Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-206, 2016 Manuscript under review for journal Atmos. Meas. Tech. Published: 4 July 2016 c Author(s) 2016. CC-BY 3.0 License.
Improving automated global detection of volcanic SO2 plumes using the Ozone Monitoring Instrument (OMI) Verity J. B. Flower, Thomas Oommen, Simon A. Carn Dept. Geological and Mining Engineering and Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, 5
MI 49931, USA Correspondence to: Verity J. B. Flower (
[email protected])
Abstract. 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 10
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 the development of an automated volcanic plume detection method utilizing daily, global observations of sulphur 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
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data are analysed using a logistic regression analysis which permits the identification of volcanic events with an overall accuracy of over 80%, and consistent plume identification when the volcanic plume SO2 loading exceeds ~400 tons. The accuracy and minimal user input requirements of the developed procedure provide a basis for the creation of an automated SO2 alert system providing volcanic alerts in regions where ground based volcano monitoring capabilities are limited. The technique could easily be adapted for use with satellite measurements of volcanic SO 2 emissions from other platforms.
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1 Introduction Volcanic eruptions pose a global hazard due to the potential for emissions to be entrained into the upper atmosphere and transported globally. The addition of these particles can result in significant impacts locally as fine particulate matter in the atmosphere can cause significant health problems (Delmelle et al., 2002, Hansell & Oppenheimer, 2004) and impacts to the aviation industry (Miller & Casadevall, 1999; Prata, 2009) in addition to alterations to the radiative transfer rates through the
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atmosphere on a global scale as seen following the eruption of Mt Pinatubo (Self et al., 1993), in order to mitigate the possible impacts of volcanic eruptions timely warning of events are essential. The installation of a global network of ground-based monitoring stations would be both costly and impractical however satellite-based monitoring provides the spatial and temporal coverage necessary to facilitate the near-real time (NRT) monitoring of global volcanism (Brenot et al., 2014). Existing techniques employ a threshold approach in order to identify volcanic eruptions however this limits the capabilities in regards
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to smaller events and can be susceptible to the effect of high background noise levels. The following work outlines a method 1
Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-206, 2016 Manuscript under review for journal Atmos. Meas. Tech. Published: 4 July 2016 c Author(s) 2016. CC-BY 3.0 License.
for the identification of volcanic plumes utilising a background correction factor. The resulting output was processed with a binary classification algorithm in order to identify the strength of the developed methodology to distinguish volcano events from control samples. The Ozone Monitoring Instrument (OMI), launched on NASA’s Aura satellite in July 2004, provides near global daily 5
monitoring of multiple atmospheric trace gases with absorption bands in the ultraviolet (UV) spectral band, and was designed to supersede the Total Ozone Monitoring Spectrometer (TOMS) instrument. Due to its strong absorption bands in the UV, sulphur dioxide (SO2) can be discerned by instruments designed to measure ozone (Krueger, 1983). The ability of satellitebased ozone monitoring instrumentation to detect and monitor volcanic SO2 emissions was first demonstrated by the identification of the eruption plume of El Chichón in 1982 (Krueger, 1983), which led to the implementation of satellite based
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UV measurements as a volcano monitoring tool (Schneider et al., 1999; Krueger et al., 2008). The low spatial resolution of the TOMS instruments precluded the measurement of SO2 in all but the largest volcanic eruptions (Carn et al., 2003), but OMI’s higher spatial resolution (13 x 24 km at nadir) permits detection of smaller eruptions and passive volcanic degassing of SO 2, whilst providing daily global coverage (Krotkov et al., 2006; Carn et al., 2013, 2016). This work utilises the continuous global coverage of OMI to identify and automatically classify volcanic eruption events based on common characteristics.
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1.1 Existing alert systems An operational alert system known as the Support to Aviation Control Service (SACS) is currently employed in the assessment of SO2 and ash emitted from volcanoes (Brenot et al., 2012). This service provides near real time (NRT) alerts of anomalously high SO2 amounts and ash indices recorded by three UV instruments; OMI and Global Ozone Monitoring Experiment-2 (GOME-2; maintained on-board two meteorological satellites MetOp-A and MetOp-B) and three infrared (IR) instruments;
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the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI; also flown on the MetOpA
and
B
platforms).
