The accuracy of total ozone computed from the Smithsonian Astrophysical
Observatory (SAO) optimal estimation (OE) ozone profile algorithm (SOE)
applied to the Ozone Monitoring Instrument (OMI) is assessed through
comparisons with ground-based Brewer spectrometer measurements from 2005 to
2008. We also compare the three OMI operational ozone products, derived from
the NASA Total Ozone Mapping Spectrometer (TOMS) algorithm, the KNMI (Royal Netherlands Meteorological
Institute)
differential optical absorption spectroscopy (DOAS) algorithm, and KNMI's
Optimal Estimation (KOE) algorithm. The best agreement is observed between
SAO and Brewer, with a mean difference of within 1 % at most individual
stations. The KNMI OE algorithm systematically overestimates Brewer total
ozone by 2 % at low and mid-latitudes and 5 % at high latitudes while the
TOMS and DOAS algorithms underestimate it by
The Dutch–Finnish Ozone Monitoring Instrument (OMI) (Levelt et al., 2006) aboard the NASA Aura satellite was launched on 15 July 2004 to continue the long-term record of satellite total ozone measurements, initiated in 1970 with the launch of the nadir-sounding Backscatter Ultraviolet instrument (BUV) aboard the Nimbus-4 spacecraft, and followed in 1978 with the launch of the Total Ozone Monitoring Spectrometer (TOMS) and Solar Backscatter Ultraviolet (SBUV) instruments aboard Nimbus-7. There are two independent operational total ozone algorithms applied to OMI measurements to produce the standard OMI total column ozone products. The OMTO3 algorithm is based on the well-known TOMS method developed at NASA Goddard Space Flight Center (GSFC) (Bhartia and Wellemeyer, 2002). The algorithms used for OMDOAO3 and OMO3PR take advantage of the spectroscopic capability of the OMI instrument. These were both developed at the Royal Netherlands Meteorological Institute (KNMI). One is based on differential optical absorption spectroscopy (DOAS) (Veefkind et al., 2006) and the other on the optimal estimation (OE) inversion technique (KNMI OE, KOE) (van Oss et al., 2001; Kroon et al., 2011). The variety of OMI operational ozone data products offers a good opportunity to compare the total ozone retrieval performance among the different algorithms and to identify their strengths and shortcomings.
An independent OE-based ozone profile algorithm, referred to as SOE here, was developed at the Smithsonian Astrophysical Observatory (SAO) (Liu et al., 2010a). It was shown capable of capturing tropospheric ozone signals in OMI measurements that are perturbed by convection, biomass burning, anthropogenic pollution, and transport of pollution. In subsequent validation studies, good agreement was found between OMI SOE ozone profiles and high resolution ozone profiles made by satellite and ozonesonde (Liu et al., 2010b; Wang et al., 2011). The SOE algorithm was shown to capture very well the ozone variability in the extratropical tropopause region through comparison with aircraft and ozonesonde measurements (Pittman et al., 2009; Bak et al., 2013).
In Liu et al. (2010a), the profile of partial ozone columns is retrieved at 24
layers and total ozone column is just the sum of partial ozone columns at
all layers. In principle, OE-based profile algorithms should have the
potential to provide more accurate total ozone estimates than the two
primary total ozone algorithms because of its use of a wider wavelength
range (270–330 nm) than that used for total ozone (Bhartia and Wellemeyer,
2002; Veefkind et al., 2006). Liu et al. (2010a) indicated that the total
ozone retrieval errors (root sum square of both random noise and smoothing
error) from SOE are typically 1–2.0 DU on average at solar zenith angle
< 80
The main objective of this study is to evaluate the retrieval performance in total ozone through comparison with 4 years (2005–2008) of Brewer observations over the Northern Hemisphere, collected from World Ozone and Ultraviolet Radiation Data Centre (WOUDC) network and the Sodanklyä Total Column Ozone Intercomparison (SAUNA) campaign. The dependence of SOE–Brewer differences on various algorithmic variables (solar zenith angle, cross-track position, cloud parameters, total ozone amount) is thoroughly examined to identify possible problems with the SOE algorithm under certain conditions. SOE total ozone columns are further evaluated for long-term stability and seasonal or daily variability. The evaluation of possible dependence on algorithmic variables and time will provide useful insights into the characteristics of this algorithm, which have not come from previous studies.
