Volcanic SO<sub>2</sub> effective layer height retrieval for the Ozone Monitoring Instrument (OMI) using a machine-learning approach

نویسندگان

چکیده

Abstract. Information about the height and loading of sulfur dioxide (SO2) plumes from volcanic eruptions is crucial for aviation safety assessing effect sulfate aerosols on climate. While SO2 layer has been successfully retrieved backscattered Earthshine ultraviolet (UV) radiances measured by Ozone Monitoring Instrument (OMI), previously demonstrated techniques are computationally intensive not suitable near-real-time applications. In this study, we introduce a new OMI algorithm fast retrievals effective height. We apply Full-Physics Inverse Learning Machine (FP_ILM) to in spectral range 310–330 nm. This approach consists training phase that utilizes extensive radiative transfer calculations generate large dataset synthetic radiance spectra geophysical parameters representing measurement conditions. The principal components addition few used train neural network solve inverse problem predict followed applying trained model real measurements retrieve plume heights. tested several major during data record. results 2008 Kasatochi, 2014 Kelud, 2015 Calbuco, 2019 Raikoke eruption cases presented here compared with heights estimated other satellite sensors. For most part, OMI-retrieved agree well lidar aerosol Cloud–Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) thermal infrared atmospheric sounding interferometer (IASI). errors be 1–1.5 km relatively signals (>40 DU). very retrieves less than 10 min an entire orbit.

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ژورنال

عنوان ژورنال: Atmospheric Measurement Techniques

سال: 2021

ISSN: ['1867-1381', '1867-8548']

DOI: https://doi.org/10.5194/amt-14-3673-2021