AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-1935-2015A model sensitivity study of the impact of clouds on satellite
detection and retrieval of volcanic ashKyllingA.arve.kylling@nilu.nohttps://orcid.org/0000-0003-1584-5033KristiansenN.StohlA.Buras-SchnellR.EmdeC.GasteigerJ.https://orcid.org/0000-0002-4401-0118NILU – Norwegian Institute for Air Research, P. O. Box 100, 2027 Kjeller, NorwaySchnell Algorithms, Am Erdäpfelgarten 1, 82205 Gilching, GermanyMeteorological Institute, Ludwig-Maximilians-University, Munich, GermanyA. Kylling (arve.kylling@nilu.no)6May2015851935194922August201418November20147April20158April2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/8/1935/2015/amt-8-1935-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/1935/2015/amt-8-1935-2015.pdf
Volcanic ash is commonly observed by infrared detectors on board Earth-orbiting satellites. In the presence of ice and/or liquid-water clouds, the detected volcanic ash signature may be altered.
In this paper the sensitivity of detection and retrieval of
volcanic ash to the presence of ice and liquid-water clouds was
quantified by simulating synthetic equivalents to
satellite infrared images with a 3-D radiative transfer model.
The sensitivity study was made for the two recent eruptions of
Eyjafjallajökull (2010) and Grímsvötn (2011) using realistic
water and ice clouds and volcanic ash clouds.
The water and ice clouds were taken from European Centre for
Medium-Range Weather Forecast (ECMWF) analysis data and the
volcanic ash cloud fields from simulations by the
Lagrangian particle dispersion model FLEXPART.
The radiative transfer simulations were made both with and without
ice and liquid-water clouds for the geometry and channels of the Spinning
Enhanced Visible and Infrared Imager (SEVIRI).
The synthetic SEVIRI images were used as input to standard reverse
absorption ash detection and retrieval methods.
Ice and liquid-water clouds were
on average found to reduce the number of detected ash-affected pixels
by 6–12 %. However, the effect was highly variable and for individual
scenes up to 40 % of pixels with mass loading >0.2gm-2 could
not be detected due to the presence of water and ice clouds.
For coincident pixels, i.e. pixels where ash was both present in the
FLEXPART (hereafter referred to as “Flexpart”) simulation and detected by the algorithm, the presence of
clouds overall increased the retrieved mean mass
loading for the Eyjafjallajökull (2010) eruption by about 13 %,
while for the Grímsvötn (2011) eruption ash-mass loadings the
effect was a 4 % decrease of the retrieved ash-mass loading.
However, larger differences were seen between scenes
(standard deviations of ±30 and ±20 % for
Eyjafjallajökull and Grímsvötn, respectively) and even
larger ones within scenes.
The impact of ice and liquid-water clouds on the
detection and retrieval of volcanic ash, implies that to fully
appreciate the location and amount of ash, hyperspectral and spectral
band measurements by satellite instruments should be combined with
ash dispersion modelling.
Introduction
Volcanic ash clouds can have a number of impacts on the environment
and society, including alteration of the radiative balance of the
atmosphere and the Earth's climate
,
and disruption to aviation
. Infrared (IR)
detectors in space are key tools for tracking and monitoring ash
clouds. Commonly used ash detection methods are variations of the
reverse absorption method
e.g. . This method
explores the brightness temperature difference (ΔT=T10.8-T12.0) between the 10.8 (T10.8) and 12.0
(T12.0) µm regions of the thermal infrared spectrum. For
volcanic ash ΔT<0, while ΔT≥0 for liquid water and
ice clouds. This method was, for example, successfully used on data
from the Spinning
Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat
Second Generation (MSG, Meteosat-9) geostationary satellite, for the
Eyjafjallajökull (2010) eruption .
After detection of
ash-affected pixels, various methods
may be used to retrieve
mass loading and effective radius of the ash
cloud. Several factors influence the infrared detection and retrieval
of volcanic ash, including ash cloud density and altitude
(temperature), ash particle composition, shape and size distribution,
the atmospheric temperature profile, humidity, and the surface
temperature .
Also, coarse-sized ash particles are not detectable by this method as
discussed by .
In addition, the presence of ice and/or liquid-water clouds may change
ΔT and affect the detection of ash and the retrieval of ash
cloud properties.
It is noted that improved versions of the reverse absorption method
(split-window) method somewhat may mitigate the effect of underlying
ice and/or liquid-water clouds see for example ( for various
approaches.)
The above-mentioned methods all require one or more threshold values
to be specified. A probabilistic method not using thresholds was
presented by . Hyperspectral sounders like the
Infrared Atmospheric Sounding Interferometer (IASI) may better
distinguish between ash and ice and water clouds due to the increased
number of available channels. Several methods have been presented to
detect ash using hyperspectral sensors; see for example
.
In retrievals, assumptions are typically made about
the composition, shape and size distribution of the ash
particles. Ash cloud temperature and surface temperature may either be
retrieved or taken from weather forecast models. The presence
of ice and/or liquid-water clouds is usually not considered.
The aim of this paper is to investigate the sensitivity of detection
and retrieval of volcanic ash on the presence of ice and liquid-water clouds.
To do so,
cases with volcanic ash in the presence of ice and/or liquid-water
clouds must be compared with very similar cases but without ice and
liquid-water clouds. Both observational and model-based investigations are
possible; however, observational approaches are difficult
due to the inherent problem in distinguishing
cloudy and cloudless cases under realistic atmospheric conditions.
Furthermore, ice and liquid-water cloud information together with volcanic ash cloud information
is needed for such an investigation, but this information is difficult
to obtain. Hence, here a model-based approach is
adopted. We choose to perform the sensitivity study for the
two recent eruptions of the Eyjafjallajökull (2010) and
Grímsvötn (2011), which have attracted a lot of interest.