The same comparisons performed between SOE total ozone and Brewer measurements have been conducted for the three operational total ozone products. Both OMTO3 and OMDOAO3 were validated previously by several groups using various reference data (e.g., Balis et al., 2007; Kroon et al., 2008; McPeters et al., 2008; Antón et al., 2009; Antón and Loyola, 2011). However, total ozone from the OMO3PR product has not yet been thoroughly evaluated against ground-based measurements. This study will therefore contribute to the assessment of that product. Despite the potential of ozone profile algorithms for improving total ozone retrieval, the successful performance of spectroscopic profile retrieval algorithms can be accomplished only when accurate calibration and forward model simulations and good knowledge of measurement errors and a priori covariance matrices are available (Liu et al., 2005; Liu et al., 2010a). In this paper, one of our interests is to see how total ozone retrieval performance differs between SOE and KOE due to the different implementations of OE they employ.
This paper is organized as follows. Section 2 briefly describes the four satellite ozone retrieval algorithms and data sets, the ground-based total ozone data, and the comparison methodology. Section 3 provides the OMI validation results using WOUDC and SAUNA data. We discuss the effect of different implementations between SOE and KOE on total column ozone retrievals in Sect. 4. Section 5 summarizes our validation results.
OMI is a nadir-viewing, ultraviolet–visible (UV-VIS) spectrometer, measuring backscattered solar radiances and irradiances over a wavelength range of 270 nm to 500 nm with two spectral channels: UV 270–370 nm and VIS 350–500 nm (Levelt et al., 2006). The UV channel is further divided into two sub-channels, UV-1 and UV-2, at about 310 nm, to allow for a design that suppresses stray light. OMI provides daily global coverage with an approximately 2600-kilometer-wide ground swath. Each swath consists of 60 and 30 cross-track pixels for UV-2/VIS and UV-1 spectra, respectively. The ground pixel size at nadir is 24 km (UV-2/VIS) and 48 km (UV-1) in the across-track direction and 13 km in the flight direction.
Main Characteristics of SOE, KOE, TOMS, and DOAS ozone algorithms.
A summary of the main characteristics of the four OMI ozone retrieval
algorithms is presented in Table 1. The principle of SAO and KNMI
algorithms, SOE and KOE, is to find an OE-based solution that corresponds to
a weighted average between measurement and a priori information, constrained
by measurement and a priori error covariance matrices (Rodgers, 2000). Both
algorithms derive ozone profile information from OMI ultraviolet spectrum
with a fitting window of
Mean biases and 1
Brewer stations selected from WOUDC.
Adjustments based on comparisons of measured and simulated earthshine
radiances for well-characterized geophysical reference conditions are
popularly known as “soft” calibrations in contrast to “hard”
calibrations, when radiometric adjustments are made solely using information
from the instruments on-board calibration hardware. A calibration
adjustment is applied to OMI level 1b radiances in the SOE algorithm
independent of space and time to correct possible calibration errors causing
cross-track and wavelength-dependent biases and part of the stray light error
(Liu et al., 2010a). This first-order correction is derived using the
average percent difference between measured and simulated radiance derived
from 2 days of Microwave Limb Sounder (MLS) data in the tropics as shown in Sect. 2.3 and Fig. 1
of Liu et al. (2010a). The a priori information (mean and error) for ozone
is taken from a monthly and latitude-dependent ozone profile climatology
constructed by McPeters et al. (2007), the “McPeters–Logan–Labow (LLM)”
climatology. The retrieval variables in the state vector include ozone
values at 24 layers from the surface to
The KOE algorithm does not perform radiometric calibration as done in the SOE algorithm, but does use a stray light correction estimated by minimizing the signatures of Fraunhofer features in the fitted residuals separately in the UV-1 and UV-2 channels. The a priori ozone mean state is defined from LLM climatology, but a constant a priori ozone error of 20 % is assumed for all latitudes and altitudes except for ozone hole conditions. The retrieval variables include ozone profiles at 18 layers from the surface to 0.3 hPa, surface albedo, cloud albedo, and stray light correction parameters. The surface albedo and cloud albedo is turned on or off depending on the cloud fraction as a state vector; for cloud fraction < 0.2, the surface albedo is fitted with fixed cloud albedo of 0.8 whereas for cloud fraction > 0.2 the cloud albedo is fitted with the fixed surface albedo of its a priori value (Kroon et al., 2011).
