AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-445-2017New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO2
dataset: algorithm description, initial results, and continuation with the
Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)LiCancan.li@nasa.govKrotkovNickolay A.https://orcid.org/0000-0001-6170-6750CarnSimonhttps://orcid.org/0000-0002-0360-6660ZhangYanSpurrRobert J. D.JoinerJoannaEarth System Science Interdisciplinary Center, University of Maryland,
College Park, MD 20742, USANASA Goddard Space Flight Center, Greenbelt, MD 20771, USADepartment of Geological and Mining Engineering and Sciences, Michigan
Technological University, Houghton, MI 49931, USART Solutions, Inc., Cambridge, MA 02138, USACan Li (can.li@nasa.gov)6February20171024454581July201613September20161January201715January2017This 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/10/445/2017/amt-10-445-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/445/2017/amt-10-445-2017.pdf
Since the fall of 2004, the Ozone Monitoring Instrument
(OMI) has been providing global monitoring of volcanic SO2 emissions,
helping to understand their climate impacts and to mitigate aviation
hazards. Here we introduce a new-generation OMI volcanic SO2 dataset
based on a principal component analysis (PCA) retrieval technique. To reduce
retrieval noise and artifacts as seen in the current operational linear fit
(LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic
features extracted directly from OMI radiances in the spectral fitting,
thereby helping to minimize interferences from various geophysical processes
(e.g., O3 absorption) and measurement details (e.g., wavelength shift).
To solve the problem of low bias for large SO2 total columns in the LF
product, the OMSO2VOLCANO algorithm employs a table lookup approach to
estimate SO2 Jacobians (i.e., the instrument sensitivity to a
perturbation in the SO2 column amount) and iteratively adjusts the
spectral fitting window to exclude shorter wavelengths where the SO2
absorption signals are saturated. To first order, the effects of clouds and
aerosols are accounted for using a simple Lambertian equivalent reflectivity
approach. As with the LF algorithm, OMSO2VOLCANO provides total column
retrievals based on a set of predefined SO2 profiles from the lower
troposphere to the lower stratosphere, including a new profile peaked at 13
km for plumes in the upper troposphere. Examples given in this study
indicate that the new dataset shows significant improvement over the LF
product, with at least 50 % reduction in retrieval noise over the remote
Pacific. For large eruptions such as Kasatochi in 2008 (∼ 1700 kt total SO2) and Sierra Negra in 2005 (> 1100 DU maximum
SO2), OMSO2VOLCANO generally agrees well with other algorithms that
also utilize the full spectral content of satellite measurements, while the
LF algorithm tends to underestimate SO2. We also demonstrate that,
despite the coarser spatial and spectral resolution of the Suomi National
Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite
(OMPS) instrument, application of the new PCA algorithm to OMPS data
produces highly consistent retrievals between OMI and OMPS. The new PCA
algorithm is therefore capable of continuing the volcanic SO2 data
record well into the future using current and future hyperspectral UV
satellite instruments.
Introduction
Volcanic emissions, while collectively a smaller source of sulfur dioxide
(SO2) than anthropogenic emissions (e.g., Bluth et al., 1993), are a
dominant and highly variable natural forcing to the Earth's climate system.
Explosive eruptions such as El Chichón in 1982 (Krueger, 1983) and Mt.
Pinatubo in 1991 (McCormick et al., 1995, and references therein) directly
inject large amounts of SO2 and other species into the stratosphere,
producing secondary sulfate aerosols that can remain at high altitudes for
years. Such large eruptions are rare, but they have been found to cause
significant perturbations to global surface temperature and atmospheric
circulation patterns (e.g., Robock and Mao, 1995; Robock et al., 2007;
Stenchikov et al., 2002). Recent studies (Ridley et al., 2014; Solomon et
al., 2011; Vernier et al., 2011) suggest that more frequent, moderate
eruptions may also be a far more important source of stratospheric aerosols
than previously thought and may have contributed to the slower recent rate
of global warming (Santer et al., 2014). SO2 emitted by passive
volcanic degassing is normally too short-lived to reach the stratosphere,
but its regional climate impact can be significant through the interaction
between secondary sulfate aerosols and clouds (Schmidt et al., 2012; Yuan et
al., 2011). In addition to their climate effects, volcanic plumes also pose
severe threats to health and human lives and aviation safety (e.g., Carn et
al., 2009; Stohl et al., 2011). To better understand volcanic climate
forcing, it is important to acquire accurate estimates of emissions from
various types of volcanic activity. Mitigating volcanic hazards, in contrast, requires global monitoring of volcanic plumes on a timely basis.
Since the first demonstration by Krueger (1983), satellite retrievals of
volcanic SO2 using ultraviolet (UV) instruments have become a critical
tool in studies of volcanism as well as in management of aviation safety.
The satellite volcanic SO2 data record dates back to the 1970s (e.g.,
Carn et al., 2016). However, earlier measurements (pre-1990s) were generally
limited to larger eruptions (e.g., Krueger et al., 1995; McPeters, 1993)
due to the small number of discrete wavelengths and relatively large
footprint size characteristic of heritage instruments such as the Total
Ozone Mapping Spectrometer (TOMS). Since the 1990s, hyperspectral UV
instruments have made measurements at hundreds of wavelengths, allowing
SO2 absorption features to be more clearly separated from interfering
processes. This has been demonstrated with the Global Ozone Monitoring
Experiment (GOME; Burrows et al., 1999; Eisinger and Burrows, 1998), GOME-2
(Nowlan et al., 2011; Rix et al., 2009), and the SCanning Imaging Absorption
spectroMeter for Atmospheric CHartographY (SCIAMACHY; Lee et al., 2008).
Among hyperspectral instruments that are currently operating in orbit, the
Dutch–Finnish Ozone Monitoring Instrument (OMI; Levelt et al., 2006),
launched on board NASA's Aura spacecraft in 2004, offers the best ground
resolution (13 × 24 km2 at nadir), along with a wide spectral
range (270–500 nm) and contiguous daily global coverage – features that make
it an effective instrument for global SO2 monitoring. In contrast to
TOMS, OMI permits the detection of weak SO2 emissions such as those
from coal-fired power plants or quiescent degassing volcanoes (e.g., Carn et
al., 2008; Hsu et al., 2012; Li et al., 2010a, b; Lu et al., 2010), even
with the at-launch algorithms (Krotkov et al., 2006, 2008; Yang et al.,
2007) that utilize only a small fraction of the available wavelengths. The
linear fit (LF) algorithm (Yang et al., 2007) is presently the operational
algorithm for NASA's standard OMI volcanic SO2 product. It uses 10 OMI
wavelengths between 310.8 and 360.2 nm and produces estimates of the
total SO2 vertical column density (VCD) assuming three different
prescribed SO2 profiles with center of mass altitudes (CMAs) at 3 km
(lower troposphere, TRL), 8 km (middle troposphere, TRM), and 18 km (lower
stratosphere, STL). The presumed CMAs were selected to represent SO2
from passively degassing volcanoes, moderate eruptions, and large explosive
eruptions, respectively. In addition to the OMI standard product, the LF
algorithm has also been implemented to produce SO2 retrievals quickly
from direct readout data, as well as near-real-time SO2 data from OMI
and the Ozone Mapping and Profiler Suite (OMPS) nadir mapper aboard the
NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi-NPP) spacecraft.
The LF algorithm has been widely used in studies of volcanic SO2 and
aviation safety applications, but it has a number of limitations. For
example, it is known to significantly underestimate SO2 VCDs in
relatively large eruptions due to signal saturation at shorter wavelengths
(e.g., Krotkov et al., 2010; Yang et al., 2009a). The algorithm also has
large biases over clean background areas, making it difficult to track
volcanic plumes from relatively small eruptions.
Several studies have demonstrated that OMI SO2 retrievals can be
improved by exploiting the full spectral content of the hyperspectral OMI
measurements. Theys et al. (2015) produced an OMI SO2 product with
reduced noise and bias using a DOAS (Differential Optical Absorption
Spectroscopy) scheme. For large volcanic signals, they used longer
wavelengths (up to 360–390 nm) where SO2 is only weakly absorbing to
avoid signal saturation. The iterative spectral fitting (ISF, and its
variant, direct spectral fitting or DSF) algorithm developed by Yang et al. (2009a) also utilized OMI spectral measurements at longer wavelengths and
produced much greater SO2 VCDs for SO2-rich eruptions such as
Sierra Negra in 2005 (Yang et al., 2009a) and Kasatochi in 2008 (Krotkov et
al., 2010) than the corresponding VCDs derived from the LF algorithm.
However, the ISF/DSF algorithm relies on computationally intensive online
radiative transfer calculations and instrument-specific soft calibration,
hindering its operational implementation.
Recently, we introduced a retrieval technique based on principal component
analysis (PCA) of satellite-measured radiances (Li et al., 2013, 2015).
Unlike DOAS or ISF/DSF, the PCA technique is a data-driven approach and uses
a set of principal components (PCs) extracted directly from satellite
radiance data in the spectral fitting. It benefits from the fact that the
PCs that account for the most spectral variance have characteristics
associated with various geophysical processes (e.g., O3 absorption,
rotational Raman scattering or RRS) or measurement details (e.g., wavelength
shift, variation in dark current). This allows these various interferences
in SO2 retrievals to be minimized without extensive forward calculation
or explicit instrument characterization, leading to an efficient
implementation with relatively small biases and noise in the retrieved
SO2 VCDs.
A flow chart of the new OMI volcanic SO2 PCA retrieval
algorithm (OMSO2VOLCANO). The first part of the algorithm, enclosed in the
dashed blue box, is essentially identical to the operational OMI planetary
boundary layer (PBL) SO2 algorithm (Li et al., 2013) and provides
input to the second part of algorithm that performs iterative spectral
fitting to retrieve volcanic SO2 VCD.
Our PCA algorithm has been implemented for operational production of the new-generation NASA standard OMI planetary boundary layer (PBL) SO2 dataset (used
for air quality studies). As compared with the previous OMI PBL SO2
dataset (Kroktov et al., 2006), the new product improves the detection limit
for point anthropogenic sources by a factor of two (Fioletov et al., 2015,
2016), enabling the detection of a large number of sources missing in
current emission inventories (McLinden et al., 2016). The reduced retrieval
bias also makes it easier to detect regional trends of SO2 (e.g.,
Krotkov et al., 2016). He et al. (2016) demonstrated that, without extensive
bias correction, it was possible to derive a clear regional SO2 trend
over the eastern US from the new PCA product but not from the previous
product. In this paper, we extend the PCA algorithm to volcanic retrievals
and introduce the new-generation NASA OMI volcanic SO2 product. We
describe the algorithm in Sect. 2 and present results for selected
scenarios in Sect. 3, including background regions and several volcanic
eruptions; the focus is on comparisons with retrievals from the current
operational LF algorithm. Another advantage of the PCA approach is its
ability to generate consistent retrievals between different instruments, and
in Sect. 4 we discuss the prospect of continuing the long-term OMI data
record with Suomi-NPP OMPS.
Algorithm description
Our new OMI volcanic SO2 algorithm (hereafter referred to as
OMSO2VOLCANO) comprises two main components: the first step (enclosed by the
dashed blue box in the algorithm flowchart; Fig. 1) is designed to
identify pixels having strong SO2 signals and provide initial estimates
of SO2 VCD (ΩSO2). The second step produces more accurate
estimates of volcanic SO2 VCD through iterative spectral fitting.
Step 1: PCA and initial fit
The first step of the OMSO2VOLCANO algorithm is essentially identical to the
operational OMI PBL SO2 algorithm that has been previously described in
detail elsewhere (Li et al., 2013) and is only briefly reviewed here. In
short, for each OMI orbit, we process the 60 rows (cross-track positions)
one at a time, employing a PCA technique to extract PCs
(νi) for the spectral range 310.5–340 nm from the
sun-normalized radiance spectra of ∼ 1000 pixels along the
flight direction (after excluding pixels with high slant column ozone). The
PCs are ranked in descending order according to the spectral variance they
each explain. If derived from SO2-free regions, the first several PCs
that account for the most of the variance are representative of geophysical
processes unrelated to SO2 such as ozone absorption and RRS, as well as
measurement details such as wavelength shift. We then obtain initial
estimates of SO2 VCD (ΩSO2_ini) and the
coefficients of the PCs (ω) by fitting
the first nν (non-SO2) PCs and the SO2 Jacobians
(∂N/∂ΩSO2) to the measured radiance
spectrum (in this case the quantity N, which is the logarithm of
the sun-normalized radiances, I):
Nω,ΩSO2=∑i=1nvωivi+ΩSO2∂N∂ΩSO2.
The SO2 Jacobians represent the sensitivity of sun-normalized
backscattered radiances (I or its logarithm, N) at the top
of the atmosphere (TOA) to a unit perturbation in ΩSO2 and
were precalculated with the vector radiative transfer code VLIDORT (Spurr,
2008). As with the PBL SO2 algorithm (Li et al., 2013), in this part of
the algorithm we use a fixed SO2 Jacobian spectrum in Eq. (1),
calculated assuming that SO2 is predominantly in the lowest 1000 m of
the atmosphere and that the observation is made under cloud-free conditions
with fixed surface albedo (0.05), surface pressure (1013.25 hPa), solar
zenith angle (30∘), viewing zenith angle (0∘), and
preset O3 and temperature profiles (with O3 VCD = 325 DU).
In most cases, we use nν= 20 PCs in the fitting.
However, in the presence of strong SO2 signals (e.g., a volcanic
plume), the inclusion of that many PCs in Eq. (1) can introduce
collinearity, as some of the leading PCs may contain an SO2 absorption
signature. To avoid this, we examine the PCs 4–20 and only use
nν=i-1 PCs if the ith
PC is found to be significantly correlated with SO2 cross sections at
the 95 % confidence level. Note that we always include the first three PCs
in fitting, as they are clearly associated with geophysical processes such
as O3 absorption (Li et al., 2013). An example of the correlation
coefficients between SO2 and PCs is given in Fig. S1 in the
Supplement to further demonstrate this technique. We then
exclude pixels with large ΩSO2_ini (outside of
±1.5 standard deviations for the row) and repeat the PCA and spectral
fitting to get updated PCs and the SO2 VCD. The output PCs and VCD are
used as input to the second step of the OMSO2VOLCANO algorithm (see Fig. 1).
Step 2: Volcanic SO2 Jacobians lookup table (LUT)
A main function of the second step of the OMSO2VOLCANO algorithm is to
determine SO2 Jacobians under various actual observation conditions. In
our spectral range of interest (∼ 310 to 340 nm), both
I (i.e., sun-normalized backscattered radiances at TOA) and the
SO2 Jacobians depend on a number of factors including satellite
geometry (solar zenith angle, SZA or θ0, viewing
zenith angle, VZA or θ, and relative azimuth angle, RAZ or
ϕ), surface reflectivity and pressure, cloud fraction and
height, aerosols, and the amount and vertical distribution of absorbing
gases (O3 and SO2). However, for an operational global retrieval
algorithm, it would be too computationally expensive to perform online
radiative transfer calculations at many wavelengths to explicitly account
for all of these factors. Moreover, some of these factors, such as the
height of the volcanic SO2 plume or the composition and size
distribution of aerosols, are usually not well known or well defined at the
time of retrieval. To simplify the problem and save computational expense,
we have constructed a number of pre-computed LUTs for volcanic
SO2 Jacobians, following an approach similar to that used in TOMS and
OMI total column O3 retrievals (Bhartia and Wellemeyer, 2002). We
assume that, to first order, the combined effects of clouds/aerosols/surface
reflectivity on SO2 Jacobians are accounted for with a simple
Lambertian equivalent reflectivity (SLER or R) derived at the
surface (Ahmad et al., 2004). We also neglect the effects of inelastic RRS
on SO2 Jacobians (RRS contribution to radiances is accounted for with
PCs in Eq. 1). On the basis of these assumptions, the backscattered
radiances at TOA (I) for Rayleigh multiple scattering can be
calculated with the following equation:
I=I0θ0,θ+I1θ0,θcosϕ+I2θ0,θcos2ϕ+RIrθ0,θ1-RSb.
The first three terms (I0, I1, and
I2) on the right-hand side of Eq. (2) are Fourier expansion
coefficients in azimuthal angle that in the case of Rayleigh scattering have
only two terms (Dave, 1964). Together, they represent the atmospheric
contribution to the radiances. The last term represents the surface
contribution, where RIr represents the TOA radiance that is
reflected once from the surface and transmitted through the atmosphere, and
(1-RSb) accounts for the effects of multiple reflections
between the surface and the atmosphere, with Sb being the
fraction of surface-reflected radiation that is scattered back to the
surface by the atmosphere. Note that these terms also depend on absorbing
gases such as O3 and SO2, which are omitted from Eq. (2) for
brevity. Equation (2) has been used widely in satellite backscattered UV (BUV)
retrievals of ozone and other minor gaseous components (e.g., Bhartia and
Wellemeyer, 2002), as it allows for the use of smaller LUTs by excluding the
azimuth dimension. Taking the partial derivative with respect to the
SO2 VCD (ΩSO2), we obtain the following equation for the
calculation of SO2 Jacobians:
∂I∂ΩSO2=∂I0θ0,θ∂ΩSO2+∂I1θ0,θ∂ΩSO2cosϕ+∂I2θ0,θ∂ΩSO2cos2ϕ+R1-RSb∂Irθ0,θ∂ΩSO2+R2Irθ0,θ1-RSb2∂Sb∂ΩSO2.
To account for O3 vertical distributions, we generated a set of
SO2 Jacobian LUTs, one for each of the 21 standard O3
climatology profiles used in OMI total O3 retrievals (each profile
represents an O3 node), employing VLIDORT (Spurr, 2008) to compute the
components in Eq. (3) (I0, I1, I2,
Ir, Sb, and their derivatives with respect to
ΩSO2) for 8 SZAs (0–81∘), 8 VZAs
(0–80∘), and 15 SO2 nodes (0–1000 DU) for the spectral range
311–342 nm at 0.05 nm resolution (see Table 1 for a list of the
nodes). This was done for four prescribed Gaussian vertical SO2
profiles with a full width at half maximum (FWHM) of ∼ 2.3 km
and different CMAs. Three of these profiles have the same plume CMAs as
those used in the LF algorithm (3 km or TRL, 8 km or TRM, and 18 km or STL).
Retrievals produced using these profiles are distributed as part of the new
operational OMI SO2 dataset (OMSO2 V1.3.0). The fourth SO2
profile, centered at 13 km altitude, was introduced for the generation of a
new TRU (upper troposphere) SO2 research product. Several eruptions
during the OMI era, including Kasatochi in 2008, injected SO2 to
altitudes between 8 and 18 km, and the TRU profile should allow for more
accurate retrievals for those eruptions.
Nodes of the solar zenith angle (SZA), viewing zenith angle (VZA),
and SO2 column amount, as used in the pre-computed SO2 Jacobians
lookup tables.
ParameterNodes SZA0∘15∘30∘45∘60∘70∘77∘81∘VZA0∘15∘30∘45∘60∘70∘75∘80∘SO2 (DU)01510501002003004005006007008009001000Step 2: Ancillary retrieval parameters, data processing, and fitting
windows
For a given pixel with SZA =θ0, VZA =θ, and RAZ =ϕ, a number of ancillary
retrieval parameters are generated at intermediate steps in the OMSO2VOLCANO
algorithm, before the volcanic SO2 Jacobians for the pixel can be
determined. Two of these parameters, namely the initial estimate of
SO2 VCD (ΩSO2_ini) and an estimate of
O3 VCD (ΩO3), come from the first stage of the
OMSO2VOLCANO algorithm (blue box in Fig. 1). ΩO3 is from the
operational OMI total column O3 product (OMTO3) for most pixels but,
for pixels having large SO2 signals (ΩSO2_ini > 5 DU), it is interpolated from neighboring pixels with
small ΩSO2_ini in order to avoid overestimation
of ΩO3 due to SO2 contamination. For the SLER
(R), we first derive R at 342.5, 354.1, and 367.04 nm by
matching the measured and calculated radiances in Eq. (2). Since
contributions from gaseous absorption and RRS processes are minimal at these
wavelengths, we use a smaller LUT and assume a fixed O3 profile (O3= 325 DU). We then fit a second-degree polynomial function to the three
wavelengths to extrapolate R to shorter wavelengths. The
interpolation implicitly accounts for the combined effects of aerosols,
clouds, and the surface on the spectral dependence of TOA radiances. An
example of the derived R is given in Fig. S2.
For each presumed SO2 profile, the algorithm then selects two
pre-computed LUTs based on the O3 VCD and the latitude of the
pixel, with the two O3 nodes bracketing the input ΩO3 for
the pixel (Ωnode1 < ΩO3 < Ωnode2). For the lookup table with
O3 VCD =Ωnode1, a total of eight SO2 Jacobian spectra are calculated using
ϕ and the derived R as input to Eq. (3) for two
SO2, two SZA, and two VZA nodes that bracket ΩSO2_ini, θ0, and θ,
respectively. Next, the eight SO2 Jacobian spectra are interpolated
in two steps, a 2-D linear interpolation with respect to the cosines of the
angles in the SZA dimension and VZA dimension followed by a 1-D linear
interpolation between the two SO2 nodes, leading to an estimated
SO2 Jacobian spectrum for SZA =θ0, VZA =θ, RAZ =ϕ, SO2
VCD =ΩSO2_ini, and O3 VCD =Ωnode1. We
repeat the above steps for the LUT with O3 VCD =Ωnode2 to obtain another estimated SO2 Jacobian spectrum for the
same input, but for a different O3 profile. A final interpolation
between the two O3 nodes is then performed to generate an estimated
SO2 Jacobian spectrum for the given pixel.
(a) A comparison of SO2 Jacobians for an idealized pixel (SZA = 30∘,
VZA = 45∘, RAZ = 90∘, R= 0.05, an SO2 plume centered at 18 km with ΩSO2= 250 DU,
and a midlatitude O3 profile with ΩO3= 375 DU) derived
from direct calculation using VLIDORT (red line) and interpolation (blue
line) from VLIDORT calculations for two bracketing SO2 nodes (200
and 300 DU) shows sizable (b) interpolation errors at wavelengths < 315 nm, caused by nonlinearity owing to the saturation of SO2
absorption signals. The relative difference between interpolated and
directly calculated SO2 Jacobians exceeds 30 % at wavelengths
< 315 nm and remains substantial at about 2 % at ∼ 316 nm (see insert). The wavelength with the maximum interpolated SO2
Jacobian, or the start of the fitting window for this particular example, is
∼ 318 nm and is marked with a vertical solid line in both
(a) and (b).
The SO2 Jacobian spectrum is convolved with the OMI slit function, and
then used along with the PCs from the first part of the algorithm (Fig. 1)
in least squares fitting to produce an updated estimate of SO2 VCD
(ΩSO2_step1) for the pixel, which is then
compared with ΩSO2_ini. If |ΩSO2_ini-ΩSO2_step1|
is greater than 0.1 DU or 1 % for pixels with SO2 VCD
> 100 DU, ΩSO2_step1 is used as input
to the LUT to generate an updated SO2 Jacobian spectrum. The
iterations continue until the results converge or the number of iterations
exceeds the upper limit (15). The same data processing steps are carried out
separately for the TRL, TRM, TRU, and STL SO2 profiles, resulting in
four final estimates of SO2 VCD for each pixel.
For volcanic SO2 retrievals, we start with a nominal fitting window of
313–340 nm, but drop the shortest wavelengths for pixels with large SO2
loading. As demonstrated in Fig. 2, saturation of large SO2
absorption signals at short wavelengths leads to errors of more than 30 %
in the interpolated Jacobians, even for this idealized example in which the
nonlinearity in SO2 absorption constitutes the only source of
interpolation error. To reduce this error, we update the fitting window at
each iteration step by locating the wavelength of the largest SO2
Jacobian, up to 326.5 nm, and excluding all shorter wavelengths. We select
326.5 nm as the upper limit for the short end of the fitting window so that
at least half of the wavelengths in the nominal window are used in the
fitting. In addition, the fitting window is allowed to move to longer
wavelengths only, otherwise in some cases the short end of the fitting
window can change back and forth between iteration steps, resulting in
non-convergence. Our approach not only helps to minimize this particular
source of interpolation error but also maintains sensitivity by ensuring
that those wavelengths with the largest SO2 Jacobians are included in
the spectral fitting. For an example of the shortest wavelengths used for
large volcanic eruptions, refer to Fig. S3.
Results: comparison with the LF algorithm
In this section, we present some examples of the volcanic SO2
retrievals generated with the new OMSO2VOLCANO algorithm. The entire OMI
data record has been reprocessed with this algorithm, and all data are
publicly available (see data availability section). Here, the focus is
mainly on the comparison between the OMSO2VOLCANO and the current
operational LF algorithm, but we will also compare results from other
algorithms wherever data are available.
(a) Level 2 OMI SO2 total vertical column density for 5 August
2006, retrieved with the OMSO2VOLCANO (PCA) algorithm assuming a lower
tropospheric SO2 plume with center of mass at 3 km altitude (TRL
profile). (b) Same as (a) but for the current operational linear fit
algorithm. Only pixels within the center 56 rows are plotted for both
products.
Background regions
While much attention has been paid to explosive volcanic eruptions and their
impacts on climate and aviation safety, the importance of degassing
volcanoes is now receiving increasing recognition (see Sect. 1), despite
the dearth of information on the strength of their emissions (e.g., Ge et
al., 2016). OMI SO2 retrievals can help to supply this information
(e.g., Carn et al., 2013; McLinden et al., 2016; Fioletov et al., 2016), but
ideally these retrievals need to have relatively low levels of noise and
biases to achieve sufficient sensitivity to detect degassing sources. Here
we compare the global lower tropospheric (TRL) SO2 VCD retrieved with
the operational LF and the OMSO2VOLCANO algorithms for 5 August 2006, a day
with no major volcanic plumes or long-range transport of anthropogenic
SO2 in the free troposphere. As shown in Fig. 3, both products detect
SO2 signals over large emission sources such as eastern China, the
Persian Gulf, and Nyiragongo volcano in eastern D.R. Congo, but the
PCA-based OMSO2VOLCANO retrievals have much smaller bias and noise levels
when compared with the LF retrievals. This is probably due to the fact that
the PCA algorithm utilizes the full spectral content of OMI measurements, as
opposed to just 10 wavelengths used in the LF algorithm. If we compare the
standard deviations of the two retrievals over the SO2-free remote
Pacific, we can see that, for the majority of latitude bands from
40∘ S to 60∘ N, the standard deviations of the
OMSO2VOLCANO retrievals are ∼ 0.2–0.3 DU, less than half of
the standard deviation of LF retrievals (Fig. 4). The reduced biases and
noise make the new OMSO2VOLCANO dataset more sensitive and stable, providing
enhanced long-term monitoring of continuously degassing sources. An example
of this capability is provided in Sect. 4.
Standard deviation of TRL total SO2 vertical column density
retrieved for 10∘ latitude bands over the remote Pacific
(170–180∘ W) on 5 August 2006 with the linear fit (blue line) and
OMSO2VOLCANO (PCA, red line) algorithms.
(a) OMI linear fit SO2 retrievals assuming a lower
stratospheric (STL) SO2 profile for orbit 21 635, the first OMI overpass
after the Kasatochi eruption on 8 August 2008. (b–c) Same as (a) but for
OMSO2VOLCANO (PCA) retrievals assuming (b) the STL SO2 profile and
(c) an upper tropospheric (TRU) SO2 profile. (d) Linear fit STL retrievals
for orbit 21 636, the second OMI overpass after the eruption. (e–f) Same as
(d) but for OMSO2VOLCANO (e) STL and (f) TRU retrievals. The gray-shaded
rows in (d–f) are masked due to the OMI row anomaly. If retrievals for these
masked rows are included in the calculation of the total loading, the total
LF STL SO2 mass in the domain for orbit 21 636 is 864.7 kt (see
Krotkov et al., 2010), whereas PCA total mass in the domain is 897.2 kt and
1204.9 kt for STL and TRU retrievals, respectively.
Kasatochi eruption in 2008
Next we examine two major volcanic eruptions during the OMI mission. The
first one, the Kasatochi eruption in August 2008, is the largest to date
during the OMI era (in terms of SO2 discharge by a single explosive
eruption) and has been studied extensively using several UV and infrared
satellite instruments (e.g., Carn et al., 2016). Previous studies suggest
that the eruption injected SO2 to between 7 and 13 km altitude with a
peak at ∼ 10 km (Kristiansen et al., 2010; Krotkov et al.,
2010; Nowlan et al., 2011; Wang et al., 2013; Yang et al., 2010; Ge et al.,
2016). The estimated total SO2 mass emitted by Kasatochi varies from
1500 to 2200 kt (kiloton, 103 t), much greater than the
∼ 850–860 kt retrieved with the LF algorithm assuming the STL
(18 km) SO2 profile for orbits 21 635 and 21 636, the two earliest OMI
overpasses after the eruption (Fig. 5). The estimated SO2 mass for
these two orbits based on LF TRM (8 km) retrievals is slightly greater, but
at ∼ 950 kt it is still biased low with respect to the other
estimates by almost a factor of 2, most likely due to signal saturation by
high SO2 loading in the young volcanic cloud. This apparent lack of
dependence of SO2 mass on plume height suggests that the signal
saturation issue with the LF algorithm is likely more serious for TRM (and
TRL) retrievals than for STL retrievals.
The OMSO2VOLCANO (PCA) retrievals (Fig. 5b and e) yield much greater
estimates of SO2 mass: ∼ 1200 kt for TRU (13 km; Fig. 5c and f) and ∼ 2000–2200 kt for TRM (8 km). The significant
difference between our STL and TRU retrievals is due to the different
SO2 Jacobians between the two profiles under large SO2 loading, as
also confirmed by forward radiative transfer calculations using VLIDORT (see
Fig. S5 for an example). We also note that
the OMSO2VOLCANO retrieved SO2 loadings for the two orbits agree to
within 2 % for STL and TRU and to within 10 % for TRM. This suggests
that the algorithm has very small cross-track biases, given that essentially
the same plume was observed with different rows of the OMI instrument in
these two consecutive orbits just ∼ 1.5 h apart. Taking the
average of the TRU and TRM retrievals, we estimate that the OMSO2VOLCANO
retrievals would have given an estimated SO2 mass of ∼ 1700 kt for these two orbits, had a 10 km profile been used. This is
∼ 10 % larger than the offline OMI ISF retrievals (1500 kt;
Yang et al., 2010) and GOME-2 optimal estimation retrievals (1600 kt; Nowlan
et al., 2011), but smaller than the estimate based on the SO2 loss rate
observed over several days following the eruption (2200 kt; Krotkov et al.,
2010). The total SO2 mass for OMI orbit 21 636 estimated with a DOAS
algorithm by Theys et al. (2015) is smaller at 1060 kt, but a plume height
of 15 km was assumed in that study. The operational GOME-2A retrievals
produced with the GDP 4.7 (GOME Data Processor) algorithm (Rix et al., 2009)
for 9 August 2008 (see Fig. S4) estimate
the overall SO2 loading at 969 kt when a 15 km plume height is assumed.
This falls in between the OMSO2VOLCANO STL and TRU retrievals. The SO2
mass based on the GDP GOME-2A 6 km retrievals, in contrast, is only
slightly greater at 1097 kt, much less than the optimal estimation
retrievals (Nowlan et al., 2011). Like the OMI LF retrievals, this weak (and
likely underestimated) dependence of SO2 mass on plume height in the
GOME-2A GDP retrievals may be due to signal saturation at shorter
wavelengths.
(a) OMI LF SO2 retrievals assuming a lower tropospheric (TRL)
SO2 profile over the Galápagos Islands, from orbit 6779 on 23
October 2005, after the eruption of Sierra Negra volcano on the previous
day. (b) Same as (a) but for OMSO2VOLCANO (PCA) TRL retrievals.
For OMI orbit 21 650 that captured the Kasatochi plume about 24 h later, the
total SO2 mass estimated with OMSO2VOLCANO is greater than that for
orbit 21 636 (∼ 1300 kt vs. ∼ 890 kt for STL,
∼ 1500 kt vs. ∼ 1200 kt for TRU, ∼ 2300 kt vs. ∼ 2000 kt for TRM). This apparent increase in the
SO2 loading over time has also been reported for other OMI retrievals
for Kasatochi including the LF (1300 kt vs. 850 kt on the first day for STL
retrievals), ISF (1600 kt vs. 1500 kt on the first day), and DOAS (1220 kt
vs. 1060 kt on the first day) algorithms, and previously for other eruptions
measured by different instruments (e.g., TOMS retrievals for the Pinatubo
eruption), suggesting that the SO2 mass may still be underestimated for
highly concentrated plumes observed shortly after eruptions. This is perhaps
due to the combined effects of SO2 signal saturation and high volcanic
ash and aerosol loadings. Increases in measured volcanic SO2 loading
beyond the first day of observation have also been attributed to conversion
of emitted H2S to SO2 (e.g., Rose et al., 2000), and the new
OMSO2VOLCANO retrievals promise more accurate assessments of this
possibility by better eliminating the uncertainties due to SO2 signal
saturation in fresh volcanic clouds. In the Kasatochi case, a preliminary
mass balance suggests that not all the observed SO2 increase
(> 100 kt) can be accounted for by conversion of detected
H2S (29 ± 10 kt of H2S reported in the Kasatochi plume by
Clarisse et al. (2011) would yield a maximum of ∼ 74 kt of
SO2), but there are also issues with satellite sensitivity to H2S
(see Clarisse et al., 2011).
Regarding the maximum SO2 VCDs, for orbit 21 635, the highest
OMSO2VOLCANO VCDs are 238, 388, and 685 DU for STL, TRU, and TRM retrievals,
respectively. In comparison, the maximum LF VCDs are smaller at 220 and 379 DU for STL and TRM, respectively. Similarly for orbit 21 636, the
OMSO2VOLCANO peak VCDs are also greater at 260, 350, and 644 DU for STL,
TRU, and TRM, respectively, compared with 246 and 274 DU for STL and TRM
retrievals in the LF product. The DOAS algorithm developed by Theys et al. (2015) appears to produce greater peak ΩSO2 for this case, with
ΩSO2 maxima of 382 DU for OMI orbit 21 635, and 565 DU for
GOME-2 data from the same day, despite assuming a 15 km SO2 profile and
giving a smaller estimate of the overall SO2 mass for the plume (see
above). The DOAS algorithm uses the spectral window of 312–326 nm for
baseline slant column SO2 amounts (SCD1). It also retrieves additional
SCDs using alternative windows (325–335 nm for SCD2 and 360–390 nm for SCD3)
and determines the final fitting window based on the comparison between the
SCDs. For most pixels, the baseline window is retained as the final window,
but for pixels with moderate (SCD1 > 40 DU and SCD2 > SCD1) and large (SCD2 > 250 DU and SCD3 > SCD2)
SO2 signals, the longer wavelength windows of 325–335 and 360–390 nm
are used, respectively (see Theys et al., 2015, for details). The
SO2 cross sections in 360–390 nm are over an order of magnitude smaller
than those at the wavelengths used in PCA retrievals. Such large differences
in the cross sections may contribute to the different peak SO2 VCDs
between the PCA and DOAS retrievals, but a more detailed comparison between
the two algorithms will be necessary to fully understand their differences.
Sierra Negra eruption in 2005
The second example we examined was the effusive eruption of Sierra Negra
(Galápagos Islands) in 2005. This eruption began on October 22 of that
year and injected large amounts of SO2 into the lower troposphere with
little ash (Yang et al., 2009a), thus representing different conditions to
the explosive Kasatochi eruption. The Sierra Negra volcanic plume was
measured using OMI LF TRL retrievals for orbit 6779 on 23 October yielding
a maximum SO2 VCD of 246 DU and a total SO2 mass of 363 kt
(Fig. 6a). The OMSO2VOLCANO TRL retrievals (Fig. 6b) for the same orbit
reveal a similar spatial distribution of the SO2 plume but give a much
greater total SO2 mass of 694 kt, almost double that of the LF
algorithm. The maximum SO2 VCD retrieved with the OMSO2VOLCANO
algorithm is extraordinarily high at ∼ 1125 DU, the largest
detected to date by satellites. A similarly large VCD has also been
retrieved for the same case with the offline OMI ISF algorithm
(> 1100 DU; Yang et al., 2009a). The good agreement between the
OMSO2VOLCANO and ISF algorithms suggests that both can produce less-biased
retrievals than the LF algorithm but, unlike the ISF algorithm, OMSO2VOLCANO
has been implemented operationally and does not require online radiative
transfer calculations and instrument-specific corrections to the radiance
data (i.e., soft calibration). One may notice that the difference between
OMSO2VOLCANO and LF is even greater for this case than for Kasatochi,
probably reflecting the more serious signal saturation issue in the LF
algorithm for TRL retrievals.
Data continuity with the Suomi-NPP OMPS instrument
Now already in its 12th year of service, OMI is expected to continue to
provide global monitoring of volcanic SO2. However, the quality and
spatial coverage of OMI measurements are presently limited by instrument
issues that have developed over time. In particular, since 2009 about half
of the 60 OMI rows have been rendered unusable by a partial blockage of the
field of view (i.e., the OMI row anomaly;
http://projects.knmi.nl/omi/research/product/rowanomaly-background.php).
Several satellite instruments currently in orbit or to be launched in the
near future will continue the UV satellite volcanic SO2 data record
(Carn et al., 2015, 2016). Particularly suitable for this task is the
OMPS nadir mapper (OMPS-NM) on board the Suomi-NPP spacecraft (Flynn et al.,
2014; Seftor et al., 2014). This platform was launched in 2011 and is flying
in a sun-synchronous orbit with a local afternoon overpass time close to
that of the Aura satellite (OMI). OMPS has already made several years' worth
of measurements that overlap the OMI mission. This makes it possible to
conduct extensive comparisons between retrievals from the two instruments
in order to evaluate their consistency. Like OMI, the 2-D “push-broom” CCD
detector of OMPS-NM covers the entire globe on a daily basis, allowing the
total SO2 mass to be estimated for complete volcanic clouds. However,
there are challenges in the construction of a consistent volcanic SO2
dataset between the two instruments, given the coarser spatial (50 × 50 km2 vs. 13 × 24 km2 at nadir) and spectral
(1 nm vs. ∼ 0.5–0.6 nm) resolution offered by OMPS-NM. For small
SO2 sources in the boundary layer, the coarser spectral resolution of
OMPS may lead to ∼ 10 % reduction in SO2 signals. More
importantly, since an OMPS pixel is several times larger than an OMI pixel,
this may lead to significant dilution of SO2 signals for isolated
small-scale plumes (see Fig. S6 for an
example). As for large volcanic eruptions, multiple OMI pixels existing in a
single OMPS pixel may have very different SO2 amounts and different
Jacobians, particularly for shorter wavelengths. As a result, the saturation
issue discussed in Sect. 3 can have different effects on OMI and OMPS
retrievals even if the two instruments are making simultaneous measurements
of the same volcanic plume (see Fig. S7 for an example). Furthermore,
differences in instrument calibration also need to be taken into account to
minimize inter-instrument biases.
