Mapping lava flows from Nyamuragira volcano (1967-2011) with satellite data and automated classification methods

The volume, location and extent of historical lava flows are important when assessing volcanic hazards, as well as the productivity or longevity of a volcanic system. We use a Landsat/Hyperion/ALI dataset and automated classification methods to map lava flows at Nyamuragira volcano (1967-2011) in the Democratic Republic of the Congo. The humid tropical climate ofNyamuragira is advantageous because its lava flows are emplaced onto heavily forested flanks, resulting in strong contrast between lava and vegetation, which contributes to efficient flow mapping. With increasing age, there is an increase in Landsat band-4 reflectance, suggesting lava flow revegetation with time. This results in a distinct spectral contrast to delineate overlapping flows emplaced ~ 5 years apart. Areal extents of the flows are combined with published lava flow thicknesses to derive volumes. The Landsat/Hyperion/ALI dataset is advantageous for mapping future flows quickly and inexpensively, particularly for volcano observatories where resources are limited

First synoptic analysis of volcanic degassing in Papua New Guinea

We report the first satellite-based survey of volcanic sulphur dioxide (SO2) degassing in Papua New Guinea, using Ozone Monitoring Instrument (OMI) data. OMI is sensitive to low-level passive degassing. These observations are useful for volcano monitoring, hazard assessment (particularly aviation hazard) and assessment of arc geochemical budgets and are of immense value in remote regions with little ground-based instrumentation, such as Papua New Guinea. We identify Bagana, Manam, Rabaul, Ulawun and Langila as the active sources of volcanic SO2 in Papua New Guinea, with Bagana being the largest source. We present an OMI SO2 time series for 2005–2008 and a total detected regional output of ∼1.8 × 109 kg SO2. About 40% of emissions were released by major eruption events at Manam (January 2005), Bagana (June 2006) and Rabaul (October 2006). Over the past century however, we estimate that major explosive eruptions contribute <5% of the arc-scale SO2 emission budget. Ground-based DOAS measurements of SO2 degassing at five of Papua New Guinea's volcanoes are compared with our OMI observations. The total OMI SO2 output is only ∼20% of the total extrapolated from DOAS, a discrepancy which we demonstrate is consistent with other volcanic arcs. Therefore, the true total regional SO2 output may be considerably higher than that detected by OMI. Uncertainties in the OMI SO2 data include the effects of in-plume chemical processing and dilution of SO2 prior to the satellite overpass, OMI's reduced sensitivity to low levels of SO2 in the planetary boundary layer and interference by meteorological clouds

First observations of volcanic eruption clouds from the L1 Earth-Sun Lagrange point by DSCOVR/EPIC

Volcanic sulfur dioxide (SO2) emissions have been measured by ultraviolet sensors on polar‐orbiting satellites for several decades but with limited temporal resolution. This precludes studies of key processes believed to occur in young (~1–3 hr old) volcanic clouds. In 2015, the launch of the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) provided an opportunity for novel observations of volcanic eruption clouds from the first Earth‐Sun Lagrange point (L1). The L1 vantage point provides continuous observations of the sunlit Earth, offering up to eight or nine observations of volcanic SO2 clouds in the DSCOVR/EPIC field of view at ~1‐hr intervals. Here we demonstrate DSCOVR/EPIC\u27s sensitivity to volcanic SO2 using several volcanic eruptions from the tropics to midlatitudes. The hourly cadence of DSCOVR/EPIC observations permits more timely measurements of volcanic SO2 emissions, improved trajectory modeling, and novel analyses of the temporal evolution of volcanic clouds

Characterising volcanic cycles at Soufriere Hills Volcano, Montserrat: Time series analysis of multi-parameter satellite data

The identification of cyclic volcanic activity can elucidate underlying eruption dynamics and aid volcanic hazard mitigation. Whilst satellite datasets are often analysed individually, here we exploit the multi-platform NASA A-Train satellite constellation to cross-correlate cyclical signals identified using complementary measurement techniques at Soufriere Hills Volcano (SHV), Montserrat. In this paper we present a Multi-taper (MTM) Fast Fourier Transform (FFT) analysis of coincident SO2 and thermal infrared (TIR) satellite measurements at SHV facilitating the identification of cyclical volcanic behaviour. These measurements were collected by the Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) (respectively) in the A-Train. We identify a correlating cycle in both the OMI and MODIS data (54–58 days), with this multi-week feature attributable to episodes of dome growth. The ~ 50 day cycles were also identified in ground-based SO2 data at SHV, confirming the validity of our analysis and further corroborating the presence of this cycle at the volcano. In addition a 12 day cycle was identified in the OMI data, previously attributed to variable lava effusion rates on shorter timescales. OMI data also display a one week (7–8 days) cycle attributable to cyclical variations in viewing angle resulting from the orbital characteristics of the Aura satellite. Longer period cycles possibly relating to magma intrusion were identified in the OMI record (102-, 121-, and 159 days); in addition to a 238-day cycle identified in the MODIS data corresponding to periodic destabilisation of the lava dome. Through the analysis of reconstructions generated from cycles identified in the OMI and MODIS data, periods of unrest were identified, including the major dome collapse of 20th May 2006 and significant explosive event of 3rd January 2009. Our analysis confirms the potential for identification of cyclical volcanic activity through combined analysis of satellite data, which would be of particular value at poorly monitored volcanic systems

