AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-5747-2016Accuracy, precision, and temperature dependence of Pandora total ozone
measurements estimated from a comparison with the Brewer triad
in TorontoZhaoXiaoyixizhao@atmosp.physics.utoronto.cahttps://orcid.org/0000-0003-4784-4502FioletovVitalivitali.fioletov@outlook.comhttps://orcid.org/0000-0002-2731-5956CedeAlexanderDaviesJonathanStrongKimberlyhttps://orcid.org/0000-0001-9947-1053Department of Physics, University of Toronto, Toronto, M5S 1A7,
CanadaEnvironment and Climate Change Canada, Toronto, M3H 5T4, CanadaNASA Goddard Space Flight Center, Greenbelt, MD 20771, USALuftBlick, Kreith, AustriaXiaoyi Zhao (xizhao@atmosp.physics.utoronto.ca) and Vitali Fioletov (vitali.fioletov@outlook.com)30November20169125747576127July201612September20168November20169November2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/5747/2016/amt-9-5747-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/5747/2016/amt-9-5747-2016.pdf
This study evaluates the performance of the recently developed
Pandora spectrometer by comparing it with the Brewer reference triad. This
triad was established by Environment and Climate Change Canada (ECCC) in the
1980s and is used to calibrate Brewer instruments around the world, ensuring
high-quality total column ozone (TCO) measurements. To reduce stray light,
the double Brewer instrument was introduced in 1992, and a new reference
triad of double Brewers is also operational at Toronto. Since 2013, ECCC has
deployed two Pandora spectrometers co-located with the old and new Brewer
triads, making it possible to study the performance of three generations of
ozone-monitoring instruments. The statistical analysis of TCO records from
these instruments indicates that the random uncertainty for the Brewer is
below 0.6 %, while that for the Pandora is below 0.4 %. However,
there is a 1 % seasonal difference and a 3 % bias between the
standard Pandora and Brewer TCO data, which is related to the temperature
dependence and difference in ozone cross sections. A statistical model was
developed to remove this seasonal difference and bias. It was based on daily
temperature profiles from the European Centre for Medium-Range Weather
Forecasts ERA-Interim data over Toronto and TCO from the Brewer reference
triads. When the statistical model was used to correct Pandora data, the
seasonal difference was reduced to 0.25 % and the bias was reduced to
0.04 %. Pandora instruments were also found to have low air mass
dependence up to 81.6∘ solar zenith angle, comparable to double
Brewer instruments.
Introduction
Routine total column ozone (TCO) measurements started in the 1920s with the
Dobson instrument (Dobson, 1968). During the International Geophysical Year,
1957, the worldwide Dobson ozone-monitoring network was formed. Stratospheric
ozone has been an important scientific topic since the 1970s and became a
matter of intense interest with the discovery and subsequent studies of the
Antarctic ozone hole (Farman et al., 1985; Solomon et al., 1986; Stolarski et
al., 1986) and depletion on the global scale (Stolarski et al., 1991;
Ramaswamy et al., 1992). To improve the accuracy and to automate the TCO
measurements, the Brewer spectrophotometer was developed in the early 1980s
(Kerr et al., 1980, 1988). In 1988, the Brewer was designated (in addition to
the Dobson) as the World Meteorological Organization (WMO) Global Atmosphere
Watch (GAW) standard for total column ozone measurement. By 2014, there were
more than 220 Brewer instruments installed around the world, with most in
operation today. To maintain the measurement stability and characterize each
individual Brewer, field instruments need to be regularly calibrated against
the travelling standard reference instrument. The travelling standard itself is
calibrated against the set of three Brewer instruments (serial numbers 8, 14,
and 15) operated by Environment and Climate Change Canada (ECCC), located in
Toronto, and known as the Brewer reference triad (BrT) (Fioletov et al.,
2005). Due to the well-known stray-light issue in the UV region (Bais et al.,
1996; Fioletov et al., 2000), the MkIII Brewer (double Brewer) was introduced
in 1992. The double Brewer has two spectrometers in series, significantly
improving UV response and measuring global UV spectral irradiance, O3,
SO2, and aerosol optical depth. The double Brewer instruments also have a
set of three instruments (serial numbers 145, 187, and 191) co-located with
BrT to form the Brewer reference triad double (BrT-D). Individual Brewer
instruments of the BrT and BrT-D are independently calibrated at Mauna Loa,
Hawaii, every 2–6 years (Fioletov et al., 2005).
The Pandora system was developed at NASA's Goddard Space Flight Center and
first deployed in the field in 2006. Pandora instruments are based on a
commercial spectrometer with stability and stray-light characteristics that
make them suitable candidates for both direct-sun and zenith-sky measurements
of total column ozone and other trace gases (Herman et al., 2009; Tzortziou
et al., 2012). Pandora instruments have been tested and deployed in multiple
scientific measurement campaigns around the world. These include the
Cabauw Intercomparison Campaign of Nitrogen Dioxide measuring Instruments
(CINDI) in the Netherlands in 2009 (Roscoe et al., 2010) and four NASA
DISCOVER-AQ campaigns since 2011 (Tzortziou et al., 2012). The Pandora
instruments have been used for validation of satellite ozone (Tzortziou et
al., 2012) and NO2 (Herman et al., 2009; Tzortziou et al., 2012)
measurements. By 2015, several long-term Pandora sites had been established
in the United States and worldwide (including Austria, Canada, the Canary
Islands, Finland, and New Zealand). In 2013, two Pandora instruments (serial
number 103 and 104) were deployed at Toronto co-located with BrT and BrT-D on
the roof of the ECCC Downsview building (43.782∘ N,
79.47∘ W).
The instrument random uncertainties of BrT were analysed by Kerr et
al. (1996) and Fioletov et al. (2005) using similar methods. These methods
both require knowledge of the extraterrestrial calibration (ETC) values, the
ozone absorption coefficients, and the Rayleigh scattering coefficients for
each instrument. Fioletov et al. (2005) reported that the random
uncertainties of individual observations from the BrT are within ±1 %
in about 90 % of all measurements. This work takes a different approach,
using a statistical variable estimation method to determine the random
uncertainties for BrT, BrT-D, and the two Pandora instruments together. The
variable estimation method follows the work of Fioletov et al. (2006) to
estimate the random uncertainties with the assumption that there is no
multiplicative bias between Pandoras and Brewers. Details of the method are
provided in Sect. 3.1. Since the instrument random uncertainties for BrT were
last reported 10 years ago using data to 2004 (Fioletov et al., 2005), this
work provides a new assessment of the performance of both the BrT and BrT-D
in recent years, along with a comparison between coincident Brewer and
Pandora measurements.
