The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to
be launched in 2019–2020 on board the GEO-KOMPSAT (GEOstationary KOrea
Multi-Purpose SATellite)-2B, contributing as the Asian partner of the global
geostationary constellation of air quality monitoring. To support this air
quality satellite mission, we perform a cross-evaluation of simulated GEMS
ozone profile retrievals from OMI (Ozone Monitoring Instrument) data based
on the optimal estimation and ozonesonde measurements within the GEMS
domain, covering from 5
The development of geostationary ultraviolet–visible (UV–VIS)
spectrometers is a new paradigm in the field of the space-based air quality
monitoring. It builds on the polar-orbiting instrument heritage for the last
40 years, which were initiated with the launch of a series of Total Ozone
Mapping Spectrometer (TOMS) instruments starting in 1978 (Bhartia et al.,
1996) and consolidated by the Global Ozone Monitoring Experiment (GOME)
(ESA, 1995), the SCanning Imaging Absorption spectroMeter for Atmospheric
CHartographY (SCIAMACHY) (Bovensmann et al., 1999), the Ozone Monitoring
Instrument (OMI) (Levelt et al., 2006), GOME-2 (EUMETSAT, 2006), the Ozone
Mapping and Profiler Suite (OMPS) (Flynn et al., 2014), and the TROPOspheric
Monitoring Instrument (TROPOMI) (Veefkind et al., 2012). Three geostationary
air quality monitoring missions, including the Geostationary Environmental
Monitoring Spectrometer (GEMS) (Bak et al., 2013a) over East Asia,
TEMPO (Tropospheric Emissions: Monitoring of Pollution; Chance et al., 2013;
Zoogman et al., 2017) over North America, and Sentinel-4 (Ingmann et al.,
2012) over Europe, are in progress to launch in the 2019–2022 time frame, to
provide unprecedented hourly measurements of aerosols and chemical
pollutants at suburban-scale spatial resolution (
GEMS will be launched in late 2019 or early 2020 on board the GEO-KOMPSAT
(GEOstationary KOrea Multi-Purpose SATellite)-2B to measure
To support the development of the GEMS ozone profile algorithm, Bak et al. (2013a) demonstrated that the GEMS spectral coverage of 300–500 nm minimizes
the loss in the sensitivity to tropospheric ozone despite the lack of most
Hartley ozone absorption wavelengths shorter than 300 nm. They further
indicated the acceptable quality of the simulated stratospheric ozone
retrievals from 212 to 3 hPa (40 km) through comparisons using Microwave
Limb Sounder (MLS) measurements. As a consecutive work, this study evaluates
simulated GEMS tropospheric ozone retrievals against ozonesonde
observations. GEMS ozone retrievals are simulated using an optimal
estimation (OE)-based fitting algorithm with OMI radiances in the spectral
range 300–330 nm in the same way as Bak et al. (2013a). The validation
effort is essential to ensuring the quality of GEMS ozone profile retrievals
and to verifying the newly implemented ozone profile retrieval scheme.
