Aerosol Retrieval Using Remote-sensed Observations by Yueqing Wang A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Statistics

Aerosol Retrieval Using Remote-sensed Observations by Yueqing Wang Doctor of Philosophy in Statistics with the Designated Emphasis in Communication, Computation, and Statistics University of California, Berkeley Professor Bin Yu, Chair Atmospheric aerosols are solid particles and liquid droplets that are usually smaller than the diameter of a human hair. They can be found drifting in the air in every ecosystem on Earth, leaving significant impacts on human health and our climate. Understanding the spatial and temporal distribution of di↵erent atmospheric aerosols, therefore, is an important first step to decode the complex system of aerosols and further, their e↵ects on public health and climate. The development of remote-sensing radiometers provides a powerful tool to monitor the amount of atmospheric aerosols, as well as their compositions. Radiometers aboard satellites measure the amount of electromagnetic solar radiation. The amount of atmospheric aerosols is further quantified by aerosol optical depth (AOD), defined as the amount of solar radiation that aerosols scatter and absorb in the atmosphere and generally prevent from reaching the Earth surface. Despite e↵orts to improve remote-sensing instruments and a great demand for a detailed profile of aerosol spatial distribution, methods needed to provide AOD estimation at a reasonably fine resolution, are lacking. The quantitative uncertainties in the amount of aerosols, and especially aerosol compositions, limit the utility of traditional methods for aerosol retrieval at a fine resolution. In Chapter 2 and 3 of this thesis, we exploit the use of statistical methods to estimate aerosol optical depth using remote-sensed radiation. A Bayesian hierarchy proves to be useful for modeling the complicated interactions among aerosols of di↵erent amount and compositions over a large spatial area. Based on the hierarchical model, Chapter 2 estimates and validates aerosol optical depth using Markov chain Monte Carlo methods, while chapter 3 resorts to an optimization-based approach for faster computation. We extend our study focus from the aerosol amount to the aerosol compositions in Chapter 4. Chapter 1 briefly reviews the characteristics of atmospheric aerosols, including the di↵erent types of aerosols and their major impacts on human health. We also introduce a major