The OMI TOMS and OMI DOAS total ozone algorithms use UV-2 measurements and thus retrievals are done at the higher UV-2 spatial resolution. The TOMS algorithm uses sun-normalized radiances at two wavelengths, 317.6 and 331.3 nm, to measure total ozone under most retrieval conditions. One wavelength is significantly absorbed by ozone and sensitive to the total column amount, and the other is insensitive to ozone. At large slant column densities, the retrieved total ozone is sensitive to assumed a priori profile shape. Information from the 312.6 nm wavelength, which is sensitive to ozone profile, is used to reduce this profile shape error (Wellemeyer et al., 1997). The algorithm is rather insensitive to calibration error that does not vary with wavelength, but it is more sensitive to wavelength-relative error (Bhartia and Wellemeyer, 2002). The TOMS algorithm uses ozone cross-section data based on Bass and Paur (1985). OMTO3 total ozone measurements largely rely on OMI's pre-launch radiometric calibration at nadir described by Dobber et al. (2006) and validated by Jaross and Warner (2008). Small residual errors in the collection 3 radiances (Dobber et al., 2008) are further reduced using soft-calibration techniques where biases and irregularities that vary with viewing angle and wavelength are estimated and reduced by comparing the measured radiances with forward model calculations. This approach is applied only to select data where the variability in ozone is low and therefore the radiances can be simulated reliably. The DOAS algorithm calculates the slant column density with a DOAS-based fitting of the measured spectrum in the spectral region 331.1–336 nm to the differential absorption cross sections of ozone using BDM cross sections, and then it estimates the vertical column density by dividing the slant column density by the air mass factor (AMF) (Veefkind et al., 2006).
In all four OMI ozone algorithms, clouds are treated as Lambertian
reflectors and partially cloudy scenes are treated using the independent
pixel approximation or mixed Lambertian surfaces. SOE uses cloud pressures
from the OMI O
The OMI ozone standard products are from the Aura Validation Data Center
(AVDC) (
The Brewer grating spectrometer has an improved optical design over the
Dobson spectrometer and is fully automated. The Brewer can be operated in
single or double monochromator configuration. The double monochromator
(MK-III model) is known to better reduce the impact of stray light on the
measurement than the single monochromator (MK-II or MK-IV) does (Kerr, 2002;
Petropavlovskikh et al., 2011). Spectral irradiance measurements can be made
by a well-maintained Brewer instrument with the precision of
Absorption coefficients based on Bass and Paur (1985) data are used in the
standard Brewer algorithm. In addition, the standard Brewer algorithm does
not consider the temperature dependence of ozone cross sections and instead
uses a fixed temperature of
We use daily mean values derived from Brewer spectrometers that are publicly
available from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC)
archive (
Comparison statistics* between OMI and Brewer total column ozone data for the Northern Hemisphere (NH), mid-latitudes, and high latitudes.
The main objective of the Sodankylä Total Column Ozone Intercomparison
(SAUNA) campaign was to assess the performance of the ground-based
instruments and algorithms used to measure total column ozone at large solar
zenith angles and high total column ozone amounts
(
A portion of the OMI radiance measurements are affected by an instrument error termed the “row anomaly” which began in June of 2007. Loose thermal insulating material in front of the instrument's entrance slit is believed to both block and scatter light, causing measurement error. The anomaly affects radiance measurements at all wavelengths for specific cross-track viewing directions which are imaged to the charge-coupled device (CCD) rows. Initially, the anomaly only affected a few rows (two positions in 2007, eight positions starting in 11 May 2008). But, since January 2009, the anomaly has spread to other rows and began to shift with time. While a large fraction of good measurements remain in the UV-2 and VIS channels used by OMTO3 and OMDOAO3, the effect of the anomaly on UV-1 measurements used by the SOE and KOE algorithms is more widespread and severe. Therefore in this study, OMI data are only used from the period of 2005–2008 when the row anomaly did not substantially affect radiance data used by any of the four algorithms.