It is well known that the Dobson and Brewer ozone retrievals exhibit
dependence on stratospheric temperature (Kerr et al., 1988; Redondas et al.,
2014; Scarnato et al., 2009). This is because the retrievals use different
wavelengths and ozone cross sections measured at fixed temperatures. Brewer
instruments have a very low temperature dependence (typically
< 0.1 % K-1) (Kerr et al., 1988; Kerr, 2002; Van Roozendael et
al., 1998; Scarnato et al., 2009; Herman et al., 2015). For example, Kerr et
al. (1988) reported a 0.07 % K-1 temperature dependence for Brewer
no. 8 (one of the BrT), and Kerr (2002) reported a 0.094 % K-1
temperature dependence for Brewer no. 14 (one of the BrT). In addition,
Scarnato et al. (2009) reported that Brewer instruments (nos. 40, 72,
and 156) exhibited less temperature dependence than Dobson instruments
(nos. 83 and 101). Redondas et al. (2014) reported a 0.133 % K-1
temperature dependence for Dobson no. 83.
The Pandora ozone retrievals are more sensitive to stratospheric
temperatures. In Herman et al. (2015), the temperature dependence for Pandora
no. 34 (0.333 % K-1) was determined by applying retrievals at a
series of different ozone temperatures from 215 to 240 K for the ozone cross
sections and then obtaining a linear fit to the percent change. As the small
Brewer temperature dependence is known, we use coincident measurements from
the BrT and BrT-D to determine the temperature dependence factors for Pandora
nos. 103 and 104, and then apply the correction to remove the difference
between Pandora and Brewer instruments.
Instruments and datasetsPandora
The Pandora spectrometer system uses a temperature-stabilized (1 ∘C)
symmetric Czerny–Turner system with a 50-micron entrance slit and
1200 lines mm-1 grating. Unlike the Brewer instruments, which only
measure intensities at selected wavelengths, the Pandora instruments, with a
2048×64 back-thinned Hamamatsu CCD detector, record spectra from 280
to 530 nm at 0.6 nm resolution (Herman et al., 2015). The spectra are
analysed using the differential optical absorption spectroscopy (DOAS)
technique (Noxon, 1975; Platt, 1994; Platt and Stutz, 2008; Solomon et al.,
1987), in which absorption cross sections for multiple atmospheric absorbers
(including ozone, NO2, SO2, HCHO, and BrO) are fitted to the
spectra (Tzortziou et al., 2012). The Daumont, Brion, and Malicet (DBM)
(Daumont et al., 1992; Brion et al., 1993, 1998) ozone cross section at an
effective temperature of 225∘ K is used in the Pandora retrievals
(Herman et al., 2015). Additional information on Pandora calibrations and
operation can be found in Herman et al. (2015).
Two commercial Pandoras (nos. 103 and 104) were used in this study with no
modifications to operational and processing algorithms (available from
SciGlob, http://www.sciglob.com/). Pandoras nos. 103 and 104 were
deployed in Toronto in September 2013, and in this work all available Pandora
data from these instruments are used. Pandora no. 104 was moved to the
Canadian oil sands region in August 2014. Following the work of Tzortziou et
al. (2012), the Pandora ozone dataset is filtered to remove data from which
the normalized root mean square (RMS) of weighted spectral fitting residuals
is greater than 0.05, and the Pandora-calculated standard uncertainty
(Tzortziou et al., 2012) in TCO is greater than 2 DU.
Brewer
The Brewer instruments use a holographic grating in combination with a slit
mask to select six channels in the UV (303.2, 306.3, 310.1, 313.5, 316.8, and
320 nm) to be detected by a photomultiplier. The first and second
wavelengths are used for internal calibration and measuring SO2
respectively. The four longer wavelengths are used for the ozone retrieval.
The total column of ozone is calculated by analysing the relative intensities
at these different wavelengths using the Bass and Paur (1985) ozone cross
sections at a fixed effective temperature of 228.3∘ K (Kerr, 2002).
Coincident measurement periods and number of data points for
comparisons between Pandora and Brewer instruments.
Pandora no. 103Pandora no. 104Brewer no. 8Coincident period18 Oct 2013–14 May 201520 Jan 2014–8 Aug 2014Coincident data points50082671Brewer no. 14Coincident period25 Nov 2013–24 Dec 201516 Feb 2014–8 Aug 2014Coincident data points77971701Brewer no. 15Coincident period31 Nov 2013–31 Jul 201420 Jan 2014–8 Aug 2014Coincident data points22971376Brewer no. 145Coincident period15 Jan 2015–24 Dec 2015N/ACoincident data points1474N/ABrewer no. 187Coincident period18 Oct 2013–23 Apr 201420 Jan 2014–23 Apr 2014Coincident data points608397Brewer no. 191Coincident period20 Nov 2013–24 Dec 201521 Jan 2014–8 Aug 2014Coincident data points53591490
Most of the instruments in the BrT (nos. 8, 14, and 15) and BrT-D (no. 145,
nos. 187, and 191) have been in operation since Pandora instruments were
deployed. However, there are a few measurement gaps for some of the Brewers.
For example, Brewers nos. 14 and 15 were recalibrated at Mauna Loa, Hawaii,
in October 2013, and Brewer no. 145 was in Spain in March 2014 for an intercomparison.
We also had to exclude some periods due to instrument malfunction and
repairs. The coincident measurement periods for the instruments are shown in
Table 1. The data from Brewer and Pandora instruments are both time-binned
(3 min) for the comparison. Following the work of Tzortziou et al. (2012),
the Brewer dataset is filtered to remove data with calculated standard
uncertainty in TCO greater than 2 DU. In addition, the Brewer dataset is
filtered for clouds by removing data for which the logarithm of the signal at
320 nm is less than the mean value minus 2 standard deviations (4 % of
data were removed with this filter).
OMI
The Ozone Monitoring Instrument (OMI) is a nadir-viewing, near-UV–Vis
spectrometer aboard NASA's Earth Observing System (EOS) Aura satellite
(launched in July 2004). The OMI instrument measures the solar radiation
backscattered by the Earth's atmosphere and surface between 270 and 500 nm
with a spectral resolution of about 0.5 nm (Levelt et al., 2006). The OMI
TCO data are retrieved using both the Total Ozone Mapping Spectrometer (TOMS)
technique (developed by NASA (Bhartia and Wellemeyer, 2002) and based on a
retrieval using four wavelengths at 313, 318, 331, and 360 nm) and the DOAS
technique (developed by KNMI (Veefkind et al., 2006; Kroon et al., 2008) and
based on the spectrum measured in the wavelength range 331.1–336.6 nm). The
OMI TCO validation done by Balis et al. (2007) shows a globally averaged
agreement of better than 1 % for OMI–TOMS data and better than 2 %
for OMI–DOAS data in comparison with Brewer and Dobson measurements.
