Satellite and ground observations of the June 2009 eruption of Sarychev Peak volcano, Matua Island, Central Kuriles

Рыбин, А. В.; Chibisova, Marina; Webley, P. W.; Steensen, Torge; Izbekov, Pavel; Neal, Christina A.; Realmuto, V. J.

doi:10.1007/s00445-011-0481-0

Cited by 50 publications

(27 citation statements)

References 41 publications

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“…The ash particles are assumed to be spherical with a monomodal log-normal aerosol size distribution, which has been shown to be a suitable representation of the size distribution of airborne volcanic ash (Wohletz et al, 1989). The distribution is characterized by a spread of 2 (Wen and Rose, 1994b;Yu et al, 2002;Rybin et al, 2011;Pavolonis et al, 2013b), and the mode radius is translated to obtain different effective radii. The refractive index used in this paper is from measurements of ash from the Eyjafjallajökull eruption (Peters, 2010).…”

Section: Forward Model Descriptionmentioning

confidence: 99%

Retrieval of ash properties from IASI measurements

et al. 2016

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Abstract. A new optimal estimation algorithm for the retrieval of volcanic ash properties has been developed for use with the Infrared Atmospheric Sounding Interferometer (IASI). The retrieval method uses the wave number range 680-1200 cm −1 , which contains window channels, the CO 2 ν 2 band (used for the height retrieval), and the O 3 ν 3 band.Assuming a single infinitely (geometrically) thin ash plume and combining this with the output from the radiative transfer model RTTOV, the retrieval algorithm produces the most probable values for the ash optical depth (AOD), particle effective radius, plume top height, and effective radiating temperature. A comprehensive uncertainty budget is obtained for each pixel. Improvements to the algorithm through the use of different measurement error covariance matrices are explored, comparing the results from a sensitivity study of the retrieval process using covariance matrices trained on either clear-sky or cloudy scenes. The result showed that, due to the smaller variance contained within it, the clear-sky covariance matrix is preferable. However, if the retrieval fails to pass the quality control tests, the cloudy covariance matrix is implemented.The retrieval algorithm is applied to scenes from the Eyjafjallajökull eruption in 2010, and the retrieved parameters are compared to ancillary data sources. The ash optical depth gives a root mean square error (RMSE) difference of 0.46 when compared to retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) instrument for all pixels and an improved RMSE of 0.2 for low optical depths (AOD < 0.1). Measurements from the Facility for Airborne Atmospheric Measurements (FAAM) and Deutsches Zentrum für Luft-und Raumfahrt e.V. (DLR) flight campaigns are used to verify the retrieved particle effective radius, with the retrieved distribution of sizes for the scene showing excellent consistency. Further, the plume top altitudes are compared to derived cloud-top altitudes from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and show agreement with RMSE values of less than 1 km.

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Section: Forward Model Descriptionmentioning

confidence: 99%

Retrieval of ash properties from IASI measurements

et al. 2016

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“…A major eruption of the volcano occurred between 11 and 16 June 2009; the eruption is described by Rybin et al (2011) and a detailed chronology of the explosion is given by Matoza et al (2011). The most energetic phases of the eruption were between 12 June and 16 June.…”

Section: Sarychev June 2009mentioning

confidence: 99%

Observations of volcanic SO<sub>2</sub> from MLS on Aura

et al. 2015

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Abstract. Sulfur dioxide (SO 2 ) is an important atmospheric constituent, particularly in the aftermath of volcanic eruptions. These events can inject large amounts of SO 2 into the lower stratosphere, where it is oxidised to form sulfate aerosols; these in turn have a significant effect on the climate. The MLS instrument on the Aura satellite has observed the SO 2 mixing ratio in the upper troposphere and lower stratosphere from August 2004 to the present, during which time a number of volcanic eruptions have significantly affected those regions of the atmosphere. We describe the MLS SO 2 data and how various volcanic events appear in the data. As the MLS SO 2 data are currently not validated we take some initial steps towards their validation. First we establish the level of internal consistency between the three spectral regions in which MLS is sensitive to SO 2 . We compare SO 2 column values calculated from MLS data to total column values reported by the OMI instrument. The agreement is good (within about 1 DU) in cases where the SO 2 is clearly at altitudes above 147 hPa.

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“…Augustine volcano Power et al, 2006;Webley et al, 2010), Okmok volcano (Larsen et al, 2009), Kasatochi volcano (Waythomas et al, 2010) and Sarychev Peak volcano (Rybin et al, 2011), where real-time monitoring was critical for understanding volcanic activity. These historic eruptions are utilized to assess the impact of SAR products on the decisionmaking made for these events.…”

Section: Assessment Of the Benefit Of Integrating Sar Into Operation mentioning

confidence: 99%

Data Processing Concepts for the Integration of Sar Into Operational Volcano Monitoring Systems

Meyer

McAlpin

Gong

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Remote Sensing plays a critical role in operational volcano monitoring due to the often remote locations of volcanic systems and the large spatial extent of potential eruption pre-cursor signals. Despite the all-weather capabilities of radar remote sensing and despite its high performance in monitoring change, the contribution of radar data to operational monitoring activities has been limited in the past. This is largely due to (1) the high data costs associated with radar data, (2) the slow data processing and delivery procedures, and (3) the limited temporal sampling provided by spaceborne radars. With this paper, we present new data processing and data integration techniques that mitigate some of the above mentioned limitations and allow for a meaningful integration of radar remote sensing data into operational volcano monitoring systems. The data integration concept presented here combines advanced data processing techniques with fast data access procedures in order to provide high quality radar-based volcano hazard information at improved temporal sampling rates. First performance analyses show that the integration of SAR can significantly improve the ability of operational systems to detect eruptive precursors. Therefore, the developed technology is expected to improve operational hazard detection, alerting, and management capabilities.

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Satellite and ground observations of the June 2009 eruption of Sarychev Peak volcano, Matua Island, Central Kuriles

Abstract: After 33 years of repose, one of the most active volcanoes of the Kurile island arc-Sarychev

Cited by 50 publications

References 41 publications

Retrieval of ash properties from IASI measurements

Retrieval of ash properties from IASI measurements

Observations of volcanic SO<sub>2</sub> from MLS on Aura

Data Processing Concepts for the Integration of Sar Into Operational Volcano Monitoring Systems

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Satellite and ground observations of the June 2009 eruption of Sarychev Peak volcano, Matua Island, Central Kuriles

Abstract: After 33 years of repose, one of the most active volcanoes of the Kurile island arc-Sarychev

Cited by 50 publications

References 41 publications

Retrieval of ash properties from IASI measurements

Retrieval of ash properties from IASI measurements

Observations of volcanic SO&lt;sub&gt;2&lt;/sub&gt; from MLS on Aura

Data Processing Concepts for the Integration of Sar Into Operational Volcano Monitoring Systems

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Product

Resources

About

Observations of volcanic SO<sub>2</sub> from MLS on Aura