In situ ozonesonde soundings have been considered to be the best reference
but should be carefully used due to the spatial and temporal irregularities
in instrument types, manufacturers, operating procedures, and correction
strategies (Deshler et al., 2017). Compared to TEMPO and Sentinel-4, the
GEMS validation activity is expected to be more challenging for the ozone
profile product because of the much sparser distribution of stations and
more irregular characteristics of ozonesonde measurements over the GEMS
domain. Continuous balloon-borne observations of ozone are only available at
the Pohang (129.23
The development of the GEMS ozone profile algorithm builds on the heritage
of the Smithsonian Astrophysical Observatory (SAO) ozone profile algorithm
which was originally developed for GOME (Liu et al., 2005), continuously
adapted for its successors including OMI (Liu et al., 2010a), GOME-2 (Cai et
al., 2012), and OMPS (Bak et al., 2017). In addition, the SAO algorithm will
be implemented to retrieve TEMPO ozone profiles (Chance et al., 2013;
Zoogman et al., 2017). In this algorithm, the well-known OE-based iterative inversion is applied to estimate the best ozone
concentrations from simultaneously minimizing between measured and simulated
backscattered UV measurements constrained by the measurement covariance
matrix, and between retrieved values and its climatological a priori values
constrained by an a priori covariance matrix (Rodgers, 2000). The impact of
a priori information on retrievals becomes important when measurement
information is reduced due to instrumental errors, instrument design
sensitivity (e.g., stray light, dark current, and read-out smear), and
physically insufficient sensitivities under certain geophysical conditions
(e.g., the reduced penetration of incoming UV radiation into the lower
troposphere at high solar zenith angles or blocked photon penetration below
thick clouds). The described OE-fitting solution
The ozone fitting window was determined to maximize the retrieval
sensitivity to ozone and minimize it to measurement error: 289–307 and
326–339 nm for GOME, 270–309 and 312–330 nm for OMI, 289–307 and
325–340 nm for GOME-2, and 302.5–340 nm for OMPS. For OMI, GOME and GOME-2,
partial ozone columns are typically retrieved in 24 layers from the surface
to
Figure 1 is a schematic diagram of the ozone profile algorithm. With the input of satellite measurements, the slit function is parameterized through cross-correlation between satellite irradiance and a high-resolution solar reference spectrum to be used for wavelength calibration and for high-resolution cross section convolution (Sun et al., 2017; Bak et al., 2019); a normalized Gaussian distribution is assumed to derive analytic slit functions for OMI. To remove the systematic errors between measured and calculated radiances, “soft calibration” is applied to measured radiances and then the logarithms of sun-normalized radiances are calculated as measurement vectors (Liu et al., 2010a; Cai et al., 2012; Bak et al., 2017). Measurement covariance matrices are constructed as diagonal matrices with components taken from the square of the measurement errors as measurement errors are assumed to be uncorrelated among wavelengths. In the OMI algorithm, a noise floor of 0.4 % (UV1) and 0.2 % (UV2) is used because OMI measurement errors underestimate other kinds of random noise errors caused by stray light, dark current, geophysical pseudo-random noise errors due to subpixel variability, motion when taking a measurement, forward model parameter error (random part), and other unknown errors into account (Huang et al., 2017). GEMS is expected to have similar retrieval sensitivity to tropospheric ozone and have at least comparable radiometric/wavelength accuracy (4 % including light source uncertainty/0.01 nm) to OMI. It is designed to provide hyperspectral radiances at a spectral resolution of 0.6 nm and spectral intervals of 0.2 nm, which are also similar to OMI (spectral resolution of 0.42–0.63 nm, sampling rate of 0.14–0.33 nm per pixel). A priori ozone information is taken from the tropopause-based (TB) ozone profile climatology which was developed for improving ozone profile retrievals in the upper troposphere and lower stratosphere (Bak et al., 2013b). The Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) model (Spurr, 2006, 2008) is used to calculate normalized radiances and weighting function matrices for the atmosphere, with Rayleigh scattering and trace-gas absorption and with Lambertian reflection for both surface and cloud (Liu et al., 2010a). The ozone algorithm iteratively estimates the best ozone profiles within the retrieval converges (typically 2–3 iterations), together with other geophysical and calibration parameters (e.g., cloud fraction, albedo, BrO, wavelength shift, Ring parameter, mean fitting scaling parameter) for a better fitting accuracy even though some of the additional fitting parameters can reduce the degrees of freedom for signal of ozone. We should note here that GEMS data processing is expected to be different from OMI mainly in two ways: (1) OMI uses a depolarizer to scramble the polarization of light. However, GEMS has polarization sensitivity (required to be less than 2 %) and performs polarization correction using an RTM-based look-up table of atmospheric polarization state and pre-flight characterization of instrument polarization sensitivity in the level 0 to 1b data processing. The GEMS polarization correction is less accurate, and hence an additional fitting process might be required in the level 2 data processing, especially for ozone profiles that are more sensitive to the polarization error compared to other trace gases. (2) GEMS has a capability to perform diurnal observations and hence diurnal meteorological input data are required to account for the temperature-dependent Huggins band ozone absorption. Hence, the numerical weather prediction (NWP) model analysis data will be transferred to the GEMS Science Data Processing Center (SDPC).