The OMI TCO products used in the present study are the Level-3 Aura/OMI daily
global TCO gridded product (OMTO3e) retrieved by the enhanced TOMS Version 8
algorithm (Balis et al., 2007). The OMTO3e data (Bhartia, 2012) are generated
by the NASA OMI science team by selecting the best pixel (shortest path
length) data from the good-quality Level-2 TCO orbital swath data (for
example, L2 observations with SZA < 70∘; details can be found in
Bhartia, 2012) that fall in the 0.25∘×0.25∘ global
grids. The OMTO3e data that come from the grid point over the ground-based site
are used in this work to validate our correction method for Pandora TCO data.
ECMWF ERA-Interim data
In this work, the ozone-weighted effective temperature was used to assess the
temperature sensitivity of Pandora ozone retrievals. Temperature and ozone
profiles were extracted from the European Centre for Medium-Range Weather
Forecasts (ECMWF) ERA-Interim data for 2013–2015 (Dee et al., 2011) with
0.5∘×0.5∘ spatial resolution on 37 standard pressure
levels, available from http://apps.ecmwf.int/datasets/. The
ozone-weighted effective temperature (Teff) is calculated based on
daily ozone and temperature profiles (at 18:00 UTC) over Toronto, defined as
Teff=∑i=630weff,i⋅Ti,weff,i=ni∑j=630nj=MMRi⋅pi/Ti∑j=630MMRj⋅pj/Tj,
where weff is the weighting function, Ti is the temperature,
ni is the ozone number density, MMRi is the ozone mass mixing
ratio, and pi is the pressure at pressure level i. In this work,
profile data on ECMWF standard pressure levels from no. 6 to no. 30
(10–800 mbar) were used to decrease the noise from variable surface
temperatures.
Ozone total column data from Pandoras, Brewers, and OMI:
(a) Pandora nos. 103 and 104 compared with OMI–TOMS;
(b) Brewer triad (Brewer nos. 8, 14, and 15) compared with
OMI–TOMS; (c) Brewer triad double (Brewer nos. 145, 187, and 191)
compared with OMI–TOMS; (d) the daily mean difference, Brewer (or
Pandora)–OMI.
Statistical uncertainty estimation
Figure 1 shows the time series of the total column ozone datasets used in
this work. The seasonal cycles of TCO from the ground-based and satellite
instruments track each other well, and the high-frequency daily variations
from all ground-based instruments are consistent.
By comparing the same quantity retrieved from different remote sensing
instruments, we can characterize the differences between them, which are a
combination of random uncertainties and systematic bias. Theoretically,
information about the random uncertainties can be derived from the
measurements themselves (Grubbs, 1948; Toohey and Strong, 2007). The
following method for doing this is described in Fioletov et al. (2006) and
briefly explained below.
Method
We define the two types of measured TCO (denoted as MB and
MP, for Brewer and Pandora respectively) as simple linear
functions of the true TCO value (X) and instrument random uncertainties
(δB and δP), and assume that there is no
multiplicative or additive bias between Pandora and Brewer, giving
MB=X+δB,MP=X+δP.
If we assume that the instrument random uncertainties are independent of the
measured TCO, the variance of M is the sum of the variances of X (around
the mean of the dataset) and δ:
σMB2=σX2+σδB2,σMP2=σX2+σδP2.
If the difference between Pandora and Brewer does not depend on X (no
multiplicative bias), and the random uncertainties of the two instruments are
not correlated, then the variance of the difference is equal to the sum of
the variance of the random uncertainties:
σMB-MP2=σδB2+σδP2.
Since we have the measured TCO and the difference between the Pandora and
Brewer datasets, the variance of the TCO and instrument random uncertainties
can be solved by
σX2=σMB2+σMP2-σMB-MP2/2,σδB2=σMB2-σMP2+σMB-MP2/2,σδP2=σMP2-σMB2+σMB-MP2/2.
Equation (6) can be used to estimate the standard deviation (SD) of
instrument random uncertainties (σδB and σδP) and the SD of ozone variability (σX). We do
not actually know the variances σMi2 and σMB-MP2; we can only estimate them, with some
uncertainty, from the available measurements. It can be shown that the
uncertainties in the σX2, σδB2, and
σδP2 estimates depend on the sum of all three
variances (σMB2, σMP2, and
σMB-MP2) and can be high even if the estimated
variance itself is low (but one or more of the variances
σMB2, σMP2, and
σMB-MP2 are high). The estimates are thus only
as accurate as the least accurate of these parameters. The variance estimates
can be improved by increasing the number of data points or by reducing
variances of X by removing some of the daily variability. To remove the
variability in X, the residual ozone here is defined as the difference
between the high-frequency TCO and the low-frequency TCO measured by an
instrument:
dMres=Mhigh-f-Mlow-f.
For example, the Brewer residual ozone could be the Brewer TCO measurements
minus the Brewer ozone daily mean for that day, whereas the corresponding
Pandora residual ozone would be the Pandora TCO measurements minus the
Pandora ozone daily mean. By subtracting the low-frequency signal, we remove
most of the ozone variability. In addition, as proposed in Fioletov et
al. (2005), to improve the removal of the bias, we can use the following
statistical model to calculate the low-frequency signal:
Mlow-f=AB⋅IB+AP⋅IP+B⋅t-t0+C⋅t-t02,
where t is the time of the measurement and t0 is the time of local
solar noon. IB is an indicator function for the Brewer instrument;
it is set to 1 if the TCO is measured by the Brewer and to 0 otherwise.
IP is the indicator function for the Pandora. The coefficients
AB, AP, B, and C are estimated by the least-squares
method for each day (for example, the calculated low-frequency signal for
Brewer and Pandora will share the same B and C terms, but they have their
own offsets AB and AP). In the following, we will refer
to the residual ozone calculated by subtracting the daily mean value as
residual type 1 and that obtained by subtracting this second-order function as
residual type 2. The present work is focused on evaluating the high-quality
TCO data. Thus to avoid the stray-light effect, in the statistical
uncertainty estimation, we only use Pandora and Brewer data with ozone
air mass factor (AMF) less than 3 (see Sect. 4 for more details about the
stray-light effect).
Definition of terminologies used in the uncertainty estimation.
