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| Titel |
piscope - A Python based software package for the analysis of volcanic SO2 emissions using UV SO2 cameras |
| VerfasserIn |
Jonas Gliss, Kerstin Stebel, Arve Kylling, Anna Solvejg Dinger, Holger Sihler, Aasmund Sudbø |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250148774
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| Publikation (Nr.) |
 EGU/EGU2017-13061.pdf |
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| Zusammenfassung |
| UV SO2 cameras have become a common method for monitoring SO2 emission rates from
volcanoes. Scattered solar UV radiation is measured in two wavelength windows, typically
around 310 nm and 330 nm (distinct / weak SO2 absorption) using interference filters. The
data analysis comprises the retrieval of plume background intensities (to calculate
plume optical densities), the camera calibration (to convert optical densities into SO2
column densities) and the retrieval of gas velocities within the plume as well as the
retrieval of plume distances. SO2 emission rates are then typically retrieved along a
projected plume cross section, for instance a straight line perpendicular to the plume
propagation direction. Today, for most of the required analysis steps, several alternatives
exist due to ongoing developments and improvements related to the measurement
technique.
We present piscope, a cross platform, open source software toolbox for the analysis of UV
SO2 camera data. The code is written in the Python programming language and emerged
from the idea of a common analysis platform incorporating a selection of the most prevalent
methods found in literature. piscope includes several routines for plume background
retrievals, routines for cell and DOAS based camera calibration including two individual
methods to identify the DOAS field of view (shape and position) within the camera
images. Gas velocities can be retrieved either based on an optical flow analysis or
using signal cross correlation. A correction for signal dilution (due to atmospheric
scattering) can be performed based on topographic features in the images. The
latter requires distance retrievals to the topographic features used for the correction.
These distances can be retrieved automatically on a pixel base using intersections of
individual pixel viewing directions with the local topography. The main features of
piscope are presented based on dataset recorded at Mt. Etna, Italy in September 2015. |
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