Introduction
The disruption of the nitrogen cycle by the human creation of reactive
nitrogen has created one of the major challenges for humankind
(Rockström et al., 2009). Global reactive nitrogen emissions into the
air have increased to unsurpassed levels (Fowler et al., 2013) and are
currently estimated to be four times larger than pre-industrial levels
(Holland et al., 1999). As a consequence the deposition of atmospheric
reactive nitrogen has increased causing ecosystems and species loss (Rodhe
et al., 2002; Dentener et al., 2006; Bobbink et al., 2010). Ammonia (NH3)
as fertilizer is essential for agricultural production and is one of the
most important reactive nitrogen species in the biosphere. NH3
emission, atmospheric transport, and atmospheric deposition are major causes
of eutrophication and acidification of soils and water in semi-natural
environments (Erisman et al., 2008, 2011). Through reactions with sulfuric
acid and nitric acid, ammonium nitrate and ammonium sulfate are formed,
which embody up to 50 % of the mass of fine-mode particulate matter (PM2.5)
(Seinfeld and Pandis., 1988; Schaap et al., 2004). PM2.5 has been
associated with various health impacts (Pope III et al., 2002, 2009).
At the same time, atmospheric aerosols impact global climate directly
through their radiative forcing effect and indirectly through the formation
of clouds (Adams et al., 2001; Myhre et al., 2013). By fertilizing
ecosystems, deposition of NH3 and other reactive nitrogen compounds
also plays a key role in the sequestration of carbon dioxide (Oren et al., 2001).
Despite the significance and impact of NH3 on the environment and
climate, its global distribution and budget are still relatively uncertain
(Erisman et al., 2007; Clarisse et al., 2009; Sutton et al., 2013). One of
the reasons is that in situ measuring of atmospheric NH3 at ambient
levels is complex due to the sticky nature and reactivity of the molecule,
leading to large uncertainties and/or sampling artefacts with the currently
used measuring techniques (von Bobrutzki et al., 2010; Puchalski et al.,
2011). Measurements are also very sparse. Currently, observations of
NH3 are mostly available in north-western Europe and central North
America, supplemented by a small number of observations made in China
(Van Damme et al., 2015b). Furthermore, there is a lack of detailed information
on its vertical distribution as only a few dedicated airborne measurements
are available (Nowak et al., 2007, 2010; Leen et al., 2013; Whitburn et al.,
2015; Shephard et al., 2015). The atmospheric lifetime of NH3 is rather
short, ranging from hours to a few days. In summary, global emission
estimates have large uncertainties. Estimates of regional emissions
attributed to source types that are different from the main regions are even more
uncertain due to a lack of process knowledge and atmospheric levels (Reis et al., 2009).
Over the last decade the development of satellite observations of NH3
from instruments such as the Cross-track Infrared Sounder (CrIS, Shephard
and Cady-Pereira, 2015), the Infrared Atmospheric Sounding Interferometer
(IASI, Clarisse et al., 2009; Coheur et al., 2009; Van Damme et al., 2014a),
the Atmospheric Infrared Sounder (AIRS, Warner et al., 2016) and the
Tropospheric Emission Spectrometer (TES, Beer et al., 2008; Shephard et al.,
2011) have shown the potential to improve our understanding of NH3
distribution. Recent studies show the global distribution of NH3
measured at a twice daily scale (Van Damme et al., 2014a,
2015a) can reveal seasonal cycles and distributions for regions where
measurements were unavailable until now. Comparisons of these observations
to surface observations and model simulations show underestimations of the
modelled NH3 concentration levels, pointing to underestimated regional
and national emissions (Clarisse et al., 2009; Shephard et al., 2011; Heald
et al., 2012; Nowak et al., 2012; Zhu et al., 2013; Van Damme et al., 2014b;
Lonsdale et al., 2017; Schiferl et al., 2014, 2016; Zondlo et al., 2016).
However, the overall quality of the satellite observations is still highly
uncertain due to a lack of validation. The few validation studies showed a
limited vertical, spatial and or temporal coverage of surface observations
for a proper uncertainty analysis (Van Damme et al., 2015b; Shephard et
al., 2015; Sun et al., 2015). A recent study by Dammers et al. (2016a)
explored the use of Fourier transform infrared (FTIR-NH3, Dammers et
al., 2015) observations to evaluate the uncertainty of the IASI-NH3
total column product. The study showed the good performance of the IASI-LUT
(look-up table; Van Damme et al., 2014a) retrieval with a high
correlation (r ∼ 0.8), but indicated an underestimation of
around 30 % due to potential assumptions of the shape of the vertical
profile (Whitburn et al., 2016; IASI-NN, neural network), uncertainty
in spectral line parameters and assumptions on the distributions of
interfering species. The study showed the potential of using FTIR
observations to validate satellite observations of NH3, but also
stressed the challenges of validating retrievals that do not provide the
vertical measurement sensitivity, such as the IASI-LUT retrieval. Since no
IASI satellite averaging kernels are provided for each retrieval, and thus
no information is available on the vertical sensitivity and/or vertical
distribution of each separate observation, it is hard to determine the cause
of the discrepancies between the observations.