DefinitionEstimated random uncertainty (σδ)Random uncertainty estimated using the statistical variable estimation method described in Sect. 3.1Mhigh-fHigh-frequency TCO measurements, averaged in 3 min binMlow-f (daily mean)Low-frequency TCO, calculated as the daily mean TCOMlow-f (2nd order function)Low-frequency TCO, calculated using the second-order function (Eq. 8)Residual type 1Mhigh-f-Mlow-f (daily mean)Residual type 2Mhigh-f-Mlow-f (2nd order function)Results
In this work, we calculate two different types of residual ozone (see Eq. 7)
as defined in Sect. 3.1 and then use them to calculate the instrument random
uncertainty with the statistical variable estimation method (Eq. 6; more
details can be found in Fioletov et al., 2006). For example, we use Eqs. (7)
and (8) to calculate two type 2 residuals for both Brewer and Pandora
(dMb-res2 and dMp-res2), and then calculate their
difference (dMb-res2-dMp-res2). Next, we calculate
their variances values σ2(dMb-res2),
σ2(dMp-res2), and σ2(dMb-res2-dMp-res2). Those variance terms are used in Eq. (6) to
estimate the random uncertainties. The residual types and relevant
terminologies are summarized in Table 2.
Estimated random uncertainties: for the Brewer instruments using
(a) residual ozone type 1 and (b) residual ozone type 2;
for the Pandora instruments using (c) residual ozone type 1 and
(d) residual ozone type 2. The black squares indicate data from
Pandora no. 103, and the red triangles indicate data from Pandora no. 104.
The error bars show the 95 % confidence bounds.
Figure 2 shows the Brewer-estimated random uncertainties obtained using the
two types of residual ozone data (Fig. 2a for residual type 1, Fig. 2b for
type 2). For example, in Fig. 2a, the estimated random uncertainty for Brewer
no. 8 using Pandora no. 103 data (residual type 1, derived from
MP103) is shown as a black square in the column for Brewer no. 8,
while its estimated random uncertainty using Pandora no. 104 data (residual
type 1, derived from MP104) is shown as a red triangle in the same
column. Figure 2 demonstrates that type 1 (Fig. 2a) and type 2 (Fig. 2b)
residual ozone data provide comparable results and confirm that Brewer
instruments have random uncertainties of 1–2 DU.
Figure 2 also shows the Pandora-estimated random uncertainties using the two
types of residual ozone data (Fig. 2c for residual type 1, Fig. 2d for
type 2). For example, in Fig. 2c, the estimated random uncertainty for
Pandora no. 103 using Brewer no. 8 data is shown as a black square in the
column of Brewer no. 8, while its estimated random uncertainties using other
Brewer data are shown by respective Brewer columns. Figure 2 demonstrates
that the Pandora instruments have estimated random uncertainties less than
1.5 DU. Slight differences in the estimated Pandora random uncertainties
were found using different Brewer instruments. This is due to the sample
size; when the sample size is large (> 1200 coincident points; see
Table 1), the Pandora-estimated random uncertainties from different
instruments are more consistent. For example, in Fig. 2c, one of the
estimated random uncertainties for Pandora no. 103 (black square in Brewer
no. 187 column) is below 0.5 DU. This result is undesirable (the value is
∼ 0.5 DU lower than the other values) but not unusual. Dunn (2009)
describes this issue in detail and points out that the low (even negative in
some cases) variance estimate is due to small sample size. In general,
Dunn (2009) concludes that, even with the correct model, the comparisons and
estimation of precision are only viable with large sample sizes. Figure 3c
shows that the low variance was indeed from the smallest sample size (608
coincident points for Pandora no. 103 vs. Brewer no. 187 and 397 for Pandora
no. 104 vs. Brewer no. 187). In addition, when using the data from the same
pair of Brewer and Pandora instruments, the estimated random uncertainty for
Pandora is consistently lower than that for Brewer by ∼ 0.5 DU.
Estimated residual ozone variability (σX) using
(a) residual ozone type 1 and (b) residual ozone type 2.
(c) Number of coincident measurements used in the statistical
uncertainty estimation. The black squares indicate data from Pandora no. 103,
and the red triangles indicate data from Pandora no. 104. The error bars show
the 95 % confidence bounds.
Fioletov et al. (2006) estimated natural ozone variability (σX)
using Eq. (6). However, because we are using the residual ozone instead of
the TCO in the statistical analysis, the σX calculated from our
method is not the estimated natural ozone variability but the estimated
residual ozone variability for the measurement period. It can be used to
characterize the difference between residual types 1 and 2. Figure 3a shows
the estimated residual ozone variability using residual type 1 data, while
Fig. 3b shows the variability using residual type 2. Figure 3a and b
demonstrate that residual type 1 data have larger variability than type 2
data, indicating that using the daily mean value as the low-frequency signal
did not fully remove the natural ozone variability. Ideally, the random
uncertainty estimate should only contain random noise caused by the
instrument and no natural ozone variation. Scatter plots of Brewer vs.
Pandora residual ozone (Fig. 4) illustrate the same results. Figure 4 shows
that the correlation coefficients for residual type 1 (R=0.813 for Brewer
no. 8 vs. Pandora no. 103, see Fig. 4a; 0.909 for Brewer no. 8 vs. Pandora
no. 104, see Fig. 4b) are higher than the ones for residual type 2 (0.333 for
Brewer no. 8 vs. Pandora no. 103, see Fig. 4c; 0.688 for Brewer no. 8 vs.
Pandora no. 104, see Fig. 4d). The low correlation coefficients for ozone
residual type 2 data indicate that the ozone variability has been largely
removed from Pandora and Brewer data. Thus when we use residual ozone type 2,
even with relatively small sample size, the estimated uncertainties for
Pandoras are still consistent with those obtained from comparisons with other
Brewers having larger sample sizes (see Fig. 2c and d, Brewer no. 187
column).
Scatter plots for residual ozone type 1 and 2, colour-coded by the
normalized density of the points. (a) Brewer no. 8 vs. Pandora
no. 103 (residual type 1), (b) Brewer no. 8 vs. Pandora no. 104
(residual type 1), (c) Brewer no. 8 vs. Pandora no. 103 (residual
type 2), (d) Brewer no. 8 vs. Pandora no. 104 (residual type 2). The
black line is the one-to-one line.
To summarize, we tested two different methods for calculating residual ozone
and applied them in the statistical uncertainty estimation. The comparison of
two residuals helps us understand more details about the variable estimation
method. Although using the daily mean value as a low-frequency signal (as in
the residual type 1 calculation) has some shortcomings, it is more
straightforward than using the complex second-order statistical model
(Eq. 8). By showing the consistency of results from both type 1 and 2 in
Fig. 2, we validated the use of the second-order statistical model (Eq. 8)
and proved some of the advantages when using type 2. For example, the
residual type 2 could work with smaller data size than the residual type 1
(without making the estimated variance unrealistic, too low, or even
negative). In general, Fig. 2 demonstrates that the Pandora TCO data have
∼ 0.5 DU smaller estimated random uncertainties than the Brewer TCO
data. The mean estimated random uncertainties for BrT and BrT-D are in the
range of 1–2 DU (∼ 0.6 %). The mean estimated random
uncertainties for Pandora nos. 103 and 104 are in the range of 0.5–1.5 DU
(∼ 0.4 %). These results confirm the quality of the TCO data, with
all eight instruments meeting the GAW requirement for a precision better than
1 % to measure ozone (WMO, 2014).