The
method
of
SO2
alert
generation
used
by
SACS
(Brenot
et
al.
2014;
http://sacs.aeronomie.be/info/index.php) involves the initial identification of an anomalously high SO 2 column amount (>2 DU). When a pixel is flagged the area is analysed in greater detail and an alert is only generated if more than half of the neighbouring pixels also display high SO2 values (>2 DU). The technique developed by Brenot et al. (2014) is subject to 25
certain limitations when utilising UV data including; the systematic noise in the data leading to false alerts and the restriction of retrievals to those that assume a SO2 plume altitude in the lower stratosphere (STL). Therefore, in the development of an algorithm based on OMI data we aim to account for variable background SO 2 levels and systematic noise, in addition to using SO2 retrievals assuming a lower plume altitude in an attempt to resolve plumes with lower SO2 amounts, lower injection altitude and more diffuse characteristics.
2
Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-206, 2016 Manuscript under review for journal Atmos. Meas. Tech. Published: 4 July 2016 c Author(s) 2016. CC-BY 3.0 License.
2 Methodology 2.1 Data collection OMI Level 2 total column SO2 (OMSO2) data are publicly available from NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC; http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/omso2_v003.shtml). These data 5
provide global coverage with a temporal resolution of 1 day at low latitudes and increasing daily observations towards the poles, where measurement swaths overlap. OMSO2 data currently provide volcanic SO2 total column amounts calculated using a linear fit (LF) algorithm (Yang et al., 2007) for three distinct layers of the atmosphere; corresponding to centre of mass altitudes (CMA) of approximately 3 km (lower troposphere; TRL), 8 km (mid-troposphere; TRM) and 17 km (lower stratosphere; STL). These altitudes are based upon atmospheric pressure levels and therefore can display slight variations
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depending upon the local conditions such as temperature profile (Carn et al., 2013). In order to obtain an accurate estimation of the SO2 column amount the appropriate retrieval must be selected based upon the known or inferred injection altitude of the volcanic plume (Yang et al., 2007), which can be poorly constrained particularly in remote regions with minimal or no monitoring capabilities (Sparks, 2012). Differences between the altitude assumed in the LF algorithm and the true altitude of the plume can lead to errors of up to 20%, provided the assumption is approximately correct (Yang et al., 2007). Due to the
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focus of this work on a variety of plumes displaying diverse eruptive characteristics, we use SO2 data from the TRL OMSO2 product in order to facilitate identification of eruptions confined to the lower troposphere. The use of one retrieval altitude reduces the need for user input or prior knowledge of the injection altitude of the plume however results in the overestimation of plume mass for features injected above the retrieval altitude therefore this method is for identification and alert purposes as opposed to accurate plume mass calculation. Previous works have provided in depth descriptions of the OMI retrieval
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algorithms (Carn et al., 2013; Krotkov et al., 2006; McCormick et al., 2013; Yang et al., 2007) with a proven track record in the assessment of volcanic and anthropogenic emissions including identification of volcanic plume sources (e.g., Carn et al., 2008; McCormick et al., 2012; Carn et al., 2013, 2016; McCormick et al., 2013), volcanic plume tracking (e.g., Carn and Prata 2010; Krotkov et al., 2010; Lopez et al., 2013) and identification of copper smelter emissions (Carn et al., 2007) and other large SO2 emission sources (e.g., Fioletov et al., 2011, 2013).
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OMI data collected since 2008 are influenced by a row anomaly (the OMI row anomaly; ORA) which results in data gaps in particular rows along the OMI measurement swath. Information on the status of this anomaly is provided by the Royal Netherlands Meteorological Institute (www.knmi.nl/omi/). The ORA data gaps combined with the variation in viewing angle produced by the 16-day orbital cycle of the Aura satellite results in varying influence on OMI SO2 measurements (Flower et al., 2016). Any eruptions identified after the formation of the ORA were investigated with greater scrutiny and excluded where
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the effect was significant.
3
Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-206, 2016 Manuscript under review for journal Atmos. Meas. Tech. Published: 4 July 2016 c Author(s) 2016. CC-BY 3.0 License.