The new CrIS fast physical retrieval (Shephard and Cady-Pereira, 2015) uses
an optimal estimation retrieval approach that provides the information
content and the vertical sensitivity (derived from the averaging kernels;
for more details see Shephard and Cady-Pereira, 2015), and robust and
straightforward retrieval error estimates based on retrieval input
parameters. The quality of the retrieval has so far not been thoroughly
examined in comparison to other observations. Shephard and Cady-Pereira (2015) used
Observing System Simulation Experiment (OSSE) studies to evaluate the
initial performance of the CrIS NH3 retrieval, and report a small
positive retrieval bias of 6 % with a standard deviation of ±20 %
(ranging from ±12 to ±30 % over the vertical profile). Note
that no potential systematic errors were included in these OSSE simulations.
Their study also shows good qualitative comparisons with the Tropospheric
Emission Spectrometer (TES) satellite (Shephard et al., 2011) and the
ground-level in situ quantum cascade laser (QCL) observations (Miller et
al., 2014) for a case study over the Central Valley in CA, USA, during the
DISCOVER-AQ campaign. However, currently there has not been an extensive
validation of the CrIS NH3 retrievals using direct comparisons with
vertical profile observations. In this study we will provide both direct
comparisons of the CrIS-retrieved profiles and ground-based FTIR
observations as well as comparisons of CrIS total column values and the FTIR and IASI.
Methods
The CrIS fast physical retrieval
CrIS was launched in late October 2011 on board the Suomi NPP platform. CrIS
follows a sun-synchronous orbit with a daytime overpass time at 13:30 LT (local
time) (ascending) and a night-time equator overpass at 01:30 LT. The instrument
scans along a 2200 km swath using a 3 × 3 array of circular-shaped pixels
with a diameter of 14 km at nadir for each pixel, which become larger ovals away
from nadir. In this study we use the NH3 retrieval as described by
Shephard and Cady-Pereira (2015). The retrieval is based on an optimal
estimation approach (Rodgers, 2000) that minimizes the differences between
CrIS spectral radiances and simulated forward model radiances computed from
the Optimal Spectral Sampling method (OSS) OSS-CrIS (Moncet et al., 2008), which is
built from the well-validated Line-By-Line Radiative Transfer Model (LBLRTM)
(Clough et al., 2005; Shephard et al., 2009; Alvarado et al., 2013) and uses
the HITRAN database (Rothman et al., 2013) for its spectral lines. The fast
computational speed of OSS facilitates the operational production of CrIS-retrieved
(level 2) products using an optimal estimation retrieval approach
(Moncet et al., 2005). The CrIS OSS radiative transfer forward model
computes the spectrum for the full CrIS LW band, at the CrIS spectral
resolution of 0.625 cm-1 (Tobin, 2012); thus the complete NH3
spectral band (near 10 µm) is available for the retrievals. However,
only a small number of microwindows are selected for the CrIS retrievals to
both maximize the information content and minimize the influence of errors.
Worden et al. (2004) provides an example of a robust spectral region
selection process that takes into consideration both the estimated errors
(i.e. instrument noise, spectroscopy errors, interfering species, etc.) and
the associated information content in order to select the optimal spectral
regions for the retrieval. The a priori profiles selection for the optimal
estimation retrievals follows the TES
retrieval algorithm (Shephard et al., 2011). Based on the relative NH3
signal in the spectra the a priori is selected from one of three possible
profiles representing unpolluted, moderate, and polluted conditions. The
initial guess profiles are also selected from these three potential profiles.
An advantage of using an optimal estimation retrieval approach is that
averaging kernels (sensitivity to the true state) and the estimated errors
of the retrieved parameter are computed in a robust and straightforward
manner (for more details see Shephard and Cady-Pereira, 2015). The total
satellite retrieved parameter error is expressed as the sum of the smoothing
error (due to unresolved fine structure in the profile), the measurement
error (random instrument noise in the radiance spectrum propagated to the
retrieval parameter), and systematic errors from uncertainties in the
non-retrieved forward model parameters and cross-state errors propagated
from retrieval to retrieval (i.e. major interfering species such as
H2O, CO2, and O3) (Worden et al., 2004). As of yet we have
not included error estimates for the systematic errors. The CrIS smoothing
error is computed, but since in these FTIR comparison results we apply the
FTIR observational operator (which accounts for the smoothing error), the
smoothing error contribution is not included in the CrIS errors reported in
the comparisons. Thus, only the measurement errors are reported for
observations used here; these errors can thus be considered the lower limit
of the total estimated CrIS retrieval error.
Figure 1 shows an example of CrIS NH3 observations surrounding one of
the ground-based FTIR instruments. This is a composite map of all days in
Bremen with observations in 2015. This figure shows the widespread elevated
amounts of NH3 across north-western Germany as observed by CrIS.
Since the goal of this analysis is to evaluate the CrIS retrievals that
provide information beyond the a priori, we only performed comparisons when
the CrIS spectrum presents a NH3 signal. We also focused our efforts on
FTIR stations that have FTIR observations with total columns larger than
5 × 1015 molecules cm-2 (∼ 1–2 ppb surface VMR (volume mixing ratio). This
restriction does mean that a number of sites of the FTIR-NH3 data set
will not be used. For comparability of this study to the results of the
IASI-LUT evaluation in an earlier study by Dammers et al. (2016a) we
include a short paragraph on the performance of the IASI-LUT and the more
recent IASI-NN product when applying similar constraints.
The location, longitudinal and latitudinal position,
altitude above sea level, and type of instrument for each of the FTIR sites
used in this study. In addition, a reference is given to a detailed site
description, when available.