Time series of Brewer no. 14–Pandora no. 103 TCO difference
colour-coded by ozone effective temperature (see Eq. 1): (a) before
applying the temperature dependence correction and (b) after
applying the correction. The dashed lines are LOWESS(0.5) fits.
Temperature dependence effect and correctionMethod
When comparing Pandora and Brewer TCO data, we can see a clear seasonal
structure and a bias in the difference and ratio. Figure 5a shows the time
series of Brewer no. 14–Pandora no. 103 TCO difference; the seasonal
amplitude is 3–4 DU, and the mean bias is 10.81 DU. Figure 5b (which uses
the corrected data) will be discussed in Sect. 4.2. The locally weighted
scatter plot smoothing (LOWESS(x)) fit (the dashed line) is based on local
least-squares fitting applied to a specified x fraction of the data
(Cleveland and Devlin, 1988). The bias between Pandora and Brewer TCO is
mainly due to the fact that both retrievals depend on the choice of ozone
absorption cross section (Scarnato et al., 2009; Herman et al., 2015). The
Brewer TCO in this work was retrieved using the standard Brewer network
operational ozone cross section (Bass and Paur, 1985), while the Pandora TCO
was retrieved using the standard Pandora network operational ozone cross
section (the DBM ozone cross section). Redondas et al. (2014) reported that
changing the Brewer operational ozone cross section from Bass and Paur (1985)
to that of Daumont et al. (1992) (DBM) will change the calculated TCO by
-3.2 %. In addition to the offset caused by the use of different ozone
cross sections, the seasonal difference between Pandora and Brewer TCO data
is due to their differing temperature dependence, which varies from
instrument to instrument because of the differences in ozone retrieval
algorithm and instrument design. Moreover, even for the same type of
instrument, the temperature sensitivity can be different due to imperfections
in the wavelength settings and slit function for each individual instrument.
We will study these differences (offset and temperature effect) by using the
standard TCO products from Pandora and Brewer instruments.
Linear regression of Brewer / Pandora TCO ratio as a function of
effective temperature minus 225 K. (a) Linear regression results;
(b) residual plot of the linear regression.
In this work, we use ECMWF ERA-Interim ozone and temperature profiles to
calculate daily ozone effective temperature (described in Sect. 2.4). Then we
use the following simple linear regression model to find the temperature
dependence factor for Pandora instruments:
MBMP=a⋅Teff-225+b,
where a is the temperature dependence factor for Pandora, b is the
(systematic) multiplicative bias between Pandora and Brewer, and 225 refers
to effective temperature of 225∘ K for ozone cross sections used in
the Pandora retrievals. Here, the MB and MP are TCO daily
means measured by the Brewer and Pandora respectively. To increase the number
of coincident data points, the MB dataset is formed by merging all
measurements from the six Brewers (see Table 1). A successfully merged
MB data point has coincident measurements from at least two
Brewers, to avoid domination by a single instrument. The coincident time
period of the MB and MP103 datasets is from October 2013
to December 2015 with 272 coincident days (points). Figure 6 shows the linear
regression results for Pandoras nos. 103 and 104. We found the “relative
temperature dependence factor” (RTDF) for Pandora no. 103 to be
0.247 ± 0.013 % K-1 (from the term a in Eq. 9), with a
2.2 ± 0.1 % multiplicative bias (from the term b in Eq. 9).
Although Pandora no. 104 only has measurements from January to April 2014 (53
coincident days), the linear regression still results in a similar
temperature dependence factor (0.255 ± 0.040 % K-1) and the
same bias as Pandora no. 103. The correlation coefficients for those two
linear regressions are 0.91 and 0.89 respectively.
We applied the Pandora temperature dependence factors to the Pandora TCO to
remove its bias and seasonal difference relative to Brewer TCO data. Similar
to the correction function used in Herman et al. (2015) for Pandora no. 34,
we used the following function to correct Pandora TCO data:
Mcorr=MP⋅a⋅Teff-225+b,
where Mcorr is corrected Pandora TCO, and other terms are as
defined for Eq. (9). For the Pandora no. 103 dataset, this becomes
Mcorr=MP103⋅0.00247⋅Teff-225+1.022,
where MP103 is the TCO data from Pandora no. 103. The temperature
dependence factor (0.247 ± 0.013 % K-1) and the
multiplicative bias (1.022) are found in Fig. 6. The same regression model
and method give a 0.255 ± 0.040 % K-1 temperature dependence
factor with a 2 % multiplicative bias to Pandora no. 104, and hence
Mcorr=MP104⋅0.00255⋅Teff-225+1.022,
where MP104 is the Pandora no. 104 TCO. For comparison, Herman et
al. (2015) derived the correction function for Pandora no. 34 as
Mcorr=MP34⋅0.00333⋅Teff-225+1,
where the 0.00333 (0.333 % K-1) is the temperature dependence
factor for Pandora no. 34. Note that this value was determined by applying
retrievals using ozone cross sections from 215 to 240 K and then obtaining a
linear fit to the percent change (Herman et al., 2015). However in this work,
the factors for Pandora nos. 103 and 104 were found by statistical analysis
(comparison) of the Pandora and Brewer TCO datasets. Thus our temperature
dependence factor combines the temperature sensitivity from both Pandora and
Brewer instruments, and describes the relative temperature sensitivity
between the Pandora and Brewer standard TCO products. We call it a “relative
temperature dependence factor” (RTDF), while that from Herman et al. (2015)
is an absolute temperature dependence factor (ATDF). Although the RTDF is a non-linear combination of ATDF from both
Pandora and Brewer (note that the Pandora used an ozone cross section at an
effective temperature of 225 K, while the Brewer used that at 223.8 K), we
can still make a simple linear estimation of the RTDF from reported ATDFs. In
fact, the reported ATDF for Pandora no. 34 (0.333 % K-1; Herman et
al., 2015) minus the reported ATDF for Brewer nos. 8 and 14 (0.07 and
0.094 % K-1; Kerr et al., 1988; Kerr, 2002) gives relative numbers
(0.26 and 0.24 % K-1) that are close to our model-calculated RTDF
(∼ 0.25 % K-1). In our correction functions (Eqs. 11–12), we
have a constant b term of 1.022 given 0.001 uncertainty, which indicates a
multiplicative bias of ∼ 2 % (not caused by the temperature effect)
between the Pandora and Brewer instruments due to their different selection
of ozone cross sections.