Our PCA-based retrieval approach can help to overcome these challenges, as
it has small biases and requires no explicit instrument-specific corrections
to the radiance data. Indeed, in a separate study (Zhang et al., 2016), we
showed that the PCA PBL SO2 algorithm was able to produce nearly
unbiased retrievals for regional SO2 pollution events between OMI and
OMPS. Here, we examine the feasibility of using OMPS to continue the
volcanic SO2 emission dataset produced from OMI, for both continuously
degassing volcanoes and large eruptions. The OMPS PCA volcanic SO2
algorithm used here is almost identical to the OMSO2VOLCANO algorithm, with
the exception of two implementation details: (1) we used fewer PCs in the
spectral fitting (up to 15 PCs for OMPS, compared with 20 for OMI), since
OMPS has approximately half the wavelengths of OMI in the same spectral
fitting window; (2) we extended the OMPS retrievals to pixels with solar
zenith angles up to 75∘ to gain more spatial coverage near the
edge of the swath at high latitudes in winter months.
(a) Daily total SO2 mass in a domain (16–21∘ N,
154–160∘ W) around the Kīlauea volcano in Hawai'i, retrieved
with OMI and OMPS using the PCA algorithm assuming a lower tropospheric
(TRL) SO2 profile. Only days with at least 90 % coverage of the
domain by either OMI (the first 24 rows) or OMPS (all 36 rows) are shown.
(b) Scatter plot between OMI- and OMPS-estimated total SO2 over the
domain for days with at least 90 % spatial coverage by both OMI (the first
24 rows) and OMPS. (c) The probability density function of the daily spatial
correlation coefficient between OMI and OMPS TRL SO2, both gridded at
0.5∘× 0.5∘ resolution. Only those days
(∼ 200 in all) when both instruments covered over 90 % of
the domain and detected over 0.5 kt of SO2 are included.
Long-term monitoring of SO2 degassing from Kīlauea
volcano
Kīlauea volcano is the most active of the five volcanoes on the island
of Hawai'i and has been in a state of near-continuous effusive eruption
since 1983. Lower tropospheric SO2 plumes emitted from Kīlauea
have been detected routinely by OMI since the start of the Aura mission
(e.g., Carn et al., 2013). Here, we compare the PCA volcanic SO2
retrievals over the Hawai'i region from both OMI and OMPS instruments
(Fig. 7). To calculate the daily SO2 mass within the domain around
Kīlauea (16–21∘ N, 154–160∘ W), we first gridded
OMI and OMPS TRL SO2 retrievals to the same 0.5∘× 0.5∘ horizontal resolution and then calculated the total SO2
loading for each instrument by summing the SO2 mass in all grid cells
with SO2 VCD > 0.4 DU. This threshold was selected to
include only signals that are at least twice the retrieval noise level for
the area (∼ 0.2 DU, see Fig. 4). To ensure consistency in
sampling, only the first 24 rows of OMI (considered to be unaffected by the
row anomaly) were used, and only those days with at least 90 % spatial
coverage by these 24 rows were included in the time series plot in Fig. 7a. We applied the same SO2 threshold and spatial coverage constraints
to the OMPS data but used all 36 rows of the OMPS-NM sensor. As shown in
Fig. 7a, OMI has detected substantial variability in the SO2 loading
over the area near Kīlauea, with a calm period until March 2008 and an
active period during 2008–2010, followed by another relatively calm period.
This is qualitatively consistent with the estimates by Carn et al. (2013)
and Ge et al. (2016) using the OMI LF retrievals and also with ground
observations (Elias and Sutton, 2012). OMPS provided more frequent
measurements during 2012–2015 due to its more complete spatial coverage. The
OMPS SO2 time series compares well with the OMI record for the period.
A regression analysis (Fig. 7b) confirms that OMPS estimates of daily
regional SO2 mass are well correlated with OMI estimates, with a
correlation coefficient (r) of 0.89 and a slope of 1.12, indicating
slight overestimates by OMPS as compared with OMI. Analysis using reduced
major-axis regression yields a slope of 1.25, still suggesting overall
consistency between OMI and OMPS. We also investigated the correlation
between the spatial distributions of the OMI and OMPS PCA retrievals on a
daily basis (Fig. 7c) and found that for the ∼ 200 days when
both instruments detected at least 0.5 kt of SO2 over the area, 84 %
of them had r of at least 0.5 (66 % had r > 0.7). Only 5 of these days had r < 0.3 (5 February 2012,
2 October 2012, 14 May 2013, 6 November 2013, and 9 November 2014) and on all 5 days
the core of the plume was covered by OMI pixels near the nadir position but
by OMPS pixels near the edge of the swath. This comparison demonstrates that
OMPS is capable of extending the long-term OMI record of SO2 emissions
from degassing volcanoes.
(a) The volcanic SO2 plume from the Kelut eruption on
13 February 2014 retrieved using the OMI PCA algorithm (OMSO2VOLCANO)
assuming the STL SO2 profile on the following day. Shaded areas are
masked due to the OMI row anomaly. (b) Same as (a) but for OMPS PCA
retrievals. (c) Same as (a) but for the merged OMI and OMPS PCA STL SO2
retrievals.
Kelut eruption in 2014
The violent explosive eruption of Mt. Kelut in Indonesia on the night of
13 February 2014 injected a volcanic plume to altitudes of ∼ 16–17 km and above (Kristiansen et al., 2015). OMSO2VOLCANO SO2
retrievals for the OMI overpass on the following day (Fig. 8a) were able
to capture the plume, but the estimated total SO2 mass of
∼ 131 kt is certainly biased low, as a sizeable part of the
plume was obscured by the OMI row anomaly. The OMPS PCA retrievals (Fig. 8b) produce a very similar spatial pattern to the OMI retrievals, and the
complete plume coverage by OMPS yields a greater estimate of total SO2
(179 kt). However, the OMPS retrievals lack some of the spatial
detail offered by OMI and have a smaller maximum SO2 VCD (47.5 DU vs.
69.3 DU); this is probably mainly due to its coarser spatial resolution (see
Fig. S8 for a more detailed explanation).