A 3-D radiative transfer model was used to simulate
images equivalent to the SEVIRI 10.8 and 12.0 µm channels for the
full duration of the two eruptions. Simulations were made both with and
without realistic water and ice clouds taken from European Centre for
Medium-Range Weather Forecast (ECMWF) analyses. The volcanic ash cloud
fields were taken from simulations by the Lagrangian particle
dispersion model Flexpart. These synthetic images were
used as input to ash detection and retrieval methods.
Simulated satellite scenes have been used by several authors to
evaluate algorithms for detection of liquid water and ice cloud
properties. For example simulated a SEVIRI scene
over Germany, with cloud input from the COSMO-EU weather model, to
compare and validate cloud retrievals. developed
a fast SEVIRI simulator to quantify cloud water path retrieval
uncertainties using the Regional Atmospheric Climate
Model. calculated synthetic Moderate Resolution
Imaging Spectroradiometer (MODIS) radiances using cloud information
from the Goddard Earth Observing System Version 5 (GEOS-5) Earth
system model. They used the simulated radiances as input to standard
MODIS retrievals and compared these with retrievals using real MODIS
data and were able to locate and quantify problems with GEOS-5 cloud
optical properties and cloud vertical distributions.
During development of satellite detection and retrieval algorithms, the
outcomes of these algorithms must be compared and tested against
“true value” data sets. These data sets may come from either
observations or simulations of the property of interest.
By simulated properties we understand properties
retrieved from simulated satellite images, whereas observed properties
are retrieved from measured images. Model properties are input to the
radiative transfer model that generated the simulated satellite
images. Several routes are possible in the comparison of different
atmospheric property data sets. have summarized
these routes in their Fig. 1 – Route I: compare observed properties
with model properties; Route II: compare observed and simulated
radiances; Route III: compare model properties with simulated
properties; and Route IV: compare observed properties with simulated
properties. Here we use Route III to quantitatively estimate the
effects of liquid water and ice clouds on detection and retrieval of
volcanic ash.
This is done by analysing synthetic SEVIRI-like images simulated with
and without ice and liquid-water clouds.
For single scenes qualitative comparisons are made following route IV.
That is, simulated scenes are visually compared with measured scenes.
However, measured images are not quantitatively used in the analysis as
the simulated scenes are based on information from the measured images.
The remainder of the paper is organized as follows. In
Sect. the simulation of the IR images is described
together with the input data. The ash detection and retrieval methods
are described in Sect. . The results are
presented in Sect. and impacts of ice and liquid-water clouds on detection and retrieval of volcanic ash clouds are
discussed in Sect. .
The total ash column as simulated by the Flexpart model (Upper
left). Only pixels with column density above 0.2 g m-2 are
shown. The total liquid water (upper centre) and ice water (upper
right) cloud columns from ECMWF analysis data. The simulated
cloudless (lower left) and cloudy (lower centre) 10.8 µm
brightness temperatures. (lower right) The measured brightness
temperature of the 10.8 µm SEVIRI channel. All data shown are
for 12:00 UTC, 15 April, 2010.
Simulation of infrared SEVIRI images
Simulation of infrared SEVIRI images in the presence of ash have been
described by and . The
latter approach is adopted here. Briefly stated the
radiative transfer is calculated by the 3-D Monte Carlo code for the
physically correct tracing of photons in cloudy atmospheres (MYSTIC)
, which gets ash cloud fields
input from the Lagrangian particle dispersion model Flexpart
and ice and liquid-water clouds from
European Centre for Medium-Range Weather
Forecast (ECMWF) analysis.
A 3-D radiative transfer calculation was adopted as it has been shown
by that brightness temperatures may be both over-
and underestimated by 1-D radiative transfer models due to cloud
shadow effects.
While used the LOWTRAN
gas absorption parameterization we
here adopt the recent, more accurate and faster REPTRAN parameterization
of .
used Flexpart to calculate the 3-D dispersion of ash
from the Eyjafallajökull (2010) eruption using optimized emissions
based on inverse modelling with SEVIRI and IASI measurements. The ash
concentrations were calculated with a horizontal resolution of
0.25∘×0.25∘ and a vertical resolution of
250 m for 25 particle size classes with radii in the range
0.125–125 µm (see for details). Examples of the
total ash column from the Flexpart model simulations are shown in the
top left panels of
Figs. –.
These two cases are shown as they demonstrate two different effects of
ice and liquid-water clouds in volcanic ash observations, as will be
discussed in more detail below. Only pixels
with column density above 0.2 g m-2 are shown. This limit was chosen
as it corresponds to the low contamination limit of 0.2 mgm-3 for
an ash cloud of 1 km vertical thickness defined in connection with the
Eyjafjallajökull eruption .
For the
Grímsvötn (2011) eruption the 3-D ash clouds estimated by
using Flexpart combined with optimized emissions
based on inversion modelling with IASI measurements, were used as input to the
radiative transfer simulations. The ash particles were assumed to be
composed of andesite, and the refractive index was taken from
.
Same as Fig. , but for 18:00 UTC, 8 May, 2010.
The ice and liquid-water clouds were taken from ECMWF analysis data
with horizontal resolution of 0.25∘×0.25∘
and 91 vertical model levels. The 2-D ECMWF ice and liquid water fields
for the level closest to the Flexpart output layer was interpolated to
the Flexpart output resolution as described by .
ECMWF data
are available every 6 h. Consequently, radiative transfer
simulations were performed for 0, 6, 12 and 18 h each day of the
eruptions (14 April–24 May, 2010, for Eyjafjallajökull; 21–27
April, 2011, for Grímsvötn). Examples of total columns of the
liquid water and ice water cloud profiles are shown in the top centre
and right plots, respectively, of
Figs. –. Surface
and atmospheric temperatures were also taken from ECMWF
analysis. Spectrally resolved surface emissivity maps were adopted
from .