Station
Lon
Lat
Altitude
FTIR instrument
Reference
(degrees)
(degrees)
(m a.s.l)
Bremen, Germany
8.85∘ E
53.10∘ N
27
Bruker 125 HR
Velazco et al. (2007)
Toronto, Canada
79.60∘ W
43.66∘ N
174
ABB Bomem DA8
Wiacek et al. (2007)
Lutsch et al. (2016)
Boulder, USA
105.26∘ W
39.99∘ N
1634
Bruker 120 HR
Pasadena, USA
118.17∘ W
34.20∘ N
350
MkIV_JPL
Mexico City, Mexico
99.18∘ W
19.33∘ N
2260
Bruker Vertex 80
Bezanilla et al. (2014)
Wollongong, Australia
150.88∘ E
34.41∘ S
30
Bruker 125 HR
Lauder, New Zealand
169.68∘ E
45.04∘ S
370
Bruker 120 HR
Morgenstern et al. (2012)
Annual mean of the CrIS-retrieved NH3 surface VMR
values around the Bremen FTIR site for 2015. The two circles show the
collocation area when for radii of 25 and 50 km.
![]()
FTIR-NH<inline-formula><mml:math id="M77" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula> retrieval
The FTIR-NH3 product used in this study is similar to the set described
in Dammers et al. (2016a) and is based on the retrieval methodology
described by Dammers et al. (2015). The retrieval methodology uses two
spectral microwindows with spectral width that depends on the NH3
background concentration determined for the observation stations and
location (wider window for stations with background concentrations less than
one ppb). NH3 is retrieved by fitting the spectral lines in the two
microwindows MW1 [930.32–931.32 cm-1 or wide: 929.40–931.40 cm-1]
and MW2 [962.70–970.00 cm-1 or wide: 962.10–970.00 cm-1]. An
optimal estimation approach (Rodgers, 2000) is used, implemented in
the SFIT4 algorithm (Pougatchev et al., 1995; Hase et al., 2004, 2006).
There are a number of species that can interfere to some extent in both
windows, with the major species being H2O, CO2 and O3 and the
minor species N2O, HNO3, CFC-12, and SF6. The HITRAN 2012
database (Rothman et al., 2013) is used for the spectral lines. A further
set of spectroscopic line parameter adjustments are added for CO2 taken
from the ATMOS database (Brown et al., 1996) as well as a set of
pseudo-lines for the broad absorptions by the CFC-12 and SF6 molecules
(created by NASA-JPL, G.C. Toon, http://mark4sun.jpl.nasa.gov/pseudo.html).
The NH3 a priori profiles are based on balloon measurements (Toon et
al., 1999) and refitted to match the local surface concentrations (depending
on the station either measured or estimated by model results). For the
interfering species a priori profiles we use the Whole Atmosphere Community
Climate Model (WACCM, Chang et al., 2008, v3548). The estimated errors in
the FTIR-NH3 retrievals are of the order of ∼ 30 %
(Dammers et al., 2015) with the uncertainties in the NH3 line
spectroscopy being the most important contributor. Based on the data
requirements in Sect. 2.1, a set of seven stations is used (Table 1). For
all sites except Wollongong in Australia we use the basic narrow spectral
windows. For Wollongong the wide spectral windows are used. For a more
detailed description of each of the stations see the publications listed in
Table 1 or Dammers et al. (2016a).
Coincidence criteria and quality flags applied to the
satellite and FTIR data. The third through fifth columns show the number of
observations remaining after each subsequent data criteria step and the
number of possible combinations between the CrIS and FTIR observations. The
first set of numbers indicate the number of CrIS observations within a
1∘ × 1∘ degree square surrounding the FTIR site.
Filter
Data criteria
No. obs.
FTIR
CrIS
Combinations
CrIS
15 661
25 855
Temporal sampling difference
Max 90 min
1576
13 959
112 179
Spatial sampling difference
Max 50 km
1514
3134
22 869
Elevation difference
Max 300 m
1505
1642
9713
Quality flag
DOFS ≥ 0.1
1433
1453
8579
IASI-NH<inline-formula><mml:math id="M101" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula>
The CrIS retrieval will also be compared with corresponding IASI/FTIR
retrievals using results from a previous study by Dammers et al. (2016a).
Both the IASI-LUT (Van Damme et al., 2014a) and the IASI-NN (Whitburn et al., 2016) retrievals from observations by the IASI
instrument aboard MetOp-A will be used. A short description of both IASI
retrievals is provided here; for a more in-depth description see the
respective publications by Van Damme et al. (2014a) and Whitburn et al. (2016).
The IASI instrument on board the MetOp-A platform is in a
sun-synchronous orbit and has a daytime overpass at around 09:30 LST (local solar
time) and a night-time overpass at around 21:30 LST. The instrument has a
circular footprint of about 12 km diameter for nadir-viewing angles with of
nadir observations along a swath of 2100 km. Both IASI retrievals are based
on the calculation of a dimensionless spectral index called the
hyperspectral range index (HRI) (Van Damme et al., 2014a). The HRI is
representative of the amount of NH3 in the measured column. The
IASI-LUT retrieval makes a direct conversion of the HRI to total column
density with the use of a look-up table (LUT). The LUT is created using a
large number of simulations for a wide range of atmospheric conditions which
link the thermal contrast (TC, the difference between the air temperature
at 1.5 km altitude and the temperature of the Earth's surface) and the HRI to
a NH3 total column density. The retrieval includes a retrieval error
based on the uncertainties in the initial HRI and TC parameters. The more
recent IASI-NN retrieval (Whitburn et al., 2016) follows similar steps but
it makes use of a neural network. The neural network combines the complete
temperature, humidity and pressure profiles for a better representation of
the state of the atmosphere. At the same time the retrieval error estimate
is improved by including error terms for the uncertainty in the profile
shape, and the full temperature and water vapour profiles. The IASI-NN
version uses the fixed profiles that were described by Van Damme et al. (2014a)
but allows for the use of third party profiles to improve the
representation of the NH3 atmospheric profile. The IASI-LUT and IASI-NN
retrievals have both been previously compared with FTIR observations
(Dammers et al., 2016a, b). They compared reasonably
well with correlations around r = 0.8 for a set of FTIR stations, with an
underestimation of around 30 % that depends slightly on the magnitude of
total column amounts, with the IASI-NN performing slightly better.
