Volcanic eruptions may generate volcanic ash and sulfur
dioxide (SO2) plumes with strong temporal and vertical variations. When
simulating these changing volcanic plumes and the afar dispersion of
emissions, it is important to provide the best available information on the
temporal and vertical emission distribution during the eruption. The
volcanic emission preprocessor of the chemical transport model WRF-Chem has
been extended to allow the integration of detailed temporally and vertically
resolved input data from volcanic eruptions. The new emission preprocessor
is tested and evaluated for the eruption of the Grimsvötn volcano in
Iceland 2011. The initial ash plumes of the Grimsvötn eruption differed
significantly from the SO2 plumes, posing challenges to simulate plume
dynamics within existing modelling environments: observations of the
Grimsvötn plumes revealed strong vertical wind shear that led to
different transport directions of the respective ash and SO2 clouds.
Three source terms, each of them based on different assumptions and
observational data, are applied in the model simulations. The emission
scenarios range from (i) a simple approach, which assumes constant emission
fluxes and a predefined vertical emission profile, to (ii) a more complex
approach, which integrates temporarily varying observed plume-top heights
and estimated emissions based on them, to (iii) the most complex method that
calculates temporal and vertical variability of the emission fluxes based on
satellite observations and inversion techniques. Comparisons between model
results and independent observations from satellites, lidar, and surface air
quality measurements reveal the best performance of the most complex source
term.
Introduction
In the past decades, there have been several eruptions with a significant
impact on aviation (e.g. Albersheim and Guffanti, 2009; Guffanti et al.,
2010; Bolić and Sivčev, 2011). Airspace closure or flight rerouting
has been required since volcanic ash may cause significant damage to turbine
engines when internal fans are exposed to elevated concentration levels over
certain time periods (Clarkson et al., 2016). During the eruption of the
Eyjafjallajökull volcano in 2010, wide areas of the European airspace
were closed for days (Bolić and Sivčev, 2012). From 15 until 22 April 2010, 104 000 flights were cancelled (Alexander, 2013). In May 2011,
the Grimsvötn eruption led to a cancellation of 1 % (∼900 of total ∼90000) of planned flights in Europe during a
period of 2 d (https://volcano.si.edu/volcano.cfm?vn=373010, last access: 20 November 2020).
Observational data, e.g. from radar, lidar, or satellite, are used to observe
locations and extent of volcanic clouds. Numerical model simulations are
performed by Volcanic Ash Advisory Centers (VAACs) to predict the dispersion
of the volcanic ash and SO2 clouds in support of emergency management.
After the Eyjafjallajöküll 2010 eruption, harmonized thresholds were
defined for aircraft alerting procedures and provided by the London and
Toulouse VAACs to support the Volcanic Ash Contingency Plan (VACP, Edition
2.0.0 – July, 2016). Low-, medium-, and high-contamination regions were
defined for volcanic ash mass concentrations: less than or equal to
2 mg/m3, greater than 2 mg/m3 and less
than or equal to 4 mg/m3, and higher than
4 mg/m3, respectively.
Characterizations of emission source terms during volcanic events are
typically extremely challenging to obtain, and best model results can only
be achieved by integrating all available observational data. Volcanic source
terms include the source strength, its vertical and temporal variations, and size, density, and shape of emitted particles. A realistic estimate
of the source term is crucial to accurately predict the transport of ash and
gases released during volcanic eruptions.
The Weather Research and Forecasting (WRF; Grell et al., 2005) model coupled
with Chemistry (WRF-Chem) is able to realistically simulate the dispersion
of ash clouds from volcanic eruptions (e.g. Webley et al., 2012; Stuefer et
al., 2013; Hirtl et al., 2019). However, the standard volcanic emission
preprocessor of WRF-Chem has some deficiencies degrading the model
performance related to the dispersion of volcanic ash and SO2 clouds.
These deficiencies can be mainly attributed to limitations of the
description of temporal and vertical variability of emission fluxes (Hirtl
et al., 2019). In other words, the WRF-Chem volcanic emission application
has been limited to using source terms based on “simple” mass eruption
rate time series. This study presents the extension of the WRF-Chem volcanic
emission preprocessor towards more complex source terms and evaluates the
results for the eruption of the Grimsvötn volcano in Iceland in May
2011.
The Grimsvötn volcano is one of the most active and well-known volcanoes
in Iceland (e.g. Gudmundsson and Björnsson, 1991; Vogfjörd et al.,
2005; Witham et al., 2007; Moxnes et al., 2014). Over the past centuries, it
has erupted about once per decade. During the most recent major eruption,
which occurred from 21 until 25 May 2011, significant amounts of
SO2 and ash were injected into the atmosphere. The Grimsvötn plume
development was observed by GOME-2 (Global Ozone Monitoring Experiment-2;
Flemming and Inness, 2013), OMI (Ozone Monitoring Instrument; Sigmarsson et al.,
2013), IASI (Infrared Atmospheric Sounding Interferometer; Moxnes et al.,
2014; Carboni al., 2016), SEVIRI (Spinning Enhanced Visible Infra-Red
Imager; Cooke et al., 2014), AIRS (Atmospheric Infrared Sounder; Chahine et
al., 2006), AATSR (Advanced Along-Track Scanning Radiometer; Virtanen et
al., 2014), and MODIS (Moderate Resolution Imaging Spectroradiometer; Tesche
et al., 2012). This study uses observations from the IASI, SEVIRI, AATSR, and
AIRS instruments. The IASI observations are used in the Bayesian inversion
technique to calculate a volcanic ash and SO2 source term and the
SEVIRI, AIRS, and AATSR for evaluation purposes. Beside satellite
observations lidar and ground station measurements from national air quality
monitoring networks are used for model evaluation.
