Volcanic SO2 Effective Layer Height Retrieval for OMI Using a Machine Learning Approach

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

Volcanic SO2 Effective Layer Height Retrieval with OMI Using a Machine Learning Driven Approach

Information about the height and loading of aerosols and sulfur dioxide (SO2) plumes from volcanic eruptions is crucial for both aviation safety and for assessing the effect of volcanic sulfate aerosols on climate. Previously, a few retrieval techniques for the SO2 layer height using backscattered Earthshine ultraviolet (BUV) radiances have been demonstrated. However, these techniques often rely on time consuming direct spectral fitting methods and on-line radiative transfer calculations, and as a result are mostly not suited for near real time applications. Here, we introduce a new machine learning based algorithm for fast retrievals of effective volcanic SO2 layer height (SO2 LH) from the Ozone Monitoring Instrument (OMI) . The first part of this method is a training phase that utilizes extensive radiative transfer calculations to generate a large dataset of synthetic BUV spectra in the 310-330 nm spectral range. The principal components of this dataset, in addition to several key physical parameters, are used to train a feed-forward neural network to predict the SO2LH. This is followed by the application phase, where the trained inverse model is used on real OMI BUV measurements to retrieve the effective SO2 LH. The algorithm has been tested on four major explosive eruptions during the OMI data record. Results for the 2008 Kasatochi, 2019 Raikoke, 2015 Calbuco and 2014 Kelud eruptions are presented here and compared with volcanic plume heights estimated with other satellite sensors. For the most part, OMI-retrieved effective SO2 LHs agree well with the lidar measurements of co-located volcanic sulfate aerosol layer height from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and thermal infrared retrievals of SO2 LH from the infrared atmospheric sounding interferometer (IASI). The errors in OMI retrieved SO2 heights are estimated to be in the 1.5-2 km range for plumes with relatively large SO2 column densities exceeding ~40 Dobson Units (1DU= 2.69 x 1016 molecules SO2 cm-2). In the application phase the retrieval of plume height is highly efficient, and takes less than 3 minutes for a full OMI orbit. This approach and similar machine learning based applications can also be readily adapted for other satellite UV-Vis spectrometers

Linking Improvements in Sulfur Dioxide Emissions to Decreasing Sulfate Wet Deposition by Combining Satellite and Surface Observations with Trajectory Analysis

Sulfur dioxide (SO2), a criteria pollutant, and sulfate (SO42) deposition are major environmental concerns in the eastern U.S. and both have been on the decline for two decades. In this study, we use satellite column SO2 data from the Ozone Monitoring Instrument (OMI), and SO42 wet deposition data from the NADP (National Atmospheric Deposition Program) to investigate the temporal and spatial relationship between trends in SO2 emissions and the downward sulfate wet deposition over the eastern U.S. from 2005 to 2015. To establish the relationship between SO2 emission sources and receptor sites, we conducted a Potential Source Contribution Function (PSCF) analysis using HYSPLIT back trajectories for five selected Air Quality System (AQS) sites - (Hackney, OH, Akron, OH, South Fayette, PA, Wilmington, DE, and Beltsville, MD) - in close proximity to NADP sites with large downward SO42 trends since 2005. Back trajectories were run for three summers (JJA) and three winters (DJF) and used to generate seasonal climatology PSCFs for each site. The OMI SO2 and interpolated NADP sulfate deposition trends were normalized and overlapped with the PSCF, to identify the areas that had the highest contribution to the observed drop. The results suggest that emission reductions along the Ohio River Valley have led to decreases in sulfate deposition in eastern OH and western PA (Hackney, Akron and South Fayette). Farther to the east, emission reductions in southeast PA resulted in improvements in sulfate deposition at Wilmington, DE, while for Beltsville, reductions in both the Ohio River Valley and nearby favorably impacted sulfate deposition. For Beltsville, sources closer than 300km from the site contribute roughly 56% observed deposition trends in winter, and 82% in summer, reflecting seasonal changes in transport pattern as well as faster oxidation and washout of sulfur in summer. This suggests that emissions and wet deposition are linked through not only the location of sources relative to the observing sites, but also to photochemistry and the weather patterns characteristic to the region, as evidenced by a west to east shift in the contribution between winter and summer. The method developed here is applicable to other regions with significant trends such as China and India, and can be used to estimate the potential benefits of emission reduction in those areas

Volcanic SO2 Layer Height from UV/Vis sensors; inter-comparisons and validation

info:eu-repo/semantics/publishe