Data criteria and quality
NH3 concentrations show large variations both in space and time as a
result of the large heterogeneity in emission strengths due to spatially
variable sources and drivers such as meteorology and land use (Sutton et
al., 2013). This high variability poses challenges in matching ground-based
point observations made by FTIR observations with CrIS downward-looking
satellite measurements which have a 14 km nadir footprint. For the pairing
of the measurement data we apply data selection criteria similar to those
described in Dammers et al. (2016a) and summarized in Table 2. To minimize
the impact of the heterogeneity of the sources, we choose a maximum of 50 km
between the centre points of the CrIS observations and the FTIR site
location. To diminish the effect of temporal differences between the FTIR
and CrIS observations, a maximum time difference of 90 min is used.
Topographical effects are reduced by choosing a maximum altitude difference
of 300 m at any point between the FTIR site location and the centre point of
the satellite pixel location. The altitude differences are calculated using
the Space Shuttle Radar Topography Mission Global product at 3 arc-second
resolution (SRTMGL3, Farr et al., 2007). To ensure the data quality of
CrIS-NH3 retrieval for version 1.0, a small number of outliers with a
maximum retrieved concentration above 200 ppb (at any point in the profile)
were removed from the comparison data set. While potentially a surface
NH3 value of 200 ppb (and above) is possible (i.e. downwind of
forest fires), it is highly unlikely to occur over the entire footprint of
the satellite instrument. Moreover, after inspecting these data points, they
seem to be affected by numerical issues in the fitting procedure (possibly
due to interfering species). As we are interested in validating the CrIS
observational information (not just a priori information), we only select
comparisons that contain some information from the satellite (degrees of
freedom for signal – DOFS – ≥ 0.1). Do note that on average the
observations have a DOFS between 0.9 and 1.1. The DOFS > 0.1
filter only removes some of the outliers at the lower end. No explicit
filter is applied to account for clouds; however, clouds will implicitly be
accounted for by quality control as CrIS will not measure a NH3
signal (e.g. DOFS < 0.1) below optically thick clouds (e.g. cloud
optical depth > ∼ 1). In addition, the CrIS
observations are matched with FTIR observations taken only during clear-sky
conditions, which mostly eliminates influence from cloud cover. Finally, the
high signal-to-noise ratios (SNR) of the CrIS instrument allows it to
retrieve NH3 from a thermal contrast approaching 0 K during daytime
observations (Clarisse et al., 2010). Given this, we decided not to apply a
thermal contrast filter to the CrIS data. No additional filters are applied
to the FTIR observations beyond the clear-sky requirement.
For both IASI retrievals, we use the same observation selection criteria as
described in Dammers et al. (2016a). The set of criteria is similar to those
used here for the CrIS observations. Observations from both IASI retrievals
are matched using the overpass time, and longitudinal and latitudinal
positions. For comparability with CrIS a spatial difference limit of 50 km
limit was used, instead of the 25 km spatial limit used in the previous
study. Furthermore we apply the thermal contrast (> 12 K,
difference between the temperatures at 1.5 km and the surface) and Earth's
skin temperature criteria to the IASI observations to match the previous study.
Observational operator application
To account for the vertical sensitivity and the influence of the a priori
profiles of both retrievals, we apply the observational operator (averaging
kernel and a priori of the retrieval) of the FTIR retrieval to the CrIS-retrieved profiles. The CrIS observations are matched to each individual
FTIR observation in time and space following the matching criteria. The FTIR
averaging kernels, a priori profiles and retrieved profiles are first
mapped to the CrIS pressure levels (fixed pressure grid, layers are made
smaller or cut off for observations above elevation to fit the fixed
pressure grid). Following Rodgers and Connor (2003) and Calisesi et al. (2005)
this results in the mapped FTIR averaging kernel,
Aftirmapped, the mapped FTIR
a priori, xftirmapped,apriori,
and the mapped FTIR-retrieved profile, xftirmapped. Then we apply the FTIR
observational operator to the CrIS observations using Eq. (1).
x^CrIS=xftirmapped,apriori+AftirmappedxCrIS-xftirmapped,aprioriΔx^abs=x^CrIS-xftirmappedΔx^rel=x^CrIS-xftirmapped/05xftirmapped+0.5⋅x^CrIS,
where xftirapriori is the FTIR
a priori profile, xftirmapped is
the interpolated FTIR profile,
Aftirmapped is the FTIR averaging kernel,
and x^CrIS is the smoothed CrIS profile.
The CrIS smoothed profile x^CrIS calculated from
Eq. (1) provides an estimate of the FTIR retrieval applied to the CrIS satellite
profile. Next we evaluate both total column and profile measurements.