Pandora relative temperature dependence factors derived from 13
sensitivity tests (shown in Table 3). (a) RTDFs,
(b) multiplicative biases, (c) correlation coefficients
(R), and (d) number of data points in sensitivity tests. The error
bars show the 95 % confidence bounds.
Summary of sensitivity tests for Pandora relative temperature
dependence factors.
Merging data from all six Brewers could lead to variation of the Brewer
temperature dependence, so we performed sensitivity tests on the dataset.
Table 3 summarizes the tests; the combined Brewer data are merged from all
available Brewer data during the data period indicated in the table. Figure 7
shows the RTDFs, multiplicative bias, correlation coefficient, and number of
data points for the 13 sensitivity tests. Tests 1 and 2 are the results
adapted from Fig. 6. Due to the small data size, the RTDF for test 2 has
larger error bars than test 1. Test 3 shows Pandora no. 103 RTDF using
combined Brewer data for the same time period as Pandora no. 104. Pandora
no. 103 has a measurement gap from August to December 2014 due to instrument
failure (see Fig. 1); hence, tests 4 and 5 use combined Brewer data for
2013–2014 (∼ 1-year coverage, before the instrument failure of Pandora
no. 103) and 2015 (1-year coverage, after Pandora no. 103 was repaired)
separately. Brewer no. 191 was one of the most reliable Brewer instruments
during the comparison period. Thus tests 6–8 use only Brewer no. 191 data;
test 6 uses all available data (2013–2015), test 7 uses only 2013–2014 data
(before the instrument failure of Pandora no. 103), and test 8 uses 2015 data
(after Pandora no. 103 was repaired). Tests 9–13 use individual Brewer data
(all available data for each individual Brewer). For the 13 tests, the RTDFs
(see Fig. 7a) are in the range of 0.24–2.9 %, and the multiplicative
biases (see Fig. 7b) are in the range of 1.7–2.5 %. The correlation
coefficients (see Fig. 7c) for most tests are above 0.8. In general, the
RTDFs found for the Pandora instruments are stable when derived from combined
Brewer data or reliable individual Brewer data. For this 2-year data period,
the derived RTDFs from BrT-D instruments are lower
(0.241–0.246 % K-1) than the ones from BrT instruments
(0.262–0.290 % K-1). However, with the large uncertainties on the
estimated RTDFs and the bias, we could not conclude whether this is due to
the different instrument designs or a sampling issue.
Scatter plots of Pandora no. 103 vs. Brewer no. 14 TCO, colour-coded
by ozone effective temperature: (a) before applying the correction
and (b) after applying the correction. The red line is a simple
linear fit, the green line is the linear fit weighted by the calculated
standard uncertainty from Pandora and Brewer TCO data, the blue line is the
linear fit with intercept set to 0, and the black line is the one-to-one
line.
ResultsPandora TCO correction
As an example, Fig. 5 shows the time series of Brewer no. 14–Pandora no. 103
TCO differences, before and after applying the Pandora correction (Eq. 11). A
clear seasonal signal is seen due to the variation of Teff before
we apply the temperature dependence correction (see Fig. 5a). Figure 8 shows
scatter plots of Pandora no. 103 versus Brewer no. 14 TCO. In Fig. 8a, the
linear regression (green line, weighted accounting for uncertainties from
both measurements; York et al., 2004) between Pandora no. 103 and Brewer
no. 14 gives a slope of 1.023, an offset of -18.486 DU, and strong
correlation (R=0.9954). Forcing the intercept to 0 gives a slope of
0.969, indicating -3.1 % mean bias. This is consistent with the work of
Redondas et al. (2014), which showed that changing the Brewer ozone cross
section from Bass and Paur to DBM changed the Brewer TCO by -3.2 %. By
colour coding the scatter points, it is obvious that this non-ideal slope and
offset are related to Teff . After applying the correction, the
seasonal Brewer–Pandora difference disappears as seen in Fig. 5b, and the
linear regression (green line) gives a slope of 1.008, an offset of
-2.678 DU, and an improved correlation (R=0.9982) (see Fig. 8b).
Linear fitting with zero intercept gives a slope of 1.001, indicating that
the correction improves the mean bias between Pandora and Brewer TCO from
-3.1 to 0.1 %.
Effective ozone temperature: (a)Teff calculated
using ECMWF ERA-Interim data (18:00 UTC over Toronto) and NASA climatology data
(monthly mean for 40–50∘ N), and (b) the difference between
these two.
To calculate the effective temperature, we use daily temperature and ozone
profiles from ECMWF ERA-Interim data at 18:00 UTC for Toronto, but Herman et
al. (2015) used monthly averaged temperature and ozone climatology data
(interpolating the climatological ozone profile to the observed TCO in order
to capture day-to-day variability; see
ftp://toms.gsfc.nasa.gov/pub/ML_climatology) for latitudes of 30–40
and 40–50∘ N to form an average suitable for Boulder
(40∘ N). To understand the difference due to the selection of
Teff, we adapted the climatology data used in Herman et al. (2015)
and used the data from 40 to 50∘ N to calculate effective ozone
temperature for Toronto (44∘ N). Figure 9 shows the comparison
between the ECMWF daily Teff and the NASA monthly climatology
Teff. A sudden cooling event happened at Toronto on 29–30 January
2014, for which the difference between the daily and monthly Teff
was -10 K. Figure 10 shows the time series of TCO difference (combined
Brewer–Pandora no. 103) before and after applying the temperature dependence
correction using both the monthly climatology Teff and daily
Teff. Because the monthly climatology Teff does not
reflect the low temperature during those two days, the correction function
(see Eq. 11) overcompensated for the temperature effect (the minimum delta
ozone value on 29 January changed from -8 DU in Fig. 10a to -14 DU in
Fig. 10b). The low-temperature event was captured by the daily
Teff; thus the compensation from the temperature effect was
reasonably small when using ECMWF daily Teff (the minimum value was
-7 DU; see Fig. 10c). In general, the ECMWF daily Teff can
better capture some ozone variation events that are associated with rapid
temperature changes.
Time series of combined Brewer–Pandora no. 103 TCO difference
colour-coded by ozone effective temperature: (a) before applying the
temperature dependence correction, (b) after applying the correction
using NASA monthly climatology Teff, and (c) after
applying the correction using ECMWF EAR-Interim daily Teff. The
sudden cooling event on 29–30 January 2014 is marked by a black box. The
dashed lines are LOWESS(0.5) fits.
