Introduction
Ammonia, along with ammonium nitrate and ammonium sulfate aerosols, is
important for the nitrogen cycle that directly or indirectly impacts air
quality, water quality and the climate. In the atmosphere ammonia is a
toxin, and it combines with sulfates and nitric acid to form ammonium
nitrate and ammonium sulfate, which constitute ∼50 % of the
mass of fine particulate matter (PM2.5) over land (e.g. Seinfeld and
Pandis, 1988). These particles form smog and, in addition to being
statistically associated with health impacts, such as bronchitis, asthma,
cardiovascular disease and lung disease, cause premature deaths (Schwartz
et al., 2002; Reiss et al., 2007; Pope et al., 2002, 2009; Crouse et al.,
2012). For example, there is a 6 and 8 % increase in the risk of
cardiopulmonary and lung cancer mortality associated with exposure to 10 µgm-3
increases in PM2.5 concentrations (Pope et al.,
2002). In terms of climate change, ammonia's contribution to atmospheric
aerosols (ammonium) has both a direct (reflection of solar radiation)
radiative forcing effect of ∼-0.35 Wm-2 since
pre-industrial times, and a potentially larger indirect (clouds) radiative
forcing effect (e.g. Charlson et al., 1991; Myhre et al., 2013).
Furthermore, reactive nitrogen (Nr) (e.g. ammonia (NH3), ammonium
(NH4), nitrogen oxide (NO)) has increased by a factor of 2 to 5 over
the last century (Reay et al., 2008; Lamarque et al., 2010), and
anthropogenic ammonia gas emissions (i.e. concentrated animal feeding
operations (CAFO), fertilizers, biofuel) are one of the IPCC AR5
Representative Pathway Concentration (RPC) species predicted to increase in
the future (Lamarque et al., 2011; Ciais et al., 2013). Increasing
atmospheric concentrations of ammonia have the potential to increase the
global deposition of reactive nitrogen to nitrogen-poor ecosystems, which in
turn increases the efficiency of the land and ocean in removing
human-induced carbon dioxide from the atmosphere, thus acting as a carbon
sink (“carbon dioxide fertilization effect” (Reay et al., 2008)). Excess
deposition in terrestrial ecosystems leads to soil acidification and loss of
biodiversity (e.g. Carfrae et al., 2004); and in coastal ecosystems causes
eutrophication, algal blooms, and loss of fish and shellfish (e.g. Paerl et
al., 2002). In spite of the significant role ammonia plays in our
environment and health, there is still limited knowledge of the magnitude
and seasonal/spatial distribution of NH3 emission sources, especially
on a global scale. Therefore, satellite observations of ammonia provide an
unprecedented opportunity to gain a greater understanding of atmospheric
ammonia concentrations and to constrain model emission, which are still
poorly known, especially outside of North America and Europe.
Observations from the NASA Aura Tropospheric Emission Spectrometer (TES)
(Beer et al., 2001) Fourier Transform Spectrometer (FTS) launched on 15 July 2004,
and the Infrared Atmospheric Sounder Interferometer (IASI)
(Clerbaux et al., 2009) FTS launched on MetOp-A (19 October 2006) and
MetOp-B (17 September 2012), have demonstrated the value of lower
tropospheric ammonia satellite measurements. For example, IASI and TES
observations have shown spatial and seasonal distributions of ambient
tropospheric ammonia concentrations globally (Clarisse et al., 2009;
Shephard et al., 2011; Van Damme et al., 2014a) and regionally (Beer et al., 2008; Clarisse et al.,
2010). Also, combining these satellite ammonia emissions with coincident
satellite observations of carbon monoxide has shown the potential of using
the satellite-derived NH3:CO enhancement ratios to identify the ammonia
emission sources and constrain NH3 emission inventories (e.g. Luo et
al., 2015). These satellite observations have been initially evaluated with
in situ ammonia surface observations. Comparisons of instantaneous twice
daily satellite boundary layer averaged observations with footprints on the
order of 5–15 km with commonly measured in situ bi-weekly averaged surface
network observations can be challenging given the obvious sampling
differences (horizontal, temporal, and vertical). Nevertheless, Pinder et
al. (2011) was able to show that the TES ammonia observations reflect
spatial gradients and seasonal trends when compared with overlapping
bi-weekly CAMNet in situ surface observations. Similar evaluations of the
IASI NH3 column retrievals have been performed using indirect surface
and aircraft comparisons (Van Damme et al., 2014a).
Satellite observations of tropospheric ammonia are also contributing to
better understanding of the ammonia emission inventories used in chemical
transport models. Both IASI and TES satellite observations have been used to
evaluate and improve ammonia emissions and transport in global (GEOS-Chem)
and regional chemistry transport (Community Multiscale Air Quality (CMAQ))
models, which have been broadly under-predicting ammonia concentrations
compared to the satellite observations, especially in large source regions
like the central valley in California, USA. Some examples include using TES
ammonia observations to provide top-down constraints on NH3 emissions
in GEOS-Chem (Zhu et al., 2013). Heald et al. (2012) used IASI observations along
with the GEOS-Chem chemical transport model to show that NH3 is likely
underestimated in California, leading to a local underestimate of ammonium
nitrate aerosol. At the same time, Walker et al. (2012) using TES observations
showed a similar under-prediction of ammonia emissions by GEOS-Chem over
California, which has among the largest concentrations of ammonia in the
USA. TES satellite and in situ observations were also used to evaluate the
new treatment of ammonia bidirectional fluxes in the CMAQ and GEOS-Chem
models (Zhu et al., 2015). In addition, insights into
the diurnal variability of animal NH3 emissions have been obtained by
combining TES satellite with in situ ground-based and aircraft observations
in order to develop and evaluated a new improved NH3 temporal emissions
profile for CMAQ (Bash et al., 2013, 2015).
While these initial studies have greatly improved our knowledge of the
magnitude, seasonal cycle and spatial distribution of NH3 emissions,
there still remain large uncertainties in ammonia emissions and in the
nitrogen cycle in general. Therefore, advancements in our understanding of
ammonia emission around the globe will benefit from recent and new satellite
ammonia observations. The Cross-track Infrared Sounder (CrIS) instrument is
a FTS operated by the USA NOAA/NASA/DoD Joint Polar Satellite System (JPSS)
program on Suomi National Polar-orbiting Partnership (NPP) satellite, which
was launched on 28 October 2011. With its good radiometric calibration and
instrument signal-to-noise ratio (SNR), CrIS also has the potential to
globally monitor ammonia and to contribute to a better understanding of
tropospheric ammonia variability over the globe. The overall objective of
this analysis is to demonstrate the capability of the CrIS instrument to
retrieve atmospheric ammonia. We present (i) the CrIS ammonia retrieval
strategy including spectral microwindows and error analysis, (ii) simulations
showing the retrieval vertical sensitivity, level-of-detectability, and
performance, (iii) the example of the first CrIS observations of elevated
ammonia over the Central Valley of California, USA, and (iv) initial
comparison of these CrIS NH3 retrievals with coincident TES satellite
and Quantum Cascade-Laser (QCL) surface observations (Miller et al., 2014).
Satellite tropospheric ammonia observations
The main governing satellite sensor characteristics for detecting ammonia in
the infrared are the measurement noise, spectral resolution, and local
overpass sampling time (as the thermal contrast is tightly correlated with
the diurnal cycle). This section details the CrIS instrument
specifications pertinent to ammonia observations, plus a summary of
comparable FTS IASI and TES sensor specifications and corresponding ammonia
measurement characteristics.
