During the Mt Kelud February 2014 eruption the ash cloud was detectable on 13–14 February in the infrared with the reverse absorption technique by, for example, the Advanced Very High Resolution Radiometer (AVHRR/3). The Infrared Atmospheric Sounding Interferometer (IASI) observed the ash cloud also on 15 February when AVHRR did not detect any ash signal. The differences between ash detection with AVHRR/3 and IASI are discussed along with the reasons for the differences, supported by radiative transfer modelling. The effect of concurrent ice clouds on the ash detection and the ash signal in the IASI measurements is demonstrated. Specifically, a radiative transfer model is used to simulate IASI spectra with ash-only, with ice cloud only and with both ash and ice clouds. It is shown that modelled IASI spectra with ash and ice clouds reproduce the measured IASI spectra better than ash-only- or ice-only-modelled spectra. The ash and ice modelled spectra that best reproduce the IASI spectra contain about a factor of 12 less ash than the ash-only spectra that come closest to reproducing the measured spectra.
During volcanic eruptions large quantities of ash may be ejected high
into the atmosphere. The ash may be observed by both UV-visible and
infrared (IR) satellite instruments. The ash make
volcanic clouds rich on ice-forming nuclei. The dominant volatile in
explosive volcanic eruptions is water and for eruptions
reaching high and cold altitudes eruption
plumes may thus contain ice.
The first detection of ice in volcanic clouds was
reported by
The Infrared Atmospheric Sounding Interferometer (IASI) onboard
the Meteorological Operational (MetOp)-A, MetOp-B, and
MetOp-C satellites,
measures infrared radiation with high spectral resolution. As such it
has the potential to provide increased information content compared
to spectral band instruments such as AVHRR/3, the Moderate Resolution
Imaging Spectroradiometer (MODIS), the Spinning Enhanced Visible and
InfraRed Imager (SEVIRI) and the Multifunctional Transport Satellite
(MSAT) imager. IASI is dedicated to operational
meteorological soundings of temperature and humidity. It may also be
used to detect and measure the amount of a number of trace
gases
During an eruption, volcanic ash may be the dominating compound in the
atmospheric column viewed by each instrument pixel; however, it is
seldom the sole compound, as ice may be present in the volcanic cloud and
meteorological clouds may also be present elsewhere in the atmospheric
column.
Concerning the latter,
The aims of the present study are to (1) investigate the combined impact of ash and water ice clouds on IASI MetOp-A spectra using radiative transfer modelling, and (2) find the combination of ash and ice clouds that best reproduce the spectra measured during the Mt Kelud 2014 eruption. In addition to IASI data, measurements from AVHRR/3, also onboard MetOp-A, are used.
Mt Kelud, Indonesia, erupted on 13 February 2014 at 22:50 LT
(UT+7). The explosive eruption declined the following days and on 21
Feb the volcano's alert status was downgraded. The eruption was
detected in data from several satellite instruments including IR
data from the geostationary MTSAT imager using IR channels similar to
the AVHRR/3 instrument
Both the IASI and AVHRR/3 instruments onboard the polar orbiting MetOp-A satellite observed the Mt Kelud 2014 ash cloud.
AVHRR/3 has three solar and three thermal channels and a spatial
resolution of 1.1 km at nadir.
The AVHRR/3 thermal channels 4 and 5 are
centered at 10.8 and 12.0
For 14 February 2014, 02:53 UTC there is an area between 7 and 10
(Left panel) The
brightness temperature difference between AVHRR/3 channels 4 and 5
(BTD
(Left panel) The
brightness temperature difference between AVHRR/3 channels 4 and 5
(BTD
IASI covers the spectral range from 645 to 2760 cm
Information pertinent to IASI data
shown in Fig.
Due to the larger spectral coverage and higher spectral resolution
several approaches may be used to detect volcanic ash with IASI
(Left panel) The difference between
a spectrum with background SO
However, the 1168 cm
If ice clouds are present somewhere in the same atmospheric column as
the ash cloud, they may also have an impact on BTD
In the right panels of Figs.
(Left panel) Measured IASI/MetOp-A spectra
for the pixels marked in the right panels of
Figs.
Modelled high resolution infrared spectra may be used to retrieve
aerosol and ice cloud properties from measured spectra. For example
Vertical profiles of temperature, pressure, H
The parts of the scenes containing ash are mostly over water; hence the
surface emissivity was set equal to sea water (MODIS – University of
California, Santa Barbara (UCSB) Emissivity Library; the measurement procedure is described in
CALIOP data indicate that the ash plume over the vent reached altitudes
between 17 and 26 km. Other clouds were present between 10 and 15 km,
For the simulations with an ice cloud, the ice cloud was assumed to be
1 km thick and vertically homogeneous. The ice particles were assumed
to consist of solid columns with
Measured spectra were compared with simulated spectra
to get an estimate of the ash mass loading, ash particle radius, ice
water content and ash cloud altitude. However, the measured IASI
spectra contain numerous trace gas absorption
lines. The ash and ice optical properties vary comparatively slowly with
wavelength; hence only wavelengths for which trace gases have minimal
influence were selected to estimate the ash and ice cloud
properties.
Characterization of atmospheric constituents by using selected IASI
channels have been done by others. For example
Spectra were simulated for three cases.
A total of 306 “ice cloud only”, 96 “ash cloud only”, and 29 376
“ash cloud and ice cloud” spectra were calculated.
For a measured spectrum the brightness temperature was extracted for
the
Both analysis of individual spectra and complete scenes were made to better understand the effect of ice and ash clouds on the measured spectra. But first examples of the effects of ash and water ice clouds on IASI brightness temperatures are demonstrated.
