Introduction
The amount of surface solar UV radiation (200–400 nm) reaching the earth's
surface has substantial impacts on human health and ecosystems (Andrady et al.,
2015; WMO, 2014). For example, about 90 % of nonmelanoma skin cancers are
associated with exposure to solar UV radiation in the US (Koh
et al., 1996). Bornman and Teramura (1993) and Caldwell et al. (1995)
showed the negative effects of UV radiation on plant growth and tissues.
Since the discovery of the significant ozone depletion in the Antarctic
region (Farman et al., 1985) and midlatitudes (Fioletov et al.,
2002), subsequent effects on surface UV levels have received attention. As a
result, great efforts have been made to monitor surface UV radiation from
both satellite and ground instruments in the past few decades (Bigelow et
al., 1998; Sabburg et al., 2002; Levelt et al., 2006; Buntoung and Webb,
2010; Lakkala et al., 2014; Pandey et al., 2016; Krzyścin et al., 2011;
Utrillas et al., 2013). Although satellite measurements provide a better
spatial coverage of the surface UV radiation, they (similar to ground-based
observations) are not only affected by instrument errors (Bernhard and
Seckmeyer, 1999), but are also subject to uncertainties in the algorithms
used to derive surface UV radiation. Therefore, evaluation of
satellite-based estimates of surface UV radiation against available ground
measurements at many locations around the world is needed to characterize
the errors toward further refinement of the surface UV estimates.
The solar spectral irradiance (in mW m-2 nm-1) is usually
measured by ground and satellite instruments. In addition, the erythemally
weighted irradiance has been widely used to describe the sunburning or
reddening effects (McKenzie et al., 2004). Erythemally weighted
irradiance or erythemal dose rate (EDR; in mW m-2) is defined as the
solar irradiance on a horizontal surface weighted with the erythemal action
spectrum (McKinlay and Diffey, 1987); it can be further divided by 25 mW m-2
to derive the UV index – an indicator of the potential for skin
damage (WMO, 2002). Hence, the UV index is commonly used as a UV exposure
measure for the general public and in epidemiological studies in
many parts of the world (Eide and Weinstock, 2005; Lemus-Deschamps and
Makin, 2012; Walls et al., 2013). In the US, several ground UV monitoring
networks that respond to changes in the surface
UV radiation have been established (Bigelow et al., 1998; Sabburg et al., 2002; Scotto et al.,
1988). Currently, the UV-B Monitoring and Research Program (UVMRP) initiated
by the US Department of Agriculture (USDA) and the NEUBrew (NOAA-EPA
Brewer Spectrophotometer UV and Ozone Network) remain as the two active
operating networks providing surface UV data in the US.
The goal of this study is to use UVMRP datasets to evaluate the Ozone Monitoring
Instrument (OMI)-based estimates of the surface UV radiation in the past decade in the US.
As a successor to the Total Ozone Mapping Spectrometer (TOMS), whose
surface UV data (such as erythemally weighted irradiance) have been
extensively evaluated in the past (Arola et al., 2005; Cede et al., 2004;
Kalliskota et al., 2000; Kazantzidis et al., 2006; McKenzie et al., 2001),
OMI data have a finer spatial and spectral resolution and thereby bear more
advanced capability for characterizing the spatial distribution of the
surface UV radiation. TOMS data records span from 1978 to 2005, and many
past studies have shown that TOMS surface UV data overestimated the ground
observational data at many sites. OMI was launched into space in July 2004
as part of the Aura satellite (Levelt et al., 2006), and it has started
to collect data from August 2004 to the present. While there have been a
number of studies evaluating the OMI surface UV data with ground
observations, these studies, as shown in Table 1, have mainly focused on
Europe (Antón et al., 2010; Buchard et al., 2008; Ialongo et al.,
2008; Kazadzis et al., 2009a; Tanskanen et al., 2007; Weihs et al., 2008;
Zempila et al., 2016), South America (Cabrera et al., 2012), high
latitudes (Bernhard et al., 2015) and the tropics (Janjai et al.,
2014). These studies evaluated OMI spectral irradiance, EDR and erythemally
weighted daily dose (EDD) within different time periods. Most comparisons show
positive bias up to 69 %, with few showing negative bias up to -10 %.