The criteria for collocating OMI with Brewer data is that it must be within
150 km between OMI pixel center and ground-based station location and on the
same day. We take only the closest match on a given day, not the average of
OMI pixels found. The location and overpass time of KOE and SOE (and,
separately, of TOMS and DOAS) collocated at one ground point are exactly the
same whereas the locations differ slightly between SOE/KOE and TOMS/DOAS.
The average distance between OMI and the ground stations is 10
Two statistical quantities, mean bias and 1
There are 35 stations available from the WOUDC archive for this validation study, as mentioned in Sect. 2.2. Twenty-seven Brewer stations among them were identified as references using a similar selection procedure as that used by Balis et al. (2007). This selection procedure is described in the rest of this section.
Figure 1 shows the relative differences between OMI and Brewer total ozone
at all 35 stations listed in Table 2. On average, both mean biases and
1
Among the four algorithms, the SOE data present the best agreement with
Brewer data at most stations; the mean difference is typically below
Same as Fig. 1, but for correlation coefficient (
The correlations between OMI and Brewer data are shown in the left panel of
Fig. 2. Two tropical stations (Paramaribo and Petaling Jaya) are excluded
from comparisons because of their small correlation coefficients compared to
the overall values of other stations. In addition, the Pohang, Mt. Waliguan,
and Alert stations, where the mean differences deviate strongly, show
inconsistencies from neighboring stations. Apart from these stations, the
comparisons present high correlation coefficient values, between 0.95 and 1,
depending on OMI algorithms and stations. The SOE and TOMS total ozone
columns show the best correlations with Brewer data (
We derive the trend of the differences [
This leaves 27 stations selected as good references to be used for the
validation of OMI total column ozone data sets. Comparison statistics are in
Table 3. For all stations in the Northern Hemisphere (NH), the average
difference between SOE and Brewer is 0.02 % (0.04 DU) with a standard
deviation of 1.81 % (5.98 DU), which generally represents an improvement
over other comparisons presented in this study as well as in previous
validation studies for other spaceborne instruments (e.g., Antón and Loyola, 2011; Koukouli et al., 2012). Overall, the SOE algorithm also
demonstrates the best agreement with Brewer among all four algorithms with
respect to correlation coefficients and linear regression results for the
NH, middle-latitude, and high-latitude regions. Despite the use of only two
or three wavelengths, the TOMS algorithm shows similar standard deviations
to the SOE algorithm (slightly smaller at mid-latitude stations, but
slightly larger at high-latitude stations) except for some larger biases of
up to
Comparison between OMI and Brewer total ozone measurements as a
function of solar zenith angle at Uccle station with single (blue) and
double (red) Brewer instruments, respectively. The mean relative biases and
1
In Fig. 3, both single and double Brewer measurements at Uccle station are compared with the four OMI data sets. This comparison with double Brewer measurements shows less scatter but insignificant SZA-dependent reduction of OMI/Brewer differences although it is known that the performance of single Brewer instruments has a distinct dependence on SZA, especially at large SZAs due to the influence of stray light (Bais and Zerefos, 1996). In addition, comparisons at other double Brewer stations also show less scatter and an even smaller trend in the OMI/Brewer differences compared to those latitudinally adjacent stations with single Brewer instruments (Figs. 1 and 2).
Figure 4 compares the daily time series of total ozone columns from OMI and
SAUNA Brewer measurements at Sodanklyä for April 2006 when
solar zenith angles are above 50
Time series of SAUNA data (Brewer reference) and OMI total column ozone for April 2006 (upper panel). Time series of the relative differences between OMI and SAUNA total ozone (lower panel).
The solar zenith angle (SZA) of the polar-orbiting satellite changes
dramatically from the tropics to the poles as well as seasonally from summer
to winter. Tropospheric ozone information available from satellite UV
measurements decreases at larger SZAs (Liu et al., 2005), and radiative
transfer simulations lose accuracy for very high SZAs (Caudill et al., 1997).