The ash, ice and liquid-water cloud fields given in latitude/longitude
coordinates were horizontally re-gridded to a 200 × 320
rectangular grid required by MYSTIC with a resolution of about
28 × 16 km. The vertical resolution was the same as
for the Flexpart simulation (i.e., 250 m). For each grid cell the ash,
ice and liquid-water cloud
optical properties were calculated as described by
.
The radiative transfer calculations were made by
the MYSTIC 3-D model, which was run within the libRadtran model
framework . While MYSTIC can handle 3-D clouds, the
libRadtran/MYSTIC framework does not allow 3-D fields of trace gases,
including water vapour. Hence a
constant water vapour profile from the subarctic summer atmosphere
from was adopted over the whole
domain. The effect of this simplification is discussed in
section . Brightness temperatures were calculated
for the 10.8 and 12.0 µm channels and the viewing geometry of
SEVIRI. Cloudy images with ash, ice and liquid-water clouds were calculated in addition to cloudless images containing
only ash (see lower left and centre plots of
Figs. -
for examples). A total of 184 (Eyjafjallajökull: 159;
Grímsvötn: 25) images were calculated for each channel (2) for
cloudy and cloudless conditions. This totals to 736 simulated images,
each taking about 2 h of CPU hours using 10 nodes on a Linux
cluster.
As MYSTIC uses the Monte Carlo method, the brightness
temperature has a statistical uncertainty which was calculated as the
standard deviation of the brightness temperature for each pixel.
The root mean square of the standard deviation was 0.15 K for both
channels which is better than the requirements
for the noise equivalent delta temperature (NEΔT) of 0.25 and 0.37 K for the
10.8 and 12.0 µm SEVIRI channels and of similar magnitude as the
actual NEΔT performance of 0.11 and 0.15 K, respectively
.
Ash detection and retrieval
The reverse absorption technique was used to identify pixels affected
by ash . A conservative cut-off temperature
difference, ΔTcut=-0.5K, was used to avoid too many false
positives. This means that pixels with ΔT<ΔTcut were
identified as containing ash. It is noted that water vapour absorption
decrease the magnitude of ΔT and may be corrected for
. No water vapour correction was applied in the analysis
presented here. At large viewing angles, the SEVIRI pixel size
increases significantly; see Fig. 1 of ; thus, data were
required to have a viewing angle smaller than 70∘.
A spatial noise reduction technique was applied to remove isolated
patches of pixels detected as ash. The spatial noise reduction was only
applied to the measured SEVIRI data and not to the simulated data due
to larger pixel sizes.
For each detection of an ash-affected pixel, the surrounding
pixels north, south, and to the east and west, were also required to
be identified as ash, otherwise the pixel was rejected. This
is a slightly stronger requirement than the spatial noise reduction
applied by .
They required that at least six out of nine pixels in a 3 × 3 surrounding block were ash-flagged for the
centre pixel to be retained.
If the optical depth (τ10.8) and effective radius (re) of
the ash cloud are known, the ash-mass loading ml for each pixel can
be calculated :
ml=43ρτreQext(re).
In Eq. it is assumed that the ash composition and
hence the extinction efficiency (Qext) and density (ρ) are
known and that the size distribution does not vary within the pixel.
The ash cloud optical depth and effective radius were retrieved using
a modification of the Bayesian optimal estimation technique described
by . They used the SEVIRI 10.8, 12.0 and
13.4 µm brightness temperatures to retrieve the ash layer
pressure, the ash column mass loading and the ash size distribution
effective radius. derived the ash cloud optical
depth and the ash size distribution effective radius from the SEVIRI
10.8 and 12.0 µm brightness temperatures using a look-up-table-based approach.
In this study we use the SEVIRI 10.8 and 12.0 µm
brightness temperatures to retrieve the independent ash cloud optical
depth, τ10.8 and the ash size distribution effective radius
by minimizing the cost function :
J(x)=(x-xb)TB-1(x-xb)+(yob-y(x))TR-1(yob-y(x)),
where the atmospheric state vector x=(τ10.8,re),, the
prior atmospheric state vector xb=(0.5,3.5), and B is
the error covariance matrix of the a priori background.
The error covariance matrix B was assumed to be diagonal, and
the variances of the state variables were set to
στ10.82=(10)2 and σre2=(10µm)2 (The latter value is from Francis et al., 2012). The values
in B are large
compared to the desired retrieval accuracy; thus the
background state only provides a weak constraint. The
observations are the brightness temperatures at 10.8 and 12.0 µm,
yob=(T10.8,T12.0), while y(x) are the
brightness temperatures for the state vector x as calculated
by the libRadtran radiative transfer model using the
DISORT radiative transfer equation solver . For the
forward calculations of y(x), the ash cloud was assumed to be
vertically homogeneous and 1 km thick in the vertical.
The measurement error covariance matrix is denoted by R. The
values for R were taken from Table 1 of
who assumed R to be diagonal with R11=(1.11K)2 and
R22=(1.11K)2. For the
forward calculations the ash particles were assumed to be spherical,
have a log-normal size distribution, and composed of andesite. The
geometric standard deviation of the size distribution was 1.5. The
andesite refractive index was taken from .
The T10.8 and T12.0 brightness temperatures also depend on
the surface temperature and the ash cloud temperature. These may
either be retrieved by including information from more channels
; obtained from weather forecasting models; or
estimated from for example the 12.0 µm image .
Here the latter
approach is chosen. For a given pixel the surface (ash cloud)
temperature is taken to be the maximum (minimum) temperature of a
block of 10 × 10 (29 × 29) surrounding pixels centred on
the simulated (measured) pixel.
Results
The effect of ice and liquid-water clouds on ash detection and
retrieval is qualitatively illustrated below for two selected SEVIRI
scenes during
the Eyjafjallajökull (2010) eruption. Further quantitative
evaluations based on cloudy and cloudless simulations for the whole
eruption periods of the Eyjafjallajökull (2010) and
Grímsvötn (2011) eruptions are given in Sects.
and , respectively.