New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO2 dataset: Algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new-generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of predefined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13 km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50% reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (∼1700 kt total SO2/ and Sierra Negra in 2005 (\u3e 1100DU maximum SO2/, OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments

Improving global detection of volcanic eruptions using the Ozone Monitoring Instrument (OMI)

Volcanic eruptions pose an ever-present threat to human populations around the globe, but many active volcanoes remain poorly monitored. In regions where ground-based monitoring is present the effects of volcanic eruptions can be moderated through observational alerts to both local populations and service providers, such as air traffic control. However, in regions where volcano monitoring is limited satellite-based remote sensing provides a global data source that can be utilised to provide near-real-time identification of volcanic activity. This paper details a volcanic plume detection method capable of identifying smaller eruptions than is currently feasible, which could potentially be incorporated into automated volcanic alert systems. This method utilises daily, global observations of sulfur dioxide (SO2) by the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite. Following identification and classification of known volcanic eruptions in 2005–2009, the OMI SO2 data, analysed using a logistic regression analysis, permitted the correct classification of volcanic events with an overall accuracy of over 80 %. Accurate volcanic plume identification was possible when lower-tropospheric SO2 loading exceeded ∼ 400 t. The accuracy and minimal user input requirements of the developed procedure provide a basis for incorporation into automated SO2 alert systems

The impact of satellite sensor viewing geometry on time-series analysis of volcanic emissions

Time-series analysis techniques are being increasingly used to process satellite observations of volcanic gas emissions and heat flux, with the aim of identifying cyclic behaviour that could inform hazard assessment or elucidate volcanic processes. However, it can be difficult to distinguish cyclic variations due to geophysical processes from those that are artefacts of the observation technique. Here, we conduct a comprehensive investigation into the origin of cyclicity in volcanic observations by analysing daily, global satellite measurements of volcanic SO2 loading by the Ozone Monitoring Instrument (OMI) and thermal infrared anomalies detected by the Moderate Resolution Imaging Spectroradiometer (MODIS). We use a fast Fourier Transform (FFT) multi-taper method (MTM) to analyse multiple phases of activity at 32 target volcanoes, utilising measurements obtained from three NASA satellite instruments (Aura – OMI, Aqua – MODIS and Terra – MODIS), and identify a common cycle (period of ~ 2.3 days), which is not considered to be of volcanic origin. Based on the presence of this cycle in multiple satellite datasets, we attribute it to variations in viewing angle during the 16-day orbit repeat cycle of sun-synchronous satellites maintained in Low Earth Orbit (LEO). A 5-point averaging correction procedure is tested on satellite observations from Kilauea volcano, Hawaii, and is found to reduce the impact of higher frequency cycles and reveal the presence of longer-period geophysical signals. In addition to the identification of a signal common to different measurement techniques, an underlying cyclical pattern was found in the OMI SO2 observations (periods of ~ 7.9 and 3.2 days) generated by the OMI Row Anomaly (ORA). We conclude that identification of the presence and magnitude of non-geophysical cyclic behaviour, which can suppress natural cycles in time-series data, and implementation of appropriate corrections, is crucial for accurate interpretation of satellite observations. The use of data averaging to suppress non-geophysical cycles imposes limits on the length of natural cycles that can be confidently identified in moderate resolution satellite observations from polar-orbiting spacecraft

The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design

The Stratospheric Sulfur and its Role in Climate (SSiRC) Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP) explores uncertainties in the processes that connect volcanic emission of sulfur gas species and the radiative forcing associated with the resulting enhancement of the stratospheric aerosol layer. The central aim of ISA-MIP is to constrain and improve interactive stratospheric aerosol models and reduce uncertainties in the stratospheric aerosol forcing by comparing results of standardized model experiments with a range of observations. In this paper we present four co-ordinated inter-model experiments designed to investigate key processes which influence the formation and temporal development of stratospheric aerosol in different time periods of the observational record. The Background (BG) experiment will focus on microphysics and transport processes under volcanically quiescent conditions, when the stratospheric aerosol is controlled by the transport of aerosols and their precursors from the troposphere to the stratosphere. The Transient Aerosol Record (TAR) experiment will explore the role of small- to moderate-magnitude volcanic eruptions, anthropogenic sulfur emissions, and transport processes over the period 1998–2012 and their role in the warming hiatus. Two further experiments will investigate the stratospheric sulfate aerosol evolution after major volcanic eruptions. The Historical Eruptions SO2 Emission Assessment (HErSEA) experiment will focus on the uncertainty in the initial emission of recent large-magnitude volcanic eruptions, while the Pinatubo Emulation in Multiple models (PoEMS) experiment will provide a comprehensive uncertainty analysis of the radiative forcing from the 1991 Mt Pinatubo eruption

