digital