Given that OMI and OMPS overpasses were only 30 min apart in this case, we
can combine the OMI and OMPS retrievals to produce an integrated SO2
map that covers the plume in its entirety whilst providing fine spatial
resolution where OMI data are available (Fig. 8c). This is facilitated by
the very good agreement between OMI and OMPS PCA retrievals and involves
assigning the SO2 VCD from the closest OMPS pixel (based on pixel
center coordinates) to each OMI row anomaly pixel. In fact, the total
SO2 mass estimated from the combined OMI+OMPS map (174 kt; Fig. 8c)
agrees with the OMPS-only estimate to within 3 %. The Kelut example
demonstrates that our OMPS PCA data can supplement OMI retrievals to provide
complete and consistent coverage for large volcanic eruptions.
Conclusions
In summary, we have developed a new-generation OMI volcanic total SO2
column amount dataset using an algorithm based on a PCA technique. The new algorithm, OMSO2VOLCANO, directly extracts
spectral features (in the form of PCs) from OMI
radiances that are associated with various geophysical processes (e.g.,
O3 absorption, RRS) and measurement details (e.g., wavelength shift).
The algorithm then fits these PCs and SO2 Jacobians (representing
instrument sensitivity to a perturbation in column SO2) to the measured
radiances in order to estimate SO2 while minimizing the impacts of
various interferences on SO2 retrievals. An LUT approach was
developed to determine SO2 Jacobians under various conditions such as
measurement geometry (solar zenith angle, viewing zenith angle, and relative
azimuth angle), SO2 amount, O3 amount (from OMI or OMPS total
O3 products), and the reflectivity of the underlying surface. To first
order, the effects of clouds and aerosols are accounted for using a SLER derived at longer wavelengths
(342.5, 354.1, 367.04 nm) and then extrapolated to our nominal fitting
window of 313–340 nm. For very large volcanic plumes, SO2 absorption
may saturate at shorter wavelengths, leading to large interpolation error.
To circumvent this problem, the spectral fitting window is updated
dynamically at each iteration step by locating the wavelength with the
maximum SO2 Jacobian in the nominal window and dropping all shorter
wavelengths. In the absence of information on SO2 plume height,
retrievals of SO2 total column amounts are given for four different
predefined SO2 profiles with peaks at 3 (TRL), 8 (TRM), 13
(TRU), and 18 km (STL), representing typical altitudes of plumes from
non-eruptive volcanic degassing, moderate eruptions, and explosive eruptions
(TRU and STL), respectively. For large eruptions, OMI radiances at short
wavelengths (< 313 nm) may contain information on the height of the
volcanic plume (e.g., Yang et al., 2009b). However, such plume height retrievals
require extensive online RT calculations that are currently too
computationally expensive to be implemented as part of an operational global
retrieval algorithm.
Comparisons with the current operational OMI volcanic SO2 product based
on the LF algorithm and other satellite datasets suggest that
the new OMSO2VOLCANO dataset has significantly lower biases and noise with
respect to the LF, with the background noise reduced by half or more over
the remote Pacific. For the Kasatochi eruption in 2008, the OMSO2VOLCANO
retrievals give an estimated SO2 total mass of ∼ 1700 kt
from OMI observations shortly after the eruption, a factor of 2 greater
than LF estimates and generally in agreement with OMI ISF (Yang et al., 2010) and DOAS (Theys et al., 2015) retrievals as
well as GOME-2 optimal estimation retrievals (Nowlan et al., 2011). For the
Sierra Negra eruption in 2005, the OMSO2VOLCANO algorithm detects a peak
SO2 column amount of over 1100 DU. This value is consistent with that
obtained using the offline ISF algorithm (Yang et al., 2009), and this
concurrence suggests that, for this case, the LF algorithm underestimates
peak SO2 amounts by a large margin. Overall, the new OMSO2VOLCANO
dataset has much improved data quality for both quiescent degassing
volcanoes and large eruptions, when compared with the LF product. Unlike the
ISF algorithm, the OMSO2VOLCANO algorithm has been implemented operationally
as it requires far less computational resources and also does not rely on
instrument-specific soft calibration.
To extend the 10-year OMI volcanic SO2 data record and to mitigate
the loss of OMI spatial coverage due to the row anomaly, we have implemented
the same PCA volcanic SO2 algorithm with the Suomi-NPP OMPS instrument
nadir mapper. Despite its coarser spatial and spectral resolution, the PCA
retrievals with OMPS are highly consistent with those from the OMSO2VOLCANO
dataset. For the area around the continuously degassing Kīlauea volcano
in Hawai'i, the OMPS and OMI estimates of daily total SO2 loading are
highly correlated (r= 0.89, slope = 1.12). For large volcanic
eruptions, OMPS and OMI PCA retrievals can be merged to achieve complete
spatial coverage and fine measurement details, as demonstrated for the Kelut
eruption in 2014. The estimated total SO2 mass within the Kelut plume
from the combined OMI–OMPS data agrees with the OMPS-only data to within
3 %, again indicating very good agreement between OMI and OMPS retrievals.
Overall, the new PCA-based OMSO2VOLCANO dataset offers enhanced sensitivity
to small volcanic degassing sources and more accurate retrievals for large
volcanic plumes as compared with the current operational OMI volcanic
SO2 product. The new dataset will help to improve our understanding of
the impacts of volcanic emissions, and the new record will be continued with
existing instruments and extended with future sensors such as the next
generation of OMPS on the Joint Polar Satellite System (JPSS) spacecraft and
TROPOMI (the TROPospheric Monitoring Instrument) on the Copernicus Sentinel
5 Precursor.
Data availability
The new-generation OMI volcanic (TRL, TRM, and STL) SO2 product based
on the OMSO2VOLCANO algorithm (OMSO2 v1.3.0) is publicly available from the
NASA Goddard Earth Sciences (GES) Data and Information Services Center
(DISC; http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI/omso2_v003.shtml). The OMI TRU SO2 data are upon request from the
corresponding author.
The Supplement related to this article is available online at doi:10.5194/amt-10-445-2017-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
The authors acknowledge the NASA Earth Science Division (ESD) Aura Science
Team program (managed by Ken Jucks) for funding of OMI SO2 product development and analysis. The
Dutch- and Finnish-built OMI instrument is part of the NASA's Earth Observing
System (EOS) Aura satellite payload. The OMI project is managed by the Royal
Meteorological Institute of the Netherlands (KNMI) and the Netherlands Space
Agency (NSO). Can Li acknowledges partial support from NASA's Earth Science New
Investigator Program in developing the OMPS SO2 algorithm (grant no. NNX14AI02G).
The authors would also like to thank the NASA OMPS ozone Product Evaluation
and Test Element (PEATE) team for updating the OMPS calibration and
producing the OMPS Level 1b and Level 2 O3 data used in this analysis.
We thank the OMI calibration team, led by KNMI, for the calibrated OMI Level
1b data used here.
Edited by: A. Richter
Reviewed by: two anonymous referees
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