Flow chart of the GEMS ozone profile retrieval algorithm.
Ozonesondes are small, lightweight, and compact balloon-borne instruments
capable of measuring profiles of ozone, pressure, temperature, and humidity
from the surface to balloon burst, usually near 35 km (4 hPa); ozone
measurements are typically reported in units of partial pressure (mPa) with
vertical resolution of
Geographic locations of the ozonesonde stations available since
2005 over the GEMS observation domain. Each symbol represents a different
type sensor: the modified Brewer–Mast (MBM), the carbon–iodine cell (CI),
and the electrochemical concentration cell (ECC). The background map
illustrates the OMI
Figure 2 displays the locations of 10 ozonesonde sites focused on in this study
within the GEMS domain bordering from 5
List of ozonesonde stations.
Seasonal mean (solid) and standard deviation (dashed) profiles of
ozonesonde soundings from 2005 to 2015 at the 10 sites listed in Table 1; 5 mPa is subtracted from standard deviations to fit the
In Fig. 3 the seasonal means and standard deviations of ozonesonde measurements are presented to show the stability and characteristics of ozonesonde measurements at each site. Instabilities of measurements are observed from New Delhi ozonesondes. High surface ozone concentrations at Trivandrum in summer are believed to be caused by measurement errors because low levels of pollutants have been reported at this site under these geolocation and meteorological effects (Lal et al., 2000). Besides Trivandrum, Naha could be regarded as a background site according to low surface ozone (Fig. 3) and precursor concentrations (Fig. 2) compared to neighboring stations and previous studies (Oltmans et al., 2004; Liu et al., 2002). In the lower troposphere, high ozone concentrations are captured at Pohang, Tsukuba, and Sapporo in the summer due to enhanced photochemical production of ozone in daytime, whereas tropical sites Naha, Hanoi, and Hong Kong show ozone enhancements in spring, mainly due to biomass burning in Southeast Asia, with low ozone concentrations in summer due to the Asian monsoon and in winter due to tropical air intrusion (Liu et al., 2002; Ogino et al., 2013). Singapore and Kuala Lumpur are supposed to be severely polluted areas, but ozone pollution is not clearly captured over the seasons. This might be explained by the morning observation time at these two stations. In addition, instabilities of Singapore measurements are noticeable, including abnormally large variability and very low ozone concentration in the stratosphere. The effect of stratospheric intrusions on the ozone profile shape is dominant at mid-latitudes (Pohang, Tsukuba, and Sapporo) during the spring and winter when the ozonepause goes down to 300 hPa, with larger ozone variabilities in the lower stratosphere and upper troposphere, whereas the ozonepause is around 100 hPa with much less variability of ozone in other seasons.
The GEMS ozone profile algorithm is applied to OMI BUV measurements for
300–330 nm to simulate GEMS ozone profile retrievals at coincident locations
listed in Table 1. The coincidence criteria between satellite and
ozonesondes are
To increase the validation accuracy, data screening is implemented for both
ozonesonde observations and satellite retrievals according to Huang et al. (2017). For ozonesonde observations, we screen ozonesondes with
balloon-bursting pressures exceeding 200 hPa, gaps greater than 3 km,
abnormally high concentration in the troposphere (> 80 DU), and
low concentration in the stratosphere (< 100 DU). Among WOUDC sites,
the Japanese and Indian datasets include a correction factor which is
derived to make better agreement between integrated ozonesonde columns and
correlated reference total ozone measurements as mentioned in Sect. 2.2.