The possible dependence of retrieval algorithms on SZA can cause
seasonal-/latitudinal- dependent retrieval biases. In Fig. 5a, the
stability of each algorithm is assessed for SZA dependence between
20
Dependence of OMI–Brewer relative mean differences and 1
Dependence of OMI–Brewer relative differences on solar zenith angle (right panels) for two groups of cloud fractions and (left panels) for three groups of OMI cross-track positions in UV-1 (left side of the positions, 1–10; nadir, 11–20; right, 21–30).
Correlations (
As indicated in Koelemeijer and Stammes (1999) and Antón and Loyola
(2011), it is important to evaluate the joint effects of satellite-viewing
geometries and clouds on ozone retrievals. In Fig. 6, the SZA dependence
is characterized by sub-groups of cloud fraction and OMI cross-track
positions, respectively. This outcome again demonstrates the stable
performance of the SOE algorithm. On the other hand, the SZA dependence of
OMI–Brewer differences derived from other algorithms varies with cloud
fraction, especially at SZA below 60
The OMI swath contains 30 and 60 cross-track pixels for the UV-1 and UV-2
channels, respectively. The viewing angles ranges from near 0
The dependence of OMI/Brewer biases on cross-track position is examined in
Fig. 5b. It shows strong cross-track dependence in the KOE data, with
the maximum biases of
The effect of clouds on trace-gas retrievals from satellite observations is
well established in the literature (Antón and Loyola, 2011). OMI ozone
algorithms use a Lambertian surface model for a cloud with a fixed albedo of
0.8, requiring the effective cloud-top pressure (or optical centroid
pressure) and effective cloud fraction to model the cloud. The accuracy of
ozone retrievals is sensitive to the uncertainties of cloud information and
cloud treatment, and therefore the validation results should be examined
with respect to cloud parameters used in retrieval algorithms (Koelemeijer
and Stammes, 1999; Antón and Loyola, 2011). It was shown in Sect. 3.2
that the effect of cloudiness on validation results becomes more pronounced
at smaller SZAs. Therefore, in order to clearly investigate the effect of
clouds on the comparison, we show relative differences for SZAs smaller than
45
Figure 5c shows the influence of cloud fraction on the OMI–Brewer
comparisons. The DOAS and TOMS results present similar negative and stable
biases for cloud fraction bins less than
Time series (monthly) of relative differences (yellow circles)
between OMI and Brewer total ozone columns over four selected latitude bands
and the 1
Figure 5d shows the influence of the cloud top pressure on the OMI–Brewer
comparisons. All of the four algorithms show no significant dependence on
cloud pressure except for high clouds (cloud top pressure <
In Fig. 5e, the differences between OMI and Brewer measurements are
plotted as a function of Brewer total ozone column in bins of 25 DU. The
dependence on the total column ozone could be attributed to the sensitivity
to profile shape of retrieved total ozone at high SZAs due to the difference
between actual and assumed a priori (climatological) ozone profiles as
indicated by Lamsal et al. (2007) and Antón et al. (2009). There
is
We examine the long-term stability and seasonal variation of the OMI total
column ozone retrievals to evaluate the four OMI algorithms. Figure 7 shows
the 4-year time series of the total ozone differences relative to Brewer
in four latitude ranges between 30
Comparison between the SOE and Brewer total ozone columns with and without soft calibration as a function of solar zenith angle (left) and cross-track position (right).
Although the SOE and KOE algorithms are similar, the SOE algorithm shows significantly better performance in retrieved total ozone. Two of the major algorithmic differences are the use of soft calibration and the use of an a priori error from the LLM climatology in the SOE algorithm vs. 20 % throughout the atmosphere in the KOE algorithm. In order to investigate whether the retrieval performance differences between two algorithms are caused by these two algorithmic differences, we perform SOE retrieval experiments with modified implementations corresponding to KOE. First, we retrieve total ozone columns using the SAO algorithm with and without soft calibration and then compare both retrievals with Brewer measurements as a function of SZA and cross-track position in Fig. 8. The use of soft calibration slightly reduces the standard deviations, SZA dependence, and cross-track dependence for most positions except for large reductions in mean biases by up to 2 % for the first two positions (UV-1 position 2 and 3). Comparing the magnitudes and patterns in the reductions vs. KOE/SOE differences in Fig. 5a and b, the KOE cross-track dependence at the left side of the OMI swath could be explained by the soft calibration, but the larger SZA and cross-track dependence (nadir to right off-nadir) cannot be explained.