(Left panel) The
brightness temperature at 773.5 cm
In the left panel of Fig.
In the right panel of Fig.
The behaviour of the brightness temperature will generally be similar for other wavelengths, albeit different in magnitude due to the wavelength dependence of the ice water and ash cloud optical properties. This wavelength dependence is used below to obtain the combination of ash and ice water clouds that best fit the measured spectra.
The measured IASI spectra for the star marked locations in the right
panels of Figs.
For the measured spectra in the left panel of
Fig.
The brightness temperature for the
pixels identified as ash in the right panels of
Figs.
The saturated ice cloud spectrum is best modelled by an atmosphere including an
ice cloud with top at 16 km and ice water content of
0.2 g m
(Left panel) Measured AVHRR/3
BT
The large footprint of IASI is covered by several AVHRR/3 pixels. For
the spectra in Fig.
(Upper left panel) The estimated ice water content.
(Upper right panel) The estimated ash mass loading.
(Lower left panel) The estimated altitude of the ice cloud top height for ice
clouds with ice water content
(Upper left panel) The estimated ice water content.
(Upper right panel) The estimated ash mass loading.
(Lower left panel) The estimated altitude of the ice cloud top. height for ice
clouds with ice water content
The frequencies of the ash mass loading and RMSD are shown in Fig.
(Upper row) The frequency of the ash mass loading (columns 1 and 2) and the RMSD (columns 3 and 4) when including all types
of simulated spectra.
(Lower row) The frequency of the ash mass loading (columns 1 and 2) and the RMSD (columns 3 and 4) when not including spectra
with both ash and ice. In columns 3 and 4, the red bars
represents ash-affected pixels while the green bars are all other pixels.
The blue bars represents the ash-affected pixels but modelled
ice spectra were used to calculate the RMSD, see text for details.
Note log-scale on
The method presented in Sect.
For 14 February there is considerable ice in the same column as the
volcanic ash, upper left panel Fig.
For both scenes the RMSD for pixels identified as ash is generally
smaller than pixels not
identified as ash, see lower right panels
Figs.
The mean and standard deviation of the
frequency distributions shown in Fig.
If the modelled spectra including both ash and ice are excluded from
the comparison and the ash-only and ice-only included,
the ash mass loading shifts to larger values as shown
in the lower row of Fig.
If all ash-identified pixels are to be reproduced with ice-only
spectra, the RMSD gets closer to the RMSD for the spectra including
both ash and ice, compare red bars in the upper row, column 3 and 4
panels of Fig.
The RMSD for the ash and ice case vs. the ice-only case for 14 February 2014 (left panel) and 15 February 2014 (right panel).
Above it was shown that modelled spectra including both ash and ice clouds better reproduced the IASI measured spectra compared to modelled spectra including only ash. While considerable detail was included in the simulated spectra some of the limitations of the study are discussed here.
Water clouds and mixed phase clouds are not considered in this
study.
The ice cloud particles were assumed to be solid columns with a
mean effective radius of 40
SO
The ash was assumed to be andesite. A different composition will have
a different refractive index which will change the spectral shape of
the simulated spectra and hence may yield other ash and ice cloud
estimates than those presented.
The shape of ash particles were assumed to be spherical. It has been shown by
For all calculations the
ash cloud was assumed to be at 18 and 1 km thick. Moving the ash
cloud up and down a few kilometres and changing its thickness will
affect the results as demonstrated by
Scatter plot (not shown) of the ash mass loading vs. the ice water
content for the ash-affected pixels show no correlation between the
two. Thus, the ash and ice clouds appear to be independent of each
other. However, for a number of pixels the colocation of the ice and
ash at the same altitude gave the best agreement with the IASI
measurements. Ice was most likely present in the volcanic cloud, in
addition meteorological water ice clouds were present at other
levels. The latter is supported by CALIOP data, Fig. 2e of
Two scenes during the Mt Kelud February 2014 eruption have been
studied using the hyperspectral IASI and broad band AVHRR/3 instruments
onboard the MetOp-A satellite. The instruments' different capabilities
to detect volcanic ash have been discussed and the spectral coverage of
IASI explored to investigate the possible presence of both ice and volcanic
ash. The main findings of the investigation are the following.
The spectral coverage and resolution of the IASI instrument gives
increased sensitivity to the presence of volcanic ash. As such,
the use of IASI data for detection of volcanic ash is a powerful and
attractive addition to the use of AVHRR/3 and similar instruments. IASI may detect ash when AVHRR/3 and similar instruments such as
SEVIRI, MODIS and MTSAT, do not detect ash. Comparisons of modelled and measured IASI spectra suggests that
during the Mt Kelud 2014 eruption both ash and ice clouds were
present simultaneously. IASI may potentially retrieve both ash and ice clouds
microphysical properties simultaneously. Underlying ice clouds reduce the ash needed to reproduce the
measured IASI spectra by about a factor of 12 on the average.
Finally, in this study IASI spectra measured during the Mt Kelud 2014 eruption were interpreted in terms of ash and ice clouds. It is noted that this interpretation does not rule out other interpretations based on other ash, ice, and water cloud assumptions not considered here.
IASI and AVHRR data are available from the Comprehensive Large
Array-data Stewardship System (CLASS) which is an electronic library
of NOAA environmental data (
Constructive comments from the two anonymous reviewers are highly appreciated. The Centre National d'Etudes Spatiales (CNES, France) developed and built IASI. It is flown onboard the MetOp satellites as part of the EUMETSAT Polar System. NILU receives IASI L1 data from EUMETSAT through the EUMETCAST near real time data distribution service. Part of this study was funded by the Norwegian Ministry of Transport and Communications. Edited by: A. Kokhanovsky