In Fig. 4, Japanese ozonesondes are compared against GEMS simulations when a
correction factor is applied or not to each CI and ECC measurement,
respectively. Morris et al. (2013) recommended restricting the application
of this correction factor to the stratospheric portion of the CI ozonesonde
profiles due to errors in the above-burst column ozone. Our comparison
results illustrate that applying the correction factor reduces the vertical
fluctuation of mean biases in ozone profile differences with insignificant
impact on their standard deviations. Therefore we decide to apply this
correction factor to the sonde profiles if this factor ranges from 0.85 to
1.15. Because of a lack of retrieval sensitivity to ozone below clouds and
lower tropospheric ozone under extreme viewing conditions, GEMS simulations
are limited to cloud fraction less than 0.5, solar zenith angles (SZAs) less than
60
Effects of applying a correction factor (CF) to
Due to the different units of ozone amount between satellites and
ozonesondes, we convert ozonesonde-measured partial pressure ozone values
(mPa) to partial column ozone (DU) at the 24 retrieval grids of the
satellite for the altitude range from surface to the balloon-bursting
altitudes. Ozonesonde measurements are obtained at a rate of a few seconds
and then typically averaged into altitude increments of 100 m, whereas
retrieved ozone profiles from nadir BUV satellite measurements have much
coarser vertical resolution of 10–14 km in the troposphere and 7–11 km in
the stratosphere, based on OMI retrievals. Consequently, satellite
observations capture only the smoothed structures of ozonesonde soundings,
especially near the tropopause, where a sharp vertical transition of ozone
within 1 km is observed, and in the boundary layer due to the insufficient
penetration of photons. Satellite retrievals unavoidably have an error
compound due to its limited vertical resolution, called “smoothing error”
in OE-based retrievals (Rodgers, 2000). It could be useful to eliminate the
effect of smoothing errors on differences between satellites and sondes to
better characterize other error sources in comparisons (Liu et al., 2010a).
For this reason, satellite data have been compared to ozonesonde
measurements smoothed to the satellite vertical resolution, together with
original sonde soundings (Liu et al., 2010b; Bak et al., 2013b; Huang et
al., 2017). The smoothing approach is
In order to define tropospheric columns, both satellite retrievals and
ozonesonde measurements are vertically integrated from the surface to the
tropopause taken from daily National Centers for Environmental Prediction
(NCEP) final (FNL) Operational Global analysis data (
Comparison statistics (mean bias in DU, 1
Witte et al. (2018) recently compared seven SHADOZ station ozonesonde
records, including Hanoi and Kuala Lumpur in the GEMS domain, with total
ozone and stratospheric ozone profiles measured by spaceborne nadir and
limb-viewing instruments, respectively. In this comparison, the Hanoi
station shows comparable or better agreement with the satellite datasets
when compared to other sites. Morris et al. (2013) and Rohtash et al. (2016)
thoroughly evaluated ozonesonde datasets over Japanese and Indian sites,
respectively, but they did not address their measurement accuracy with
respect to those at other stations. Validation of GOME TOC by Liu et al. (2006) showed relatively larger biases at Japanese CI stations, and
validation of OMI TOC by Huang et al. (2017) showed both larger biases and
standard deviations at the Indian MBM sites. In South Korea, regular
ozonesonde measurements are taken only from Pohang, but these measurements
have been insufficiently evaluated; only the stratospheric parts of these
measurements were quantitatively assessed against satellite solar
occultation measurements by Halogen Occultation Experiment (HALOE) from 1995
to 2004 in Hwang et al. (2007), but only 26 pairs were compared despite the
coarse coincident criteria (48 h in time,
Time series of tropospheric ozone columns (DU) of GEMS-simulated ozone profile retrievals (blue) and ozonesonde measurements convolved with GEMS averaging kernels (red) from 2005 to 2015 at 10 stations listed in Table 1.
