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Volcanic eruptions eject large amounts of ash and trace gases such as sulfur dioxide (SO 2) into the atmosphere. A significant difficulty in mitigating the impact of volcanic SO 2 clouds on air traffic safety is that these gas emissions can be rapidly transported over long distances. The use of space-borne instruments enables the global monitoring of volcanic SO 2 emissions in an economical and risk-free manner. Within the European Space Agency (ESA) Sentinel-5p + Innovation project, the S5P SO 2 layer height (S5P + I: SO2LH) activities led to the improvements of the retrieval algorithm and generation of the corresponding near real-time S5P SO 2 LH products. These are currently operationally provided, in near real-time, by the German Aerospace Center (DLR) within the framework of the Innovative Products for Analyses of Atmospheric Composition (INPULS) project. The main aim of this paper is to present its extensive verification, accomplished within the S5P + I: SO2LH project, over major recent volcanic eruptions, against collocated space-borne measurements from the IASI/Metop and CALIOP/CALIPSO instruments as well as assess its impact on the forecasts provided by the Copernicus Atmospheric Monitoring Service (CAMS). The mean difference between S5P and IASI observations for the Raikoke 2019, the Nishinoshima 2020 and the La Soufrière-St Vincent 2021 eruptive periods is ∼ 0.5 ± 3 km, while for the Taal 2020 eruption, a larger difference was found, between 3 ± 3 km and 4 ± 3 km. The comparison of the daily mean SO 2 LH further demonstrates the capabilities of this near real-time product, with slopes between 0.8 and 1 and correlation coefficients ranging between 0.6 and 0.8. Comparisons between the S5P SO 2 LH and the CALIOP/CALIPSO ash plumes revealed an expected bias at - 2.5 ± 2 km, considering that the injected SO 2 and ash plume locations do not always coincide over an eruption. Furthermore, the CAMS assimilation of the S5P SO 2 LH product led to much improved model output against the non-assimilated IASI LH, with a mean difference of 1.5 ± 2 km, compared to the original CAMS analysis, and improved the geographical spread of the Raikoke volcanic plume following the eruptive days. [ABSTRACT FROM AUTHOR]

The Copernicus Atmosphere Monitoring Service (CAMS), operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission, provides daily analyses and 5 d forecasts of atmospheric composition, including forecasts of volcanic sulfur dioxide (SO2) in near real time. CAMS currently assimilates total column SO2 products from the GOME-2 instruments on MetOp-B and MetOp-C and the TROPOMI instrument on Sentinel-5P, which give information about the location and strength of volcanic plumes. However, the operational TROPOMI and GOME-2 data do not provide any information about the height of the volcanic plumes, and therefore some prior assumptions need to be made in the CAMS data assimilation system about where to place the resulting SO2 increments in the vertical. In the current operational CAMS configuration, the SO2 increments are placed in the mid-troposphere, around 550 hPa or 5 km. While this gives good results for the majority of volcanic emissions, it will clearly be wrong for eruptions that inject SO2 at very different altitudes, in particular exceptional events where part of the SO2 plume reaches the stratosphere. A new algorithm, developed by the German Aerospace Centre (DLR) for GOME-2 and TROPOMI, optimized in the frame of the ESA-funded Sentinel-5P Innovation– SO2 Layer Height Project, and known as the Full-Physics Inverse Learning Machine (FP_ILM) algorithm, retrieves SO2 layer height from TROPOMI in near real time (NRT) in addition to the SO2 column. CAMS is testing the assimilation of these products, making use of the NRT layer height information to place the SO2 increments at a retrieved altitude. Assimilation tests with the TROPOMI SO2 layer height data for the Raikoke eruption in June 2019 show that the resulting CAMS SO2 plume heights agree better with IASI plume height data than operational CAMS runs without the TROPOMI SO2 layer height information and show that making use of the additional layer height information leads to improved SO2 forecasts. Including the layer height information leads to higher modelled total column SO2 values in better agreement with the satellite observations. However, the plume area and SO2 burden are generally also overestimated in the CAMS analysis when layer height data are used. The main reason for this overestimation is the coarse horizontal resolution used in the minimizations. By assimilating the SO2 layer height data, the CAMS system can predict the overall location of the Raikoke SO2 plume up to 5 d in advance for about 20 d after the initial eruption, which is better than with the operational CAMS configuration (without prior knowledge of the plume height) where the forecast skill is much more reduced for longer forecast lead times. [ABSTRACT FROM AUTHOR]

