Coupling data science and numerical simulations to empower atmospheric and environmental research
type: CreativeWork
description: We have entered the age of Data Science. Massive data from numerical simulations of the Earth system are now common in atmospheric and environmental research. However, state-of-the-art Earth System Models are subject to limitations because of the multiscale nature of the Earth system, where processes on scales smaller than the computational grid resolution remain unresolved and can only rely on simplified representations. These simplified representations introduce large yet frequently poorly characterized uncertainties in climate simulations. Therefore, making sense and making use of these simulation data remains a fundamental challenge. This dissertation tackles two critical representations in the Earth System Models: aerosol representation and urban representation. We couple Data Science and numerical simulations to create a suite of tools for addressing and overcoming the limitations: (1) Coarse graining & Regionalizing: Simulating a particle-resolved model to calculate the aerosol mixing state index (a metric to describe the chemical composition across the aerosol) at the global scale is computationally expensive. We develop data-driven emulators (surrogate models) that are learned from particle-resolved aerosol simulations to predict submicron aerosol mixing state indices from Earth System Model simulations. Different mixing state indices exhibit unique spatial and seasonal distributions via the global maps of aerosol mixing state indices. An unsupervised learning-based approach is developed to regionalize the global mixing state indices. We illustrate that regionalization can capture the variability in aerosol mixing state distribution. (2) Model benchmarking: Aerosols are often represented by several overlapping subpopulations with pre-defined parameters (known as “modes”) that cannot fully resolve mixing state. To quantitatively evaluate the error in mixing state represented by a modal model, we take the machine learning-enabled particle-resolved surrogate m…
abstract: We have entered the age of Data Science. Massive data from numerical simulations of the Earth system are now common in atmospheric and environmental research. However, state-of-the-art Earth System Models are subject to limitations because of the multiscale nature of the Earth system, where processes on scales smaller than the computational grid resolution remain unresolved and can only rely on simplified representations. These simplified representations introduce large yet frequently poorly characterized uncertainties in climate simulations. Therefore, making sense and making use of these simulation data remains a fundamental challenge. This dissertation tackles two critical representations in the Earth System Models: aerosol representation and urban representation. We couple Data Science and numerical simulations to create a suite of tools for addressing and overcoming the limitations: (1) Coarse graining & Regionalizing: Simulating a particle-resolved model to calculate the aerosol mixing state index (a metric to describe the chemical composition across the aerosol) at the global scale is computationally expensive. We develop data-driven emulators (surrogate models) that are learned from particle-resolved aerosol simulations to predict submicron aerosol mixing state indices from Earth System Model simulations. Different mixing state indices exhibit unique spatial and seasonal distributions via the global maps of aerosol mixing state indices. An unsupervised learning-based approach is developed to regionalize the global mixing state indices. We illustrate that regionalization can capture the variability in aerosol mixing state distribution. (2) Model benchmarking: Aerosols are often represented by several overlapping subpopulations with pre-defined parameters (known as “modes”) that cannot fully resolve mixing state. To quantitatively evaluate the error in mixing state represented by a modal model, we take the machine learning-enabled particle-resolved surrogate m…
author: Zheng, Zhonghua
language: en
keywords: Data Science, Atmospheric Aerosols, Urban Environments
date published: 2021-03-05T21:45:35Z
date modified: 2026-07-22T22:24:50Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/109594