Figure 1 shows ash and SO2 clouds observed by the IASI instrument for 23 May 2011. The comparison between ash and SO2 observations
clearly reveals different dispersion patterns. While SO2 was first
transported to the north of Iceland and then towards Greenland and the
Canadian and US east coast, volcanic ash was transported to the south of
Iceland and then towards the northern UK and eastern Scandinavia. The
separation of the ash and SO2 clouds was caused by different injection
heights and vertical wind shear (Moxnes et al., 2014). Forecast models,
which did not take into account the different release heights at the early
stage of the eruption, produced unrealistic forecasts as shown by
comparisons to satellite data (Tesche et al., 2012; Cooke et al., 2014).
Prata et al. (2017) provided observational perspectives on the event and
advised using separate source terms for ash and SO2. The motivation to
further develop the volcanic emission preprocessor of WRF-Chem was to
improve the capabilities of the model to also simulate complex eruption
cases.
IASI SO2(a) and total ash (b) observations on 23
May 2011, 22:00 UTC.
For this study, three source terms, based on different assumptions and
observational data, are applied in the model simulations. The emission
scenarios range from (i) a simple approach, which assumes constant emission
fluxes and a predefined vertical emission profile, to (ii) a more complex
approach, which integrates temporarily varying observed plume heights and
estimates emissions based on observed plume heights, to (iii) the most
complex method that calculates temporal and vertical variability of the
emission fluxes based on satellite observations and inversion techniques.
The remainder of this paper is divided into the following sections: Sect. 2 provides a technical description of the extension of the WRF-Chem volcanic
emission preprocessor. Section 3 describes the WRF-Chem model setup and
emission scenarios. The results and model evaluation with different
observations (satellite, lidar, and surface air quality measurements) can be
found in Sect. 4. Summary and conclusions are given in Sect. 5.
Extension of the volcanic preprocessor of the WRF-Chem model
The WRF-Chem model simulates emission, transport, mixing, and chemical
transformation of trace gases and aerosols simultaneously with the
meteorology. The model enables the use of various options for dynamic cores and
physical parameterizations (Skamarock et al., 2008). The online approach
(meteorology with air chemistry; see e.g. Baklanov et al., 2014) accounts
for a numerically consistent air quality forecast.
In the official release of WRF-Chem v4.2 (code is available at
https://github.com/wrf-model/WRF/releases, last access: 20 November 2020), volcanic emission sources can be
considered only in a very simplified way. The model can simulate the
dispersion of volcanic emissions specified by the initial plume height,
erupted mass (ash and SO2), duration of the eruption, and
aerosol bin size distribution (up to 10 bin sizes).
If erupted mass is not known, it can be calculated applying the Mastin
formula (Mastin et al., 2009), which relates plume height hplume (in
metres) to the emitted mass per time step memitted (kg/s).
memitted=2600⋅(0.0005⋅hplume)4.1494
For the vertical source term structure, a 75/25 umbrella-shaped plume is
applied: 25 % of the mass from vent height to a certain height
(∼73 % of plume height) of the plume and then 75 % of the
mass distributed to a parabolic distribution until plume-top height. For
real-time applications, this is a straightforward approach, as the
development of the volcanic emission cannot be predicted.
Stuefer et al. (2013) had extended the volcanic emission preprocessor with
time-variant emissions, which can either be specified directly as mass
fluxes or calculated with the Mastin equation based on temporarily varying
plume heights (implemented in WRF-Chem version 3.4).
We extended the WRF-Chem capability towards user-defined volcanic source
emission data that are read in through an external file. These emission
fluxes (kg/m/s) comprise vertically resolved time series of ash and
SO2, as shown in Table 1. The date and time entries refer to the start
of the emission interval, and the specified height (above ground level, AGL)
refers to the lower limit of the height interval. Emissions of the last time
step and the topmost level are the upper bounds of the highest sub-column
and the last time step (therefore emissions are set to zero). The emission
fluxes can be estimated by any suitable method. They can for example be
produced with Bayesian inversion techniques, as included in the third
emission scenario in this study.
Example input data from the May 2011 Grimsvötn event for the
new volcanic emission preprocessor.
Date (inTime (inHeightAsh emissionsSO2 emissionsyyyymmdd)hhmmss)(a.g.l. in metres)(kg/m/s)(kg/m/s)201105211500007501.01120.00022011052115000012500.97130.01072011052115000017500.88870.03472011052115000022500.76030.07482011052115000027500.00.0201105211800007500.00570.00002011052118000012500.07530.00002011052118000017500.19960.00002011052118000022500.34840.00092011052118000027500.00.0201105212100007500.00000.22102011052121000012500.00530.21352011052121000017500.03410.19972011052121000022500.08970.18202011052121000027500.00.0201105220000007500.00.02011052200000012500.00.02011052200000017500.00.02011052200000022500.00.02011052200000027500.00.0
As the emission fluxes have to be provided at heights above ground, the
preprocessor (linearly) interpolates the input values for each column to
the model levels of WRF-Chem (see Fig. 2). Depending on the difference
between the model terrain height of the vent and the real vent height, an
offset can be defined to account for deviations due to the limited model
resolution. Finally, the resulting total volcanic emission, which is used
for the WRF-Chem simulation is scaled in order to ensure mass conservation
(can be violated due to interpolation effects). The routines have already
been used in the frame of a volcanic eruption exercise for an artificial
eruption of Etna (Hirtl et al., 2020).
Linear interpolation between input data (blue) and WRF-Chem model
levels (green) of the emission flux (red, kg/m/s).
WRF-Chem model simulationsModel setup
WRF-Chem simulations were performed from 21 to 26 May 2011. The model
domain extended from northern Africa to the north of Greenland and from
eastern Newfoundland to western Russia. Model resolution was 12 km
horizontally and 47 levels vertically from the surface up to 50 hPa.