The Copernicus Atmosphere Monitoring Service (CAMS), operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission, provides daily analyses and 5-day forecasts of atmospheric composition, including forecasts of volcanic sulphur dioxide (SO2) in near-real time. CAMS currently assimilates total column SO2 retrievals from the GOME-2 instruments on MetOp-B and -C and the TROPOMI instrument on Sentinel-5P which give information about the location and strength of volcanic plumes. However, the operational TROPOMI and GOME-2 retrievals do not provide any information about the height of the volcanic plumes and therefore some prior assumptions need to be made in the CAMS data assimilation system about where to place the resulting SO2 increments in the vertical. In the current operational CAMS configuration, the SO2 increments are placed in the mid-troposphere, around 550 hPa or 5 km. While this gives good results for the majority of volcanic emissions, it will clearly be wrong for eruptions that inject SO2 at very different altitudes, in particular exceptional events where part of the SO2 plume reaches the stratosphere. A new algorithm, developed by DLR for GOME-2 and TROPOMI and optimized in the frame of the ESA-funded Sentinel-5P Innovation-SO2 Layer Height Project, the Full-Physics Inverse Learning Machine (FP_ILM) algorithm, retrieves SO2 layer height from TROPOMI in NRT in addition to the SO2 column. CAMS is testing the assimilation of these data, making use of the NRT layer height information to place the SO2 increments at a retrieved altitude. Assimilation tests with the TROPOMI SO2 layer height data for the Raikoke eruption in June 2019 show that the resulting CAMS SO2 plume heights agree better with IASI plume height retrievals than operational CAMS runs without the TROPOMI SO2 layer height information and that making use of the additional layer height information leads to improved SO2 forecasts than when using the operational CAMS configuration. By assimilating the SO2 layer height data the CAMS system can predict the overall location of the Raikoke SO2 plume up to 5 days in advance for about 20 days after the initial eruption. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. This approach offers a promising prospect of using physics-based machine learning applications to other instruments. [ABSTRACT FROM AUTHOR]

The accurate determination of the location, height, and loading of sulfur dioxide (SO2) plumes emitted by volcanic eruptions is essential for aviation safety. The SO2 layer height is also one of the most critical parameters with respect to determining the impact on the climate. Retrievals of SO2 plume height have been carried out using satellite UV backscatter measurements, but, until now, such algorithms are very time-consuming. We have developed an extremely fast yet accurate SO2 layer height retrieval using the Full-Physics Inverse Learning Machine (FP_ILM) algorithm. This is the first time the algorithm has been applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform. In this paper, we demonstrate the ability of the FP_ILM algorithm to retrieve SO2 plume layer heights in near-real-time applications with an accuracy of better than 2 km for SO2 total columns larger than 20 DU. We present SO2 layer height results for the volcanic eruptions of Sinabung in February 2018, Sierra Negra in June 2018, and Raikoke in June 2019, observed by TROPOMI. [ABSTRACT FROM AUTHOR]

Precise knowledge of the location and height of the volcanic SO2 plumes is essential for accurate determination of SO2 emitted by volcanic eruptions for aviation control applications, but so far very time-consuming to retrieve from UV satellite data. The SO2 height is furthermore one of the most critical parameters that determine the impact on the climate. We have developed an extremely fast yet accurate SO2 layer height retrieval algorithm using the Full-Physics Inverse Learning Machine (FP_ILM) algorithm, which, for the first time, is applied to TROPOMI aboard Sentinel-5 Precursor. In this work we demonstrate the ability of the FP_ILM algorithm to retrieve layer heights in near-real time applications with an accuracy of better than 2 km for SO2 total columns larger than 20 DU and show SO2 layer height results for selected volcanic eruptions. [ABSTRACT FROM AUTHOR]

Precise knowledge of the location and height of the volcanic SO2 plume is essential foraccurate determination of SO2 emitted by volcanic eruptions for aviation control applications,but so far very time-consuming to retrieve from UV satellite data. We have developed anextremely fast yet accurate SO2 layer height retrieval algorithm using the Full-PhysicsInverse Learning Machine (FP-ILM) algorithm, which, for the first time, is applied toTROPOMI aboard Sentinel-5 Precursor. Conceptually, the FP-ILM consists of a training phase, in which the inversion operator isobtained using synthetic data generated using a radiative transfer (RT) model, and anoperational phase, in which the inversion operator is applied to satellite measurements. Themain advantage of the FP-ILM over classical direct fitting approaches is that thetime-consuming training phase involving complex RT modeling is performed offline; theinverse operator itself is robust and computationally simple and therefore extremelyfast. In this presentation we demonstrate the ability of the FP-ILM algorithm to retrievelayer heights in near-real time applications with an accuracy of better than 2 kmand show SO2 layer height results for selected volcanic eruptions measured bySentinel-5 Precursor/TROPOMI, including the Krakatau and Etna eruption end of2018. [ABSTRACT FROM AUTHOR]