The effect of ice and liquid-water clouds on the simulated 10.8 µm
brightness temperatures can be seen in
Figs. –. In
the cloudless simulations (lower left plots) the ash cloud is clearly
visible by comparison to the locations of Flexpart modelled ash
cloud. Other variability in the cloudless T10.8 simulations is
caused by variations in surface emissivity and surface temperature,
for example over Greenland, the Alps and the mountain ranges of
Norway. Including ice and liquid-water clouds in the image simulations
changes T10.8 dramatically (lower centre).
For qualitative comparison and demonstration of the realism of the
simulations, we also show the 10.8 µm brightness
temperatures as measured by SEVIRI for the
same time as the simulated images, in the lower right plots of
Figs. –. There
are clear similarities between the cloudy simulated and measured
images. Common features coupled to the addition
of ice and liquid-water clouds are clearly visible; for example the
cloud systems over Iceland and Sweden in
Fig. , and over the east coast of
Greenland and northern Spain in
Fig. . The ash cloud is
seen in both simulated and measured images, at least in areas with
sufficient high ash-mass loadings and homogeneous cloud fields.
However, numerous differences between the simulated cloudy and
measured images discussed by , are
evident, for example the too warm brightness temperatures in the North
Sea in the simulations. These differences are attributed to
inaccurate representation of the cloud and temperature fields
used as input to the radiative transfer simulations,
the coarser spatial resolution in the simulations and time-mismatch
between the ECMWF fields and the SEVIRI observations.
The ash detection technique as described previously in Sect. 3, was
applied to the cloudless and cloudy simulated images.
This provides an evaluation of the impact of ice
and water clouds on the ash
detection. Figs. –
show examples of the ash detection on the simulated
data from Figs. –. The
pixels with Flexpart ash columns above the low contamination limit
(0.2 g m-2) are compared with the pixels flagged as ash by the
reverse absorption technique in the cloudless simulation (left) and
cloudy simulation (centre) of
Figs. –.
Ash detection: Pixels flagged as ash by the reverse absorption
technique in the cloudless simulation (left) and cloudy simulation
(centre) compared to pixels with Flexpart ash columns above the low
contamination limit (0.2 g m-2). The colour coding of the pixels
are as follows – coincident (green): pixel identified as ash and
contains ash in the Flexpart model simulation; false positive (red):
pixel identified as ash, but does not contain ash;
false negative (blue): pixel not flagged as ash, but
contains ash. (right)
The ΔT=T10.8-T12.0 brightness
temperature difference calculated from simulated SEVIRI images with
ash and clouds. Only data points with ΔT<-0.5 K are shown.
Data is for 12:00 UTC, 15 April, 2010.
Same as Fig. , but for 18:00 UTC, 8 May, 2010.
The Flexpart columns with ash columns above the low contamination
limit are given as the union of blue and green pixels. The green
pixels are termed coincident pixels and are those with Flexpart column
above the low contamination limit and identified as containing ash
using the ash detection method described previously in Sect. 3. The
blue-coloured pixels are false negatives, i.e. prescribed as ash from
the Flexpart simulations, but detection by the simulated
image/detection framework failed. The red pixels are identified as ash, but
they contain no ash according to the Flexpart simulations;
consequently, they are false positives. The ΔT=T10.8-T12.0<-0.5 K brightness temperature differences
calculated from simulated SEVIRI images including ash and clouds for
the same situations are shown in the right plots of Figs. –.
For the Eyjafjallajökull (2010) and Grímsvötn (2011)
eruptions, time series of data similar to those in
Figs. –
were generated. These data and
Figs. –
are further discussed in Sects. and below.
Ash retrieval: the ash-mass loading retrieved from cloudless
simulated SEVIRI images (left), and including clouds
(middle). The difference (cloudy–cloudless) between the ash-mass
loading retrieved for pixels identified as ash in both the cloudy
and cloudless simulations (right). All data
representative for 12:00 UTC, 15 April, 2010.
The next step is to apply the ash retrieval technique as described
previously in Sect. , to the cloudless and cloudy
simulated images.
This allows an evaluation of the
impact of clouds on the ash retrieval. Examples of
retrieved ash-mass loading for the simulated scenes in
Figs. and
are shown in Figs. and
, respectively.
Same as Fig. , but data for 18:00 UTC, 8 May, 2010.
The retrieved ash-mass loadings based on the cloudless simulated
images (left plots Figs. and
) show the same maxima and minima structures as
the Flexpart ash distributions (Figs. and
), but are smaller in magnitude; see
discussion in Sect. for an explanation.
Including clouds causes both over- and under-estimates
of the ash-mass loading compared to the cloudless situation (middle
and right plots Figs. and
; see end of Sect.
for discussion).
For the two cases in
Figs. –.
the above detection and retrieval methods were also applied to the
SEVIRI measurements. The pixels identified as ash and the ash-mass
loading retrieved from the measured SEVIRI data for these two cases
are shown in Fig. .
By comparing the SEVIRI simulated cloudy ΔT in
Figs. –
with the SEVIRI measured ΔT in
Fig. and by comparing
SEVIRI simulated cloudless and cloudy retrievals in
Figs. and
with the SEVIRI measured retrievals
in Fig. , it is tempting to conclude that
the cloudy simulations better represent the measurements, at least
for the 15 April when the ice and liquid-water clouds have a larger
effect (cf. left and middle plot of Fig. ).
However, a direct comparison between the SEVIRI simulated ash
retrieval and the SEVIRI measured ash retrieval is non-trivial as the
simulated data have a coarser spatial resolution compared
to the measured SEVIRI data.
A thorough and complete comparison of the SEVIRI simulated ash
retrieval and the SEVIRI measured ash retrieval for the
Eyjafjallajökull (2010) and Grímsvötn (2011) eruptions is
beyond the scope of this work.