For the first validation step, following Dammers et al. (2016a), who
evaluated the IASI-LUT (Van Damme et al., 2014a) product, we sum the
individual profile (x^CrIS) to obtain a column total to
compare to the FTIR total columns. This step gives the opportunity to
evaluate the CrIS retrieval in a similar manner as was done with the
IASI-LUT retrieval. If multiple FTIR observations match a single CrIS
overpass we also average those together into a single value as well as each
matching averaged CrIS observation. Therefore, it is possible to have
multiple FTIR observations, each with multiple CrIS observations all
averaged into a single matching representative observation. For the profile
comparison this averaging is not performed to keep as much detail available
as possible. An important point to make is that this approach assumes that
the FTIR retrieval gives a better representation of the truth. While this
may be true, the FTIR retrieval will not match the truth completely. For
readability we assume that the FTIR retrieval indeed gives a better
representation of the truth, and in the next sections we will describe the case
in which we apply the FTIR observational operator to the CrIS values. For
the tenacious reader we included a similar set of results in the appendix,
using the CrIS observational operator instead of the FTIR observational
operator, as the assumption of the FTIR being true is not exactly right.
Correlation between the FTIR and CrIS total columns using
the coincident data from all measurement sites. The horizontal and vertical
bars show the total estimated error on each FTIR and CrIS observation. The
colouring on the scatter indicates the mean DOF of each the CrIS coincident
data. The trend line shows the results of the regression analysis.
![]()
FTIR vs. CrIS comparison scatter plots showing the
correlations for each of the individual stations, with estimates of error
plotted for each value. The trend lines show the individual regression
results. Note the different ranges on the x and y axis. The results for the
Boulder (green line) and Lauder (pink line) sites are shown in the same panel.
![]()
Results and discussion
Total column comparison
The total columns are averaged as explained in Sect. 2.4 to show a direct
comparison of FTIR measurements with CrIS observations in Fig. 2. A 3σ
outlier filter was applied to calculate the regression statistics. The
filtered outliers are displayed in grey, and may be caused by low
information content (DOFS) and terrain characteristics. For the regression
we used the reduced major axis regression (Bevington and Robinson, 1992),
accounting for possible errors both in the x and y values. There is an
overall agreement with a correlation of r = 0.77 (P < 0.01,
N = 218) and a slope of 1.02 (±0.05). At the lower range of values the CrIS
column totals are higher than the observed FTIR values. The CrIS
retrieval possibly overestimates due to the low sensitivity to low concentrations.
Without the sensitivity the retrieval will find a value more closely to the
a priori, which may be too high. Figure 3 shows the comparisons at each
station. When the comparisons are broken down by station (Fig. 3), the
correlation varies from site to site, from a minimum of 0.28 in Mexico City
(possibly due to retrieval errors associated with the highly irregular
terrain) to a maximum of 0.84 in Bremen. Similarly to Mexico City the
comparison also shows an increase in scatter for Pasadena, where the FTIR
site is also located on a hill. In Toronto and Bremen there is good
agreement when NH3 is elevated (> 20 × 1015 molecules cm-2),
and low bias in the CrIS total columns for intermediate values
(between 10 and 20 × 1015 molecules cm-2) except for the outlying
observation in Bremen, which is marked as an outlier by our 3σ
filter used for Fig. 2. In Wollongong, there is less agreement between the
instruments. There are two comparisons with large CrIS to FTIR ratios while
most of the other comparisons also show a bias for CrIS. For both cases the
bias can be explained by the heterogeneity of the ammonia concentrations in
the surrounding regions. The two outlying observations were made during the
end of November 2012, which coincides with wildfires in the surrounding
region. Furthermore the Wollongong site is located on the coast, which will
increase the occurrences in which one instrument observes clean air from the
ocean while the other observes inland air masses.
Results of the total column comparisons of the FTIR to
CrIS, FTIR to IASI-LUT and FTIR to IASI-NN. N is the number of averaged
total columns, MD is the mean difference [1015 molecules cm-2],
MRD is the mean relative difference [frac, in %]. Take note that the
combined value N does not add up with all the separate sites as observations
have been included for FTIR total columns > 5 × 1015 molecules cm-2.
Retrieval
Column total range
N
MD in 1015
MRD in %
FTIR mean in
in molecules cm-2
(1σ)
(1σ)
1015 (1σ)
CrIS-NH3
< 10.0 × 1015
93
3.3 (4.1)
30.2 (38.0)
7.5 (1.5)
CrIS-NH3
> = 10.0 × 1015
109
0.4 (5.3)
-1.39 (34.4)
16.7 (8.5)
IASI-LUT
< 10.0 × 1015
229
-2.7 (3.0)
-63.6 (62.6)
7.1 (1.4)
IASI-LUT
> = 10.0 × 1015
156
-5.1 (4.2)
-50.2 (43.6)
14.8 (6.7)
IASI-NN
< 10.0 × 1015
212
-2.2 (3.6)
-57.0 (68.7)
7.1 (1.4)
IASI-NN
> = 10.0 × 1015
156
-5.0 (5.1)
-52.5 (49.7)
14.8 (6.7)
Plots of the mean absolute and relative differences
between CrIS and IASI, as a function of NH3 total column. Observations
are separated into bins of total columns. Panel (a) shows the mean
absolute difference (MD). Panel (b) shows the mean relative
difference. The bars in these top two panels show the 95 % confidence
interval for each value. Panel (c) shows the mean of the observations
in each bin. The number of observations in each set is shown in the bottom panel.