Meteorological fields used as initial and boundary conditions were derived
from the European Centre for Medium-range Weather Forecasts (ECMWF).
Parameterization of physical processes included the Mellor–Yamada–Nakanishi
and Niino Level 2.5 planetary boundary layer (PBL) schemes (Hong et al., 2006), the Grell
three-dimensional (3D) ensemble cumulus parameterization (Grell and Freitas,
2014), and the Rapid Radiative Transfer Model for Global (RRTMG) long-wave
and short-wave radiation schemes (Iacono et al., 2008).
All simulations considered 10 volcanic ash bins and SO2
(chem_opt =402). Total fine ash was assumed to be composed
of the finest four bins (the other bins were set to zero): 12.7 % of
particles within 0.01 to 3.9 µm in diameter, 18.2 % within 3.9 to
7.8 µm, 29.1 % within 7.8 to 15.6 µm, and 40.0 % within 15.6
to 31.0 µm. This is consistent with the FLEXPART (FLEXible PARTicle
dispersion model; Stohl et al., 2005) model simulations that are used as
input for the Bayesian inversion (emission scenario 3) to calculate an a
posteriori source term (see Sect. 3.2). It uses the size distribution
which represents the bin size range to which the IASI satellite observations
are mainly sensitive to.
Volcanic emission scenarios
Three emission scenarios (further designated as S1, S2, and S3) were
selected to test the sensitivity of ash and SO2 dispersion to volcanic
emissions. Underlying complexity of the source terms ranges from a very
simple first guess to a sophisticated a posteriori source term, which was
derived with satellite observations and inverse modelling.
Simple volcanic emission source terms can be derived from the eruption plume
height (Mastin et al., 2009; see also Sect. 2). During the Grimsvötn
eruption in 2011, plume height measurements were performed with weather
radars (e.g. Petersen et al., 2012) and made available by the Icelandic
Meteorological Office (IMO). The time series of observed plume heights AGL
from the Keflavík radar is shown in Fig. 3.
Observed plume heights (a.g.l.) from the Keflavík radar from 21
until 25 May 2011 during the eruption of the Grimsvötn volcano.
The first emission scenario (S1) used only the first observed plume height
(15 km) and assumed constant emissions of ash and SO2 for the eruption
which was assumed to last 2 d. This is a very rough estimate, though a
common approach to get a first idea of the dispersion of the volcanic plume.
The associated uncertainties increase rapidly, in particular if eruption
characteristics change. An ash emission rate of about 0.01 Tg/s was
estimated with the Mastin formula (Eq. 1) for ash. For SO2, the total
emitted mass was assumed to be 1 Tg, yielding a constant emission rate of
about 5787 kg/s for the 2 d. The vertical source term structure was
modelled as a 75/25 umbrella-shaped plume.
The second emission scenario (S2) was based on the entire observed plume
height time series. The same plume heights were assumed for ash and SO2
even though Prata et al. (2017) found that observed plume heights were more
linked to SO2 than to ash. Ash emission rates were computed with the
Mastin equation for each time step. Based on the total amount of IASI ash
and SO2 measurements for the 4 d of the eruption, the hourly
emission rates were further constrained with these satellite observations
following Moxnes et al. (2014). The total emitted mass used in the
simulations was scaled to 0.4 Tg for ash and to 0.36 Tg for SO2. The
magnitude of the SO2 emission is reasonable, as shown by Flemming and Inness (2013), who estimated a total emitted mass of SO2 of 0.32 Tg. After
scaling, volcanic emission rates ranged from 67 to 12 080 kg/s for ash
and from 60 to 10 872 kg/s for SO2. The vertical structure of the
source term was again modelled as a 75/25 umbrella-shaped plume but considering
different plume heights.
The third emission scenario (S3) uses the source terms produced with the
Bayesian inversion technique, using FLEXPART runs and observations from the
IASI instrument. The source term files were provided by Moxnes et al. (2014), who also described the method in detail. The source terms are shown
in Fig. 4, with a vertical resolution of 1000 m. In contrast to S1 and S2,
the vertical structure of these emissions does not follow an umbrella-shaped
plume. While maximum SO2 emissions (up to 11541 kg/s) were found at
altitudes between about 5 and 12 km above sea level (a.s.l.) in the morning
of 22 May, ash emissions were largest (7539 kg/s) at lower altitudes
(below approximately 2 km a.s.l.) in the morning on 23 May.
Temporal evolution of hourly-resolved vertical (height a.s.l.)
SO2(a) and ash (b) emissions from FLEXPART inverse modelling
based on IASI data. Data obtained from Moxnes et al. (2014).
According to Fig. 4, the highest ash emissions are below 5 km a.s.l., while the
SO2 emission peaks are located at altitudes between 5 and 13 km.
Figure 5 summarizes the temporal evolution of the emission rates of all
three scenarios. While SO2 emissions are highest during the early phase
of the eruption, the highest ash emissions occur after 22 May, 20:00 UTC,
when SO2 emissions are already low. The comparison of the three
scenarios reveals average SO2 emissions for the simple S1 source term
but distinctively higher S1 ash emissions compared to S2 and S3 (note the
logarithmic y axis).
Emission rates for all three emission scenarios for SO2(a) and ash (b).
Model inter-comparison of predicted ash considering aviation regulation aspects
To evaluate the performance of the three emission scenarios in a first step,
the model runs are intercompared for the first 2 d of the eruption.
Focus is set on ash concentration levels, which are important for aviation
aspects. All regions with volcanic ash mass concentration greater than or
equal to 4 mg/m3 are considered high-contamination
areas (ICAO, 2016). Passenger aircraft are advised not to fly through
regions of volcanic ash concentrations that exceed 4 mg/m3. This
threshold is therefore most important for aviation aspects.