To further evaluate the effect of clouds on volcanic
ash retrieval, data corresponding to
Figs. and
were calculated for all simulated
scenes of the Eyjafjallajökull (2010) and Grímsvötn (2011)
eruptions.
Eyjafjallajökull (2010)
All simulated
satellite scenes for the total duration of
the Eyjafjallajökull (2010) eruption period (14 April–20 May) were
analysed to quantify the effect of clouds. Time series
for coincidence and false positive ash detections (as in
Figs. –),
as well as retrieved total ash-mass loadings
(as in Figs. and
), were generated from all simulated
scenes. Figure shows the time series
for the ash detection analysis.
(Left plots) The
ΔT=T10.8-T12.0 brightness temperature difference
calculated from SEVIRI measurements. (right plots) The ash-mass
loading retrieved from measured SEVIRI data. Data are for 12:00 UTC, 15
April, 2010 (upper plots) and 18:00 UTC, 8 May, 2010 (bottom plots).
Only data points with ΔT<-0.5 K are shown.
Ash detection
time series for the Eyjafjallajökull (2010) eruption: the
percentage of simulated pixels identified as ash (green
lines). Dashed lines are for cloudless and solid lines for cloudy
simulations. (red lines) The percentage of false positive ash
pixels with respect to the total number of pixels in the
image. (black lines) The percentage of false negative ash
pixels with respect to the total number of pixels in the
image. (blue line) The percentage of pixels with
Flexpart ash-mass loading above 0.2 g m-2.
The percentage of pixels in a scene with Flexpart ash above the low
contamination limit is shown by the blue line. The percentage of
coincidences, i.e. Flexpart ash pixels identified as ash by the
reverse absorption technique, is shown by the green lines. The solid
(dashed) green line pertains to simulations with (without) ice and
liquid-water clouds. The red lines are the percentages of false
positives, that is pixels that are identified as ash by the reverse
absorption technique, but do not contain ash according to the Flexpart
data (Flexpart column smaller than 0.2 g m-2). The number of false
negatives, that is pixels that do contain
ash but are not detected, are shown in black. The solid (dashed) black
and green lines adds up to the blue line.
A number of interesting features are present in
Fig. .
Far fewer pixels are identified as ash than are present in the
Flexpart simulated ash fields (used as input to the detection
method).
Clouds on average reduce the number of pixels
identified as ash (compare solid and dashed green lines), but the
magnitude of the impact of clouds varies.
The number of false positives exhibits a diurnal variation. The
diurnal variation is larger for the cloudless simulations.
For the whole eruption period only 14.6 % (22.1 %) of the pixels with
ash above the low contamination limit (0.2 g m-2) are identified as
ash for the cloudy (cloudless) simulation. If a limit of 1.0 g m-2
is used, the number of pixels identified as ash increases to 54.7 % (74.7 %)
for the cloudy (cloudless) simulation.
For coincident pixels there appears to be no strong
dependence in the ash detection on the satellite viewing angle as
demonstrated by the green lines in Fig. .
Ash detection as a
function of viewing angle for the Eyjafjallajökull (2010)
eruption: the frequency of pixels identified as ash in the
Flexpart simulations (blue line), false positive pixels from ash
detection (red line) and coincidences (green line). Solid
(dashed) lines represent cloudy (cloudless) simulations.
For satellite viewing angles smaller than 51∘, the detection
efficiency is high (compare blue and green lines in
Fig. ).
The number of false positives increases strongly with
increasing viewing angle (red lines in
Fig. ), indicating that at large
viewing angles ash detection is less reliable. Interestingly, the number
of false positives is larger for the cloudless than for the cloudy
simulations. The cloudless false positives are mostly found over land
(Scandinavia) and are larger at night than at day. This is caused by
strong atmospheric temperature inversions near the surface when the
surface cools more strongly than the overlying atmosphere during
nighttime; see and
Eq. 5. In April the ECMWF surface
temperatures over
Scandinavia exhibited comparatively large diurnal variations. These
variations declined in magnitude at the end of April and into May, as
is reflected by the smaller number of false positives towards the end of the
period shown. The presence of clouds obscures the surface and
consequently reduces the diurnal variation for those pixels affected
by clouds. The pixels not affected by clouds will still have diurnal
variation. Hence, the number of false positives is generally reduced
with the presence of clouds (compare solid and dashed red lines in
Fig. ). As stated in
Sect. the
water vapour profile used in the radiative transfer calculations, is
constant over the domain. This may result in an overly humid atmosphere at
certain locations and as a result, further increases the number of
false positives. See also discussion in Sect. .
The relative frequency of false negatives (undetected ash pixels
normalized to the number of Flexpart pixels) as a function
of Flexpart ash-mass loading and ash cloud altitude for the
Eyjafjallajökull (2010) eruption. Results
from cloudy simulation. Cloudless results are similar.
To further understand why far fewer pixels are identified as ash than
are present in the Flexpart simulated ash fields, the frequency of
false negatives relative to the number of Flexpart pixels is
calculated and shown in Fig. as
a function of ash cloud mass loading and altitude.
It is seen that most ash pixels that miss detection either have a mass
loading less than 0.5 g m-2 or are below the altitude of 3 km. There are
also ash pixels missing detection around 10 km. These are associated
with increased emissions of ash on 15 May and are
missed due to the presence of clouds. There are
also pixels missed around the altitude of 5 km for mass loadings larger than
5 g m-2.
The ash clouds below the altitude of 3 km may be missed due to either overlying or
overlapping clouds or too small temperature difference
with the underlying surface, where the radiatively effective surface
under the ash cloud is the Earth's surface or an opaque liquid-water
cloud. The mostly small difference between the number of false negatives
between cloudless and cloud simulations (black lines in
Fig. ) indicates that for
the situation during the Eyjafjallajökull (2010) eruption, the
small temperature difference between the Earth's surface and the ash
cloud due to the low altitude of the ash cloud and small mass loading
of the dispersed ash, were the main reasons for the rather large
number of false negatives.