Next we examine the effect of using a 20 % a priori error relative to the mean a priori profile in the SAO total column ozone retrievals and found no significant differences with total column ozone retrievals using the natural a priori error in LLM (results not shown here). Therefore we conclude that the large KOE/SOE differences are mainly caused by other implementation details such as those in radiative transfer simulations and fitting of variables other than ozone, which will cause differences in fitting residuals.
Average fitting residuals in UV-1 and UV-2 channels for an orbit of
retrievals (orbit 09987) on 1 June 2006 using
Figure 9 compares the average fitting residuals in UV-1 and UV-2 channels
for one orbit of retrievals on 1 June 2006 using SOE and KOE as a function
of SZA. For the SAO fitting results shown in Fig. 9b, we turned off the
soft calibration and the use of common mode (average fitting residuals
derived from one orbit of retrievals). Both SOE and KOE fitting residuals show
the strong SZA dependence, but SAO is smaller by a factor of 2–3. Moreover,
the use of soft calibration in SAO algorithm leads to much larger
differences in fitting results between two algorithms, especially in UV-2,
where total and tropospheric ozone information mostly originates, by a
factor of 2 (at larger SZAs) to 5 (at smaller SZAs), as shown in Fig. 9d and e. This implies significant differences in the retrieved total and
tropospheric ozone columns between two algorithms. In addition, the KOE
fitting residuals in both UV-1 and UV-2 channels show a peak at SZAs of
The OMI total column ozone data processed with SOE and the three OMI
operational algorithms (KOE, TOMS, and DOAS) are evaluated using 4 years
(2005–2008) of Brewer measurements at 27 stations identified as good
references using a selection procedure similar to that of Balis et al. (2007). The agreement between SOE and Brewer is within
The SOE improvements to total ozone retrievals are distinct, with
insignificant dependence in the total ozone differences as a function of
various algorithmic variables; even the SZA dependence is unaffected by both
cloud fraction and cross-track position. However, the SOE biases show
significant deviation at high-altitude clouds of
A high correlation between SOE and DOAS monthly biases is identified. The common features of their seasonal-dependent errors are a weak seasonal variation in mid-latitude bands and a distinct seasonal variation in high-latitude bands with winter maximum biases and summer minimum biases. The KOE monthly biases have significant seasonal variability for all latitude bands and their seasonal dependences are highly correlated with the features of SZA-dependent biases at mid-latitudes. Comparable seasonal variability is found in TOMS differences at mid-latitudes. A comparison with the SAUNA campaign data shows that all four OMI total ozone columns represent the daily total ozone variations well.
Finally, we have demonstrated that the use of SAO soft calibration reduces the SZA and cross-track dependences of OMI–Brewer differences and fitting residuals, especially in UV-1 at smaller SZAs. However, this reduction cannot explain all of the differences in total ozone retrieval performance between the KOE and SOE algorithms. The use of different a priori error covariance matrices is immaterial to the retrieved total ozone. Other differing algorithm details, including radiative transfer simulations and fitting of variables other than ozone cause significantly larger fitting residuals for KOE by a factor of 2–3.
It is important to discuss the possible impacts of cross sections on the
evaluation of algorithm performances as different cross sections are used in
the OMI and Brewer algorithms. In 2009, the WMO/GAW(Global Atmosphere Watch)-IO3C(International Ozone Commission) established the
ACSO (Absorption Cross Sections of Ozone,
The Brewer ozone data used in this study were obtained though the WOUDC and SAUNA archive. The authors would like to thank the OMI science team for providing the satellite data and P. Veefkind and M. Koukouli for providing useful comments regarding the validation results. This research was supported by the Eco Innovation Program of KEITI (ARQ201204015), South Korea. Research at the Smithsonian Astrophysical Observatory was funded by NASA and the Smithsonian Institution. Edited by: M. Van Roozendael