2.2 Volcanic plume quantification As a test dataset for our plume identification technique, we identified 79 volcanic eruptions at 27 different volcanoes (Table 1) using the Volcanoes of the World (VOTW) database curated by the Smithsonian Institution’s Global Volcanism Program (GVP; http://volcano.si.edu/). Note that, as a result of the way in which eruptions are defined in the VOTW database, several 5
of the eruptions listed in Table 1 actually correspond to the onset of extended periods of volcanic activity, rather than discrete eruptions. For each identified eruption, total SO2 mass detected by OMI was obtained for the registered day of the eruptive event (or the start of the period of unrest) with the preceding and subsequent days analysed where no corresponding plume could be identified on the reported day of eruption. This allowance accounts for any inaccuracies in the assigned eruption date, and allows for the identification of eruption plumes generated after the Aura overpass time (~1345 local time) resulting in a
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delay in detection. Identification and quantification of volcanic SO2 emissions is complicated by the presence of variable biases and noise levels in the data. These variations are influenced by several factors including the latitude of the volcano, time of year, proximity to pollution sources, and the presence of meteorological clouds (Krotkov et al., 2006; Yang et al., 2007). In our analysis, three methods (M1, M2, and M3; Table 1) were used to quantify the SO 2 loading detected at each location, with the goal of distinguishing volcanic SO2 from background noise. The procedures were developed with the intention of
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allowing the calculation of volcanic SO2 loading with minimal user input, reducing the possible effects of human error in the classification of what constitutes the bounds of an identified plume. Method 1 (M1) and Method (M2) differ only in the geographic extent over which OMI SO 2 columns are integrated to obtain total SO2 mass (Fig. 1). For each eruption analysed, M1 calculates integrated SO2 mass in a 4°×4° box centred over the volcano location (thus capturing plumes regardless of wind direction). The 4°×4° box encompasses an area which captures most small-
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moderate volcanic plumes with few instances of dispersion of emissions outside the region; however, this relatively large sample area also potentially includes increased background noise, which could generate false alerts in locations with higher noise levels. As an alternative to M1, M2 uses a 2°×2° region which, whilst more susceptible to possible plume dispersion beyond the defined limits, is less influenced by noise contamination (Fig. 1). Manual inspection indicated that plume dispersion beyond the defined geographic limits was only an issue for the largest eruptions in Table 2. Figure 1 shows an example of a
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small volcanic SO2 plume at Piton de la Fournaise volcano (Réunion); here, the M2 region captures most of the SO 2 plume that is visually apparent, only excluding some very diffuse SO 2 further downwind that is included in the M1 region. A third method (M3) was developed in an attempt to intrinsically account for the variable noise levels in SO 2 data collected in different geographic regions (Carn et al. 2013). We posit that in order to effectively develop a volcanic plume detection methodology without a significant number of false alerts a background noise correction may be necessary. Our technique is
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analogous to contextual thermal infrared (TIR) anomaly detection procedures used at active volcanoes, where a background radiance value is calculated as a reference against which anomalously high radiance values can be compared (e.g., Wright et al. 2002; Murphy et al., 2011). In the M3 method, the 2°×2° region (M2) is considered the active emission region with a background SO2 offset value derived from the total SO2 mass in the 4°×4° M1 region (Eq. 1). 4
Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-206, 2016 Manuscript under review for journal Atmos. Meas. Tech. Published: 4 July 2016 c Author(s) 2016. CC-BY 3.0 License.
