scholarly journals Probabilistic retrieval of volcanic SO<sub>2</sub> layer height and partial column density using the Cross-track Infrared Sounder (CrIS)

2020 ◽  
Vol 13 (11) ◽  
pp. 5891-5921
Author(s):  
David M. Hyman ◽  
Michael J. Pavolonis

Abstract. During most volcanic eruptions and many periods of volcanic unrest, detectable quantities of sulfur dioxide (SO2) are injected into the atmosphere at a wide range of altitudes, from ground level to the lower stratosphere. Because the fine ash fraction of a volcanic plume is, at times, colocated with SO2 emissions, global tracking of volcanic SO2 is useful in tracking the hazard long after ash detection becomes dominated by noise. Typically, retrievals of SO2 vertical column density (VCD) have relied heavily on hyperspectral ultraviolet measurements. More recently, infrared sounders have provided additional VCD measurements and estimates of the SO2 layer altitude, adding significant value to real-time monitoring of volcanic emissions and climatological analyses. These methods can provide fast and accurate physics-based retrievals of VCD and altitude without regard to solar irradiance, meaning that they are effective day and night and can observe high-latitude SO2 even in the winter. In this study, we detail a probabilistic enhancement of an infrared SO2 retrieval method, based on a modified trace gas retrieval, to estimate SO2 VCD and altitude probabilistically using the Cross-track Infrared Sounder (CrIS) on the Joint Polar Satellite System (JPSS) series of satellites. The methodology requires the characterization of real SO2-free spectra aggregated seasonally and spatially. The probabilistic approach replaces altitude and VCD estimates with probability density functions for the layer height and the partial VCD at multiple heights, fully quantifying the retrieval uncertainty and allowing the estimation of SO2 partitioning by layer. This framework adds significant value over basic VCD and altitude retrieval because it can be used to assign probabilities of SO2 occurrence to different atmospheric intervals. We highlight analyses of several recent significant eruptions, including the 22 June 2019 eruption of Raikoke volcano, in the Kuril Islands; the mid-December 2016 eruption of Bogoslof volcano, in the Aleutian Islands; and the 26 June 2018 eruption of Sierra Negra volcano, in the Galapagos Islands. This retrieval method is currently being implemented in the VOLcanic Cloud Analysis Toolkit (VOLCAT), where it will be used to generate additional cloud object properties for real-time detection, probabilistic characterization, and tracking of volcanic clouds in support of aviation safety.

2020 ◽  
Author(s):  
David M. Hyman ◽  
Michael J. Pavolonis

Abstract. During most volcanic eruptions and many periods of volcanic unrest, detectable quantities of sulfur dioxide (SO2) are injected into the atmosphere at a wide range of altitudes, from ground level to the lower stratosphere. Because the fine ash fraction of a volcanic plume is, at times, collocated with SO2 emissions, global tracking of volcanic SO2 is useful in tracking the hazard long after ash detection becomes dominated by noise. Typically, retrievals of SO2 loading have relied heavily on hyperspectral ultraviolet measurements. More recently, infrared sounders have provided additional loading measurements and estimates of the SO2 layer altitude, adding significant value to real-time monitoring of volcanic emissions as well as climatological analyses. These methods leverage the relative simplicity of infrared radiative transfer calculations, providing fast and accurate physics-based retrievals of loading and altitude. In this study, we detail a probabilistic enhancement of an infrared SO2 retrieval method, based on a modified trace-gas retrieval, to estimate SO2 loading and altitude probabilistically using the Cross-track Infrared Sounder (CrIS) on the Joint Polar Satellite System (JPSS) series of satellites. The methodology requires the characterization of real SO2-free spectra aggregated seasonally and spatially. The probabilistic approach replaces loading and altitude estimates with non-parametric probability density functions, fully quantifying the retrieval uncertainty. This framework adds significant value over basic loading and altitude retrieval because it can be readily incorporated into Monte Carlo forecasting of volcanic emission transport. We highlight results including successes and challenges from analysis of several recent significant eruptions including the 22 June 2019 eruption of Raikoke volcano, Kuril Islands; the mid-December 2016 eruption of Bogoslof volcano; and the 26 June 2018 eruption of Sierra Negra volcano, Galapagos Islands. This retrieval method is currently being implemented in the VOLcanic Cloud Analysis Toolkit (VOLCAT), where it will be used to generate additional cloud object properties for real-time detection, characterization, and tracking of volcanic clouds in support of aviation safety.


2021 ◽  
Author(s):  
Maria-Elissavet Koukouli ◽  
Konstantinos Michailidis ◽  
Pascal Hedelt ◽  
Isabelle A. Taylor ◽  
Antje Inness ◽  
...  