Figure 11 shows time series of the monthly average TCO difference in
percentage before and after applying the temperature dependence correction
for eight pairs of instruments (six individual Brewers vs. Pandora no. 103,
combined Brewer vs. Pandora no. 103, and combined Brewer vs. Pandora
no. 104). Figure 11a shows that both Pandora nos. 103 and 104 have similar
offsets relative to the Brewers before applying the correction to Pandora
data. In addition, the seasonal variations are consistent when comparing
Pandora no. 103 to six individual Brewers (see Fig. 11a). After applying the
TCO corrections (Fig. 11b), the seasonal differences decreased from ±1.02
to ±0.25 % for Pandora no. 103 and from ±0.40 to ±0.25 %
for Pandora no. 104, as did the offset which decreased from 2.92 to
-0.04 % for Pandora no. 103 and from 2.11 to -0.01 % for Pandora
no. 104. The 1σ uncertainty in Fig. 11b shows that, statistically,
the corrected Pandora datasets have no significant seasonal differences or
offsets compared to the Brewer datasets.
Monthly mean time series of the (Brewer - Pandora) / Brewer
% TCO difference: (a) before applying the Pandora temperature
dependence correction and (b) after applying the correction. The
shaded regions represent 1σ uncertainty.
Comparison with OMI
To further validate the temperature dependence correction for the Pandora
data, we used OMI ozone data (version OMTO3e). Pandora data are averaged
within ±10 min of OMI overpass times. In Fig. 12, scatter plots of OMI
vs. Pandora TCO are shown in panels a and b; OMI vs. corrected Pandora TCO
(using Eqs. 11 and 12 with the correction functions found from our
statistical model) is shown in panels c and d; and OMI vs. corrected Pandora TCO
(using Eq. 13 with the correction function from Herman et al., 2015) is shown
in panels e and f. All the Pandora TCO corrections shown in Fig. 12 used the
same Teff calculated with the ECMWF ERA-Interim daily ozone data.
Scatter plots of OMI TCO vs. Pandora TCO for (a) Pandora
no. 103 without TCO correction, (b) Pandora no. 104 without TCO
correction, (c) Pandora no. 103 with correction using Eq. (11),
(d) Pandora no. 104 with correction using Eq. (12),
(e) Pandora no. 103 with correction using Eq. (13), and
(f) Pandora no. 104 with correction using Eq. (13).
Figure 12a and c show that, after applying the TCO correction
(Eq. ) to Pandora no. 103, the slope of the linear regression
improved from 0.987 to 0.990, the offset improved from 14.84 to -3.59 DU,
the correlation coefficient improved from 0.987 to 0.991, and the mean bias
between OMI and Pandora improved from 3.1 to 0.02 %. Similar improvement
is seen in the comparison between Pandora no. 104 and OMI (see Fig. 12b
and d), although the size of the coincident measurement dataset is smaller,
with the mean bias improving from 1.5 to -0.6 %. In addition, Fig. 12e
and f show that, by using the correction function from Herman et al. (2015),
the comparisons also improve, although 1.9 % (1.4 %) bias remains for
Pandora no. 103 (no. 104) (indicated by the slop of linear fit with force the
intercept to 0; see the green lines in Fig. 12). Note that the ATDF in Herman
et al. (2015) is only 0.08 % K-1 higher than our RTDF.
Figure 13a and b shows the monthly mean time series of the OMI–Pandora TCO
percentage difference, before and after applying the three correction
functions. All three correction models reduced the difference between Pandora
and OMI. Our relative correction model (Eqs. 11 and 12) reduces the seasonal
difference (indicated by the δ of the percentage monthly delta ozone)
between Pandora no. 103 and OMI from ±1.68 to ±1.00 %, with the
mean bias decreasing from 2.65 to -0.19 % (the mean of the percentage
monthly delta ozone). Pandora no. 104 has a similar improvement. The absolute
correction model (Eq. 13) reduces the seasonal difference between Pandora
no. 103 and OMI to 0.87 %, with the mean bias decreased to 1.71 %.
The reduction in the mean bias between Pandora and OMI is better for the
relative correction model. This result (-0.19± 1.00 % mean bias)
is consistent with Balis et al. (2007), who showed that the global average
difference between OMI–TOMS and Brewer instruments is within 0.6 %, and
that the difference in the 40–50∘ N band (Toronto is at
44∘ N) is close to 0 (see their Fig. 1).
Monthly mean time series of the (OMI - Pandora) / OMI %
TCO difference: (a) before applying the correction,
(b) after applying the correction using Eqs. (11)–(13), and
(c) the difference between the corrections. The shaded regions
represent the 1σ uncertainty.
Balis et al. (2007) reported that the time series of globally averaged
differences between OMI–TOMS and Brewer instruments shows almost no annual
variation, and the OMI–TOMS data theoretically have no temperature
dependence (McPeters and Labow, 1996; Bhartia and Wellemeyer, 2002). By using
our relative correction, the corrected Pandora TCO should have similar
performance to the Brewer TCO. Figure 13c shows the difference between the
absolute correction method and the relative correction method. Although both
methods removed some of the seasonal signal (reduced from 1.68 to 1.00 %
for the relative correction and to 0.87 % for the absolute correction),
Fig. 13c shows that there is still a weak seasonal signal residual
(0.39 %) left between these two methods.
Stray-light effect
It is well known that direct-sun UV spectrometers are affected by stray light
when the solar zenith angle (SZA) is too large. In general, when the ozone
AMF is larger than 3 (SZA > 70∘), the retrieved TCO will show an
unrealistic decrease with increasing SZA (thus this effect is also known as
the air mass dependence effect). In general, the stray light from longer
wavelengths results in overestimation of the UV signal at short wavelengths
and makes the measured UV signal in that part of the spectrum less sensitive
to TCO. The double Brewer spectrometer was introduced in 1992, which uses two
spectrometers in series to reduce the stray light (Bais et al., 1996; Wardle
et al., 1996; Fioletov et al., 2000). The BrT-D has the advantage of very low
internal stray-light fraction (10-7, stray-light signal divided by total
signal) compared to BrT (10-5) in the 300–330 nm spectral range
(Fioletov et al., 2000; Tzortziou et al., 2012). For Pandora instruments, a
UV340 filter is used to remove most of the stray light that originates from
wavelengths longer than 380 nm (Herman et al., 2015). A typical UV340 filter
has a small leakage (5 %) at ∼ 720 nm, which misses the detector
and hits the internal baffles. Further stray-light correction is done by
subtracting the signal of pixels corresponding to 280 to 285 nm (which
contain almost zero direct illumination) from the rest of the spectrum.