For this purpose, we illustrate tropospheric ozone columns (TOCs) as a
function of time for individual stations listed in Table 1, measured with
three different types of ozonesonde instruments and retrieved with GEMS
simulations (Fig. 5), respectively. The goal of this comparison is to
identify any abnormal deviation of ozonesonde measurements relative to
satellite retrievals, so we exclude the impact of the different vertical
resolutions between instruments and satellite retrievals in this comparison
by convolving ozonesonde data with satellite averaging kernels. At
midlatitude sites (Pohang, Sapporo, and Tsukuba) both ozonesonde and
simulated retrievals show the distinct seasonal TOC variations with values
ranging from
Same as Fig. 5 but for absolute differences of tropospheric ozone columns (DU) between ozonesonde measurements and GEMS-simulated retrievals.
Mean biases and 1
In Fig. 6 time-dependent errors in differences of TOC between ozonesonde and
simulated GEMS retrievals are evaluated with the corresponding comparison
statistics in Table 2. Simulated retrievals show a strong correlation of
Same as Fig. 8 but for validating OMI standard ozone profiles (OMO3PR) produced by the KNMI OE-based algorithm.
Same as Fig. 8 but for validating OMI research ozone profiles (OMPROFOZ) produced by the SAO OE-based algorithm.
Figure 7 compares differences of ozone profiles between ECC ozonesondes and
GEMS-simulated retrievals at each station. Among ECC ozonesondes,
Singapore's are in the worst agreement with GEMS simulations in both terms
of mean biases and standard deviations, which could be explained by the
discrepancy in collocation time. Sonde observations at Japan, Pohang, Hong
Kong, and Hanoi stations, where balloons were launched in the afternoon
(
The GEMS-simulated retrievals are assessed against ECC ozonesonde soundings
at five stations (Hong Kong, Pohang, Tsukuba, Sapporo, and Naha) identified
as a good reference in the previous section. The comparison statistics
include mean bias and standard deviation in the absolute/relative
differences, correlation coefficients, linear regression results (slope (a),
intercept (b), error); the error of the linear regression is defined as
We simulate GEMS ozone profile retrievals from OMI BUV radiances in the
range 300–330 nm using the OE-based fitting during the period 2005–2015 to
ensure the performance of the algorithm against coincident ozonesonde
observations. There are 10 ozonesonde sites over the GEMS domain from WOUDC,
SHADOZ and KMA archives. This paper gives an overview of these ozonesonde
observation systems to address inhomogeneities in preparation, operation,
and correction procedures which cause discontinuities in individual
long-term records or among stations. Comparisons between simulated GEMS TOCs
and ozonesondes illustrate a noticeable dependence on the instrument type.
Indian ozonesonde soundings measured by MBM show severe deviations in
seasonal time series of TOC compared to coherent GEMS simulations and
ozonesonde observations measured in a similar latitude regime. At Japanese
stations, CI ozonesondes underestimate ECC ozonesondes by 2 DU or more and a
better agreement with GEMS simulations is found when ECC measurements are
compared. Therefore, only ECC ozonesonde measurements are selected as a
reference, in order to ensure a consistent, homogeneous dataset.
Furthermore, ECC measurements at Singapore, Kuala Lumpur, and Hanoi are
excluded. At Singapore and Kuala Lumpur, observations were performed in the
morning and thereby are inconsistent with GEMS retrievals simulated at the
OMI overpass time in the afternoon. In addition, the observation time for
Kuala Lumpur is inconsistent itself compared to other stations; its standard
deviation is
The ozonesonde data used in this study were obtained though the WOUDC,
SHADOZ, and KMA archives. The WOUDC dataset is available at
JB and KHB designed the research; JHK and JK provided oversight and guidance; JB conducted the research and wrote the paper; XL and KC contributed to the analysis and writing.
The authors declare that they have no conflict of interest.
Research at the Smithsonian Astrophysical Observatory was funded by NASA and the Smithsonian Institution. Research at Pusan National University was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2019-01-02-053). This work was also supported by MOE as the Public Technology Program based on Environmental Policy (2017000160001).
This research was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (grant no. NIER-2019-01-02-053).
This paper was edited by Jean-Luc Attié and reviewed by two anonymous referees.