Figure 6 shows the 4 mg/m3 contour lines of maximum sub-column (between
WRF-Chem model levels) volcanic ash for all emission scenarios for 22
and 23 May, 00:00 and 12:00 UTC. Since emission rates of scenario S1 are
much higher than those of S2 and S3 (see Fig. 5), ash-rich regions are
distinctively larger for S1 than for the other scenarios. This is most
visible on 23 May, 12:00 UTC, when the S1 cloud spreads from Greenland and
Iceland towards the UK. Neither the S2 nor the S3 scenario shows any
significant area with an ash concentration exceeding 4 mg/m3. This
illustrates how crucial it is to carefully estimate the emission rates.
Comparison between scenarios S2 and S3 reveals a higher ash concentration on
22 May for S2 but lower ash contamination on the day after. This can be
explained with corresponding emission rates (Fig. 5). An evaluation of the
source-term performance and investigation of corresponding ash and SO2
dispersion from all WRF-Chem simulations can be found in the next section,
where model runs will be compared with independent observations.
Maximum sub-column concentrations of total ash indicated via the 4 mg/m3 isoline for each grid cell predicted for the first 2 d (22
and 23 May 2011, 00:00 and 12:00 UTC) after the eruption start for the three
emission scenarios simulated with WRF-Chem.
Evaluation of WRF-Chem simulations with observationsComparison of volcanic ash and SO2 with satellite data
In this section, the model simulations are compared to satellite
observations of ash and SO2 from different instruments. SEVIRI is an
instrument on board the geostationary METEOSAT (Schmetz et al., 2002)
satellite, which observes any point within its field of view every 15 min (over Europe every 5 min), AATSR was an instrument on board
ENVISAT (mission ended in 2012), which was in a sun-synchronous orbit with
an Equator crossing time of 10:00 local time. Several studies exist in which
data of the two instruments have been used to analyse volcanic eruptions.
Virtanen et al. (2014) have developed a (plume and cloud) height estimate
algorithm for AATSR, which has been validated and compared to other
satellite-based instruments and in situ data. The method was applied to the
Eyjafjallajökull eruption in 2010 and performed reasonably well. Kylling et
al. (2015) compared SEVIRI with IASI observations for the Grimsvötn
eruption and found deviations in mass loadings of about a factor of 2
between the instruments, with the higher concentrations measured by SEVIRI,
for the plume going northward.
Results from scenario S1 are not considered here because the
model intercomparison already indicated a strong overestimation of ash
simulated with S1 (Fig. 6). The model simulations for the scenarios S2 and
S3 are compared to total column ash from SEVIRI and AATSR observations for
23 May 2011 in Fig. 7. The ash cloud was observed south of Iceland by both
SEVIRI and aerosol optical thickness (AOT) from the AATSR, which were in
good agreement. The simulation based on S3 performs well and reproduces the
location of the cloud. The maximum total ash concentration based on S3 was
higher than that of SEVIRI with 10.7 g/m2 compared to 3.9 g/m2,
respectively. Based on S2, however, the highest ash concentrations (maximum 4.5 g/m2) are simulated in the northwest of Iceland due to wrong
assumptions of the emitted ash-plume-top heights. On the next day (24 May;
see Fig. A1) the scenarios S2 and S3 further drift apart, again with S3
being in better agreement with the observations. While most of the observed
cloud moves towards the east (to the UK and Scandinavia), SEVIRI also
detected some ash north of Iceland, which is assumed to be noise in the
data, not present in AATSR and in the model. Ash mass loading of the cloud
northeast of Scotland is as high as 3.7 g/m2 in SEVIRI data and 0.8 g/m2 in the S3 model run.
Total ash columns from WRF-Chem simulations (S2 and S3), SEVIRI
ash mass loading and AOT from AATSR on 23 May 2011 12:00.
Observations from the AIRS instrument, a hyperspectral imager on the polar-orbiting EOS Aqua satellite, are used for comparison of SO2. AIRS
has a spatial resolution of 13.5 km and has already been used to study other
volcanic eruptions, such as the Etna eruption in 2002, published by Carn et
al. (2005). They showed that comparisons with MODIS observations indicated
that AIRS is likely to underestimate SO2 in the vicinity of the volcano
due to the presence of dense ash.
Simulated SO2 concentrations from all WRF-Chem runs and SO2
observed by AIRS are shown in Fig. 8 for 23 May 2011. A total of 2 d after the
eruption the SO2 cloud was transported towards the north. All model
scenarios reproduce this pattern in general but show differences in plume
width and in the distance of the plume from the vent. AIRS data also showed
SO2, which was transported towards the east, but this could not be
reproduced by the model simulations. The maximum observed SO2
concentration was about 95 DU, which was detected northwest of Iceland.
The highest SO2 concentrations from model simulations range from about
60 DU in S2 to 910 DU in S1. During the next days (Fig. A2) differences
between the model and the observations increase.
SO2 total columns (DU) from WRF-Chem simulations (S1, S2, and
S3) compared to the AIRS observations on 23 May 12:00.
The comparison of the WRF-Chem simulations with satellite observations
revealed that the proper prediction of the location of the ash and SO2
plumes for the Grimsvötn 2011 eruption is only possible when the source
terms are treated separately.
Comparison with ground-based observations
Measurements from two lidar stations and several ground-based in situ
observations are used to further evaluate the S3 model simulation. Both S1-
and S2-based simulations did not show relevant ash concentrations at these
locations.
Lidar profiles at selected stations
Vertical profiles of volcanic ash are compared with measurements from lidars
(pink dots in Fig. 9) in Stockholm (Tesche et al., 2007; Althausen et al.,
2009; Tesche et al., 2012) and Cabauw. On 24 May, the model simulates
that a narrow, elongated band of ash was transported over the northern European
mainland. The cloud ranged from the Netherlands up to northern Scandinavia
(Fig. 9). It slowly approached Stockholm (Fig. 9 northern pink dot), where
maximum ash column concentrations were found at about 23:00 UTC.
Simulated maximum total ash column for each grid cell on 24
May at 19:00 (a), 23:00 (b), and 25 May 03:00 UTC (c). The pink
dots indicate the locations of the lidar in Stockholm and Cabauw, and the orange
dots indicate the location of the ground stations.
The lidar measurements in Stockholm (Fig. 10) revealed ash arrival a few
hours later, on 25 May between 03:00 and 04:00 UTC. The temporal offset is,
however, relatively small, considering that Stockholm is far away from the
source region and that the ash cloud has already been transported for a
couple of days.
Min–max observed ash concentrations values at the lidar Stockholm
(25 May 02:00 until 08:00) compared to the WRF-Chem maximum ash
concentrations (24 May 19:00 until 25 May 03:00 UTC) for each vertical
level.
The vertical profile of modelled maximum ash concentration (average from 24
May 19:00 UTC until 25 May 03:00 UTC) based on S3 over Stockholm is below
the ash mass concentration estimates from Tesche et al. (2012) that were
based on lidar measurements between 02:00 and 08:00 UTC. While the model
predicts maximum ash concentrations
(<100µg/m3) within a thick vertical layer between 500 and
2500 m, the lidar observations revealed a sharp peak at about 1000 m with
values between 50 µg/m3 (lower estimate) and
450 µg/m3 (upper estimate). The maximum (as well as
the minimum) curve is based on different assumptions when calculating the
mass from the extinction coefficient. According to Tesche et al. (2012), the
minimum curve is more likely to represent the real observed ash
concentrations.
Volcanic ash was also detected by the lidar at Cabauw on 25 May, 16:30 UTC.
Figure 11 shows a qualitative comparison of the observed backscattering
coefficient profile and the S3-based modelled ash concentration profile, both
normalized to 1. Both data sets clearly show enhanced aerosol concentrations
between about 500 and 2000 m with the peaks at 1250 and 1500 m in the
lidar and model data, respectively. The predicted vertical extension of the
ash layer shows a very good agreement with the observation at the Cabauw
station.
Vertical (scaled) profiles of WRF-Chem S3-based total ash and
backscattering coefficients from the EARLINET lidar at Cabauw on 25 May
2011, 16:30 UTC.
Comparison with PM10 observations at selected ground stations
For the days of the Grimsvötn eruption, surface measurements of PM10 are
available from several stations in northern Europe (orange dots in Fig. 9).
These data have already been used by others to investigate the eruption and
to evaluate dispersion models (e.g. Prata and Prata, 2012; Tesche et al.,
2012; Moxnes et al., 2014). The WRF-Chem output (the finest three ash bins
corresponding to the size range of PM10) from scenario S3 was interpolated
to the station locations and compared for the 2 d of the volcanic ash
cloud overpass on 24 and 25 May. Figure 12 shows the time series of the
observed hourly data for the stations.
Time series of observed PM10 (µg/m3)
ground concentrations (solid line) and WRF-Chem (S3) simulations (dotted
line) for 24 and 25 May 2011.
In general, the observed PM10 concentrations are slightly higher than the
model prediction. This is not only true for PM10 peaks, when a large portion
of PM10 can be attributed to volcanic ash, but also for the entire time
period. This model bias is caused by missing anthropogenic and biogenic
aerosol emissions as well as secondary aerosol formation yielding zero PM
concentrations before and after the volcanic ash overpass. These
contributions were not considered in the simulations as the emphasis of
this study was on the ash and SO2 emitted by the volcano.
For most of the ground stations, the plume arrival is simulated well by the
model, although the model underestimates the observations. In Aberdeen, a
temporal shift of about 6 h is observed. At this station the modelled
peak is later compared to the observed peak. This is in contrast to the
station in Oslo where the simulated peak arrives about 6 h earlier. The
ground observations in Stockholm reveal that the time of the plume arrival
is captured by the model very well in contrast to the lidar observation,
which indicates a temporal shift of about 4 h.
Note that the simulation of the dispersion of ash and SO2 over
long distances is subject to large uncertainties. Uncertainty in the
emission and the meteorology (e.g. vertical mixing) has a strong impact on
the dispersion and causes deviations between model and observations,
especially for this complex case.
Conclusions
The developments presented in this paper permit the integration of complex source
profiles into the emission preprocessor of the WRF-Chem model. Such
temporarily and vertically resolved emissions of ash and SO2 can be
obtained, e.g. by inverse modelling exploiting satellite observations. The
simple structure of the input data format allows integration of source term
characteristics from any suitable method.
Model runs with three emission scenarios were conducted and evaluated for
the eruption of the Grimsvötn volcano in 2011. This eruption was unique
because ash and SO2 injection heights were separated and a vertical
wind shear led to different transport directions of the respective clouds
after the eruption. Model performance for ash and SO2 dispersion was
therefore highly sensitive to the source geometry.
The first model scenario neglected different emission geometries of ash and
SO2. It used the first observed plume height (15 km) as plume-top
height for ash and SO2 and assumed constant emission fluxes for the
entire eruption period which was estimated to last for 2 d. Emission
fluxes were calculated empirically (Mastin et al., 2009) and distributed
vertically in a 75/25 umbrella-shaped plume (Stuefer et al., 2013). The
second scenario was based on the entire observed plume-top-height time
series, which was, again, assumed to be the same for ash and SO2. After
scaling empirically derived emission fluxes (Moxnes et al., 2014), emitted
mass was distributed again in a 75/25 umbrella-shaped plume while
considering different plume heights. The third scenario was based on
emission fluxes obtained by the inversion of volcanic ash and SO2
column observations from the IASI instrument applying the FLEXPART model to
link an a priori source term and satellite total column observations. This
source term includes different emission characteristics of ash and SO2,
both in the temporal and in the vertical dimensions.
Evaluation of the model simulations revealed the best performance of the most
complex third emission scenario (S3). Improper emission heights of scenario
1 (S1) resulted in overestimated emission fluxes and produced too high ash
concentrations. Furthermore, the ash cloud dispersed into the wrong
direction. For the second emission scenario (S2), the simulated magnitudes
of the concentrations of ash and SO2 were in good agreement with the
satellite observations, although the location of the ash cloud was wrong due
to incorrect ash plume-top heights, which were in reality lower than those
of SO2. This underpins the utility of separate ash and SO2 source
terms with reasonable temporal and vertical variability as used in S3. This
simulation did not only reproduce the location of ash and SO2 clouds
correctly, but also ash concentration values close to the surface.
Validation of simulated vertical ash concentration profiles also revealed a
good agreement with observations, although the ash cloud was dispersed
already for a few days on the way to the measurement locations. The PM10
fraction of the ash was compared to ground stations in northern Europe. The
model underestimates the observations because no other PM10 sources
(anthropogenic, biogenic, sea salt, etc.) were considered in the
simulations. The prediction of the cloud overpass time was well accomplished
for most of the stations by the model run using the complex emission source
term S3.
Our analysis showed that volcanic ash can also have an impact on air quality
when the cloud touches the ground. Especially for volcanic events which
significantly affect surface air pollution, forecast models can support
authorities to warn the public.
Fast access to on-site measurements, e.g. from volcano observatories, is
important to constrain dispersion models during an emergency crisis.
Decisions must be based on the best available information. Updated source term
estimates and model hindcasts can help to better understand and predict the
transport of ash and gases. Volcanic ash observations from satellite
instruments are sometimes limited in accuracy; thus models may help to
interpret satellite retrievals. This is crucial for the aviation sector,
which is highly vulnerable to “airborne” hazards. Accurate model
predictions are not only important to ensure aircraft safety, but can also
avoid air space closures or flight reroutings, which can save millions of
dollars.
Total ash columns from WRF-Chem simulations (S2 and S3), SEVIRI
ash mass loading, and AOT from AATSR on 22 to 24 May 2011 12:00.
SO2 total columns (DU) from WRF-Chem simulations (S1, S2, and
S3) compared to the AIRS observations on 22 to 24 May 12:00.
Glossary
AATSRAdvanced Along-Track Scanning Radiometera.g.l.Above ground levela.s.l.Above surface levelAIRSAtmospheric Infrared SounderAOTAerosol optical thicknessDUDobson unitsECMWFEuropean Centre for Medium-Range Weather ForecastsEARLINETEuropean Aerosol Research Lidar NetworkENVISATEnvironmental SatelliteEOSEarth Observing SystemEUNADICS-AVEuropean Natural Airborne Disaster Information and Coordination System for AviationFLEXPARTFLEXible PARTicle dispersion modelGOME-2Global Ozone Monitoring ExperimentIASIInfrared Atmospheric Sounding InterferometerICAOInternational Civil Aviation OrganizationIMOIcelandic Meteorological OfficeLidarLight detection and rangingMETEOSATMeteorological satelliteMODISModerate Resolution Imaging SpectroradiometerNOAANational Oceanic and Atmospheric AdministrationOMIOzone Monitoring InstrumentPBLPlanetary boundary layerPMParticulate matterRRTMGRapid Radiative Transfer Model for Global radiation schemesSEVIRISpinning Enhanced Visible Infra-Red ImagerUTCCoordinated universal timeVAACsVolcanic Ash Advisory CentresVACPVolcanic Ash Contingency PlanVASTVolcanic Ash Strategic initiative TeamWRF-ChemWeather Research and Forecasting (WRF) model coupled with Chemistry
Data availability
Data are available upon request from the
corresponding author (marcus.hirtl@zamg.ac.at).
Author contributions
MH conceptualized and prepared the paper with
contributions from all co-authors. MH developed the new WRF-Chem code and
conducted the model simulations and data processing for the evaluation with
the observational data. MH, BSP, and MM collected the observational data from
different sources and prepared the figures for the paper. MH interpreted the
data with support from BSP and MS. MS and RB helped with WRF-Chem setup and
data preparation. CM, DA, and MM provided important support for the source
term inversion part by providing and interpreting the data which were used
for the model scenario S3.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Analysis and prediction of natural airborne aviation hazards”. It is not associated with a conference.
Acknowledgements
Satellite data were made available via the Volcanic Ash Strategic initiative
Team (VAST) project web page http://vast.nilu.no/ (last access: 20 November 2020). The authors acknowledge
EARLINET for providing aerosol LIDAR profiles available at https://data.earlinet.org/ (last access: 20 November 2020).
Financial support
This work has been conducted within the framework of the EUNADICS-AV
project, which received funding from the European Union's Horizon 2020
research programme for societal challenges – Smart, Green and Integrated
Transport under grant agreement no. 723986. This work has also been
supported by the BMWFW (Federal Ministry of Science, Research and Economics)
through funding of the LUFTLEER project (2020). The publication in part is
the result of research sponsored by the Cooperative Institute for Alaska
Research with funds from the National Oceanic and Atmospheric Administration
under cooperative agreement NA13OAR4320056 with the University of Alaska.
Review statement
This paper was edited by Matthias Themessl and reviewed by two anonymous referees.
ReferencesAlbersheim, S. and Guffanti M.: The United States national volcanic ash
operations plan for aviation, Nat. Hazards, 51, 275–285, 10.1007/s11069-008-9247-1, 2009.
Alexander, D.: Volcanic ash in the atmosphere and risks for civil aviation:
a study in European crisis management, Int. J. Disast. Risk Sc., 4, 9–19,
2013.Althausen, D., Engelmann, R., Baars, H., Heese, B., Ansmann, A., Müller,
D., and Komppula, M.: Portable Raman lidar PollyXT for automated profiling
of aerosol backscatter, extinction, and depolarization, J. Atmos. Ocean.
Tech., 26, 2366–2378, 10.1175/2009JTECHA1304.1, 2009.Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, 10.5194/acp-14-317-2014, 2014.Bolić, T. and Sivčev, Ž.: Eruption of Eyjafjallajökull in
Iceland: Experience of European air traffic management, Transp. Res. Rec.,
2214, 136–143, 10.3141/2214-17, 2011.
Bolić, T. and Sivčev, Ž.: Air Traffic Management in Volcanic
Ash Events in Europe: a Year After Eyjafjallajökull Eruption, Transportation Research Board 91st Annual Meeting, Washington, D.C., USA, 22–26 January 2012, No.12-3009, 2012.Carboni, E., Grainger, R. G., Mather, T. A., Pyle, D. M., Thomas, G. E., Siddans, R., Smith, A. J. A., Dudhia, A., Koukouli, M. E., and Balis, D.: The vertical distribution of volcanic SO2 plumes measured by IASI, Atmos. Chem. Phys., 16, 4343–4367, 10.5194/acp-16-4343-2016, 2016.Carn, S. A., Strow, L. L., de Souza-Machado, S., Edmonds, Y., and Hannon,
S.: Quantifying tropospheric volcanic emissions with AIRS: The 2002 eruption
of Mt. Etna (Italy), Geophys. Res. Lett., 32, L02301,
10.1029/2004GL021034, 2005.Chahine, M. T., Pagano, T. S., Aumann, H. H., Atlas, R., Barnet, C.,
Blaisdell, J., Chen L., Divakarla, M., Fetzer, E. J., Goldberg, M., Gautier,
C., Granger S., Hannon, S., Irion, F. W., Kakar, R., Kalnay, E.,
Lambrigtsen, B. H., Lee. S.-Y., Le Marshall, J., McMillan, W. W., McMillin,
L., Olsen, E. T., Revercomb, H., Rosenkranz, R., Smith, W. L., Staelin, D.,
Strow, L. L., Susskind, J., Tobin, D., Wolf, W., and Zhou, L.: AIRS:
Improving weather forecasting and providing new data on greenhouse gases,
B. Am. Meteorol. Soc., 87, 911–926,
10.1175/BAMS-87-7-911, 2006.
Clarkson, R. J., Majewicz, E. J., and Mack, P.: A re-evaluation of the 2010
quantitative understanding of the effects volcanic ash has on gas turbine
engines, P. I. Mech. Eng. G.-j. Aer., 230, 2274–2291, 2016.Cooke, M. C., Francis, P. N., Millington, S., Saunders, R., and Witham, C.:
Detection of the Grímsvötn 2011 volcanic eruption plumes using
infrared satellite measurements, Atmos. Sci. Lett., 15, 321–327,
10.1002/asl2.506, 2014.Flemming, J. and Inness, A.: Volcanic sulfur dioxide plume forecasts based
on UV satellite retrievals for the 2011 Grímsvötn and the 2010
Eyjafjallajökull eruption, J. Geophys. Res.-Atmos., 118, 10172–10189,
10.1002/jgrd.50753, 2013.Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, 10.5194/acp-14-5233-2014, 2014.Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G.,
Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry with in
the WRF model, Atmos. Environ., 39, 6957–6975,
10.1016/j.atmosenv.2005.04.027, 2005.
Gudmundsson, M. T. and Björnsson, H.: Eruptions in Grímsvötn,
Vatnajökull, Iceland, 1934–1991, Jökull, 41, 21–45, 1991.Guffanti, M., Schneider, D. J., Wallace, K. L., Hall, T., Bensimon, D. R.,
and Salinas, L. J.: Aviation response to a widely dispersed volcanic ash and
gas cloud from the August 2008 eruption of Kasatochi, Alaska, USA, J.
Geophys. Res., 115, D00L19, 10.1029/2010JD013868, 2010.Hirtl, M., Stuefer, M., Arnold, D., Grell, G., Maurer, C., Natali, S.,
Scherllin-Pirscher, B., and Webley, P.: The effects of simulating volcanic
aerosol radiative feedbacks with WRF-Chem during the Eyjafjallajökull
eruption, April and May 2010, Atmos. Environ., 198, 194–206,
10.1016/j.atmosenv.2018.10.058, 2019.Hirtl, M., Arnold, D., Baro, R., Brenot, H., Coltelli, M., Eschbacher, K., Hard-Stremayer, H., Lipok, F., Maurer, C., Meinhard, D., Mona, L., Mulder, M. D., Papagiannopoulos, N., Pernsteiner, M., Plu, M., Robertson, L., Rokitansky, C.-H., Scherllin-Pirscher, B., Sievers, K., Sofiev, M., Som de Cerff, W., Steinheimer, M., Stuefer, M., Theys, N., Uppstu, A., Wagenaar, S., Winkler, R., Wotawa, G., Zobl, F., and Zopp, R.: A volcanic-hazard demonstration exercise to assess and mitigate the impacts of volcanic ash clouds on civil and military aviation, Nat. Hazards Earth Syst. Sci., 20, 1719–1739, 10.5194/nhess-20-1719-2020, 2020.Hong, S. Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Wea. Rev., 134,
2318–2341, 10.1175/MWR3199.1, 2006.Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos.,
113, D13103, 10.1029/2008JD009944, 2008.International Civil Aviation Organization: Volcanic Ash Contingency
Plan – Eur and Nat Regions, Edition 2.0.0, availabe at:
https://www.icao.int/EURNAT/EUR and NAT Documents/EUR+NAT VACP.pdf
(last access: 20 November 2020), 2016.Kylling, A., Kristiansen, N., Stohl, A., Buras-Schnell, R., Emde, C., and Gasteiger, J.: A model sensitivity study of the impact of clouds on satellite detection and retrieval of volcanic ash, Atmos. Meas. Tech., 8, 1935–1949, 10.5194/amt-8-1935-2015, 2015.Mastin, L. G., Guffanti, M., Servranckx, R., Webley, P., Barsotti, S., Dean,
K., Durant, A., Ewert, J. W., Neri, A., Rose, W. I., Schneider, D., Siebert,
L., Stunder, B., Swanson, G., Tupper, A., Volentik, A., and Waythomas, C.