![]()
The mean absolute (MD) and relative difference (MRD) are calculated
following Eqs. (4) and (5);
MRD=1N∑i=1NCrIScolumni-FTIRcolumni×1000.5×FTIRcolumni+0.5×CrIScolumniMD=1N∑i=1NCrIScolumni-FTIRcolumni
with N being the number of observations.
We evaluate the data by subdividing the comparisons over a set of total
column bins as a function of the FTIR total column value of each individual
observation. The bins (with a range of 5 × 1015 to
25 × 1015 molecules cm-2 with iterations steps of
5 × 1015 molecules cm-2) give a better representation of the performance of the retrieval
as it shows the influence of the retrieval as a function of the magnitude of the
total column densities. The results of these total column comparisons are
presented in Fig. 4. Table 3 summarizes the results for each of the FTIR
to satellite column comparisons into two total column bins, which splits the
comparisons between smaller and larger than 10 × 1015 molecules cm-2.
A few combinations of the IASI-NN and FTIR retrievals have a
small denominator value that causes problems in the calculation of the MRD.
A 3σ outlier filter based on the relative difference is applied to
remove these outliers (< 10 × 1015 molecules cm-2, only
the IASI-NN set). The statistical values are not given separately by site
because of the low number of matching observations for a number of the sites.
The CrIS/FTIR comparison results show a large positive difference in both
the absolute (MD) and relative (MRD) for the smallest bin,
(5.0–10.0 × 1015 molecules m-2). The rest of the CrIS/FTIR comparison bins
with NH3 values > 10.0 × 1015 agree very well with a
nearly constant bias (MD) around zero, and a standard deviation of the order
of 5.0 × 1015, which slightly dips below zero in the middle bin. The
standard deviation over these bins is also more or less constant, and the
weak dependence on the number of observations in each bin indicates that
most of the effect is coming from the random error on the observations. The
relative difference becomes systematically smaller with increasing column
total amounts, and tends towards zero with a standard deviation
∼ 25–50 %, which is on the order of the reported estimated
errors of the FTIR retrieval (Dammers et al., 2015).
For a comparison with previous reported satellite results, we included
both the IASI-LUT (Van Damme et al., 2014a) and the IASI-NN (Whitburn et
al., 2016) comparisons with the FTIR observations. To put the results of
this study into perspective of the IASI-LUT and IASI-NN products we added
Fig. A1 to the Appendix, which shows the total column comparison for both
products. The IASI products show similar differences as a function of
NH3 column bins, which is somewhat different from the CrIS/FTIR
comparison results. The absolute difference (MD) is mostly negative with the
smallest factor for the smallest total column bin, with a difference around
-2.5 × 1015 (std = ±3.0 × 1015,
N = 229) molecules cm-2,
which slowly increases as a function of the total column. However,
the relative difference (MRD) is at its maximum for the smaller bin with a
difference of the order -50 % (std = ∼ ±50 %,
N = 229) which decreases to ∼ -10–25 % (std = ±25 %)
with increasing bin value. For both the IASI-NN and IASI-LUT
retrievals we find an underestimation of the total columns, which originates
mostly from a large systematic error in combination with more randomly
distributed error sources such as the instrument noise and interfering
species, which are similar to results reported earlier for IASI-LUT (Dammers et al., 2016b).
A number of factors, besides the earlier reported FTIR uncertainties, can
explain the differences between the FTIR and CrIS measurements. The small
positive bias found for CrIS points to a small systematic error. The higher
SNR, from both the low radiometric noise and high spectral resolution,
enables it to resolve smaller gradients in the retrieved spectra, which
can potentially provide greater vertical information and detect smaller
column amounts (lower detection limit). This could explain the larger MRD
and MD CrIS differences at the lower end of the total column range. However,
a number of standalone tests with the FTIR retrieval showed only a minor
increase in the total column following a decrease in spectral resolution,
which indicates that the spectral resolution itself is not enough to explain the difference.
Profile comparison
The CrIS-satellite- and FTIR-retrieved profiles are matched using the
criteria specified above in Table 2 and compared. It is possible for a CrIS
observation to be included multiple times in the comparison as there can be
more than one FTIR observation per day, and/or, the possibility of multiple
satellite overpasses that match a single FTIR observation.
A representative profile example
An example of the profile information contained in a representative CrIS and
FTIR profile is shown in Fig. 5. Although the vertical sensitivity and
distribution of NH3 differs per station this is fairly
representative. The FTIR usually has a somewhat larger DOFS of the order of 1.0–2.0,
mostly depending on the concentration of NH3 compared to the
CrIS total of ∼ 1 DOFS. Figure 5a shows an unsmoothed FTIR
averaging kernel [vmr vmr-1] of a typical FTIR observation. The
averaging kernel (AVK) peaks between the surface and ∼ 850 hPa,
which is typical for most observations. In specific cases with plumes
passing over the site, the averaging kernel peak is at a higher altitude,
matching the location of the NH3 plume. The CrIS averaging kernel
(Fig. 5b) usually has a maximum somewhere in between 680 and 850 hPa depending on the
local conditions. This particular observation has a maximum near the
surface, an indication of a day with high thermal contrast. Both the FTIR
and CrIS concentration profiles have a maximum at the surface with a
continuous decrease that mostly matches the a priori profile in a shape
following the low DOFS. This is visible for layers at the lower pressures
(higher altitudes) where the FTIR and CrIS a priori and retrieved volume
mixing ratios become similar and near zero. The absolute difference between
the FTIR and CrIS profiles can be calculated by applying the FTIR
observational operator to the CrIS profile, as we described in Sect. 2.5.