This study differs from the past studies in the following ways. Firstly, we
conducted a comprehensive evaluation of the OMI surface UV data from 2005
to 2017 covering the continental US. The evaluation was made for
erythemally weighted irradiance at both local solar noon and satellite
overpass times, and the evaluation statistics not only concern mean bias (MB) but
also the probability density function (PDF), cumulative distribution function (CDF)
and variability of the UV data. Secondly, a trend analysis of the
surface UV irradiance from both ground observation and OMI was performed,
with a special focus on the effects of the temporal sampling. The analysis
addresses whether the once-per-day sampling from the polar-orbiting satellite
would have any inherent limitation for the trend analysis of surface UV
data. Finally, the error characteristics in the OMI surface UV data were
examined to understand the underlying sources (such as from treatment of
clouds and assumption of constant atmospheric conditions between the local
solar noon and satellite overpass time). The investigation yields
recommendations for future refinement of the OMI surface UV algorithm.
The paper is organized as follows: Sect. 2 describes the satellite and
ground observational data, the methodology is discussed in Sect. 3, Sect. 4
presents the results and Sect. 5 summarizes the findings.
Summary of previous studies evaluating OMI surface UV data against
ground observation. Most of the comparisons shown here are for all-sky
conditions unless noted otherwise.
Study
Location
OMI dataa
Ground instrument
Time periods
Biasb
Kazadzis et
Thessaloniki,
Spectral
Brewer MK III
Sep 2004–Dec 2007
30 % (305 nm), 17 % (324 nm),
al. (2009a)
Greece
(op)
13 % (380 nm)c
Antón et al.
El Arenosillo,
Spectral
Brewer MK III
Oct 2004–Dec 2008
14.2 % (305 nm), 10.6 % (310 nm),
(2010)
Spain
(op)
8.7 % (324 nm)d
EDR (op)
12.3 %
Zempila et al.
Thessaloniki,
Spectral
NILU-UV multi-filter
Jan 2005–Dec 2014
31 % (305 nm), 29.5 % (310 nm),
(2016)
Greece
(op)
radiometer
6.1 % (324 nm), 14.0 % (380 nm)e
Spectral
33.6 % (305 nm), 28.6 % (310 nm),
(noon)
5.6 % (324 nm), 13.2 % (380 nm)
Buchard et al.
Villeneuve-
EDR (op)
spectroradiometerf
Oct 2005–Feb 2007
32.5 %h
(2008)
d'Ascq, France
UVB-1, YESg
69.3 %
EDD
spectroradiometer
Oct 2005–Jul 2006
17.1 %
Briançon,
EDD
spectroradiometer
Oct 2004–Sep 2005
7.9 %
France
Ialongo et al.
Rome, Italy
EDR
Brewer MKIV
Sep 2004–Jul 2006
33 %i
(2008)
(noon)
UVB-1, YES
30 %
Tanskanen et
17 sites
EDD
18 instruments
Sep 2004–Mar 2006
up to 50 %k
al. (2007)j
Bernhard et
13 stations
EDD
13 instruments
Sep 2004–Dec 2012
-1 % to 24 %m
al. (2015)l
Weihs et al.
Vienna, Austria
UV index
biometer
May–Jul 2007
-10 % to 50 %o
(2008)n
(op)
Janjai et al.
Thailand
UV index
multichannel UV
2008–2010
43.6 %, 43.5 %, 28.7 %,
(2014)p
(op)
radiometer
21.9 %q
Cabrera et al.
Santiago, Chile
UV index
PUV-510r
2005–2007
47 %s
(2012)
(noon)
aSpectral represents the OMI spectral irradiance data,
EDR is the erythemal dose rate and EDD is the erythemally weighted daily dose.
Op corresponds to the OMI data at its overpass time while noon means the data
at local solar noon time. b The validation statistic shown here is
the bias with each study using slightly different ways of calculation.
c The bias here is calculated as the median (OMI/Ground - 1)×100.
d The bias is calculated as 100×1N∑i=1NOMI-GroundOMI,
where N is the total number of data points. e The bias is
calculated as the mean (OMI - Ground)/Ground × 100.
f The spectroradiometer used here is thermally regulated Jobin
Yvon H10 double monochromator. g The broadband UVB-1 is from
Yankee Environmental System (YES). h The bias is calculated as
100×1N∑i=1NOMI-GroundGround,
where N is the total number of data points. i Same as h.
j This study evaluated OMI surface EDD at 17 ground sites representing
different latitudes, elevations and climate conditions with 18 instruments,
which include single and double Brewer spectrophotometers, NIWA UV spectrometer
systems, a DILOR XY50 spectrometer and SUV spectroradiometers. k The
bias is calculated as in c. For sites significantly affected by
absorbing aerosols or trace gases, the bias can be up to 50 %.
l This study evaluated OMI EDD at 13 ground stations located
throughout the Arctic and Scandinavia from 60 to 83∘ N. The instruments
installed include a single-monochromator Brewer spectrophotometer and GUV-541 and
GUV-511 multi-filter radiometers from Biospherical Instrument Inc. (BSI).
m Same as c. n This study evaluated OMI UV
index at 6 ground stations in the city of Vienna, Austria, and its surroundings.