Relevant instrument characteristics
Cross-track Infrared Sounder (CrIS)
CrIS is in a sun-synchronous orbit (824 km) with a mean local daytime
overpass time of 13:30 in the ascending node, and a mean local nighttime
overpass time of 01:30 in the descending node. CrIS provides soundings of the
atmosphere over three wavelength bands in the infrared. For retrieved ammonia we
focus on the 9.14–15.38 µm (650–1095 cm-1) range, as the main
NH3 infrared absorbing spectral region is between 960 and 970 cm-1.
In this spectral region CrIS's spectral resolution is 0.625 cm-1
(Tobin, 2012). CrIS is an across-track scanning instrument with
a 2200 km swath width (±50∘) with the total angular field of
view consisting of a 3×3 array of circular pixels of 14 km diameter each
(nadir spatial resolution). While the spectral and spatial resolution of
CrIS is less fine than that of TES, its across-track scanning swath provides a
greater spatial coverage which is more similar to IASI. CrIS, with a spectral
resolution similar to IASI, and ∼4 times decrease in spectral
noise (∼0.04 K at 280 K) in the ammonia spectral region
(Zavyalov et al., 2013), has the potential to detect smaller NH3
concentrations than is currently possible with IASI. For example, the
Clarisse et al. (2010) sensitivity study showed that “a reduction of the
IASI noise by a factor of 2 (equally 0.1 K) would significantly improve the
sensitivity to NH3 and boundary sensitivity would start at zero thermal
contrast during the daytime”.
Infrared Atmospheric Sounder Interferometer (IASI)
IASI is a FTS instrument launched in a sun-synchronous orbit with overpass
times of 09:30 and 21:30 mean local time. It measures thermal infrared
radiation in the spectral range from 645–2760 cm-1 with a spectral
resolution 0.5 cm-1 apodized and noise of ∼0.15–0.2 K at
280 K at 950 cm-1. IASI is a scanning instrument with a 2400 km swath
made up of 2×2 arrays of 12 km diameter pixels. Under conditions of elevated
ammonia and favourable thermal contrast, IASI has peak sensitivity to
atmospheric ammonia in the boundary layer (Clarisse et al., 2010). Van Damme
et al. (2014b) using the IASI NH3 Hyperspectral Range Index (HRI)
retrieval method provide a minimum detection total column amount of
∼1.7×1016 moleccm-2 under favourable retrieval
conditions (thermal contrast ∼10 K), which is the most
relevant quantity from the HRI retrieval. In a more recent overview HRI
evaluation provided by Van Damme et al. (2015) they report these minimum
detection total column values as corresponding to surface concentrations of
3.05 µgNH3m-3 (thermal contrast of 10 K) and 1.74 µgNH3m-1
(thermal contrast of 20 K), which for comparison purposes with
CrIS (and TES) represents estimated minimum surface volume mixing ratio
values of ∼4.3 ppbv (thermal contrast of 10 K) and
∼2.4 ppbv (thermal contrast of 20 K) (values extracted from
supplemental Fig. R1 in Van Damme et al., 2015). These results are
fairly consistent with the earlier IASI NH3 optimal estimation
retrieval (which is more similar to the CrIS retrieval) results by Clarisse
et al. (2010), which states that under atmospheric states with large thermal
contrasts the lower bound minimum detection threshold is a profile with a
surface value of ∼3 ppbv.
Tropospheric emission spectrometer (TES)
TES has less dense spatial coverage than the scanning satellites (e.g. IASI,
CrIS), but has a higher spectral resolution of 0.1 cm-1 (0.06 cm-1 unapodized). TES is in a
sun-synchronous orbit that has both a daytime ascending orbit with a local
overpass time of 13:30 mean solar time, providing favourable conditions for
high thermal contrast and thus increased sensitivity to boundary layer
NH3 (Clarisse et al., 2010), and a nighttime descending orbit with a
corresponding 01:30 local overpass time. The smaller satellite footprint of
TES (5km×8 km) also allows for the potential to detect more
localized NH3 sources. The TES instrument has good SNR with brightness
temperature noise of ∼0.1–0.2 K at 280 K in the NH3 region
(Worden et al., 2006; Shephard et al., 2008), which is similar to IASI. It
also has relative radiometric calibration that is stable over time (Connor
et al., 2011), which is important for long-term trend studies. The
combination of the high spectral resolution and good SNR of the TES
instrument in the NH3 region provides increased sensitivity to
NH3
mixing ratios near the surface from satellite observations and the selection
of spectral regions (microwindows) that reduce the impact of interfering
species, and consequently systematic errors in the retrievals. Shephard et
al. (2011) showed that the TES NH3 retrievals have (i) a minimum
detection level of ∼0.4 ppbv in the representative volume
missing ratio (RVMR), which corresponds to a profile with a surface volume
mixing ratio of ∼1 ppbv, and (ii) typically have peak
sensitivity in the boundary layer between 900–700 hPa (1–3 km).