The largest absolute difference (Fig. 5d) is found at the surface, which is
also generally where the largest absolute NH3 values occur. The FTIR
smoothed relative difference (red, striped line) peaks at the pressure where
the sensitivity of the CrIS retrieval is highest (∼ 55 %),
which goes down to ∼ 20–30 % for the higher altitude and
surface pressure layers. Overall the retrievals agree with most of the
difference explained by the estimated errors of the individual retrievals.
For an illustration of the systematic and random errors on the FTIR and CrIS
profiles shown in Fig. 5; see the figures in the Appendix. For the FTIR error
profile see Fig. A2 (absolute error) and Fig. A3 (relative error) and for the
CrIS measurement error profile see Fig. A4. Please note that we only show
the diagonal error covariance values for each of the errors, which is common
practice. The total column of our example profile is ∼ 20 × 1015 molecules cm-2
which is a slightly larger value than average.
The total random error is < 10 % for each of the layers, mostly
dominated by the measurement error, which is somewhat smaller than average
(Dammers et al., 2015) following the larger NH3 VMR. A similar value is
found for the CrIS measurement error with most layers showing an error
< 10 %. The FTIR systematic error is around ∼ 10 %
near the surface and grows to 40 % for the layers between
900 and 750 hPa. The error is mostly due to the errors in the NH3 spectroscopy
(Dammers et al., 2015). The shape of the relative difference between the
FTIR and CrIS closely follows the shape systematic error on the FTIR profile,
pointing to that error as the main cause of difference.
Example of the NH3 profile comparison for an FTIR
profile matched with a CrIS profile measured around the Pasadena site. With
(a) the FTIR averaging kernel, (b) the CrIS averaging kernel. For both
averaging kernels the black dots show the matrices diagonal values.
(c) shows the retrieved profiles of both FTIR (blue) and CrIS (cyan) with
the FTIR values mapped to the CrIS pressure layers. Also shown are the FTIR
a priori (green), the CrIS a priori (purple), the CrIS-retrieved profile
smoothed with the FTIR averaging kernel [CrIS (FTIR AVK)] (yellow) and the
FTIR profile smoothed with the CrIS averaging kernel [FTIR (CrIS AVK)](red).
In (d), the blue line is the absolute difference between the FTIR
profile (blue, c) and the CrIS profile smoothed with the FTIR
averaging kernel (yellow, c) with the red line as the corresponding
relative difference.
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Profile comparison for all stations combined.
Observations are combined following pressure “bins”, i.e. the midpoints of
the CrIS pressure grid. Panel (a) shows the mean profiles of the FTIR
(blue), (b) the profiles of FTIR with the CrIS averaging kernel applied to
it (red), (c) the FTIR averaging kernel diagonal values, and (d) shows the
absolute difference [VMR] between profiles (f) and (a). The second row shows
the CrIS mean profile in (e), (f) shows the profiles of CrIS with the FTIR
averaging kernel applied, (g) the CrIS averaging kernel diagonal values,
(h) the relative difference [Fraction] between the profiles in (f) and (a). Each
of the boxes edges are the 25th and 75th percentiles, the black lines in
each box is the median, the red square is the mean, the whiskers are the
10th and 90th percentiles, and the grey circles are the outlier values
outside the whiskers.
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Summary of the absolute and relative actual error as a
function of the VMR of NH3 in the individual FTIR layers. The box edges
are the 25th and 75th percentiles, the black line in the box is
the median, the red square is the mean, the whiskers are the 10th and
90th percentiles, and the grey circles are the outlier values outside
the whiskers. Only observations with a pressure greater than 650 hPa are
used. The top panel shows the absolute difference for each VMR bin, the
bottom panel shows the relative difference for each VMR bin.
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All paired data
In Fig. 6 all the individual site comparisons were merged. The Mexico City
site was left out of this figure because of the large number of observations
in combination with a difference in pressure grid due to the high altitude
of the city obscuring the overall analysis and biasing the results towards the
results of one station. Similar to the single profile example, the FTIR
profile peaks near the surface for most observations, slowly going towards
zero with decreasing pressure. When compared to the representative profile
example a number of differences emerge. A number of FTIR observations peak
further above the surface and are shown as outliers, which drag the mean
further away from the median values. The combined CrIS profile in Fig. 6
shows a similar behaviour, although for the lowest pressure layer it has a
lower median and mean compared to the layer above. The difference between
Figs. 5 and 6e derives mostly from the number of observations used in
the box plot, many with weak sensitivity at the surface. Similar to the
single profile example in Fig. 5, the FTIR averaging kernels in Fig. 6c peak on
average near or just above the surface (with the diagonal elements of
the AVK's shown in the figure). The sensitivity varies a great deal between
the observations as shown by the large spread of the individual layers. The
CrIS averaging kernels (Fig. 6g) usually peak in the boundary layer around
the 779 hPa layer with the two surrounding layers having somewhat similar
values. The instrument is less sensitive to the surface layer as is
demonstrated by the large decrease in the AVK near the surface, but this
varies depending on the local conditions. We find the largest absolute
differences in the lower three layers, as was seen in the example in Fig. 5,
although the differences decrease rather than increase. The
relative difference shows a similar shape to Fig. 5. Overall both retrievals
show agreement. The relative differences in the single-level retrieved
profile values in Fig. 6h show an average difference in the range of
∼ 20 to 40 % with the 25th and 75th percentiles at
around 60–80 %, which partially follows from our large range of
concentrations. The absolute difference shows an average difference in the
range of -0.66 to 0.87 ppb around the peak sensitivity levels of the CrIS
observations (681 to 849 hPa). The lower number of surface observations
follow on from the fact that only the Bremen site is located at an altitude low
enough for the CrIS retrieval to provide a result at this pressure level.
