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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 ... -
Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modelling using VolcanicAshInversion v1.2.1, within the operational eEMEP volcanic plume forecasting system (version rv4_17)
(Peer reviewed; Journal article, 2024)Accurate modeling of ash clouds from volcanic eruptions requires knowledge about the eruption source parameters including eruption onset, duration, mass eruption rates, particle size distribution, and vertical-emission ... -
Impact of Biomass Burning on Arctic Aerosol Composition
(Peer reviewed; Journal article, 2024)Emissions from biomass burning (BB) occurring at midlatitudes can reach the Arctic, where they influence the remote aerosol population. By using measurements of levoglucosan and black carbon, we identify seven BB events ... -
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 ... -
Emission ensemble approach to improve the development of multi-scale emission inventories
(Peer reviewed; Journal article, 2024)Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential ...