Formaldehyde (HCHO) has been measured from space for more
than 2 decades. Owing to its short atmospheric lifetime, satellite HCHO
data are used widely as a proxy of volatile organic compounds (VOCs; please
refer to Appendix A for abbreviations and acronyms), providing constraints
on underlying emissions and chemistry. However, satellite HCHO products from
different satellite sensors using different algorithms have received little
validation so far. The accuracy and consistency of HCHO retrievals remain
largely unclear. Here we develop a validation platform for satellite HCHO
retrievals using in situ observations from 12 aircraft campaigns with a chemical
transport model (GEOS-Chem) as the intercomparison method. Application to
the NASA operational OMI HCHO product indicates negative biases (-44.5 %
to -21.7 %) under high-HCHO conditions, while it indicates high biases (+66.1 % to
+112.1 %) under low-HCHO conditions. Under both conditions, HCHO a priori
vertical profiles are likely not the main driver of the biases. By providing
quick assessment of systematic biases in satellite products over large
domains, the platform facilitates, in an iterative process, optimization of
retrieval settings and the minimization of retrieval biases. It is also
complementary to localized validation efforts based on ground observations
and aircraft spirals.
Introduction
Formaldehyde (HCHO) is ubiquitous in the troposphere due to its high product
yields from atmospheric oxidation of volatile organic compounds (VOCs).
Methane mainly controls the tropospheric background, whereas regional
enhancements are contributed largely by short-lived non-methane VOCs
(NMVOCs) emitted from the biosphere, human activities, and wildfires. HCHO
is detectable from space using solar ultraviolet backscattered radiation
between 325 and 360 nm (Chance et al., 2000). HCHO vertical column densities
(VCDs; in units of molecules cm-2) are obtained after the retrieval
process and the consideration of a priori information. Because of the short
atmospheric lifetime of HCHO (a few hours), satellite HCHO VCD has been used
as a localized proxy for NMVOC emissions (e.g., Palmer et al., 2003; Shim et
al., 2005; Stavrakou et al., 2009; Marais et al., 2012; Barkley et al.,
2013; Zhu et al., 2014; Zhu et al., 2017a; Cao et al., 2018; Surl et al.,
2018). In addition, previous applications of HCHO retrievals also include
evaluating surface ozone sensitivity (Jin and Holloway, 2015; Jin et al.,
2017), quantifying cancer risks of ambient HCHO (Zhu et al., 2017b),
estimating organic aerosol abundance (Liao et al., 2019), and mapping
hydroxyl (OH) radicals (Wolfe et al., 2019). However, validation of
satellite HCHO products from different satellite sensors using different
algorithms has received little attention so far. Validation exercises over
different regions in different seasons remain extremely limited. Here we
develop a validation platform built with HCHO observations from 12 aircraft
campaigns over the United States, eastern Asia, and the remote Pacific
Ocean. We further apply it to the NASA operational HCHO product and report
the validation results.
HCHO has been continuously observed from space for more than 2 decades
since the launch of GOME (1996–2003) (Chance et al., 2000; De Smedt et al., 2008) and
SCIAMACHY (2003–2012) (Wittrock et al., 2006; De Smedt et al., 2008).
Presently available observations are from OMI (2004–) (De Smedt et al.,
2015; González Abad et al., 2015), GOME-2A (2006–) (De Smedt et al.,
2012), OMPS (2011–) (Li et al., 2015; González Abad et al., 2016),
GOME-2B (2012–) (De Smedt et al., 2012), and TROPOMI (2018–) (De Smedt et
al., 2018). Hourly HCHO observations (in daytime) will be made available
from a constellation of geostationary satellites to be launched in the
coming 1–3 years, including GEMS (2020) (Kim et al., 2020; Kwon et al.,
2019) over eastern Asia, TEMPO (2022) (Zoogman et al., 2017) over North
America, and Sentinel-4 (2023) (Courrèges-Lacoste et al., 2017) over
Europe. HCHO retrieved from the above satellites generally follows a
two-step approach, slant column density (SCD) fitting and conversion of it
to VCD using localized air mass factors (AMFs), with retrieval errors being
introduced in each step (Marais et al., 2012; De Smedt et al., 2015;
González Abad et al., 2015; Hewson et al., 2015; Kwon et al., 2017;
Herman et al., 2018; Nowlan et al., 2018).
Previous validation of HCHO satellite data sets is often conducted by
directly comparing coincident satellite pixels and observation points.
Wittrock et al. (2006) and Vigouroux et al. (2009) found SCIAMACHY HCHO
columns are unbiased compared with ground-based measurements over remote
regions. De Smedt et al. (2015) reported OMI and GOME2 data are -20 % to
-40 % biased against observed vertical profiles. Wang et al. (2017)
reported biases in OMI and GOME2 data of -12 % to -20 % over
eastern China from May to December. A recent study showed monthly bias in
OMI data ranges from -11 % in summer to +26 % in winter in Beijing
between 2010 and 2016 (Wang et al., 2019). Comparison with aircraft
observations indicated that GOME data are +16 % biased during summer
over eastern Texas in the United States (Martin et al., 2004) and that OMI
data are biased by -37 % in October over Guyana (Barkley et al., 2013).
Tan et al. (2018) found OMPS data are -18 % biased against ship-based
measurements in June over the East China Sea.
Such direct validation approaches, however, face three practical challenges.
First, they require the averaging of extensive observations to reduce large
random noises associated with individual satellite retrievals. Second, they
fail to make full use of precise in situ observations. Low earth orbit (LEO)
satellites pass over a certain location within a fixed time window up to a
couple of times per day, meaning only a small fraction of observations are
coincident with satellite pixels and thus suitable for the purpose of direct
validation. Finally, reliability of validation results is unclear for areas
beyond the observation sites/domains.
Alternatively, Zhu et al. (2016) proposed an indirect validation approach
with a chemical transport model (CTM) as the intercomparison method. This
approach increases considerably the range of data and conditions that can be
used for validation and therefore reduces random noises in satellite
retrievals through averaging. Using this approach, Zhu et al. (2016) found
current HCHO satellite products are biased by -20 % to -51 % against
the SEAC4RS (Toon et al., 2016) aircraft measurements over the
Southeastern United States during the summer of 2013. Here we follow this
indirect validation approach to develop a validation platform for satellite
HCHO retrievals using observations from 12 aircraft campaigns all over the
world, as discussed below.
HCHO observations from aircraft campaigns
Figure 1 shows flight tracks of 12 aircraft campaigns used in this study.
Detailed information is summarized in Table 1. The 12 aircraft campaigns are
selected based on the data availability and spatial representativeness.
Together, the 12 aircraft campaigns offer exceptional opportunities for
validation of satellite HCHO retrievals with extensive observations over the
United States (C1–C9; DISCOVER-AQ California 2013, NOMADSS, SENEX,
DISCOVER-AQ Texas 2013, DISCOVER-AQ Colorado 2014, FRAPPÉ, WINTER,
SONGNEX, and WE-CAN, respectively), eastern Asia (C10; KORUS-AQ), and the
remote Pacific Ocean (C11–C12; ATom-1 and ATom-2). The aircraft campaigns
have great spatial coverage over HCHO hotspots, such as the Southeastern
United States (C2 and C3) dominated by strong biogenic isoprene emissions
(Guenther et al., 2012), the Houston area (C4) featured with high anthropogenic
NMVOCs (Zhu et al., 2014), and the Western United States (C9) influenced by
wildfires. The campaigns also survey different seasons of the year, enabling
assessment of seasonal biases in satellite HCHO products.
Flight tracks of the 12 aircraft campaigns used in this study.
Panel (a) shows the spatial coverage (green rectangles) of the campaigns.
Panel (b) (inset) zooms in on campaigns over the United States. Aircraft
campaigns are numbered as C1–C12. Table 1 summarizes detailed information
of the 12 campaigns. Formaldehyde (HCHO) mixing ratios along aircraft flight
tracks are shown in panel (c)–(n). Color bar saturates at 5 ppbV. The green
rectangle in panel (c)–(n) is the same as that in (a) and (b), indicating
spatial domain of a certain campaign. The same domain is also defined in
Table 1.
Overview of the 12 aircraft campaigns used in this study.
Campaign IDCampaign nameRegionDatePlatformHCHO instrument(s)aDomainbReferencescC1DISCOVER-AQ California 2013California, U.S.16 Jan–6 Feb 2013NASA P-3BDFGAS (4 %)31.4–39.3∘ N 123.3–117.8∘ W1, 2C2NOMADSSSoutheastern U.S.3 Jun–14 Jul 2013NSF/NCAR C130TOGA (15 %)d31.3–38.2∘ N 96.8–82.7∘ W3C3SENEXSoutheastern U.S.3 Jun–10 Jul 2013NOAA WP-3DISAF (10 %)31.2–41.0∘ N 95.2–82.4∘ W4C4DISCOVER-AQ Texas 2013Texas, U.S.4–29 Sep 2013NASA P-3BDFGAS (4 %)29.1–30.5∘ N 95.95–94.65∘ W1, 2C5DISCOVER-AQ Colorado 2014Colorado, U.S.17 Jul–10 Aug 2014NASA P-3BDFGAS (4 %)38.5–42.1∘ N 105.4–103.4∘ W1, 2C6FRAPPÉColorado, U.S.26 Jul–18 Aug 2014NSF/NCAR C130CAMS (4 %) TOGA (15 %)d38.5–42.0∘ N 109.3–102.4∘ W5, 6C7WINTERNortheastern U.S.3 Feb–13 Mar 2015NSF/NCAR C-130ISAF (10 %) TOGA (15 %)d39.0–41.8∘ N 72.2–67.9∘ W7C8SONGNEXWestern U.S.19 Mar–27 Apr 2015NOAA WP-3DISAF (10 %)30.0–50.0∘ N 111.0–100.0∘ W8C9WE-CANWestern U.S.26 Jul–13 Sep 2018NSF/NCAR C-130PTR-ToF-MS (60 %)36.0–48.0∘ N 123.0–109.0∘ W9, 10C10KORUS-AQSouth Korea26 Apr–18 Jun 2016NASA DC-8CAMS (4 %)34.6–37.8∘ N 125.7–129.6∘ W11C11ATom-1Pacific Ocean29 Jul9–23 Aug 2016NASA DC-8ISAF (10 %)60.0∘ S–50∘ N 179.0–141.0∘ W12C12ATom-2Pacific Ocean26 Jan–21 Feb 2017NASA DC-8ISAF (10 %) TOGA (15 %)d60.0∘ S–50∘ N 179.0–141.0∘ W12
a Instrument accuracy is given in parentheses. During C6,
C7, and C12, HCHO is measured by two independent instruments.
b Shown as green rectangles in Fig. 1.
c (1) Crawford and Pickering (2014); (2) DISCOVER-AQ Science Team (2014); (3) Emmons (2016); (4) Warneke et al. (2016); (5) Pfister et al. (2017); (6) Richter et al. (2015); (7) UCAR/NCAR – Earth Observing Laboratory et al. (2016); (8) National Oceanic and Atmospheric Administration (NOAA) (2017); (9) Pollack et al. (2019); (10) Hu and Permar (2019); (11) KORUS-AQ (2016); (12) Wofsy
et al. (2018).
d TOGA has an accuracy of 15 % or better.
During the aircraft campaigns, HCHO observations were made along the flight
tracks with multiple instruments, including (1) NCAR Difference Frequency
Generation Absorption Spectrometer (DFGAS) (Weibring et al., 2006, 2007,
2010); (2) Trace Organic Gas Analyzer (TOGA) (Apel et al., 2003; 2010;
2015); (3) In Situ Airborne Formaldehyde instrument (ISAF) (Cazorla et al.,
2015); (4) Compact Atmospheric Multispecies Spectrometer (CAMS) (Fried et
al., 2011; Richter et al., 2015); and (5) Proton Transfer Reaction
Time-of-Flight Mass Spectrometer (PTR-ToF-MS) (Müller et al., 2014). The
instrument accuracy (1σ level) is 4.5 %, 15 % (lower limit;
https://airbornescience.nasa.gov/instrument/TOGA, last access: 23 October 2020), 10 % (Cazorla et al.,
2015), 4 % (Richter et al., 2015), and 60 % (Hu and Permar, 2019) for
DFGAS, TOGA, ISAF, CAMS, and PTR-ToF-MS, respectively. The corresponding
instrument detection limits are 40–100 ppt (Nowlan et al., 2018), 20 ppt
(Wofsy et al., 2018), 36 ppt (Cazorla et al., 2015), ∼40 ppt
(Richter et al., 2015), and 300 ppt (Hu and Permar, 2019), respectively.
HCHO observations from different instruments are generally consistent. Zhu
et al. (2016) reported ISAF to be in good agreement with CAMS during the
SEAC4RS campaign with a correlation coefficient (r) of 0.99 and a slope
of 1.10. ISAF is also found to be consistent (r=0.98) with DFGAS during the DC3
campaign (Barth et al., 2015), with a slope of 1.07 (Liao et al., 2019).
Figure 2 shows point-to-point comparisons among 1 min averaged TOGA, ISAF,
and CAMS HCHO observations aboard the aircraft. There is a high correlation
in the mixed layer (here and elsewhere defined as below 2 km; r=0.86) and
free troposphere (>2 km; r=0.93) between TOGA and CAMS during
the FRAPPÉ campaign with a reduced major axis (RMA) regression slope of
1.05±0.01. During the WINTER campaign, TOGA generally matches with
ISAF (r=0.72) within the mixed layer. However, consistency between the two
instruments begins to fall apart in the free troposphere (r=0.33), which is
likely driven by sampling differences. TOGA correlates highly with ISAF
during the ATom-2 (C12) campaign in both the mixed layer (r=0.83) and free
troposphere (r=0.82), but overall it is 48 % higher than ISAF, likely due
to the fact that the two instruments are independently calibrated. In this
study, we use CAMS data for FRAPPÉ (C6) and ISAF data for both WINTER (C7)
and ATom-2 (C12), given their higher accuracies.
Comparisons between 1 min averaged HCHO observations from multiple
instruments. (a) Observations from TOGA and CAMS instruments aboard the
NSF/NCAR C-130 during the FRAPPÉ campaign. (b) Observations from TOGA
and ISAF instruments aboard the NSF/NCAR C-130 during the WINTER campaign.
(c) Observations from TOGA and ISAF instruments aboard the NASA DC-8 during
the ATom-2 campaign. HCHO data points are colored by atmospheric pressure.
Reduced major axis (RMA) regression slopes and intercepts are shown along
with the correlation coefficient (r), sample size (N), RMA regression line
(blue), and 1:1 line (black).
Figure 3 shows mean vertical profiles measured from the 12 aircraft
campaigns. For campaigns conducted over/near land (C1–C10), aircraft
observations show a higher level of HCHO within the mixed layer as a result of
biogenic and anthropogenic NMVOC emissions. In the free troposphere, HCHO
starts to drop sharply due to short lifetimes of highly reactive NMVOCs,
such as isoprene (∼1 h) and HCHO itself (∼2 h). We see enhanced HCHO (>3 ppb) in 3–5 km during the WE-CAN
(C9) campaign, which is caused by intensive primary and secondary production
of HCHO from wildfires in the Western United States. Mean HCHO over the
remote Pacific Ocean (C11–C12) declines with altitudes through the
troposphere (below 12 km), suggesting oxidation of well-mixed methane as the
dominant source of the tropospheric background HCHO.
Mean HCHO vertical profiles as observed during the 12 aircraft
campaigns (Table 1) and simulated by GEOS-Chem. GEOS-Chem is sampled along
the flight tracks at the time and locations of the measurements. We only use
observed and modeled HCHO values within the study area, defined by the green
rectangle for each campaign in Fig. 1. HCHO values are vertically binned
in increments of 500 m. Shading gives the standard deviation in the
observations. Observed (black) and modeled (red) HCHO column densities
(1015 molecules cm-2) are given along with relative biases (in
parentheses) in modeled column densities. The relative biases are further
used as correction factors for GOES-Chem columns. Observed column densities
are computed using mean observed mixing ratio (black lines), temperature,
and pressure. Modeled column densities are computed according to GEOS-Chem
HCHO vertical profiles (red lines) as well as temperature and pressure from
GEOS-FP. Notice that the scale in panel (k) and (l) is different from that in
other panels.
GEOS-Chem as the intercomparison method
The indirect validation approach requires a CTM to bridge sampling gaps
between aircraft observations and satellite retrievals (Zhu et al., 2016).
Here we use GEOS-Chem version 12.0.0 (10.5281/zenodo.1343547, The International GEOS-Chem User Community, 2018) as the
intercomparison method for validation of satellite HCHO columns using
aircraft observations. With a detailed representation of the tropospheric chemistry of
ozone–NOx–VOCs–aerosol–halogens, the GEOS-Chem
model has been used extensively in several studies to simulate HCHO,
including comparisons with in situ observations (Jaeglé et al., 2015; Zhu et al.,
2016; Chan Miller et al., 2017; Liao et al., 2019). Zhu et al. (2016) and
Chan Miller et al. (2017) found that GEOS-Chem provides an unbiased
simulation of SEAC4RS and SENEX aircraft observations within the mixed
layer over the Southeastern United States in summer, including horizontal
patterns and mean vertical profiles. In winter, GEOS-Chem is biased by
-32 % compared against aircraft observations below 300 m over the
Northeastern United States (Jaeglé et al., 2015).
The GEOS-Chem model is driven by the Goddard Earth Observing System–Forward
Processing (GEOS-FP) assimilated meteorological data, produced by the NASA
Global Modeling and Assimilation Office (GMAO) (Molod et al., 2012). The
GEOS-FP meteorological data have a native horizontal resolution of
0.25∘×0.3125∘, with 72 vertical pressure
levels and 3 h temporal frequency (1 h for surface variables and mixed layer
depths). Biogenic VOC emissions are from the MEGAN 2.1 model (Guenther et
al., 2012) as implemented in GEOS-Chem by Hu et al. (2015). Anthropogenic
emissions are based on the NEI2011 inventory (EPA, 2015) over the United
States and the MIX inventory (Li et al., 2017) over the eastern Asia
region. Fire emissions are from the fourth-generation Global Fire Emissions
Database (GFED4) (Giglio et al., 2013). Surface-driven vertical mixing up to
the mixing depth is based on the nonlocal mixing scheme of Holtslag and
Boville (1993), as implemented in GEOS-Chem by Lin and McElroy (2010).
We run the GEOS-Chem model at a 2∘×2.5∘
resolution to simulate the ATom-1 (C11) and ATom-2 (C12) campaigns as HCHO
over the remote Pacific Ocean is relatively homogeneously distributed due to
methane oxidation. Over the continents, we use the native resolution
(0.25∘×0.3125∘, nested version) in GEOS-Chem
to better represent heterogeneities in emissions and chemistry during the
aircraft campaigns (C1–C10) over North America (130–60∘ W, 9.75–60∘ N) and eastern Asia
(70–140∘ E, 15–55∘ N). Dynamic
boundary conditions for the nested simulations are from global 2∘×2.5∘ runs. Global and nested simulations are spun up
for 10 months and 1 month, respectively, to remove the sensitivity to initial
conditions. GEOS-Chem is sampled along the flight tracks at the time and
locations of the aircraft measurements.