Retrieval strategy
NH3 retrieval methodology
The ammonia retrieval strategy used here follows closely the TES
NH3
retrieval approach (Shephard et al., 2011). It is based on an optimal
estimation approach that minimizes the difference between the observed
spectral radiances and a nonlinear radiative transfer model driven by the
atmospheric state, subject to the constraint that the estimated state must
be consistent with an a priori probability distribution for that state
(Rodgers, 2000). If the estimated retrieved state vector, x^, is close to the
actual true state, x, then it can be expressed through a linear
retrieval as
x^=xa+Ax-xa+Gn+GKbb-ba,
where xa is the a priori constraint vector. A
priori information is a necessity as the retrieval is an ill-posed problem
(can have many potential solutions). For these ammonia retrievals the
retrieved profile values are expressed as the natural logarithm of the
volume mixing ratio (VMR), since the values span many orders of magnitude in
the vertical. G is the gain matrix (or “contribution function
matrix”) describing the sensitivity of the retrieval to the measurements
(and thus measurement error), which maps from measurement (spectral
radiance) space into retrieval space. The vector n represents the noise on the spectral radiances. The vector
b
contains non-retrieved parameters that affect the modelled radiance (e.g. concentrations of interfering gases) that are not included in the retrieved
state vector. The ba holds the corresponding a priori
values, and
Kb=∂L/∂b is the Jacobian
describing the dependency of the forward model radiance L on
the vector b. The fast forward model OSS-CrIS (Moncet et al.,
2008), which is built from the Line-By-Line Radiative Transfer Model
(LBLRTM) (Clough et al., 2005; Shephard et al., 2009; Alvarado et al.,
2013), is used for these retrievals.
Plot of the CrIS spectral microwindow selection for NH3
retrievals. The top panel is the model-simulated CrIS observation for a
reference atmosphere (plotted in black). Overplotted in colour are various
simulated model calculations computed from the reference atmospheric profile
perturbed separately by 10 % H2O, 10 % CO2,
10 % O3, and NH3 increased to a polluted
profile. The bottom panel shows the residual (reference – perturbation) top
of the atmosphere (TOA) brightness temperatures. The diamonds represent
spectral points in the NH3 microwindows.
The averaging kernel, A, describes the sensitivity of the retrieval to
the true state:
A=∂x^∂x=KTSn-1K+Sa-1-1KTSn-1K=GK.
The Jacobian K (sometimes also called the “weighting function”)
describes the sensitivity of the forward model radiances to the state vector
(K=∂L/∂x). The Sn is the noise covariance matrix, representing the noise in
the measured radiances, and Sa is the a priori covariance
matrix for the retrieval. For profile retrievals, the rows of A are
functions with some finite width that give a measure of the vertical
resolution of the retrieval. The sum of each row of A represents an
estimate of the fraction of retrieval information that comes from the
measurement rather than the a priori (Rodgers, 2000) at the corresponding
altitude, provided the retrieval is relatively linear. The trace of the
averaging kernel matrix gives the number of degrees of freedom for signal
(DOFS) from the retrieval.