Six biometers (Model 501, Solar Light Company, Inc.) were used. o The bias is
calculated as (OMI/Ground - 1)×100 and the result for
clear-sky conditions is shown here. p This study evaluated OMI UV index at four
tropical sites in Thailand with each site having different time periods of data
between 2008 and 2010. The ground instrument installed is a multichannel
UV radiometer (GUV-2511) manufactured by BSI. q The bias is
calculated as h, representing the four sites, respectively.
r PUV-510 is a multichannel filter UV radiometer centered at 305,
320, 340 and 380 nm. sThe bias is calculated as (OMI - Ground)/OMI × 100.
Data
OMI data
OMI aboard the NASA Aura spacecraft is a nadir-viewing spectrometer
(Levelt et al., 2006) that measures solar reflected and backscattered
radiances in the range of 270 to 500 nm with a spectral resolution of
about 0.5 nm. The 2600 km wide viewing swath and the sun-synchronous orbit
of Aura provides a daily global coverage, with an equatorial crossing time
at ∼13:45 LT (local time). The spatial resolution varies from
13×24 km2 (along × cross) at nadir to 50×50 km2 near the edge.
OMI retrieves total column ozone, total column amount of trace gases,
SO2, NO2, HOCO, aerosol characteristic and surface UV (Levelt
et al., 2006).
OMI data products and validation statistics used in the current study.
Full name
Acronym
Unit
Data products
Full-sky overpass time erythemal dose rate
OP_FS EDR
mW m-2
Full-sky solar noon erythemal dose rate
Noon_FS EDR
mW m-2
Aerosol optical depth
AOD
unitless
Aerosol absorption optical depth
AAOD
unitless
Validation statistics
Mean bias
MB
mW m-2
Normalized mean bias
NMB
unitless
Root-mean-square error
RMSE
mW m-2
Normalized centered root-mean-square difference
NRMSD
unitless
Normalized standard deviation
NSD
unitless
The OMI surface UV algorithm has its heritage from the TOMS UV algorithm
developed at the NASA Goddard Space Flight Center (GSFC) (Eck et al., 1995;
Herman et al., 1999; Krotkov et al., 1998, 2001, 2002; Tanskanen
et al., 2006). In the first part of the algorithm, the
surface-level UV irradiance at each OMI pixel under clear-sky conditions is
estimated from a lookup table that is computed from a radiative transfer
model for different values of total column ozone, surface albedo and solar zenith angle (SZA).
The lookup table was called twice, once to calculate the surface
UV irradiance at the satellite overpass time and once at the local solar noon.
The only difference between these two lookup tables is the SZAs, with one
representing the SZAs at the overpass time and the other representing the
solar noon, while the total column ozone and cloud optical thickness (COT)
are assumed to stay constant. The second step is to correct the clear-sky
surface UV irradiance for a given OMI pixel due to the effects of cloud and
nonabsorbing aerosols. The cloud-correction factor is derived from the
ratio of measured backscatter irradiances and solar irradiances at 360 nm
along with OMI total column ozone amount, surface monthly minimum Lambertian
effective reflectivity (LER) and surface pressure. The effects of absorbing
aerosols are also adjusted in the current surface UV algorithm based on a
monthly aerosol climatology as described in Arola et al. (2009).
The second step of the cloud correction mentioned above follows radiative
transfer calculations that assume a homogeneous, plane-parallel water-cloud
model with Rayleigh scattering and ozone absorption in the atmosphere
(Krotkov et al., 2001). The COT is assumed to be spectrally
independent and the cloud-phase function follows the C1-cloud model
(Deirmendjian, 1969). This cloud model is also used to calculate the
angular distribution of 360 nm radiance at the top of the atmosphere, which
is used to derive an effective COT. The effective COT is the same as the
actual COT for a homogeneous cloud plane-parallel model. The effective COT
is saved to a lookup table to use for cloud correction.
OMI surface UV data products (or OMUVB in shorthand) include (a) spectral
irradiance (mW m-2 nm-1) at 305, 310, 324 and 380 nm at both the
local solar noon and OMI overpass time; (b) erythemal dose rate (mW m-2)
at both the local solar noon and OMI overpass time; and (c) erythemally
weighted daily dose (J m-2). The spectral irradiances
assume a triangular slit function with full width at half maximum of 0.55 nm.