Figure 3 shows GEOS-Chem mean HCHO profiles. Previous studies (Scarino et
al., 2014; Millet et al., 2015; Zhu et al., 2016) found GEOS-FP mixing depth
in summer is biased low compared with observations by a factor of
30 %–50 %, which may partially contribute to the underestimation of
HCHO in the mixed layer (Fig. 3) by GEOS-Chem over the United States
(C1–C6, C9) and South Korea (C10). On top of that, underestimation of
highly reactive VOC emissions as reported by Zhu et al. (2014) may be
another reason for the lower simulated HCHO over the Houston area (C4).
GEOS-Chem generally reproduces the observed vertical distribution of HCHO in
the free troposphere. Exceptions are for campaigns surveying the Western
United States in summer (C5, C6, and C9), likely caused by uncertainties in
GFED4 fire emissions in the model.
By integrating the mean vertical profiles in Fig. 3, we estimate, for each
aircraft campaign, a mean observed HCHO column, a mean GEOS-Chem modeled
HCHO column, and the regional bias associated with the GEOS-Chem model as
informed by comparison between observed and modeled HCHO columns. Figure 3
shows the regional bias for each aircraft campaign, which is later applied
as the correction factor in the validation exercises.
Application to the NASA operational HCHO product
The NASA operational OMI HCHO product is based on the Smithsonian Astrophysical
Observatory (SAO) HCHO retrieval algorithm (González Abad et al., 2015).
Briefly, the algorithm follows a two-step approach. First, a reference
sector correction term (ΩS0) is subtracted from the fitted
total SCD (ΩS), yielding the reference-sector-corrected SCD
(ΔΩS):
ΔΩS=ΩS-ΩS0.
Following Khokhar et al. (2005) and De Smedt et al. (2008), the reference
sector correction (ΩS0) represents a daily post-processing
normalization for the retrieved SCD, calculated as the difference between
the retrieved SCD over the Pacific Ocean and the GEOS-Chem VCD climatology
over the Pacific Ocean multiplied by the satellite air mass factor (AMF)
(González Abad et al., 2015, 2016). ΔΩS is then converted to VCD (Ω) by applying
the corresponding AMF:
Ω=ΔΩSAMF.
The AMF depends on a number of factors, including solar zenith angle (θZ), satellite viewing angle (θV), cloud characteristics,
scattering properties of the atmosphere and surface, and HCHO a priori profiles.
Following Palmer et al. (2001), it is computed as the product of a
geometrical AMF (AMFG) and a correction with scattering weights w applied to
the vertical shape factors S:
3AMF=AMFG∫PS0w(p)S(p)dp4AMFG=secθZ+secθV.
Here the integration is over the pressure (p) coordinate from the surface
(PS) to the top of atmosphere. S is the normalized vertical profile of
HCHO mixing ratios C(p):
S(p)=C(p)ΩAp∫PS0C(p)ΩApdp,
where ΩA(p) is the partial air column density at p and w measures
the sensitivity of the backscattered radiation to HCHO. The OMI SAO HCHO product
provides Ω, ΩS, ΔΩS, AMFG (in
term of θZ and θV), AMF, S, and w for each pixel.
Uncertainties associated with Ω are 30 %–100 %, contributed by
uncertainties in both AMF (∼35 %) and ΔΩS
(30–100 %) (González Abad et al., 2015).
Here we use the DISCOVER-AQ 2013 (C1) flight campaign as an example to
demonstrate the validation process. Validation of the OMI SAO HCHO product
starts with the selection of satellite pixels. This is done for each
campaign within the corresponding study period (Table 1) and domain (defined
in Table 1; shown in Fig. 1) based on the following criteria: (1) pass quality
checks (MainDataQualityFlag =0), (2) have cloud fraction less than 0.3, (3) have θZ less than 60∘, and (4) have VCD within the
range of -8.0×1015 to 7.6×1016 molecules cm-2. The last criterion is set based on 3 times
the fitting uncertainty (30 %–100 %) and a typical VCD value between
4.0×1015 and 4×1016 molecules cm-2 (González Abad et al., 2015). We then compute
campaign-averaged GEOS-Chem HCHO columns by sampling the model according to
OMI's schedule. The original GEOS-Chem columns are further scaled using
correction factors informed by comparison of model and aircraft columns
(Fig. 3). Figure 4 shows campaign-averaged HCHO columns for both OMI SAO
and corrected GEOS-Chem over the study domain (California, United States).
Campaign-averaged OMI and corrected GEOS-Chem HCHO columns for other
campaigns (C2–C12) are in the Supplement. Poor spatial correlations between
OMI and corrected GEOS-Chem columns during some campaigns (Fig. 4 and
Supplement) likely reflect large uncertainties in OMI columns. Finally, we
compare spatially and temporally averaged HCHO columns, during the study
period and over the study domain, as reported by the OMI SAO product and
modeled by GEOS-Chem (with correction) to estimate the regional systematic
bias in the OMI SAO HCHO product. Detailed validation results are summarized in
Table 2.
HCHO vertical column densities over California, United States,
during DISCOVER-AQ California 2013 (C1; 16 January–6 February). Panel (a) shows data from the OMI SAO HCHO product. Panel (b) shows GEOS-Chem
model results sampled on the OMI schedule (see text) and scaled by a factor
of 1.53 to correct for the bias relative to aircraft measurements (Fig. 3). OMI and GEOS-Chem results are regridded onto the 0.5∘×0.5∘ grids. The green rectangles represent the study
domain (same as that in Fig. 1), which is also defined in Table 1. Here
and elsewhere, validation results (e.g., in Table 2) are limited to the grids
marked with black open circles, which represent grids that are sampled by
the aircraft (i.e., intercepted with flight tracks in Fig. 1).
HCHO columns and validation results over the 12 aircraft campaignsa.
a Results are spatially and temporally averaged values for the
grids sampled by the aircraft (marked with open circles in Figs. 4 and
S1–S11) during the study periods (defined in Table 1). HCHO columns
(GEOS-Chem columns, ΩS, ΩS0, Ωavg,
and Ωcomp) are in units of 1015 molecules cm-2.
For each aircraft campaign, biases relative to the corrected GEOS-Chem
column are given in parentheses.
b Sampled from the GEOS-Chem models according to OMI's
schedule.
c Corrected with the factors informed by comparison of
observed and modeled HCHO columns (Fig. 3).
d Spatial correlation between GEOS-Chem and OMI HCHO column
over the study region (green rectangles in Fig. 1).
e SCD computed using vertical column density without reference
sector correction (“ColumnAmount” data field in the OMI SAO HCHO product) and
air mass factor (AMF).
f SCD correction term recomputed using averaged OMI ΩS, Ωavg, and AMF following Eq. (1).
g Mean VCD by directly averaging valid satellite pixels.
h VCD recomputed using averaged OMI ΩS, ΩS0, and AMF following Eq. (1).
i Recomputed using averaged OMI AMFG, observed mean HCHO
shape factors (Fig. 5), and mean OMI scattering weights (Fig. 5)
following Eqs. (3)–(5).
j VCD computed using recomputed AMF, averaged OMI ΩS, and averaged OMI ΩS0 following Eqs. (1)–(2).
k Using CAMS observations.
l Using ISAF observations.
m Results reported by Zhu et al. (2016) over the Southeastern
United States (30.5–39.0∘ N, 95.0–81.5∘ W) during 5 August–25 September 2013.
Results are based on a different version of the GEOS-Chem model.
Air mass factor (AMF) components over the 12 aircraft campaigns.
Each panel shows mean scattering weights (w; dashed blue line) and shape
factors (S; solid blue line) from the OMI SAO HCHO product averaged over the
corresponding grids sampled by the aircraft (marked with open circles in
Figs. 4 and S1–S11) during the campaign period (defined in Table 1), as
well as the product of the two (dotted blue line) from which mean AMF is
derived by vertical integration using Eq. (4). Each panel also shows
observed HCHO shape factors (black solid) from the mean HCHO profile in
Fig. 3. We use mean HCHO profiles from ATom-1 and ATom-2 (Fig. 3) to
fill observations above 6 km. Also shown is the product (dotted black line)
of mean OMI scattering weights (dashed blue line) and observed HCHO shape
factor (black solid). Mean AMF values are given in the legend computed using
OMI (blue) and observed (black) shape factors.
We see from Table 2 that relative biases in the OMI HCHO product depend on both
locations and seasons, ranging from -44.5 % in summer (C9) to
+112.1 % in spring (C8), both over the Western United States. Overall,
the relative biases in the OMI SAO product fall into two categories. First, the
product is negatively biased (-44.5 % to -21.7 %) under high-HCHO
conditions (arbitrarily defined as mean HCHO column >1.0×1016 molecules cm-2), such as summertime
Southeastern (C2, C3, C4) and Western (C9) United States as well as
summertime South Korea (C10). A similar bias (-37.0 %) in the OMI SAO HCHO
product is reported by Zhu et al. (2016) for summertime Southeastern United
States. Second, the product is highly biased (+66.1 % to +112.1 %)
under low-HCHO conditions, such as the Western United States (C5, C6, and
C8), wintertime United States (C1 and C7), and the remote Pacific Ocean (C11
and C12). Our work points to a higher bias (∼70 %) in OMI
SAO retrievals over the remote Pacific Ocean compared with the bias
(∼10 %) reported by Wolfe et al. (2019). This is likely
driven by a number of factors: (1) Wolfe et al. (2019) use all data, whereas
we only use data over the Pacific region (Fig. 1); (2) selection criteria
(e.g., θZ and data filtering) for OMI pixels are different;
(3) mean observed HCHO column is computed from individual profiles in Wolfe
et al. (2019), while it is computed based on a mean profile in this study;
(4) and finally, the relative bias metric is more sensitive to absolute bias
under low-HCHO conditions.
In both cases, a priori vertical profiles used in the SAO HCHO retrieval algorithm
are likely not the main drivers of the biases. The SAO HCHO algorithm samples
HCHO shape factors (S) from a monthly mean climatology based on GEOS-Chem
simulations in 2007 at a spatial resolution of 2∘×2.5∘, which may not be able to represent the spatial
heterogeneity in chemistry, nor able to model temporal variations in
emissions. In the first case, after recomputing the AMF with observed HCHO
shape factors following Eqs. (3)–(5), relative biases in HCHO reduce
slightly on average from -36.5 % to -32.6 % (C3, C4, C9, and C10 in
Table 2). The largest improvement is seen for C3 and C4, where relative
biases are reduced on average from -35.4 % to -27.4 %, because using
observed HCHO shape factors results in lower AMF by correcting
underestimated a priori HCHO within the mixed layer (Fig. 5). During the WE-CAN
campaign (C9), recomputed AMF is slightly higher than that reported by OMI
(Table 2) because of elevated HCHO around 3–5 km from wildfire plumes
(Fig. 5). In the second case, using observed HCHO shape factors, however,
barely reduces biases in the OMI SAO HCHO product (Table 2).
Reference-sector-corrected SCD (ΔΩS=ΩS–ΩS0) and/or other AMF components (such as surface
reflectivity and cloud correction) are likely the main drivers of high
biases. This can be further examined with aircraft observations and OMI HCHO
pixels over the remote Pacific Ocean (C11 and C12), where the contribution of
ΩS0 to ΔΩS is much lower (∼18 %; Table 2). Integration of ATom1 (C11) and ATom2 (C12) vertical
profiles indicates a Pacific background HCHO VCD of ∼3.0×1015 molecules cm-2 (Fig. 3), comparable with
previous measured values (2.8×1015 to
4.6×1015 molecules cm-2) over the remote North Pacific
Ocean (Singh et al., 2009) and modeled results (4.5×1015 molecules cm-2) (Wolfe et al., 2019). This is equivalent to a
background SCD of ∼4.7×1015 molecules cm-2, with AMF computed using observed HCHO shape factors (Fig. 5).
OMI SAO SCD (ΩS) and reference-sector-corrected SCD (ΔΩS) are much higher than such estimated background SCD values by
a factor of ∼2.0 (Table 2), pointing to potential issues with
SCD fitting and/or reference sector correction in the SAO HCHO retrieval
algorithm.
We attribute the remaining biases to increased impact of interferers
(e.g., O3 and BrO, O2–O2, and water vapor) when HCHO signals are
weak. We also find that OMI SAO HCHO VCD correlates moderately (r=0.38 to
0.66) with surface albedo during some campaigns (i.e., C1, C5, and C6),
suggesting possible bias introduced by using a reflectance climatology
(Kleipool et al., 2008) in the retrievals. In summary, high biases under
low-HCHO conditions are likely driven by both reference sector correction
and SCD fitting. An updated SAO product is being developed to minimize the
biases by optimizing the two processes accordingly.
Conclusions
We have used HCHO observations from 12 aircraft campaigns, together with the
GEOS-Chem chemical transport model as an intercomparison method, to develop
a validation platform for satellite HCHO retrievals. The validation platform
offers an alternative way to quickly assess systematic biases in satellite
products over large spatial domains and longer temporal periods,
facilitating optimization of retrieval settings and the minimization of
retrieval biases. Application to the NASA operational HCHO product (SAO
retrievals) indicates that relative biases range from -44.5 % to
+112.1 % depending on locations and seasons. The product is negatively
biased (-44.5 % to -21.7 %) under high-HCHO conditions, such as
summertime Southeastern United States, while it is positively biased (+66.1 %
to +112.1 %) under low-HCHO conditions, such as wintertime United States
and the remote Pacific Ocean. Under both conditions, HCHO a priori vertical profiles are
likely not the main driver of the biases. Our work points to the need for
improvement in the OMI SAO HCHO product to correct the systematic biases,
particularly, optimization of the HCHO slant column fitting and reference
sector correction.
Abbreviations and acronyms
AMFAir mass factorAMFGGeometrical air mass factorATomAtmospheric Tomography missionCAMSCompact Atmospheric Multi-Species SpectrometerCTMChemical transport modelDC3Deep Convective Clouds and Chemistry ExperimentDFGASDifference Frequency Generation Absorption SpectrometerDISCOVER-AQDeriving Information on Surface Conditions from COlumn and VERticallyResolved Observations Relevant to Air QualityFRAPPÉFront Range Air Pollution and Photochemistry ÉxperimentGEMSGeostationary Environment Monitoring SpectrometerGEOS-FPGoddard Earth Observing System–Forward ProcessingGFED4Fourth-generation Global Fire Emissions DatabaseGMAOGlobal Modeling and Assimilation OfficeGOME(-2)Global Ozone Monitoring Experiment(-2)HCHOFormaldehydeISAFIn situ airborne formaldehydeKORUS-AQKorea-United States Air QualityLEOLow Earth orbitMEGANModel of Emissions of Gases and Aerosols from NatureNEINational Emissions InventoryNMVOCsNon-methane VOCsNOMADSSNitrogen, Oxidants, Mercury, and Aerosol Distributions, Sources, and SinksOMIOzone Monitoring InstrumentOMPSOzone Mapping and Profiler SuitePTR-ToF-MSProton-Transfer-Reaction Time-of-Flight Mass SpectrometerRMAReduced major axisSAO(Harvard) Smithsonian Astrophysical ObservatorySCDSlant column densitySCIAMACHYScanning Imaging Absorption spectroMeter for Atmospheric ChartographySEAC4RSStudies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional SurveysSENEXSoutheast NexusSONGNEXShale Oil and Natural Gas NexusTEMPOTropospheric Emissions: Monitoring of PollutionTOGATrace Organic Gas AnalyzerTROPOMITROPOspheric Monitoring InstrumentVCDVertical column densityVOCsVolatile organic compoundsWE-CANWestern wildfire Experiment for Cloud chemistry, Aerosol absorption and NitrogenWINTERThe Wintertime INvestigation of Transport, Emissions, and Reactivity campaign
Validation platform
Source code
The validation platform (R scripts) is available at
10.7910/DVN/KG3XNC (Zhu, 2019).
Please download all the files and follow the instructions in NOTE to
install required R packages and run the script.
Sample scripts for processing aircraft, chemical transport model (CTM), and satellite data are
available at https://www.acmrsg.org/datasets (last access: 23 October 2020).
Please use process.R and compute.R for steps 2–5.
Ingesting additional field campaign data
To process additional field campaign data, a user needs to sample the CTM
along the flight tracks. This can be done with GEOS-Chem flight diagnostic
(http://wiki.seas.harvard.edu/geos-chem/index.php/Planeflight_diagnostic, last access: 23 October 2020). Please follow process.R (line 117–381) for processing
GEOS-Chem flight diagnostic output files. For other CTMs, similar
functionality may need to be developed/used.
Preparing CTM data
A user also needs to prepare CTM data according to satellite schedules. Please
follow process.R (line 384–593) for more.
Processing new HCHO retrievals
To validate new HCHO retrievals, a user needs to read, clean, filter, and
regrid level 2 satellite data. Please follow process.R (line 595–884) for
more.
Validation of new HCHO retrievals
Finally, we have a sample script (compute.R) for computing and plotting the
validation results.
Code and data availability
The validation platform (R scripts) is available at
10.7910/DVN/KG3XNC (Zhu, 2019).
The GEOS-Chem model is available at 10.5281/zenodo.1343547 (the International GEOS-Chem User Community, 2018).
OMI-SAO HCHO data were downloaded from
http://disc.sci.gsfc.nasa.gov/Aura/dataholdings/OMI/omhcho_v003.shtml (last access: 23 October 2020, González Abad et al., 2015.).
Aircraft observations are available respectively as follows.
DISCOVER-AQ California 2013 (C1):
https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html (last access: 23 October 2020, the DISCOVER-AQ
California Science Team, 2013).
NOMADSS (C2): https://www.eol.ucar.edu/field_projects/nomadss/ (last access: 23 October 2020, the NOMADSS Science
Team, 2013).
SENEX (C3): https://www.esrl.noaa.gov/csd/projects/senex/ (last access: 23 October 2020, the SENEX Science Team, 2013).
DISCOVER-AQ Texas 2013 (C4):
https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html (last access: 23 October 2020, the DISCOVER-AQ Texas
Science Team, 2013).
DISCOVER-AQ Colorado 2014 (C5):
https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html (last access: 23 October 2020, the DISCOVER-AQ
Colorado Science Team, 2014).
FRAPPÉ (C6): http://catalog.eol.ucar.edu/FRAPPE/ (last access: 23 October 2020, the FRAPPÉ Science Team, 2014).
WINTER (C7): http://catalog.eol.ucar.edu/winter/ (last access: 23 October 2020, the WINTER Science Team, 2015).
SONGNEX (C8): https://www.esrl.noaa.gov/csd/projects/songnex/ (last access: 23 October 2020, the SONGNEX Science Team, 2015).
WE-CAN (C9): https://www.eol.ucar.edu/field_projects/we-can/ (last access: 23 October 2020, the WE-CAN Science Team, 2018).
KORUS-AQ (C10): https://www-air.larc.nasa.gov/missions/korus-aq/ (last access: 23 October 2020, the KORUS-AQ Science Team, 2016).
ATom-1 (C11): https://daac.ornl.gov/ATOM/campaign/ (last access: 23 October 2020, the ATom-1 Science Team, 2016).
ATom-2 (C12): https://daac.ornl.gov/ATOM/campaign/ (last access: 23 October 2020, the ATom-2 Science Team, 2017).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-12329-2020-supplement.
Author contributions
LZ, GGA, and CRN conducted the research and wrote the paper. CCM and KC helped with the OMI data. ECA, JPD, AF, TFH, RSH, LH, JK, FNK, WP, JMSC, and GMW provided aircraft HCHO observations.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge contributions from science teams of the 12 aircraft
campaigns. This work is funded by NOAA Atmospheric Chemistry Carbon Cycle
and Climate NA18OAR4310108, NASA Aura Science Team NNX17AH47G, NASA Science
of TERRA, AQUA, and SUOMI NPP 80NSSC18K0691, and NASA Making Earth System
Data Records for Use in Research Environments 80NSSC18M0091 grants. This
work is supported by the Center for Computational Science and Engineering at
the Southern University of Science and Technology. Lei Zhu acknowledges support from the
Smithsonian Astrophysical Observatory (SAO) Visiting Scientist
Fellowship. The 2018 WE-CAN campaign was supported by the National Science
Foundation (grants NSF AGS-1650275, AGS-1650786, AGS-1650288, AGS-1650493, and AGS-1652688).
Lu Hu and Wade Permar would like to acknowledge operational, technical, and scientific
support provided by NCAR's Earth Observing Laboratory, sponsored by the
National Science Foundation. This material is based upon work supported by
the National Center for Atmospheric Research, which is a major facility
sponsored by the National Science Foundation under Cooperative Agreement No.
1852977. The NASA Goddard Space Flight Center (GSFC) team acknowledges
support for the ATom campaign from the NASA Earth Venture Suborbital-2
Program and support for DC3 and SEAC4RS campaigns from NASA.
Financial support
This research has been supported by the NOAA (grant no. NA18OAR4310108) and the NASA (grant nos. NNX17AH47G, 80NSSC18K0691, and 80NSSC18M0091).
Review statement
This paper was edited by Michel Van Roozendael and reviewed by two anonymous referees.
ReferencesApel, E. C., Hills, A. J., Lueb, R., Zindel, S., Eisele, S., and Riemer, D.
D.: A Fast-GC/MS system to measure C2 to C4 carbonyls, and
methanol aboard aircraft, J. Geophys. Res., 108, 8794,
10.1029/2002JD003199, 2003.Apel, E. C., Emmons, L. K., Karl, T., Flocke, F., Hills, A. J., Madronich, S., Lee-Taylor, J., Fried, A., Weibring, P., Walega, J., Richter, D., Tie, X., Mauldin, L., Campos, T., Weinheimer, A., Knapp, D., Sive, B., Kleinman, L., Springston, S., Zaveri, R., Ortega, J., Voss, P., Blake, D., Baker, A., Warneke, C., Welsh-Bon, D., de Gouw, J., Zheng, J., Zhang, R., Rudolph, J., Junkermann, W., and Riemer, D. D.: Chemical evolution of volatile organic compounds in the outflow of the Mexico City Metropolitan area, Atmos. Chem. Phys., 10, 2353–2375, 10.5194/acp-10-2353-2010, 2010.Apel, E. C., Hornbrook, R. S., Hills, A. J., Blake, N. J., Barth, M. C.,
Weinheimer, A., Cantrell, C., Rutledge, S. A., Basarab, B., Crawford, J.,
Diskin, G., Homeyer, C. R., Campos, T., Flocke, F., Fried, A., Blake, D. R.,
Brune, W., Pollack, I., Peischl, J., Ryer- son, T., Wennberg, P. O.,
Crounse, J. D., Wisthaler, A., Mikoviny, T., Huey, G., Heikes, B.,
O'Sullivan, D., and Riemer, D. D.: Upper tropospheric ozone production from
lightning NOx-impacted convection: Smoke ingestion case study from the
DC3 campaign, J. Geophys. Res.-Atmos., 120, 2505–2523, 2015.ATom-1 Science Team: ATom-1 merge data, available at: https://daac.ornl.gov/ATOM/campaign/ (last access: 23 October 2020), 2016.ATom-2 Science Team: ATom-2 merge data, available at: https://daac.ornl.gov/ATOM/campaign/ (last access: 23 October 2020), 2017.Barkley, M. P., De Smedt, I., Van Roozendael, M., Kurosu, T. P., Chance, K.,
Arneth, A., Hagberg, D., Guenther, A., Paulot, F., Marais, E., and Mao, J.:
Top-down isoprene emissions over tropical South America inferred from
SCIAMACHY and OMI formaldehyde columns, J. Geophys. Res.-Atmos., 118,
6849–6868, 2013.Barth, M. C., Cantrell, C. A., Brune, W. H., Rutledge, S. A., Crawford, J.
H., Huntrieser, H., Carey, L. D., MacGorman, D., Weisman, M., Pickering, K.
E., Bruning, E., Anderson, B., Apel, E., Biggerstaff, M., Campos, T.,
Campuzano-Jost, P., Cohen, R., Crounse, J., Day, D. A., Diskin, G., Flocke,
F., Fried, A., Garland, C., Heikes, B., Honomichl, S., Hornbrook, R., Huey,
L. G., Jimenez, J. L., Lang, T., Lichtenstern, M., Mikoviny, T., Nault, B.,
O'Sullivan, D., Pan, L. L., Peischl, J., Pollack, I., Richter, D., Riemer,
D., Ryerson, T., Schlager, H., St Clair, J., Walega, J., Weibring, P.,
Weinheimer, A., Wennberg, P., Wisthaler, A., Wooldridge, P. J., and Ziegler,
C.: The Deep Convective Clouds and Chemistry (DC3) Field Campaign, B. Am.
Meteorol. Soc., 96, 1281–1309, 10.1175/Bams-D-13-00290.1,
2015.Cao, H., Fu, T.-M., Zhang, L., Henze, D. K., Miller, C. C., Lerot, C., Abad, G. G., De Smedt, I., Zhang, Q., van Roozendael, M., Hendrick, F., Chance, K., Li, J., Zheng, J., and Zhao, Y.: Adjoint inversion of Chinese non-methane volatile organic compound emissions using space-based observations of formaldehyde and glyoxal, Atmos. Chem. Phys., 18, 15017–15046, 10.5194/acp-18-15017-2018, 2018.Cazorla, M., Wolfe, G. M., Bailey, S. A., Swanson, A. K., Arkinson, H. L., and Hanisco, T. F.: A new airborne laser-induced fluorescence instrument for in situ detection of formaldehyde throughout the troposphere and lower stratosphere, Atmos. Meas. Tech., 8, 541–552, 10.5194/amt-8-541-2015, 2015.Chance, K., Palmer, P. I., Spurr, R. J. D., Martin, R. V., Kurosu, T. P.,
and Jacob, D. J.: Satellite observations of formaldehyde over North America
from GOME, Geophys. Res. Lett., 27, 3461–3464, 2000.Chan Miller, C., Jacob, D. J., Marais, E. A., Yu, K., Travis, K. R., Kim, P. S., Fisher, J. A., Zhu, L., Wolfe, G. M., Hanisco, T. F., Keutsch, F. N., Kaiser, J., Min, K.-E., Brown, S. S., Washenfelder, R. A., González Abad, G., and Chance, K.: Glyoxal yield from isoprene oxidation and relation to formaldehyde: chemical mechanism, constraints from SENEX aircraft observations, and interpretation of OMI satellite data, Atmos. Chem. Phys., 17, 8725–8738, 10.5194/acp-17-8725-2017, 2017.Courrèges-Lacoste, G. B., Sallusti, M., Bulsa, G., Bagnasco, G.,
Veihelmann, B., Riedl, S., Smith, D. J., and Maurer, R.: The Copernicus
Sentinel 4 mission: a geostationary imaging UVN spectrometer for air quality
monitoring, Proceedings Volume 10423, Sensors, Systems, and Next-Generation
Satellites XXI, 1042307, 10.1117/12.2282158, 2017.
Crawford, J. H. and Pickering, K. E.: Discover-AQ: Advancing strategies for air quality observations in the next decade, EM Air Waste Manag. Assoc., 9, 4–7, 2014.De Smedt, I., Müller, J.-F., Stavrakou, T., van der A, R., Eskes, H., and Van Roozendael, M.: Twelve years of global observations of formaldehyde in the troposphere using GOME and SCIAMACHY sensors, Atmos. Chem. Phys., 8, 4947–4963, 10.5194/acp-8-4947-2008, 2008.De Smedt, I., Van Roozendael, M., Stavrakou, T., Müller, J.-F., Lerot, C., Theys, N., Valks, P., Hao, N., and van der A, R.: Improved retrieval of global tropospheric formaldehyde columns from GOME-2/MetOp-A addressing noise reduction and instrumental degradation issues, Atmos. Meas. Tech., 5, 2933–2949, 10.5194/amt-5-2933-2012, 2012.De Smedt, I., Stavrakou, T., Hendrick, F., Danckaert, T., Vlemmix, T., Pinardi, G., Theys, N., Lerot, C., Gielen, C., Vigouroux, C., Hermans, C., Fayt, C., Veefkind, P., Müller, J.-F., and Van Roozendael, M.: Diurnal, seasonal and long-term variations of global formaldehyde columns inferred from combined OMI and GOME-2 observations, Atmos. Chem. Phys., 15, 12519–12545, 10.5194/acp-15-12519-2015, 2015.De Smedt, I., Theys, N., Yu, H., Danckaert, T., Lerot, C., Compernolle, S., Van Roozendael, M., Richter, A., Hilboll, A., Peters, E., Pedergnana, M., Loyola, D., Beirle, S., Wagner, T., Eskes, H., van Geffen, J., Boersma, K. F., and Veefkind, P.: Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project, Atmos. Meas. Tech., 11, 2395–2426, 10.5194/amt-11-2395-2018, 2018.DISCOVER-AQ California Science Team: DISCOVER-AQ merge data,
available at: https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html(last access: 23 October 2020), 2013.DISCOVER-AQ Colorado Science Team: DISCOVER-AQ Colorado merge data,
available at: https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html (last access: 23 October 2020), 2014.DISCOVER-AQ Science Team: DISCOVER-AQ P-3B Aircraft In-situ Trace Gas
Measurements, NASA Langley Atmospheric Science Data Center DAAC, 10.5067/Aircraft/DISCOVER-AQ/Aerosol-TraceGas, 2014.DISCOVER-AQ Texas Science Team: DISCOVER-AQ Texas merge data,
available at: https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html (last access: 23 October 2020), 2013.Emmons, L.: Merged Data Files containing all C-130 1 Second Observations,
Version 1.0, UCAR/NCAR – Earth Observing Laboratory,
available at: https://data.eol.ucar.edu/dataset/373.045 (last access: 10 July 2019),
2016.EPA: National Emissions Inventory, version 2 Technical Support Document,
2015, available at:
https://www.epa.gov/sites/production/files/2015-10/documents/nei2011v2_tsd_14aug2015.pdf (last access: 9 July 2019), 2015.FRAPPÉ Science Team: FRAPPÉ merge data, available at:
http://catalog.eol.ucar.edu/FRAPPE/ (last access:
23 October 2020), 2014.Fried, A., Cantrell, C., Olson, J., Crawford, J. H., Weibring, P., Walega, J., Richter, D., Junkermann, W., Volkamer, R., Sinreich, R., Heikes, B. G., O'Sullivan, D., Blake, D. R., Blake, N., Meinardi, S., Apel, E., Weinheimer, A., Knapp, D., Perring, A., Cohen, R. C., Fuelberg, H., Shetter, R. E., Hall, S. R., Ullmann, K., Brune, W. H., Mao, J., Ren, X., Huey, L. G., Singh, H. B., Hair, J. W., Riemer, D., Diskin, G., and Sachse, G.: Detailed comparisons of airborne formaldehyde measurements with box models during the 2006 INTEX-B and MILAGRO campaigns: potential evidence for significant impacts of unmeasured and multi-generation volatile organic carbon compounds, Atmos. Chem. Phys., 11, 11867–11894, 10.5194/acp-11-11867-2011, 2011.Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily,
monthly, and annual burned area using the fourth- generation global fire
emissions database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328,
10.1002/jgrg.20042, 2013.González Abad, G., Liu, X., Chance, K., Wang, H., Kurosu, T. P., and Suleiman, R.: Updated Smithsonian Astrophysical Observatory Ozone Monitoring Instrument (SAO OMI) formaldehyde retrieval, Atmos. Meas. Tech., 8, 19–32, 10.5194/amt-8-19-2015, 2015.González Abad, G., Vasilkov, A., Seftor, C., Liu, X., and Chance, K.: Smithsonian Astrophysical Observatory Ozone Mapping and Profiler Suite (SAO OMPS) formaldehyde retrieval, Atmos. Meas. Tech., 9, 2797–2812, 10.5194/amt-9-2797-2016, 2016.Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, 10.5194/gmd-5-1471-2012, 2012.Herman, J., Spinei, E., Fried, A., Kim, J., Kim, J., Kim, W., Cede, A., Abuhassan, N., and Segal-Rozenhaimer, M.: NO2 and HCHO measurements in Korea from 2012 to 2016 from Pandora spectrometer instruments compared with OMI retrievals and with aircraft measurements during the KORUS-AQ campaign, Atmos. Meas. Tech., 11, 4583–4603, 10.5194/amt-11-4583-2018, 2018.Hewson, W., Barkley, M. P., Gonzalez Abad, G., Bösch, H., Kurosu, T., Spurr, R., and Tilstra, L. G.: Development and characterisation of a state-of-the-art GOME-2 formaldehyde air-mass factor algorithm, Atmos. Meas. Tech., 8, 4055–4074, 10.5194/amt-8-4055-2015, 2015.Holtslag, A. and Boville, B.: Local versus nonlocal boundary-layer diffusion
in a global climate model, J. Climate, 6, 1825–1842, 1993.Hu, L. and Permar, W.: PTR-ToF-MS Measurements of NMVOCs, HONO, HCN,
CH3CN Data, Version 1.0. UCAR/NCAR – Earth Observing Laboratory.
10.26023/K9F4-2CNH-EQ0W, 2019.Hu, L., Millet, D. B., Baasandorj, M., Griffis, T. J., Turner, P., Helmig,
D., Curtis, A. J., and Hueber, J.: Isoprene emissions and impacts over an
ecological transition region in the US Upper Midwest inferred from tall
tower measurements, J. Geophys. Res.-Atmos., 120, 3553–3571, 2015.International GEOS-Chem User Community: GEOS-Chem 12.0.0 release, Zenodo, 10.5281/zenodo.1343547, 2018.Jaeglé, L., Thornton, J. A., Brown, S. S., Shah, V., Lopez-Hilfiker, F.,
Lee, B. H., Haskins, J., Fibiger, D. L., McDuffie, E. E., Sparks, T., Ebben,
C. J., Wooldridge, P. J., Cohen, R. C., Veres, P. R., Weinheimer, A. J.,
Montzka, D. D., Dibb, J. E., Schroder, J. C., Jost, P. C., Day, D. A.,
Jimenez, J. L., Sullivan, A., Guo, H., Weber, R. J., Green, J. R., Fiddler,
M. N., Bililign, S., Campos, T. L., Apel E. C., Blake, N. J., Hall, S. R.,
Ullmann, K., Wolfe, G. M., DiGangi, J. P., Hanisco, T. F., and Leen, J. B.:
Sources, Chemistry, and Transport of Pollutants over the Eastern United
States During the WINTER 2015 Aircraft Campaign, AGU Fall Meeting, 2015.Jin, X. and Holloway, T.: Spatial and temporal variability of ozone
sensitivity over China observed from the Ozone Monitoring Instrument, J.
Geophys. Res.-Atmos., 120, 7229–7246, 10.1002/2015JD023250,
2015.Jin, X., Fiore, A. M., Murray, L. T., Valin, L. C., Lamsal, L. N., Duncan,
B., Folkert Boersma, K., De Smedt, I., Abad, G. G., Chance, K., and
Tonnesen, G. S.: Evaluating a space-based indicator of surface
ozone-NOx-VOC sensitivity over midlatitude source regions and
application to decadal trends, J. Geophys. Res.-Atmos., 122, 439–461,
10.1002/2017JD026720, 2017.Khokhar, M., Frankenberg, C., Roozendael, M. V., Beirle, S., Kuhl, S.,
Richter, A., Platt, U., and Wagner, T.: Satellite observations of
atmospheric SO2 from volcanic eruptions during the time-period of
1996–2002, Adv. Space Res., 36, 879–887, 10.1016/j.asr.2005.04.114,
2005.Kim, J., Jeong, U., Ahn, M., Kim, J. H., Park, R. J., Lee, H., Song, C. H.,
Choi, Y., Lee, K., Yoo, J., Jeong, M., Park, S. K., Lee, K., Song, C., Kim,
S., Kim, Y., Kim, S., Kim, M., Go, S., Liu, X., Chance, K., Chan Miller, C.,
Al-Saadi, J., Veihelmann, B., Bhartia, P. K., Torres, O., González Abad,
G., Haffner, D. P., Ko, D. H., Lee, S. H., Woo, J., Chong, H., Park, S. S.,
Nicks, D., Choi, W. J., Moon, K., Cho, A., Yoon, J., Kim, S., Hong, H., Lee,
K., Lee, H., Lee, S., Choi, M., Veefkind, P., Levelt, P., Edwards, D. P.,
Kang, M., Eo, M., Bak, J., Baek, K., Kwon, H., Yang, J., Park, J., Han, K.
M., Kim, B., Shin, H., Choi, H., Lee, E., Chong, J., Cha, Y., Koo, J., Irie,
H., Hayashida, S., Kasai, Y., Kanaya, Y., Liu, C., Lin, J., Crawford, J. H.,
Carmichael, G. R., Newchurch, M. J., Lefer, B. L., Herman, J. R., Swap, R.
J., Lau, A. K., Kurosu, T. P., Jaross, G., Ahlers, B., Dobber, M., McElroy,
C., and Choi, Y.: New Era of Air Quality Monitoring from Space: Geostationary
Environment Monitoring Spectrometer (GEMS), B. Am. Meteorol. Soc., 101, E1–E22, 10.1175/BAMS-D-18-0013.1, 2020.Kleipool, Q. L., Dobber, M. R., de Haan, J. F., and Levelt, P. F.: Earth
surface reflectance climatology from 3 years of OMI data, J. Geophys.
Res.-Atmos., 113, D18308, 10.1029/2008JD010290, 2008.KORUS-AQ Science Team: An International Cooperative Air Quality Field Study in Korea,
available at: https://www-air.larc.nasa.gov/missions/korus-aq/ (last access:
22 October 2019), 10.5067/Suborbital/KORUSAQ/DATA01, 2016.Kwon, H.-A., Park, R. J., Jeong, J. I., Lee, S., González Abad, G., Kurosu, T. P., Palmer, P. I., and Chance, K.: Sensitivity of formaldehyde (HCHO) column measurements from a geostationary satellite to temporal variation of the air mass factor in East Asia, Atmos. Chem. Phys., 17, 4673–4686, 10.5194/acp-17-4673-2017, 2017.Kwon, H.-A., Park, R. J., González Abad, G., Chance, K., Kurosu, T. P., Kim, J., De Smedt, I., Van Roozendael, M., Peters, E., and Burrows, J.: Description of a formaldehyde retrieval algorithm for the Geostationary Environment Monitoring Spectrometer (GEMS), Atmos. Meas. Tech., 12, 3551–3571, 10.5194/amt-12-3551-2019, 2019.Li, C., Joiner, J., Krotkov, N. A., and Dunlap, L.: A newmethod for global
retrievals of HCHO total columns from the Suomi National Polar-orbiting
Partnership Ozone Mapping and Profiler Suite, Geophys. Res. Lett., 42,
2515–2522, 2015.Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, 10.5194/acp-17-935-2017, 2017.Liao, J., Hanisco, T. F., Wolfe, G. M., St. Clair, J., Jimenez, J. L., Campuzano-Jost, P., Nault, B. A., Fried, A., Marais, E. A., Gonzalez Abad, G., Chance, K., Jethva, H. T., Ryerson, T. B., Warneke, C., and Wisthaler, A.: Towards a satellite formaldehyde – in situ hybrid estimate for organic aerosol abundance, Atmos. Chem. Phys., 19, 2765–2785, 10.5194/acp-19-2765-2019, 2019.Lin, J.-T. and McElroy, M.: Impacts of boundary layer mixing on pollutant
vertical profiles in the lower troposphere: Implications to satellite remote
sensing, Atmos. Environ., 44, 1726–1739, 2010.Marais, E. A., Jacob, D. J., Kurosu, T. P., Chance, K., Murphy, J. G., Reeves, C., Mills, G., Casadio, S., Millet, D. B., Barkley, M. P., Paulot, F., and Mao, J.: Isoprene emissions in Africa inferred from OMI observations of formaldehyde columns, Atmos. Chem. Phys., 12, 6219–6235, 10.5194/acp-12-6219-2012, 2012.Martin, R. V., Parrish, D. D., Ryerson, T. B., Nicks Jr., D. K., Chance, K.,
Kurosu, T. P., Jacob, D. J., Sturges, E. D., Fried, A., and Wert, B. P.:
Evaluation of GOME satellite measurements of tropospheric NO2 and HCHO
using regional data from aircraft campaigns in the southeastern United
States, J. Geophys. Res.-Atmos., 109, D24307, 10.1029/2004JD004869,
2004.Millet, D. B., Baasandorj, M., Farmer, D. K., Thornton, J. A., Baumann, K., Brophy, P., Chaliyakunnel, S., de Gouw, J. A., Graus, M., Hu, L., Koss, A., Lee, B. H., Lopez-Hilfiker, F. D., Neuman, J. A., Paulot, F., Peischl, J., Pollack, I. B., Ryerson, T. B., Warneke, C., Williams, B. J., and Xu, J.: A large and ubiquitous source of atmospheric formic acid, Atmos. Chem. Phys., 15, 6283–6304, 10.5194/acp-15-6283-2015, 2015.Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.-S., and
Eichmann, A.: The GEOS-5 Atmospheric General Circulation Model: Mean Climate
and Development from MERRA to Fortuna, NASA/TM–2012, 104606, 28, 1–124,
2012.Müller, M., Mikoviny, T., Feil, S., Haidacher, S., Hanel, G., Hartungen, E., Jordan, A., Märk, L., Mutschlechner, P., Schottkowsky, R., Sulzer, P., Crawford, J. H., and Wisthaler, A.: A compact PTR-ToF-MS instrument for airborne measurements of volatile organic compounds at high spatiotemporal resolution, Atmos. Meas. Tech., 7, 3763–3772, 10.5194/amt-7-3763-2014, 2014.National Oceanic and Atmospheric Administration (NOAA): SONGNEX 2015 CSD
Data Archive, Earth System Research Laboratory, Chemical Sciences Division,
available at: https://www.esrl.noaa.gov/csd/projects/songnex/ (last access:
22 October 2019), 2017.NOMADSS Science Team: NOMADSS merge data,
available at: https://www.eol.ucar.edu/field_projects/nomadss/ (last access: 23 October 2020), 2013.Nowlan, C. R., Liu, X., Janz, S. J., Kowalewski, M. G., Chance, K., Follette-Cook, M. B., Fried, A., González Abad, G., Herman, J. R., Judd, L. M., Kwon, H.-A., Loughner, C. P., Pickering, K. E., Richter, D., Spinei, E., Walega, J., Weibring, P., and Weinheimer, A. J.: Nitrogen dioxide and formaldehyde measurements from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator over Houston, Texas, Atmos. Meas. Tech., 11, 5941–5964, 10.5194/amt-11-5941-2018, 2018.Palmer, P. I., Jacob, D. J., Chance, K., Martin, R. V., Spurr, R. J. D.,
Kurosu, T. P., Bey, I., Yantosca, R., Fiore, A., and Li, Q.: Air mass factor
formulation for spectroscopic measurements from satellites: Application to
formaldehyde retrievals from the Global Ozone Monitoring Experiment, J.
Geophys. Res.-Atmos., 106, 14539–14550, 2001.Palmer, P. I., Jacob, D. J., Fiore, A. M., Martin, R. V., Chance, K., and
Kurosu, T. P.: Mapping isoprene emissions over North America using
formaldehyde column observations from space, J. Geophys. Res.-Atmos., 108,
4180, 10.1029/2002JD002153, 2003.Pfister, G., Flocke, F., Hornbrook, R., Orlando, J., Lee, S., Schroeder, J.,
and NASA Langley Research Center: Process-Based and Regional Source Impact
Analysis for FRAPPEì and DISCOVER-AQ 2014, available at:
https://www.colorado.gov/airquality/tech_doc_repository.aspx?action=open&file=FRAPPE-NCAR_Final_Report_July2017.pdf (last access: 2 November 2019), 2017.Pollack, I. B., Lindaas, J., Roscioli, J. R., Agnese, M., Permar, W., Hu, L., and Fischer, E. V.: Evaluation of ambient ammonia measurements from a research aircraft using a closed-path QC-TILDAS operated with active continuous passivation, Atmos. Meas. Tech., 12, 3717–3742, 10.5194/amt-12-3717-2019, 2019.Richter, D., Weibring, P., Walega, J. G., Fried, A., Spuler, S. M., and
Taubman, M. S.: Compact highly sensitive multi-species airborne mid-IR
spectrometer, Appl. Phys. B, 119, 119–131, 2015.Scarino, A. J., Obland, M. D., Fast, J. D., Burton, S. P., Ferrare, R. A., Hostetler, C. A., Berg, L. K., Lefer, B., Haman, C., Hair, J. W., Rogers, R. R., Butler, C., Cook, A. L., and Harper, D. B.: Comparison of mixed layer heights from airborne high spectral resolution lidar, ground-based measurements, and the WRF-Chem model during CalNex and CARES, Atmos. Chem. Phys., 14, 5547–5560, 10.5194/acp-14-5547-2014, 2014.SENEX Science Team: SENEX merge data,
available at: https://www.eol.ucar.edu/field_projects/nomadss/ (last access: 23 October 2020), 2013.Shim, C., Wang, Y., Choi, Y., Palmer, P. I., Abbot, D. S., and Chance, K.:
Constraining global isoprene emissions with Global Ozone Monitoring
Experiment (GOME) formaldehyde column measurements, J. Geophys. Res.-Atmos.,
110, D24301, 10.1029/2004JD005629, 2005.SONGNEX Science Team: SONGNEX merge data,
available at: https://www.esrl.noaa.gov/csd/projects/songnex/ (last access: 23 October 2020), 2015.Stavrakou, T., Müller, J.-F., De Smedt, I., Van Roozendael, M., van der Werf, G. R., Giglio, L., and Guenther, A.: Global emissions of non-methane hydrocarbons deduced from SCIAMACHY formaldehyde columns through 2003–2006, Atmos. Chem. Phys., 9, 3663–3679, 10.5194/acp-9-3663-2009, 2009.Singh, H. B., Brune, W. H., Crawford, J. H., Flocke, F., and Jacob, D. J.: Chemistry and transport of pollution over the Gulf of Mexico and the Pacific: spring 2006 INTEX-B campaign overview and first results, Atmos. Chem. Phys., 9, 2301–2318, 10.5194/acp-9-2301-2009, 2009.Surl, L., Palmer, P. I., and González Abad, G.: Which processes drive observed variations of HCHO columns over India?, Atmos. Chem. Phys., 18, 4549–4566, 10.5194/acp-18-4549-2018, 2018.Tan, W., Liu, C., Wang, S., Xing, C., Su, W., Zhang, C., Xia, C., Liu, H., Cai, Z., and Liu, J.: Tropospheric NO2, SO2, and HCHO over the East China Sea, using ship-based MAX-DOAS observations and comparison with OMI and OMPS satellite data, Atmos. Chem. Phys., 18, 15387–15402, 10.5194/acp-18-15387-2018, 2018.Toon, O. B., Maring, H., Dibb, J., Ferrare, R., Jacob, D. J., Jensen, E. J.,
Luo, Z. J., Mace, G. G., Pan, L. L., Pfister, L., Rosenlof, K. H., Redemann,
J., Reid, J. S., Singh, H. B., Thompson, A. M., Yokelson, R., Minnis, P.,
Chen, G., Jucks, K. W., and Pszenny, A.: Planning, implementation, and
scientific goals of the Studies of Emissions and Atmospheric Composition,
Clouds and Climate Coupling by Regional Surveys (SEAC4RS) field
mission, J. Geophys. Res.-Atmos., 121, 4967–5009,