𝑀3 = 𝑀2 −
𝑀1−𝑀2
(1)
3
Eruptive events that post-date the appearance of the ORA were manually assessed in order to identify whether the ORA data gap significantly impacted the detection of SO2, such as complete masking of the plume in extreme cases (Flower et al., in prep). Additional factors impacting the selection of eruptive events are the presence of meteorological clouds, which can 5
effectively mask any volcanic plume at lower altitudes from a satellite-based sensor (Carn et al., 2013; Krotkov et al., 2006), and the seasonal variation in UV radiation at high latitudes. Cloud masking is due to the high UV albedo of clouds and this, coupled with low UV irradiance, can make SO2 detection at high latitudes during winter months particularly challenging (Telling et al., 2015). Hence the majority of the eruptions analysed here are located at latitudes below 30°. 2.3 Control samples
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A control group is required to assess whether volcanic eruptions can be distinguished from background SO 2 levels. Therefore, for each volcanic eruption analysed (Table 2) a control SO2 mass was calculated using each of the three incorporated methodologies (M1, M2 and M3) for a second date at the same volcano. Assignment of control group analysis dates was limited to a period between 1st January 2005 and 31st December 2009. The 2009 cut-off date was employed due to the increasing influence of the ORA after this time, in an attempt to reduce the influence of data gaps on the model output. Control dates
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were assigned for comparison with each identified volcanic eruption, using an online random number generator (Haahr, 2015; http://www. random.org) to assign a value between 0 and 1825 to each data point. These random values were used to determine the number days from the beginning of the analysis period at which to assign a control date (Table 2). The identified dates were then assigned to each target volcano alphabetically, with a corresponding number of events assigned to each location as number of volcanic eruption analyses performed (Table 2).
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2.4 Modelling techniques Modelling procedures were conducted with the Weka 3 software package; a collection of algorithms that can be implemented for data mining tasks (Hall et al., 2009) provided by the University of Waikato (http://www.cs.waikato.ac.nz/ml/weka/). Significant differences in measured SO2 mass were found between the samples due to variations in eruption magnitude, background noise levels and SO2 emission strength displayed by the incorporated volcanoes preventing the calculation of a
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flat emission threshold for the classification of the eruptive events. Within the Weka 3 package, a simple logistic regression analysis (Eq. 2) was found to be an effective technique for the classification of volcanic and non-volcanic events. Simple logistic regression is a binary classification technique, here defining volcanic (v) and non-volcanic control (c) events facilitating the development of a linear model constructed from a transformed target variable (Witten & Frank, 2005). The logistic regression equation used here assigns the probability P of the occurrence
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of a volcanic eruption or degassing event; 𝑃 =1−
1 1+𝑒 −(𝑎+𝑏𝑋)
,
(2) 5
Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2016-206, 2016 Manuscript under review for journal Atmos. Meas. Tech. Published: 4 July 2016 c Author(s) 2016. CC-BY 3.0 License.
where e is the base of the natural logarithm, a is the probability when the independent variable (X) is equal to zero, and b represents the rate at which probabilities vary with incremental changes in X, which in this case is the volcanic plume SO2 mass measured in tons. Output of a logistic regression analysis is assessed against a series of validation statistics that test the accuracy of the generated 5
model. These statistics include overall accuracy, precision and recall, in addition to Receiver Operating Characteristic (ROC) curves. In this analysis, the overall accuracy relates to the percentage of c,orrectly classified events in both the volcanic and control (non-volcanic) samples; however, this statistic alone cannot account for preferential classification of one sample over another (Oommen et al. 2010). Hence precision and recall statistics, characterised by values between 0 and 1, are incorporated in order to identify whether preferential classification is occurring. Precision relates to the accuracy of prediction of a single
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sample group (volcanic or non-volcanic) whilst recall measures the effectiveness of the predictions themselves (Oommen, et al. 2010). In the context of this study, if a volcanic classification has a precision of 0.9, then 90% of the events predicted as being volcanic in nature are volcanic events, whilst the remaining 10% are misclassified as non-volcanic and will be termed here as ‘missed alerts’. In contrast, a recall value of 0.8 would correspond to 80% of observed volcanic events being correctly classified, but this does not take into account any non-volcanic events which are misclassified as volcanic, referred to here as
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‘false alerts’. The final validation statistic used here is the ROC curve, which represents a method for assessing the rate of accurately classified events against possible falsely classified events. ROC values relate to the accuracy of the classification system implemented with a value of 1 indicating accurate prediction of all events (Oommen et al., 2010; Witten & Frank, 2005). Logistic regression model calculation was conducted using the k-fold cross validation technique incorporated into the Weka 3
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software package. This method segregates the data into k partitions, allowing k-1 folds of the data to be used as a training set with the remaining data used for validation purposes. This method is then repeated with each of the k partitions being used to validate the corresponding model from which it was withheld, with the final statistics comprising an average of the output of all k models (Oommen et al. 2010). We implement a k value of 10 due to the associated reduction in bias compared to k values