Abstract. Volcanic eruptions eject large amounts of ash and trace gases such as sulphur dioxide (SO2) into the atmosphere. A significant difficulty in mitigating the impact of volcanic SO2 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 SO2 emissions in an economical and risk-free manner. Within the European Space Agency (ESA) Sentinel-5p+ Innovation project, the S5P SO2 Layer Height (S5P+I: SO2 LH) activities led to the improvements on the retrieval algorithm and generation of the corresponding near-real-time S5P SO2 LH products. These are currently operationally provided, in near-real-time, by the German Aerospace Center (DLR) in 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: SO2 LH project, over major recent volcanic eruptions, against collocated space-born 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 and 4 ± 3 km. The comparison of the daily mean SO2 layer heights further demonstrates the capabilities of this near-real-time product, with slopes between 0.8 and 1 and correlations ranging between 0.6 and 0.8. Comparisons between the S5P+I: SO2 LH and the CALIOP/CALIPSO ash plume height are also satisfactory at −2.5 ± 2 km, considering that the injected SO2 and ash plumes’ locations do not always coincide over an eruption. Furthermore, the CAMS assimilation of the S5P+I: SO2 LH product led to much improved model output against the non-assimilated IASI layer heights, 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.


2021 ◽  
Author(s):  
Pascal Hedelt ◽  
MariLiza Koukouli ◽  
Konstantinos Michaelidis ◽  
Taylor Isabelle ◽  
Dimitris Balis ◽  
...  

&lt;p&gt;Precise knowledge of the location and height of the volcanic sulfur dioxide (SO&lt;sub&gt;2&lt;/sub&gt;) plume is essential for accurate determination of SO&lt;sub&gt;2&lt;/sub&gt; emitted by volcanic eruptions, however so far not available in operational near-real time UV satellite retrievals. The FP_ILM algorithm (Full-Physics Inverse Learning Machine) enables for the first time to extract the SO&lt;sub&gt;2&lt;/sub&gt; layer height information in a matter of seconds for current UV satellites and is thus applicable in NRT environments.&lt;/p&gt;&lt;p&gt;The FP_ILM combines a principal component analysis (PCA) and a neural network approach (NN) to extract the information about the volcanic SO&lt;sub&gt;2&lt;/sub&gt; layer height from high-resolution UV satellite backscatter measurements. So far, UV based SO&lt;sub&gt;2 &lt;/sub&gt;layer height retrieval algorithms were very time-consuming and therefore not suitable for near-real-time applications like aviation control, although the SO&lt;sub&gt;2&lt;/sub&gt; LH is essential for accurate determination of SO&lt;sub&gt;2&lt;/sub&gt; emitted by volcanic eruptions.&lt;/p&gt;&lt;p&gt;In this presentation, we will present the latest FP_ILM algorithm improvements and show results of recent volcanic eruptions.&lt;/p&gt;&lt;p&gt;The SO&lt;sub&gt;2&lt;/sub&gt; layer height product for Sentinel-5p/TROPOMI is developed in the framework of the SO&lt;sub&gt;2&lt;/sub&gt;&amp;#160;Layer Height (S5P+I: SO&lt;sub&gt;2&lt;/sub&gt; LH)&amp;#160;project, which is part of ESA Sentinel-5p+ Innovation project (S5P+I). The S5P+I project aims to develop novel scientific and operational products to exploit the potential of the S5P/TROPOMI capabilities. The S5P+I: SO&lt;sub&gt;2&lt;/sub&gt; LH&amp;#160;project is dedicated to the generation of an SO&lt;sub&gt;2&lt;/sub&gt;&amp;#160;LH product and its extensive verification with collocated ground- and space-born measurements.&lt;/p&gt;


2021 ◽  
Vol 14 (5) ◽  
pp. 3673-3691
Author(s):  
Nikita M. Fedkin ◽  
Can Li ◽  
Nickolay A. Krotkov ◽  
Pascal Hedelt ◽  
Diego G. Loyola ◽  
...  

Abstract. 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.


2020 ◽  
Author(s):  
Nikita M. Fedkin ◽  
Can Li ◽  
Nickolay A. Krotkov ◽  
Pascal Hedelt ◽  
Diego G. Loyola ◽  
...  

Abstract. 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.


2021 ◽  
Author(s):  
Tom Etchells ◽  
Lucy Berthoud ◽  
Kieran Wood ◽  
Andrew Calway ◽  
Matt Watson