The presence of clouds tends to obscure ash clouds
compared to cloudless skies (compare solid and dashed
green lines in
Fig. ). The effect of
clouds varies as the overlap with the ash cloud
changes.
The mean of the number of pixels detected (excluding false positives)
as ash relative to Flexpart ash pixels for each scene in the cloudy
simulations was fairly constant between the first (14–21 April) and
second (5–21 May) eruption periods, being 13.0 % ± 9 % and
15.6 % ± 14.8 %, respectively. For the cloudless simulations these numbers
are 25.2 % ± 17.0 % and 21.4 % ± 16.0 %, indicating that the
presence of clouds reduced ash detection more in the first period (by
12.2 %) than in the second period (5.8 %). The large standard
deviations indicate large variability between scenes.
Upon inspection of individual scenes it is found that clouds may
obscure up to 40 % of the Flexpart pixels identified as
ash. No or small cloud effects are present on days 15 April and 6–8
May. It is noted that for some cases (8 May) slightly more pixels are
identified as ash for the cloudy than for the cloudless simulation, although the differences are small.
Ash retrieval time series for the Eyjafjallajökull (2010)
eruption: total ash cloud mass from the Flexpart model
(blue line) and as retrieved from simulated
cloudless (green dashed line) and cloudy
(green solid line) SEVIRI scenes (top). The
difference between the cloudy and cloudless
simulation from the above plot (bottom). Note that only coincident pixels
are included in both plots.
Further, the presence of clouds on the total ash-mass
retrieval for the whole Eyjafjallajökull (2010) eruption period was
assessed. The total ash cloud mass for each scene was calculated from
ash-mass loading retrievals for cloudless and cloudy simulated SEVIRI
scenes of which examples are shown in
Figs. and
. Time series of the ash-mass
loading for pixels detected as ash and with Flexpart ash columns above
the low contamination limit are shown in the upper
plot of Fig. . Notice that only coincident pixels (i.e., Flexpart ash present and
also detected) were used for these calculations.
The presence of clouds mainly gives a larger ash-mass
loading estimate compared to a cloudless sky except for 7–8 May, as
seen in the lower
plot of Fig. .
For the whole eruption the cloudless (cloudy)
simulation underestimates the Flexpart mass by about 38 % (25 %).
Grímsvötn (2011)
The impact of clouds on ash detection and retrieval is
further analysed for the whole duration of the Grímsvötn
eruption, 21–27 May 2011. The modelled and retrieved ash-mass loadings
for the whole period are shown as mosaics in
Fig. .
Modelled and retrieved ash-mass loadings for the
Grímsvötn (2011) eruption between 21–27 May 2011 shown as
mosaics of 6 hourly fields. (upper left) Flexpart model
simulation, (lower left) retrieved from measured SEVIRI images,
(upper right) retrieved from simulated cloudy SEVIRI images,
(lower right) retrieved from simulated cloudless SEVIRI
images. Note that composites of all individual 6-hourly scenes
were constructed by taking for each pixel the maximum value of
all scenes. For the measured SEVIRI data (lower
left), all pixels with longitude >10∘ W for the
22nd and 23rd , and for all subsequent days pixels with latitude
>63∘ N or longitude >25∘ W or
longitude >30∘ E have been removed, as they are considered
false positives.
The upper left plot illustrates the transport of ash as modelled by
Flexpart at 6-hourly (00:00, 06:00, 12:00, 18:00 h) intervals. The periodic
pattern is due to the 6-hourly sampling. The upper right (lower
right) plot shows the ash-mass loading retrieved from the simulated
cloudy (cloudless) SEVIRI images. The lower left panel shows ash-mass
loading retrieved from SEVIRI measurements for the same 6 hourly
intervals. During the start of the eruption, the ash (and SO2) was
transported northwards. A strong signal is seen in the measured SEVIRI
image (lower left). Note that the mass loadings presented here for the
northwards plume are about a factor 2 larger than those derived from
IASI measurements and presented by . SEVIRI also
tracks the south-easterly movement of the ash cloud for the later
phases of the eruption. This compares well with the IASI data
presented by in their Fig. 2. To fully understand the
reasons for the difference between SEVIRI and IASI in the northwards
plume and the agreement in the south-east plume requires detailed
comparison of the SEVIRI and IASI retrieval, which is beyond the scope
of this study.
It is noted that the emissions used for the Flexpart estimated ash
fields for the Grímsvötn (2011) eruption were based on IASI data
, while for the Eyjafjallajökull (2010) eruption
they were based on both IASI and SEVIRI data . This
implies that the qualitative comparisons of the
simulated and measured SEVIRI images to the Flexpart model simulation
are fully independent only for the Grímsvötn case.
Similar to Fig. , but
for the Grímsvötn (2011) eruption.
The cloudy simulation (upper right panel in
Fig. ) shows no ash south
and south-east of Iceland as is seen in the Flexpart and measured
SEVIRI images. Some of this ash is present in the cloudless
simulations (lower right plot,
Fig. ), but far less than in
the Flexpart
simulation. Figure
further illustrates the number of pixels that are identified as ash by
the detection algorithm. The number of Flexpart pixels with ash-mass loading above the
contamination limit is shown by the blue line, while the percentage of
ash pixels identified as ash for the cloudy and cloudless simulations
are shown as solid and dashed green lines, respectively. For the
eruption 3.6 % (10 %) of the ash pixels above the low contamination
limit are detected for the cloudy (cloudless) simulation.
If a limit of 1.0 g m-2 is used, the number of pixels identified as
ash increases to 4.8 % (15.1 %) for the cloudy (cloudless) simulation.
Similar to
Fig. , but
for the Grímsvötn (2011) eruption. The frequency of pixels
identified as ash in the Flexpart simulations (blue line),
false positive pixels from ash detection (red line) and
coincidences (green line) are shown. Solid (dashed) lines represent
cloudy (cloudless) simulations.