However, a very small (but unknown) amount of this stray light may scatter
onto the detector (Herman et al., 2015). Tzortziou et al. (2012) tested the
stray-light effect for Pandora no. 34 and Brewer no. 171 and concluded that
the Pandora stray-light fraction (∼ 10-5) was comparable to the
single Brewer. Pandora ozone retrievals are accurate up to a slant column
between 1400 and 1500 DU or 70 and 80∘ SZA, depending on the TCO
amount (Herman et al., 2015).
Brewer no. 14 / Pandora no. 103 TCO ratio vs. ozone air mass
factor: (a) before and (b) after applying the Pandora
temperature dependence correction. The points are grouped by effective
temperature (from 215 to 240 K, in 5 K bins), and the linear fits for each
group are colour-coded. The black line and linear fit are for the whole
dataset.
In this work, to assess the air mass dependence, we compared Brewer TCO to the
corrected Pandora TCO data. Figure 14 shows an example of the
Brewer / Pandora ratio as a function of ozone AMF (reported value in
Brewer data) before and after applying the TCO correction (Eq. 11), with the
data points grouped by effective temperature. Before applying the correction
(Fig. 14a), the linear fits show consistently low (-0.1 to 0.5 %)
relative AMF dependence between Brewer and Pandora (defined as the slope of
the linear fit) for each Teff group. However, the linear fit to the
whole dataset (all effective temperatures, black line) shows that the
relative AMF dependence is -0.007. Figure 14b shows that the correction
changed the slope of the black line to -0.001; removing the temperature effect
for the Pandora dataset thus reduces the relative AMF dependence from -0.7
to -0.1 %. To characterize only the air mass dependence, we therefore
removed the temperature dependence effect from the Pandora dataset.
Percentage difference between Pandoras (nos. 103 and 104) and
Brewers (grouped as BrT and BrT-D) as a function of ozone air mass factor. On
each box, the central mark is the median, the edges of the box are the 25th
and 75th percentiles, and the whiskers extend to the most extreme data points
not considered outliers.
To show how the different instrument designs affect the stray-light
performance, we merged the six Brewer datasets into two groups (BrT and
BrT-D) to compare with the corrected Pandora data. Figure 15 shows the
(Brewer - Pandora) / Brewer percentage difference as a function of
ozone AMF. In Sects. 3 and 4, the TCO data with ozone AMF > 3 were
discarded. The purpose of this filter was to ensure that only the best
direct-sun measurements (with low air mass dependence) from both instruments
were used. However, to study the instrument performance for large AMFs, and
also to characterize the performance of Brewer and Pandora instruments, we
changed the AMF threshold from 3 to 6. Figure 15 indicates that Pandora, BrT,
and BrT-D instruments have similar air mass dependence for ozone AMF < 3
(∼ 71∘ SZA), consistent with the result reported by Tzortziou
et al. (2012). Pandora and BrT-D have similar AMF dependence up to ozone AMF
of 5.5–6 (80.6–81.6∘ SZA), but Pandora and BrT diverge above AMF of
3–4 (71–76∘ SZA). In general, these results indicate the Pandora
and BrT-D instruments have very good stray-light control.
Conclusions
The instrument random uncertainty, TCO temperature dependence, and ozone
air mass dependence have been determined using two Pandora and six Brewer
instruments. In general, Pandora and Brewer instruments both have very low
random uncertainty (< 2 DU) in the total column ozone measurements, with
that for Pandora being ∼ 0.5 DU lower than Brewer. This indicates that
Pandora instruments could provide more precise measurements than the Brewer
for the study of small-scale (temporal and magnitude) atmospheric changes.
This work confirms the quality of the TCO data, with all eight instruments
meeting the GAW requirement for a precision better than 1 % (WMO, 2014);
however, the Brewer instruments have smaller ozone temperature dependence
than the Pandoras.
By using the ECMWF ERA-Interim and Brewer ozone data in the statistical
method, we successfully corrected the Pandora TCO to decrease its temperature
dependence. We found relative temperature dependence factors of
0.247 % K-1 for Pandora no. 103 and 0.255 % K-1 for
Pandora no. 104 against the Brewer instruments. This relative temperature
dependence factor is comparable to the absolute temperature dependence
factors previously found for Pandora (0.333 % K-1, by applying
retrievals with different ozone cross sections, Herman et al., 2015) and
Brewers (0.07–0.094 % K-1; Kerr et al., 1988; Kerr, 2002). In
addition, a 2 % multiplicative bias was found between the Pandora and
Brewer standard TCO products, which is due to the different ozone cross
sections used in the retrievals. After applying the corrections, the annual
seasonal difference between Pandora and Brewer instruments decreased from
±1.02 to ±0.25 %, and the mean bias decreased from 2.92 to
0.04 %. In addition to using model ozone data (ECMWF ERA-Interim for our
case) to calculate the effective ozone temperature, it could also be
estimated from Brewer or Pandora measurements (Kerr, 2002; Tiefengraber et
al., 2016), albeit at a cost of decreased TCO measurement precision. An
effective ozone temperature algorithm is under development for the Pandora.
The future operational Pandora ozone retrieval algorithm will use this
derived effective ozone temperature to minimize the temperature dependence of
the ozone product (Tiefengraber et al., 2016).
This study confirmed that the Pandora and Brewer TCO data have negligible
air mass dependence when the ozone AMF < 3. The Pandora and BrT
instruments have similar air mass dependence (relative air mass dependence
<± 0.1 %) up to 71∘ SZA (AMF < 3); the Pandora and BrT-D
instruments have very good stray-light control, and their AMF dependence is
comparably low up to 81.6∘ SZA (within 1 % up to AMF = 5.5
and within 1.5 % up to AMF = 6).
Data availability
Data from the BrT, BrT-D, and Pandora instruments are available through
Environment and Climate Change Canada (contact Vitali Fioletov,
vitali.fioletov@canada.ca). The final version of the Brewer data is (or will
be) available from the World Ozone and UV Data Centre (10.14287/10000001).
OMTO3e data are available from the GES DISC: 10.5067/Aura/OMI/DATA3002 (GES DISC, 2004). Any
additional data may be obtained from Xiaoyi Zhao
(xizhao@atmosp.physics.utoronto.ca).
Acknowledgements
X. Zhao was partially supported by the NSERC CREATE Training Program in
Arctic Atmospheric Science. We thank ECMWF for providing the ERA-Interim model
data and the NASA OMI ozone retrieval team for providing the OMTO3e
data. Edited by:
M. Van Roozendael Reviewed by: three anonymous referees
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