10.1002/2015jd024297, 2016.UCAR/NCAR – Earth Observing Laboratory, Jaeglé, L., and Shah, V.: 1s Merged
dataset of all C-130 observations and GEOS-Chem near-realtime simulations
for WINTER, Version 1.1. UCAR/NCAR – Earth Observing Laboratory,
10.5065/D68C9TDX, 2016.Vigouroux, C., Hendrick, F., Stavrakou, T., Dils, B., De Smedt, I., Hermans, C., Merlaud, A., Scolas, F., Senten, C., Vanhaelewyn, G., Fally, S., Carleer, M., Metzger, J.-M., Müller, J.-F., Van Roozendael, M., and De Mazière, M.: Ground-based FTIR and MAX-DOAS observations of formaldehyde at Réunion Island and comparisons with satellite and model data, Atmos. Chem. Phys., 9, 9523–9544, 10.5194/acp-9-9523-2009, 2009.Wang, Y., Beirle, S., Lampel, J., Koukouli, M., De Smedt, I., Theys, N., Li, A., Wu, D., Xie, P., Liu, C., Van Roozendael, M., Stavrakou, T., Müller, J.-F., and Wagner, T.: Validation of OMI, GOME-2A and GOME-2B tropospheric NO2, SO2 and HCHO products using MAX-DOAS observations from 2011 to 2014 in Wuxi, China: investigation of the effects of priori profiles and aerosols on the satellite products, Atmos. Chem. Phys., 17, 5007–5033, 10.5194/acp-17-5007-2017, 2017.Wang, Y., Wang, Z., Yu, C., Zhu, S., Cheng, L., Zhang, Y., and Chen, L.:
Validation of OMI HCHO Products Using MAX-DOAS observations from 2010 to
2016 in Xianghe, Beijing: Investigation of the Effects of Aerosols on
Satellite Products, Remote Sens., 11, 203,
10.3390/rs11020203, 2019.Warneke, C., Trainer, M., de Gouw, J. A., Parrish, D. D., Fahey, D. W., Ravishankara, A. R., Middlebrook, A. M., Brock, C. A., Roberts, J. M., Brown, S. S., Neuman, J. A., Lerner, B. M., Lack, D., Law, D., Hübler, G., Pollack, I., Sjostedt, S., Ryerson, T. B., Gilman, J. B., Liao, J., Holloway, J., Peischl, J., Nowak, J. B., Aikin, K. C., Min, K.-E., Washenfelder, R. A., Graus, M. G., Richardson, M., Markovic, M. Z., Wagner, N. L., Welti, A., Veres, P. R., Edwards, P., Schwarz, J. P., Gordon, T., Dube, W. P., McKeen, S. A., Brioude, J., Ahmadov, R., Bougiatioti, A., Lin, J. J., Nenes, A., Wolfe, G. M., Hanisco, T. F., Lee, B. H., Lopez-Hilfiker, F. D., Thornton, J. A., Keutsch, F. N., Kaiser, J., Mao, J., and Hatch, C. D.: Instrumentation and measurement strategy for the NOAA SENEX aircraft campaign as part of the Southeast Atmosphere Study 2013, Atmos. Meas. Tech., 9, 3063–3093, 10.5194/amt-9-3063-2016, 2016.WE-CAN Science Team: WE-CAN merge data,
available at: https://www.eol.ucar.edu/field_projects/we-can/ (last access: 23 October 2020), 2018.Weibring, P., Richter, D., Fried, A., Walega, J., and Dyroff, C.:
Ultra-high-precision mid-IR spectrometer II: system description and
spectroscopic performance, Appl. Phys. B, 85, 207–218,
10.1007/s00340-006-2300-4, 2006.Weibring, P., Richter, D., Walega, J. G., and Fried, A.: First demonstration
of a high performance difference frequency spectrometer on airborne
platforms, Opt. Express, 15, 13476–13495,
10.1364/OE.15.013476, 2007.Weibring, P., Richter, D., Walega, J. G., Rippe, L., and Fried, A.:
Difference frequency generation spectrometer for simultaneous multispecies
detection., Opt. Express, 18, 27670–27681, 10.1364/OE.18.027670,
2010.WINTER Science Team: WINTER merge data, available at: http://catalog.eol.ucar.edu/winter/ (last
access: 23 October 2020), 2015.Wittrock, F., Richter, A., Oetjen, H., Burrows, J. P., Kanakidou, M.,
Myriokefalitakis, S., Volkamer, R., Beirle, S., Platt, U., and Wagner, T.:
Simultaneous global observations of glyoxal and formaldehyde from space,
Geophys. Res. Lett., 33, L16804, 10.1029/2006GL026310, 2006.Wofsy, S. C., Afshar, S., Allen, H. M., Apel, E., Asher, E. C., Barletta,
B., Bent, J., Bian, H., Biggs, B. C., Blake, D. R., Blake, N., Bourgeois,
I., Brock, C. A., Brune, W. H., Budney, J. W., Bui, T. P., Butler, A.,
Campuzano-Jost, P., Chang, C. S., Chin, M., Commane, R., Correa, G.,
Crounse, J. D., Cullis, P. D., Daube, B. C., Day, D. A., Dean-Day, J. M.,
Dibb, J. E., DiGangi, J. P., Diskin, G. S., Dollner, M., Elkins, J. W.,
Erdesz, F., Fiore, A. M., Flynn, C. M., Froyd, K., Gesler, D. W., Hall, S.
R., Hanisco, T. F., Hannun, R. A., Hills, A. J., Hintsa, E. J., Hoffman, A.,
Hornbrook, R. S., Huey, L. G., Hughes, S., Jimenez, J. L., Johnson, B. J.,
Katich, J. M., Keeling, R. F., Kim, M. J., Kupc, A., Lait, L. R., Lamarque,
J.-F., Liu, J., McKain, K., Mclaughlin, R. J., Meinardi, S., Miller, D. O.,
Montzka, S. A., Moore, F. L., Morgan, E. J., Murphy, D. M., Murray, L. T.,
Nault, B. A., Neuman, J. A., Newman, P. A., Nicely, J. M., Pan, X.,
Paplawsky, W., Peischl, J., Prather, M. J., Price, D. J., Ray, E., Reeves,
J. M., Richardson, M., Rollins, A. W., Rosenlof, K. H., Ryerson, T. B.,
Scheuer, E., Schill, G. P., Schroder, J. C., Schwarz, J. P., St.Clair, J.
M., Steenrod, S. D., Stephens, B. B., Strode, S. A., Sweeney, C., Tanner,
D., Teng, A. P., Thames, A. B., Thompson, C. R., Ullmann, K., Veres, P. R.,
Vieznor, N., Wagner, N. L., Watt, A., Weber, R., Weinzierl, B., Wennberg,
P., Williamson, C. J., Wilson, J. C., Wolfe, G. M., Woods, C. T., and Zeng,
L. H.: ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols, ORNL
DAAC, Oak Ridge, Tennessee, USA, 10.3334/ornldaac/1581,
2018.Wolfe, G. M., Nicely, J. M., St. Clair, J. M., Hanisco, T. F., Liao, J.,
Oman, L. D., Brune, W. B., Miller, D., Thames, A., González Abad, G.,
Ryerson, T. B., Thompson, C. R., Peischl, J., McKain, K., Sweeney, C.,
Wennberg, P. O., Kim, M., Crounse, J. D., Hall, S. R., Ullmann, K., Diskin,
G., Bui, P., Chang, C., and Dean-Day, J.: Mapping hydroxyl variability
throughout the global remote troposphere via synthesis of airborne and
satellite formaldehyde observations, P. Natl. Acad. Sci. USA, 116, 11171–11180,
10.1073/pnas.1821661116, 2019.Zhu, L.: Global validation platform for satellite HCHO retrievals, Harvard Dataverse, V2, 10.7910/DVN/KG3XNC, 2019.Zhu, L., Jacob, D. J., Mickley, L. J., Marais, E. A., Cohan, D. S., Yoshida,
Y., Duncan, B. N., González Abad, G., and Chance, K. V.: Anthropogenic
emissions of highly reactive volatile organic compounds in eastern Texas
inferred from oversampling of satellite (OMI) measurements of HCHO columns,
Environ. Res. Lett., 9, 114004, 10.1088/1748-9326/9/11/114004, 2014.Zhu, L., Jacob, D. J., Kim, P. S., Fisher, J. A., Yu, K., Travis, K. R., Mickley, L. J., Yantosca, R. M., Sulprizio, M. P., De Smedt, I., González Abad, G., Chance, K., Li, C., Ferrare, R., Fried, A., Hair, J. W., Hanisco, T. F., Richter, D., Jo Scarino, A., Walega, J., Weibring, P., and Wolfe, G. M.: Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC4RS aircraft observations over the southeast US, Atmos. Chem. Phys., 16, 13477–13490, 10.5194/acp-16-13477-2016, 2016.
Zhu, L., Mickley, L. J., Jacob, D. J., Marais, E. A., Sheng, J., Hu, L.,
Gonzaìlez Abad, G., and Chance, K.: Long-term (2005–2014) trends in
formaldehyde (HCHO) columns across North America as seen by the OMI
satellite instrument: Evidence of changing emissions of volatile organic
compounds, Geophys. Res. Lett., 44, 7079–7086,
10.1002/2017GL073859, 2017a.Zhu, L., Jacob, D. J., Keutsch, F. N., Mickley, L. J., Scheffe, R., Strum,
M., Abad, G. G., Chance, K., Yang, K., Rappengluck, B., Millet, D. B.,
Baasandorj, M., Jaeglé, L., and Shah, V.: Formaldehyde (HCHO) As a Hazardous
Air Pollutant: Mapping Surface Air Concentrations from Satellite and
Inferring Cancer Risks in the United States, Environ. Sci. Technol., 51,
5650–5657, 10.1021/acs.est.7b01356, 2017b.Zoogman, P., Liu, X., Suleiman, R. M., Pennington, W. F., Flittner, D. E.,
Al-Saadi, J. A., Hilton, B. B., Nicks, D. K., Newchurch, M. J., Carr, J. L.,
Janz, S. J., Andraschko, M. R., Arola, A., Baker, B. D., Canova, B. P., Chan
Miller, C., Cohen, R. C., Davis, J. E., Dussault, M. E., Edwards, D. P.,
Fishman, J., Ghulam, A., Gonzaìlez Abad, G., Grutter, M., Herman, J. R.,
Houck, J., Jacob, D. J., Joiner, J., Kerridge, B. J., Kim, J., Krotkov, N.
A., Lamsal, L., Li, C., Lindfors, A., Martin, R. V., McElroy, C. T., McLin-
den, C., Natraj, V., Neil, D. O., Nowlan, C. R., OSullivan, E. J., Palmer,
P. I., Pierce, R. B., Pippin, M. R., Saiz-Lopez, A., Spurr, R. J. D.,
Szykman, J. J., Torres, O., Veefkind, J. P., Veihelmann, B., Wang, H., Wang,
J., and Chance, K.: Tropospheric emissions: Monitoring of pollution (TEMPO),
J. Quant. Spectrosc. Ra., 186, 17–39, 10.1016/j.jqsrt.2016.05.008,
2017.