&lt;p&gt;Large volcanic eruptions can pose significant hazards over a range of domains. One such hazard is volcanic ash becoming suspended in the atmosphere. This can lead to significant risks to aviation, with the potential to cause severe or critical damage to jet engines. As such, the effective measurement and forecasting of ash contaminated airspace is of vital importance. Forecasts are generally produced using volcanic ash atmospheric transportation and dispersion models (ATDMs). Among the inputs to these models are eruption source parameters such as cloud-top height and cloud volume. One method of providing estimates of these source parameters, and to aid in characterising the size, shape, and distribution of a volcanic plume, is the reconstruction of the outer hull of the plume using multi-angle imagery.&lt;/p&gt;&lt;p&gt;When considering platforms for generating this imagery, satellites provide a range of advantages. These include the potential for global coverage, the wide range of viewing angles during an orbital pass, and being safely removed from any potential volcanic hazards. This method of plume reconstruction has been previously demonstrated by the authors using simulated satellite imagery of a model volcanic plume. However, the simple model plume used during this previous work was static and did not evolve with time, an assumption that is not realistic.&lt;/p&gt;&lt;p&gt;This presentation builds on the previous work and assess the efficacy of satellite imagery-based plume reconstruction under conditions closer to real-world, namely with a plume that is evolving with time. The time evolving plume model is produced via a Blender particle simulation. The images required for reconstruction are then generated at multiple user-determined time intervals and locations. A Space Carving reconstruction method is then applied to the imagery to generate the reconstructed plume. Performance and reconstruction accuracies are investigated by comparison of the reconstructed plume with the &amp;#8216;ground-truth&amp;#8217; simulation model. The impacts of a range of variables on the reconstruction performance are investigated, including plume size, imager properties, satellite orbit, and the use of additional satellites. The accuracy of the Blender plume simulation is compared with more mature plume simulations such as the University of Bristol PlumeRise model. These comparison models were not themselves used for the reconstruction process due to issues with the generation of accurate imagery.&lt;/p&gt;&lt;p&gt;The improved simulation environment presented in this work further demonstrates the efficacy of a satellite-based reconstruction process for the measurement and forecasting of volcanic ash, potentially leading to improvements in hazard monitoring and aviation safety.&lt;/p&gt;


2018 ◽  
Vol 69 (8) ◽  
pp. 2278-2282
Author(s):  
Stelian Ioan Morariu ◽  
Letitia Doina Duceac ◽  
Alina Costina Luca ◽  
Florina Popescu ◽  
Liliana Pavel ◽  
...  

Maintaining the soil in optimal parameters is vital for mankind, given its essential role in providing the alimentary base, as well as its extremely slow formation and regeneration (hundreds or thousands of years). The direct and indirect pollution of the soil and especially its chemical pollution represent a corollary of other types of pollution, given that it is produced by solid, liquid and gaseous residues. It may be involved in a wide range of diseases (respiratory, cardiovascular, digestive, renal, haematological, osteoarticular, neurological) of allergic, infectious, degenerative or neoplastic nature, from infancy to the old age. Although there are natural causes of soil pollution (e.g. volcanic eruptions), most pollutants come from human activities, which are the most incriminated in its pollution, degradation and erosion at an accelerated pace. The growing concern of all nations for the adoption of measures to limit the chemical pollution of the soil is partially found so far in viable and effective solutions intended to combat soil contamination and degradation and ensure its restoration. Chemical industrialization leads to technical and scientific progress, but at the same time it can develop related pathologies, which means that the role of the occupational health physician is essential in ensuring prophylaxis and the early detection of occupational diseases. Besides that, the role of the pediatrician is equally precious for the detection of specific diseases caused by chemical pollutants to children, because they will develop into adults with pathological stigma.The chemical pollution of the soil is a major challenge for ecologists, given that it is an important risk factor for many types of afflictions. It requires maximum attention from civil society, health care professionals and government institutions. The specialist in occupational medicine, as well as the pediatrician bear an essential responsibility in both, prevention and treatment.


Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.


2021 ◽  
Vol 83 (2) ◽  
Author(s):  
S. Engwell ◽  
L. Mastin ◽  
A. Tupper ◽  
J. Kibler ◽  
P. Acethorp ◽  
...  

AbstractUnderstanding the location, intensity, and likely duration of volcanic hazards is key to reducing risk from volcanic eruptions. Here, we use a novel near-real-time dataset comprising Volcanic Ash Advisories (VAAs) issued over 10 years to investigate global rates and durations of explosive volcanic activity. The VAAs were collected from the nine Volcanic Ash Advisory Centres (VAACs) worldwide. Information extracted allowed analysis of the frequency and type of explosive behaviour, including analysis of key eruption source parameters (ESPs) such as volcanic cloud height and duration. The results reflect changes in the VAA reporting process, data sources, and volcanic activity through time. The data show an increase in the number of VAAs issued since 2015 that cannot be directly correlated to an increase in volcanic activity. Instead, many represent increased observations, including improved capability to detect low- to mid-level volcanic clouds (FL101–FL200, 3–6 km asl), by higher temporal, spatial, and spectral resolution satellite sensors. Comparison of ESP data extracted from the VAAs with the Mastin et al. (J Volcanol Geotherm Res 186:10–21, 2009a) database shows that traditional assumptions used in the classification of volcanoes could be much simplified for operational use. The analysis highlights the VAA data as an exceptional resource documenting global volcanic activity on timescales that complement more widely used eruption datasets.


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