Implemented for these retrievals is an iterative maximum likelihood solution
using the Levenberg–Marquardt method strategy (i.e. Clough et al., 1995;
Rodgers, 2000):
xn+1=xn+KTSn-1K+Sa-1+γSa-1-1KTSn-1R-L+Sa-1xa-xn,
where γSa-1 is the
Levenberg–Marquardt term, with γ being the Levenberg–Marquardt
parameter or penalty function. R is the measured spectral
radiance from the sensor (i.e. CrIS), and [R-L]
represents the spectral residuals being minimized in the retrieval. This numerical
iterative approach is needed to account for the nonlinearities in the
forward model spectral radiance calculations of the atmospheric state.
Without the Levenberg–Marquardt term, this method will generally only be
satisfactory for problems where the residuals are small and the initial
guess is sufficiently close to the solution (linear region). Implementing
the Levenberg–Marquardt method provides checks when the initial guess does
not satisfy this condition from one iterative step to the next, and then
only minimizes the cost function over a “trust region” in which the
retrieval is considered linear with respect to the step size, before
proceeding to the next iteration step (Bowman et al., 2006; Moré, 1978).
CrIS NH3 microwindows
It is often desired to perform the retrievals in spectral regions that are
dominated by the species of interest. Determining the spectral regions in
which to perform the retrievals (referred to as microwindows if over small
spectral domains) can depend on a number of factors. However, the general
goal is to obtain the maximum amount of information content while minimizing
the impact of systematic errors such as from cross-state interfering species
and spectroscopic line parameters errors (laboratory measured spectroscopy
lines may have different uncertainties) (Worden et al., 2004).
Figure 1 shows the spectral region used for the
CrIS NH3 retrievals. For the long-wave infrared this is considered a
relatively “clean” window region in terms of contributions from strong
spectroscopic lines. However, as shown in Fig. 1,
there is still the potential impact from a number of species such as
H2O, CO2 and O3 that need to be considered in terms of
selection of NH3 spectroscopic retrieval regions. The column amounts
used in the simulated spectrum are provided in Table 1. Utilizing microwindows can also have a practical advantage of reducing
the computational burden of the high-spectral resolution forward model
calculations, and the storage size of output retrieval parameters (i.e. Jacobians).
Column amounts used in the CrIS simulated forward model calculations in
Fig. 1.
Molecule
Background
Enhanced
(moleccm-2)
(moleccm-2)
H2O
5.42×1022
5.96×1022
CO2
8.09×1021
8.49×1021
O3
7.35×1018
8.08×1021
NH3
1.05×1014
4.91×1016
A priori vector and constraints
The a priori profiles (vectors) and constraints are those built for the TES
NH3 retrievals (Shephard et al., 2011). In summary, both the a priori
profiles and covariance matrices are derived from global distributions of
NH3 from the chemical transport model GEOS-Chem (Zhu et al., 2013) for
three categories of NH3 profiles:
Polluted: represents all profiles with surface NH3≥5 ppbv.
Moderately polluted: represents all profiles with 1ppbv≥NH3<5 ppbv
at the surface or NH3<1 ppbv at the surface, but NH3>1 ppbv
between the surface and 500 hPa; this profile type seeks to represent those cases in which
the local emissions are less than the important transport into the region.
Unpolluted: all profiles with NH3<1 ppbv between the surface and 800 hPa.
Since the NH3 concentrations are highly variable in time and space, and
not well known globally from target scene-to-scene, we followed the same two-parameter a priori selection algorithm developed for TES. The selection
algorithm uses the scene SNR of the CrIS NH3 infrared spectral
signature and the thermal contrast between the surface and the bottom level
of the atmosphere (see Shephard et al., 2011, for further details).
Comparison methodology
One thing that needs to be considered when comparing infrared satellite
inferred retrieved profiles for species with limited information, such as
ammonia, is that the true vertical resolution of the retrieved parameter is
often more coarse than the reported retrieval vertical levels. One of the
main reasons for performing retrievals at more levels than there are
independent pieces of information is to capture the vertical sensitivity as
it varies from profile-to-profile depending on the atmospheric state.
