Providing Wavelength Resolved Irradiance Measurements by Using Machine Learning




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Sunlight incident on the Earth’s atmosphere is essential for life and is the driving force for atmospheric photo-chemistry. Atmospheric photo-chemistry is central to understanding urban air quality and the host of associated human health impacts. In this dissertation, two solutions were proposed to address the current lack of real-time wavelength-resolved solar irradiance data across cities. Our first solution is based on the machine learning calibration of low-cost light sensors. These calibrated sensors have a strong performance and can be readily deployed at scale across dense urban environments to measure the wavelength resolved irradiance on a neighborhood scale. This work has been published in MDPI (Zhang et al., 2021). Our second solution is based on the comprehensive dataset from public environmental sensors. We developed another machine learning model to estimate the wavelength resolved solar irradiance from solar zenith angle, earth distance, and multiple environmental dataset, such as relative humidity, total column ozone, earth surface reflectance, and radar reflectivities in the sky. All these factors can be accessed from the public datasets of weather stations and remote sensing systems. Using this solution, wavelength resolved solar irradiance can be estimated in a neighborhood scale, without implementing any additional sensors.



Physics, Atmospheric Science