F.: A multidisciplinary effort to assign realistic source parameters to
models of volcanic ash-cloud transport and dispersion during eruptions, J.
Volcanol. Geoth. Res., 186, 10–21, 10.1016/j.jvolgeores.2009.01.008,
2009.Moxnes, E. D., Kristiansen, N. I., Stohl, A., Clarisse, L., Durant, A.,
Weber, K., and Vogel, A.: Separation of ash and sulfur dioxide during the
2011 Grímsvötn eruption, J. Geophys. Res.-Atmos., 119, 7477–7501,
10.1002/2013JD021129, 2014.Petersen, G. N., Bjornsson, H., Arason, P., and von Löwis, S.: Two weather radar time series of the altitude of the volcanic plume during the May 2011 eruption of Grímsvötn, Iceland, Earth Syst. Sci. Data, 4, 121–127, 10.5194/essd-4-121-2012, 2012.Prata, A. J. and Prata, A. T.: Eyjafjallajökull volcanic ash
concentrations determined from Spin Enhanced Visible and Infrared Imager
measurements, J. Geophys. Res., 117, D00U23, 10.1029/2011JD016800, 2012.Prata, F., Woodhouse, M., Huppert, H. E., Prata, A., Thordarson, T., and Carn, S.: Atmospheric processes affecting the separation of volcanic ash and SO2 in volcanic eruptions: inferences from the May 2011 Grímsvötn eruption, Atmos. Chem. Phys., 17, 10709–10732, 10.5194/acp-17-10709-2017, 2017.
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and
Ratier, A.: An introduction to Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, 2002.Sigmarsson, O., Haddadi, B., Carn, S., Moune, S., Gudnason, J., Yang, K.,
and Clarisse, L.: The sulfur budget of the 2011 Grímsvötn eruption,
Iceland, Geophys. Res. Lett., 40, 6095–6100, 10.1002/2013GL057760, 2013.Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda,
M. G., Huang, X.-Y., and Wang, W.: A Description of the Advanced Research
WRF Version 3, NCAR Technical Note TN-468+STR, 113 pp., 2008.Stohl, A., Forster, C., Frank, A., Seibert, P., and Wotawa, G.: Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2, Atmos. Chem. Phys., 5, 2461–2474, 10.5194/acp-5-2461-2005, 2005.Stuefer, M., Freitas, S. R., Grell, G., Webley, P., Peckham, S., McKeen, S. A., and Egan, S. D.: Inclusion of ash and SO2 emissions from volcanic eruptions in WRF-Chem: development and some applications, Geosci. Model Dev., 6, 457–468, 10.5194/gmd-6-457-2013, 2013.Tesche, M., Ansmann, A., Müller, D., Althausen, D., Engelmann, R., Hu,
M., and Zhang, Y.: Particle backscatter, extinction, and lidar ratio
profiling with Raman lidar in South and North China, Appl. Opt., 46,
6302–6308, 10.1364/AO.46.006302, 2007.Tesche, M., Glantz, P., Johansson, C., Norman, M., Hiebsch, A., Ansmann, A.,
Althausen, D., Engelmann, R., and Seifert, P.: Volcanic ash over Scandinavia
originating from the Grímsvötn eruptions in May 2011, J. Geophys.
Res., 117, D09201, 10.1029/2011JD017090, 2012.Virtanen, T. H., Kolmonen, P., Rodríguez, E., Sogacheva, L., Sundström, A.-M., and de Leeuw, G.: Ash plume top height estimation using AATSR, Atmos. Meas. Tech., 7, 2437–2456, 10.5194/amt-7-2437-2014, 2014.Vogfjörd, K. S., Jakobsdóttir, S. S., Gudmundsson, G. B., Roberts,
M. J., Ágústsson, K., Arason, T., Geirsson, H., Karlsdóttir, S.,
Hjaltadóttir, S., Ólafsdóttir, U., Thorbjarnardóttir, B.,
Hafsteinsson, G., Sveinbjörnsson, H., Stefánsson, R., and
Jónsson, T. V.: Forecasting and monitoring a subglacial eruption in
Iceland, Eos Trans. AGU, 86, 245–248, 10.1029/2005EO260001, 2005.Webley, P. W., Steensen, T., Stuefer, M., Grell, G., Freitas, S., and
Pavolonis, M.: Analyzing the Eyjafjallajökull 2010 eruption using
satellite remote sensing, lidar and WRF-Chem dispersion and tracking model,
J. Geophys. Res., 117, D00U26, 10.1029/2011JD016817, 2012.
Witham, C. S., Hort, M. C., Potts, R., Servranckx, R., Husson, P., and
Bonnardot, F.: Comparison of VAAC atmospheric dispersion models using the 1
November 2004 Grimsvötn eruption, Met. Apps., 14, 27–38, 10.1002/met.3, 2007.