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NILU [3449]
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A Machine Learning Approach to Retrieving Aerosol Optical Depth Using Solar Radiation Measurements
(Peer reviewed; Journal article, 2024)Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning ... -
Estimation of the atmospheric hydroxyl radical oxidative capacity using multiple hydrofluorocarbons (HFCs)
(Peer reviewed; Journal article, 2024)The hydroxyl radical (OH) largely determines the atmosphere's oxidative capacity and, thus, the lifetimes of numerous trace gases, including methane (CH4). Hitherto, observation-based approaches for estimating the atmospheric ... -
Estimating stratospheric polar vortex strength using ambient ocean-generated infrasound and stochastics-based machine learning
(Peer reviewed; Journal article, 2024)There are sparse opportunities for direct measurement of upper stratospheric winds, yet improving their representation in subseasonal-to-seasonal prediction models can have significant benefits. There is solid evidence ... -
Simulations of Sky Radiances in Red and Blue Channels at Various Aerosol Conditions Using Radiative Transfer Modeling
(Journal article, 2023)We conducted a theoretical analysis of the relationship between red-to-blue (RBR) color intensities and aerosol optical properties. RBR values are obtained by radiative transfer simulations of diffuse sky radiances. Changes ... -
PM2.5 Retrieval Using Aerosol Optical Depth, Meteorological Variables, and Artificial Intelligence
(Journal article, 2032)Particulate matter (PM) is one of the major air pollutants that has adverse impacts on human health. The aim of this study is to present an alternative approach for retrieving fine PM (particles with an aerodynamic diameter ...