However, due to this “oversampling”, the minor trace gas species (i.e. NH3) profiles often have several levels that are substantially
influenced by the a priori profile (i.e. containing little information from
the measurement). Depending on the purpose of the comparison, or the
quantity the satellite retrieved observations are being compared against,
there are a number of possible satellite comparison methods that can be
implemented that take into account the true satellite retrieval sensitivity.
One approach often utilized when comparing the retrieved satellite profile,
xc, against other profiles is to “map” the comparison data to the
satellite levels using a linear weighted average and to apply the satellite
averaging kernel and the a priori to the mapped in situ profile:
xcest=xa+Axcmapped-xa.
This comparison approach accounts for the satellite retrieval a priori bias
and the sensitivity and vertical resolution by applying the satellites
averaging kernel, A, and a priori, xa, to
the comparison profile xcmapped (i.e. model or in situ). This method obtains an estimated profile,
xcext, which represents what the satellite
would measure for the same air mass sampled by the in situ measurements or model.
Differences between xcext and
x^ are presumed to be associated with the satellite measurement
error on the retrieval and systematic errors resulting from parameters that
were not well represented in the forward model (e.g. temperature,
interfering gases, and instrument calibration), which are the latter two
terms in Eq. (1). This procedure is used to compare the simulated
modelled NH3 profiles with the satellite-retrieved profiles.
Conclusions
This study presents a robust CrIS NH3 retrieval that demonstrates the
capabilities of utilizing CrIS to measure tropospheric ammonia. Based on
both CrIS simulations and real observations there are a number of insights
gained in terms of the ability of CrIS to measure tropospheric ammonia. The
peak CrIS sensitivity to NH3 is typically in the range of
900–750 hPa
(∼1.0–2.5 km) depending on the atmospheric conditions. It
has a minimum NH3 detection limit of a profile with a peak value of
∼1 ppbv (typically at the surface). The retrievals have
limited information content with at most one piece of information (DOFS),
which provides more of an average boundary layer mixing ratio value (or a
partial column type) measurement as opposed to a true atmospheric
NH3
profile, which would have a number of independent pieces of information in
the vertical. The information content and sensitivity varies from
profile-to-profile depending on the atmospheric conditions, with increased
thermal contrast and ammonia concentrations providing improved measurement
sensitivity. The retrieval performance based on simulations where the truth
is known shows a small positive bias of ∼6 % with a
standard deviation of ∼±20 % (ranging from ±12 to ±30 % over the vertical profile). Considering that these are
ideal conditions where everything except the ammonia is known perfectly,
these should be considered as lower bounds on the actual errors.
Retrievals from CrIS observations on 28 January 2013 during the DISCOVER-AQ
field study over the Central Valley in California, USA, correlate well with
nearby QCL and TES observations. CrIS values at ∼1 km compare
quantitatively very well with the TES observations, and the differences are
generally within the error estimates. The CrIS ammonia distribution map over
a large domain including the Central Valley (USA), demonstrates its
ability to capture the expected spatial distribution in the ammonia values
from elevated values in the valley from anthropogenic agriculture emissions
to lower ammonia values in the unpolluted (“clean”) surrounding
mountainous regions.
There are a number of refinements to the retrieval strategy that will be
addressed in the future to facilitate more routine global operational CrIS
retrievals. Some of these potential improvements include the following: (i) accounting for
impact of clouds on the NH3 retrieval (Shephard et al., 2011) either
through screening, or more desirably retrieving the clouds (Kulawik et al.,
2006; Eldering et al., 2008), (ii) further exploring the impact of
interfering species (i.e. water vapour) on systematic errors on the ammonia
retrieval as CrIS has a 0.625 cm-1 spectral resolution, and (iii) refinement
of the CrIS surface property retrievals (i.e. surface temperature
and emissivity) in the ammonia spectral region to further reduce their
impact on the ammonia retrievals.