Similar to
Fig. , but
for the Grímsvötn (2011) eruption.
The dependence of Flexpart ash pixels and detected and false positive
pixels on viewing angle is presented in
Fig. . As for the Eyjafjallajökull (2010) eruption,
Fig. , the number of false positives
increases strongly with viewing angle and is larger for the cloudless
than the cloudy simulation.
The frequency of false negatives as a function of ash-mass loading and
ash cloud altitude is given in
Fig. . The pattern is similar to the Eyjafjallajökull (2010)
eruption. Most ash pixels that miss detection are either at altitudes
lower than 4 km or have a mass loading less than 0.5 g m-2. At the
start of the eruption the plume travelled northwards at altitudes of
about 10–12 km. The pixels missed at this altitude have an overly
small mass loading to be detected.
The total ash cloud mass for coincident pixels is shown in
Fig. . Only data up to 24 May is shown as for the cloudy simulation ash is
detected only for the first few days of the eruption; see
Figs. and
.
For the coincident pixels in Fig. the
cloudless (cloudy) mass overestimates the Flexpart mass by 28 %
(24 %). This is opposite to the underestimation we found in
Sect. for the Eyjafjallajökull (2010)
eruption. However, for
shorter time periods, 14–16 May, overestimates were also present for
the Eyjafjallajökull (2010) eruption Fig. .
Similar to Fig. , but for the
Grímsvötn (2011) eruption.
Discussion
The detection of ash-affected pixels depends on the difference
between the surface temperature and the ash cloud temperature. The
effective ash emissions were generally at higher (about 6 km)
altitudes for Eyjafjallajökull compared to Grímsvötn
(2–3 km, except for 22 May); see Fig. 2 in and Fig. 3
in , respectively. The overall lower altitude of the
Grímsvötn ash explains why relatively less of it was detected
in the simulations presented in Sect. ,
due to smaller temperature differences between the ash cloud and the
surface and more mixing with low altitude clouds.
For the Grímsvötn (2011) eruption ash was detected over
the North Sea by both SEVIRI (see lower left plot in
Fig. ) and IASI see
Fig. 2 in . The lack of detected ash in the cloudy
simulated scenes (upper right plot in
Fig. ), and the presence in
the cloudless simulated scenes, lower right plot
Fig. , indicate that the
liquid water and ice clouds used in the cloudy simulations did not well
represent the real cloud situations. This may be due to the clouds
being misplaced in altitude and/or horizontal position such as to
obscure the Flexpart ash cloud.
The detected ash pixels relative to Flexpart ash pixels with ash
loading >0.2 g m-2 was on average 14.6 % (22.1 %) for the cloudy
(cloudless) simulation for the Eyjafjallajökull (2010) eruption,
and 3.6 % (10.0 %) for the Grímsvötn (2011) eruption. These
numbers increased to 54.7 % (74.7 %) for the Eyjafjallajökull (2010)
eruption and to 4.8 % (15.1 %) for the Grímsvötn (2011)
eruption if only Flexpart ash pixels with ash loading >1.0 g m-2
were considered. These detection efficiencies are low, but are based
on the automated use of the reverse absorption technique alone. In an
operational setting during a volcanic crisis, information from several
satellites and instruments would be used together with aircraft and
surface observations if available. Furthermore, once an eruption is
identified, ash transport models would be used and judged together
with other information to best derive the extent of the ash cloud and
forecast its development.
As described in Sect. a constant water vapour
profile was used over the whole domain. For a single scene on 11 May 2010 for the
Eyjafjallajökull (2010) eruption estimated that
the fixed water vapour profile on average increased the
10.8–12.0 µm brightness temperature difference by 0.07 K for
pixels
identified as ash. As a result, for the single scene they
investigated, about 8 % of ash-affected pixels missed detection by
assuming a fixed water vapour profile. Consequently, the overall
detection efficiency would increase by including a spatially varying
water vapour profile. Since we are mostly interested in the difference
in ash detection and retrieval between the cloudless and cloudy
simulated scenes, which are similarly affected by the assumption of a
constant water vapour profile, it is not anticipated that a constant
water vapour profile will affect the results presented.
The ash-mass loadings retrieved from the simulated images for
coincident pixels are generally
lower than the Flexpart ash-mass loadings for the Eyjafjallajökull
(2010) eruption, see Fig. . For the whole eruption
period the Flexpart mean ash mass for coincident pixels was
1.75 × 108 kg. This compares to 1.09 × 108 kg and
1.32 × 108 kg for the cloudless and cloudy simulations. The
opposite occurred for the Grímsvötn (2011) eruption; the
Flexpart mean ash for coincident pixels was 7.19 × 106 kg
while it was higher for the cloudless (9.17 × 106 kg) and
cloudy (8.90 × 106 kg) simulations. However, the standard
deviations are large being 30 and 20 % for Eyjafjallajökull and
Grímsvötn, respectively. Hence, the cloud impact varies
considerably between scenes. Furthermore, inspection of the right plot
of Figs. and
reveals both under- and
overestimates of the mass loading due to the presence of clouds within
a single scene. For individual pixels the difference may be larger
than 100 %.
The ash mass retrieved depends on the surface temperature. For the
present retrieval this was deduced from T12.0, see
Sect. . In the presence of
clouds T12.0 will be lower compared to a
cloudless sky. For an ash cloud overlying a cloud
ΔT will be smaller than if the cloud was not
present. Both these factors interact to cause both
over- and underestimates of the ash-mass loading.
Clouds do affect the brightness temperatures and hence the retrieval
of ash mass. For cloudless scenes, one might expect that the simulated
cloudless mass loading retrievals should agree with the mass loading
from the Flexpart model.