Due to this difference in retrieval layering, the remaining 227 observations
mostly follow from matching observations in Bremen, which is located in a
region of significant NH3 emissions.
The switch between negative and positive values in the absolute difference
(see Fig. 6d) occurs in the two lowest layers dominated by the Bremen
observations and provides insight into the relation between absolute
differences as a function of retrieved concentration. Figure 7 shows a summary
of the differences as a function of the individual NH3 VMR layer
amounts. As seen before in the column comparison, e.g. Figs. 2 and 4, the CrIS
retrieval gives larger total columns than the FTIR retrieval for the small
values of VMR. For increasing VMRs, this slowly tends to a negative absolute
difference with a relative difference in the range of 20–30 %. However,
note that the number of compared values in these high VMR bins are by far
lower than in the first three bins leading to a relatively smaller effect in the
total column and merged VMR figures (Figs. 2 and 6) from these high VMR
bins. We now combine the results of Figs. 6 and 7 with Fig. 8 to create a
set of subplots showing the difference between both retrieved profiles as a
function of the maximum VMR of each retrieved FTIR profile. For the layers
with pressure less than 681 hPa we generally find agreement, which is
expected but not very meaningful, since there is not much NH3 (and thus
sensitivity) in these layers and any differences are smoothed out by the
application of the observational operator. The relative differences for
these layers all lie around ∼ 0–20 %. For the lowest two VMR
bins we again find that CrIS gives larger results than the FTIR, around the
CrIS sensitivity peak in the layer centred around 849 hPa, and to a lesser
extent in the layer below. At these VMR levels (< 2 ppb) the
NH3 signal approaches the spectral noise of the CrIS measurement,
making the retrievals more uncertain. The switch lies around 2–3 ppb, where
the difference in the SNR between the instruments becomes less of an issue.
Also easily observed is the relation between the concentration and the
absolute and relative differences. This can be explained by the difference
in sensitivity of the instruments, and the measurement noise of both
instruments. For the largest VMR bin [> 4.0 ppb] we find that
CrIS is biased for the four lowest layers. Differences are largest in the
surface layer where only a few observations are available, almost all from
the Bremen site. Most of these CrIS observations have a peak satellite
sensitivity at a higher altitude than the FTIR. Assuming that most of the
NH3 can be found directly near the surface, with the concentration
dropping off with a sharp gradient as a function of altitude, it is likely
that these concentrations are not directly observed by the satellite but are
observed by the FTIR instruments. This difference in sensitivity should be
at least partially removed by the application of the observational operator
but not completely, due to the intrinsic differences between both
retrievals. The CrIS retrieval uses one of three available a priori
profiles, which is chosen following a selection based on the strength of
NH3 signature in the spectra. The three a priori profiles (unpolluted,
moderately polluted and polluted) are different in both shape and
concentration. Out of the entire set of 2047 combinations used in Fig. 8,
only six are from the non-polluted a priori category. About one-third of the
remaining observations use the polluted a priori, which has a sharper peak
near the surface (see Fig. 5c) compared to the moderately polluted profile,
which is used by two-thirds of the CrIS retrievals shown in this work. Based on
the results as a function of retrieved VMR (as measured with the FTIR so not
a perfect restriction), it is possible that the sharper peak at the surface
as well as the low a priori concentrations are restricting the retrieval.
The dependence of the differences on VMR can also possibly follow from
uncertainties in the line spectroscopy. In the lower troposphere there is a
large gradient in pressure and temperature and the impact of any uncertainty
in the line spectroscopy is greatly enhanced. Even for a day with large
thermal contrast and NH3 concentrations (e.g. Fig. 5), the difference
between both the CrIS and FTIR retrievals was dominated by the line
spectroscopy. This effect is further enhanced by the higher spectral
resolution and reduced instrument noise of the FTIR instrument, which
potentially makes it more able to resolve the line shapes.
Summary of differences as a function of maximum volume
mixing ratio (VMR). The maximum VMR of each FTIR profiles is used for the
classification. Absolute (a) and relative profile differences (b)
following the FTIR and CrIS (FTIR AVK applied) profiles. Observations
are following pressure layers, i.e. the midpoints of the CrIS pressure grid.
The box edges are the 25th and 75th percentiles, the black line in
the box is the median, the red square is the mean, the whiskers are the
10th and 90th percentiles, and the grey circles are the outlier values outside the whiskers.
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To summarize, the overall differences between both retrievals are quite
small, except for the lowest layers in the NH3 profile where CrIS has
less sensitivity. The differences mostly follow the errors as estimated by
the FTIR retrieval and further effort should focus on the estimated errors
and uncertainties. A way to improve the validation would be to add a third
set of measurements with a better capability to vertically resolve NH3
concentrations from the surface up to ∼ 750 hPa (i.e. the
first 2500 m). One way to do this properly is probably by using airplane
observations that could measure a spiral around the FTIR path coinciding
with a CrIS overpass. The addition of the third set of observations would
improve our capabilities to validate the satellite and FTIR retrievals and
point out which retrieval specifically is causing the absolute and relative
differences at each of the altitudes.