Radiation-Based Analytic Approaches to Investigate the Earth’s Atmosphere

Radiation, propagating through Earth’s atmosphere, plays an important role in the Earth system. Solar radiation is the major source of energy, followed by thermal infrared radiation emitted by the Earth. The total radiative energy budget affects dynamic, thermodynamics, photochemical and biological processes. In addition, by measuring the reflected and emitted radiation at a distance (e.g., satellite or aircraft), we can detect and monitor the physical characteristics of a region which can help researchers get a better understanding of Earth’s atmosphere. Therefore, radiation-based analytic approaches are powerful tools in Earth Science. This thesis focuses on using radiation-based analytic tools to study the Earth’s atmosphere and to understand human impacts on the Earth system. First, we develop novel machine learning methods for hyperspectral radiative transfer simulations. Hyperspectral technique is one of the most popular and powerful methods for atmospheric remote sensing and is widely used for temperature, gas, aerosol, and cloud retrievals. However, accurate forward radiative transfer simulations are computationally expensive since they require a larger number of monochromatic radiative transfer calculations. We, therefore explore the feasibility of machine learning techniques for fast hyperspectral radiative transfer simulations that perform calculations at a small fraction of hyperspectral wavelengths and extend them across the entire spectral range. The machine learning-based approach achieves better performance than the traditional principal component analysis (PCA) method. Second, we evaluate modeled hyperspectral infrared spectra against satellite all-sky observations. The national weather centers obtain data from hyperspectral infrared sounders on a global scale. The cloudless scenario of this data is used to initialize weather forecasts, including temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these satellites are sensitive to the vertical distribution of ice and liquid water in the clouds, this information is not fully utilized. In this study, we evaluate how well the modeled spectra compare to AIRS observations using different cloud overlap models. We hope that this information can be used to verify clouds in the National Meteorological Center model and to initialize forecasts in the future. In the last chapter, we use radiation-based analytic approaches to study human impacts on the Earth system. In the first study case, we show that the radiative forcing due to geospatially redistributed anthropogenic aerosols mainly determined the spatial variations of winter extreme weather in the Northern Hemisphere during 1970-2005, which is a unique transition period for global aerosol forcing. In the second case, we review satellite and ground-based observations and conduct state-of-art atmospheric model simulations during the COVID-19 lockdown period. The halted human activities during the COVID-19 pandemic in China provided a unique experiment to assess the efficiency of air-pollution mitigation.