However, although the ash type, density and particle shape are the
same in the retrieval and the Flexpart simulations there are also
differences. Particularly, the retrieval method assumes a log-normal particle size
distribution (see Sect. ), which is different
from the size distribution of the Flexpart simulated ash
particles. Indeed, the Flexpart size distribution is different for
each voxel making up the ash cloud field. It is also noted that
according to , Flexpart may have too little
mass for particles with radii in the 0.5–5 µm range. This is the
size range where the retrieval method discussed here is most
sensitive. The ash cloud thickness is also different in the Flexpart
simulations and in the retrieval. In the latter, a fixed 1 km thick ash
layer is assumed while for the simulated images the vertical
distribution from Flexpart is used.
The number of false positives increases with viewing angle for both
the Eyjafjallajökull (2010) and Grímsvötn (2011) eruptions,
Figs. and
. However, large viewing angles
may also increase the ash signal due to longer path through the ash
cloud.
This was demonstrated by who found ΔT to
be larger for MODIS (small viewing angles) than for GOES (Geostationary
Operational Environmental Satellites, large viewing angles) for
the Cleveland (2001) eruptions. Hence, in this case the larger viewing
angle produced a stronger, more negative ΔT, ash
signal.
Conclusions
The sensitivity of detection and retrieval of
volcanic ash to the presence of ice and liquid-water clouds has
been quantified by simulating synthetic equivalents to
satellite infrared images with a 3-D radiative transfer model.
For the sensitivity study, realistic ice and liquid-water clouds and
volcanic ash clouds representative for the Eyjafjallajökull (2010)
and Grímsvötn (2011) eruptions were used. The
ash cloud fields from the Lagrangian particle dispersion model
Flexpart, have been input to
the MYSTIC 3-D radiative transfer model to simulate SEVIRI-like 10.8 and
12.0 µm brightness temperatures with and without the presence of
liquid water and ice clouds from ECMWF analysis data. Images of
brightness temperatures were
simulated at 6-hourly intervals limited by the temporal resolution
of the liquid water and ice clouds fields from ECMWF. Ash-affected
pixels were detected in the images based on the reverse absorption
technique. Furthermore, optimal estimation was used to retrieve ash-mass loading. Comparisons of the detected and retrieved ash from
images with and without liquid water and ice clouds showed the following.
The detection efficiency (detected ash pixels relative to
Flexpart ash pixels with ash loading >0.2 g m-2) was on
average 14.6 % (22.1 %) for the cloudy
(cloudless) simulation for the Eyjafjallajökull (2010) eruption,
and 3.6 % (10.0 %) for the Grímsvötn (2011)
eruption. These numbers increased to 54.7 % (74.7) for the
Eyjafjallajökull (2010) eruption and to 4.8 % (15.1 %) for the
Grímsvötn (2011) eruption, if only Flexpart ash pixels with
ash loading >1.0 g m-2 were considered. It is noted that these
numbers are obtained by an automated version of the reverse
absorption technique. In a real volcanic crisis, the use of
other analysis methods and instruments together with expert
judgment, may significantly improve the knowledge of the ash cloud
extent.
The mostly small difference between the number of false negatives
between cloudless and cloudy simulations (black lines in
Figs. and
) indicates that for
the situation during the eruptions, the
small temperature difference between the Earth's surface and the ash
cloud was the main reason for the rather large number of false
negatives. The small temperature difference was due to the low
altitude of the ash cloud.
The presence of clouds mostly led to
identification of fewer ash-affected pixels
(Figs. and
). On average,
during the full duration of the eruptions, ice and liquid-water
clouds were found to decrease the number of detected ash pixels by
about 6–12 %. However, variations were large between scenes and
clouds reduced ash detection by up to 40 % for individual
scenes. Dispersed and thinned ash clouds were most likely to go
undetected. For a few cases more ash pixels were identified in the
presence of clouds.
Diurnal variations were seen in the number of false
positives. These mostly occurred over cloudless land areas and were
caused by large diurnal variations in surface temperatures while the
atmospheric temperature remained comparatively constant (nighttime
temperature inversions).
The number of false positives increased with increasing viewing
angle and the results indicate that care should be used for data
with viewing angles larger than about 69∘.
The number of false positives for the cloudless simulations
increased more with viewing angle than the cloudy simulations.
It is noted that
due to geometry the magnitude of the ash signal will increase
with increasing viewing angle.
The presence of ice and liquid-water clouds gave both smaller
(4 % Grímsvötn) and larger (13 %
Eyjafjallajökull) mean ash-mass loading compared to the
cloudless situation for coincident
pixels, i.e. pixels where ash was both present in the Flexpart
simulation and detected by the algorithm. However, large differences
were seen between scenes (standard deviation of ±30 % and
±20 % for Eyjafjallajökull and Grímsvötn,
respectively) and even larger within scenes.
The results suggest that a two-layer retrieval (ash cloud overlying
liquid-water cloud) is needed to further improve ash-mass loading
estimates under cloudy conditions . Also, detection
methods that explore the temporal
behaviour of ash clouds between consecutive satellite images may prove
fruitful see for example. The ultimate goal may
be the direct assimilation of satellite-observed radiances in a weather
forecast model that also emits and transports ash.
Ice and liquid-water clouds interfere with the detection and retrieval
of volcanic ash. During a volcanic ash situation, the complexity of
the situation suggests that hyperspectral and spectral band
measurements by satellite instruments
should be combined with inverse ash dispersion modelling
and judged by experts to obtain the best understanding
of where and how much ash is present.
The present analyses pertain to the situation during the
Eyjafjallajökull (2010) and Grímsvötn (2011) eruptions. For
other eruptions taking place under other meteorological situations and
with other eruption heights the impact of clouds may be
different.
Acknowledgements
This work received support from the FP7 project FUTUREVOLC “A
European volcanological supersite in Iceland: a monitoring system
and network for the future”, (Grant agreement no: 308377), the
Norwegian Research Council (Contract 224716/E10) and the Norwegian
Ministry of Transport and Communications. EUMETSAT are acknowledged
for providing SEVIRI data via EUMETCast.
Edited by: B. Kahn
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