Version 2 Ozone Monitoring Instrument SO2 product (OMSO2 V2): New anthropogenic SO2 vertical column density dataset

The Ozone Monitoring Instrument (OMI) has been providing global observations of SO2 pollution since 2004. Here we introduce the new anthropogenic SO2 vertical column density (VCD) dataset in the version 2 OMI SO2 product (OMSO2 V2). As with the previous version (OMSO2 V1.3), the new dataset is generated with an algorithm based on principal component analysis of OMI radiances but features several updates. The most important among those is the use of expanded lookup tables and model a priori profiles to estimate SO2 Jacobians for individual OMI pixels, in order to better characterize pixel-to-pixel variations in SO2 sensitivity including over snow and ice. Additionally, new data screening and spectral fitting schemes have been implemented to improve the quality of the spectral fit. As compared with the planetary boundary layer SO2 dataset in OMSO2 V1.3, the new dataset has substantially better data quality, especially over areas that are relatively clean or affected by the South Atlantic Anomaly. The updated retrievals over snow/ice yield more realistic seasonal changes in SO2 at high latitudes and offer enhanced sensitivity to sources during wintertime. An error analysis has been conducted to assess uncertainties in SO2 VCDs from both the spectral fit and Jacobian calculations. The uncertainties from spectral fitting are reflected in SO2 slant column densities (SCDs) and largely depend on the signal-to-noise ratio of the measured radiances, as implied by the generally smaller SCD uncertainties over clouds or for smaller solar zenith angles. The SCD uncertainties for individual pixels are estimated to be 0.15-0.3DU (Dobson units) between 40 S and 40 N and to be 0.2-0.5DU at higher latitudes. The uncertainties from the Jacobians are approximately 50 %-100% over polluted areas and are primarily attributed to errors in SO2 a priori profiles and cloud pressures, as well as the lack of explicit treatment for aerosols. Finally, the daily mean and median SCDs over the presumably SO2-free equatorial east Pacific have increased by only 0.0035DU and 0.003DU respectively over the entire 15-year OMI record, while the standard deviation of SCDs has grown by only 0.02DU or 10%. Such remarkable long-term stability makes the new dataset particularly suitable for detecting regional changes in SO2 pollution

Estimation of anthropogenic and volcanic SO2 emissions from satellite data in the presence of snow/ice on the ground

Early versions of satellite nadir-viewing UV SO2 data products did not explicitly account for the effects of snow/ice on retrievals. Snow-covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses. This leads to increased uncertainties of satellite emission estimates and potential seasonal biases due to the lack of data in winter months for some high-latitudinal sources. In this study, we investigated how Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite SO2 measurements over snow-covered surfaces can be used to improve the annual emissions reported in our SO2 emissions catalogue (version 2; Fioletov et al., 2023). Only 100 out of 759 sources listed in the catalogue have 10 % or more of the observations over snow. However, for 40 high-latitude sources, more than 30 % of measurements suitable for emission calculations were made over snow-covered surfaces. For example, in the case of Norilsk, the world's largest SO2 point-source, annual emission estimates in the SO2 catalogue were based only on 3–4 summer months, while the addition of data for snow conditions extends that period to 7 months. Emissions in the SO2 catalogue were based on satellite measurements of SO2 slant column densities (SCDs) that were converted to vertical column densities (VCDs) using site-specific clear-sky air mass factors (AMFs), calculated for snow-free conditions. The same approach was applied to measurements with snow on the ground whereby a new set of constant, site-specific, clear sky with snow AMFs was created, and these were applied to the measured SCDs. Annual emissions were then estimated for each source considering (i) only clear-sky and snow-free days, (ii) only clear-sky with snow days, and (iii) a merged dataset (snow and snow-free conditions). For individual sources, the difference between emissions estimated for snow and snow-free conditions is within ±20 % for three-quarters of smelters and oil and gas sources and with practically no systematic bias. This is excellent consistency given that there is typically a factor of 3–5 difference between AMFs for snow and snow-free conditions. For coal-fired power plants, however, emissions estimated for snow conditions are on average 25 % higher than for snow-free conditions; this difference is likely real and due to larger production (consumption of coal) and emissions in wintertime.</p