The EDD is computed by applying the trapezoidal integration method to the
hourly EDR with the assumption that the total column ozone and COT remain
the same throughout the day. In addition, the OMUVB products include
information on data quality related to row anomaly, SZA and COT, which are
used in the present study. We also use the aerosol products from the OMAERUV
algorithm (Torres et al., 2007). The OMI OMAERUV algorithm uses two
wavelengths in the UV region (354 and 388 nm) to derive aerosol extinction
and absorption optical depth. The aerosol products (OMAERUV) retrieve
aerosol optical depth (AOD), aerosol absorption optical depth (AAOD) and
single scattering albedo at 354, 388 and 500 nm.
In the current study, both OMI level 2 (v003) and level 3 (v003) products
are used. The level 2 products provide swath-level data products while level 3
products are gridded daily products on a 1∘ × 1∘
horizontal grid. Two variables from OMUVB level 2
products (Table 2) are used: (1) full-sky solar noon erythemal dose rate
denoted as Noon_FS EDR and (2) full-sky overpass time erythemal
dose rate denoted as OP_FS EDR. In addition, full-sky solar
noon EDR from the OMUVBd (“d” denotes daily) level 3 products and AOD and AAOD
from OMAERUVd level 3 products are used. These level 3 datasets are mainly
used for conducting trend analysis in Sect. 4.4 unless noted otherwise, while
the rest of the data analysis uses the level 2 datasets. All the datasets are
from January 2005 to December 2017 and row anomaly is checked during data
analysis for level 2 datasets.
Ground observation data
Currently, the UVMRP operates 36 climatological sites for long-term
monitoring of surface UV radiation around different ecosystem regions
(https://uvb.nrel.colostate.edu/UVB/uvb-network.jsf, last access: 21 January 2019). Of the
36 climatological sites, five are located in New Zealand, South Korea, Hawaii,
Alaska and Canada, while 31 sites are in the continental US, with the
majority of them located in agricultural or rural areas and a few in urban
areas. Among these 31 sites, one site started operation after 2014 and one
after 2006, and all other sites started earlier than 2006. In the current
study, we use the one site in Canada and 30 of the 31 sites in the
continental US and we exclude one site where operation started after 2014 (Fig. 1).
All sites measure global irradiance using a UVB-1 pyranometer manufactured by
Yankee Environmental Systems (YES). Since 1997, these broadband radiometers
have been calibrated and characterized annually at the Central UV
Calibration Facility (CUCF), located in Boulder, and have then been cycled
through Mauna Loa Observatory (MLO), Hawaii, for calibration after around 2009.
The annual characterization process includes laboratory tests for spectral
and cosine response change in the radiometer. For the calibration, the UVMRP
broadband radiometer is collocated with three of CUCF's YES UVB-1 standard
radiometers (the triad) and a precision spectroradiometer in the field for
2 weeks. The absolute calibration factor of each UVMRP radiometer is
determined by comparing its voltage output to the standard triad, which is
in turn frequently calibrated against the collocated spectroradiometer.
Because the spectral response functions of the UVMRP broadband radiometer do
not precisely match the erythemal action spectrum (McKinlay and Diffey,
1987), corrections that depend on SZA and total column ozone are needed.
More detailed calibration and characterization procedures are described in
Lantz et al. (1999). The erythemal UV irradiance used in the current
work is prepared with SZA-dependent calibration factors that assume total
column ozone is 300 DU (Gao et al., 2010). Past studies have shown that
the UVMRP broadband radiometer differs from the triad by 0.1 %–2.8 % for SZA
ranging from 20 to 80∘ (Seckmeyer et al., 2005;
McKenzie et al., 2006). The calibration from the spectroradiometer to the
standard triad results in an uncertainty of approximately ±5 % and
the overall uncertainty for the UVMRP broadband radiometers has been
estimated at approximately ±6 % (Kimlin et al., 2005). The YES
UVB-1 instrument takes measurement every 15 s, which are aggregated into 3 min averages.
In this work, we use the 3 min averaged erythemally weighted irradiance at
31 sites in the continental US and information for each site is described
in Table S1 in the Supplement. Except for site TX41, for which data are available
since August 2006, we use data from January 2005 to December 2017 for the rest of the sites.
Map of OMI level 3 EDR (mW m-2) at solar noon time under full-sky
conditions averaged over 2005–2017, overlaid with 31 ground observational sites
averaged over 2005–2017 around solar noon time with ΔT=±5 min.
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Methods
Spatial collocation and temporal averaging of data
Since OMI data represent an average over a ground pixel (∼13×24 km2
for nadir viewing and ∼50×50 km2 for
off-nadir viewing) and ground measurements are point measurements that cover
a small area, previous work in Table 1, studies of Kazadzis et al. (2009b) and
Zempila et al. (2018), and studies using TOMS data
have investigated the effects of the selection of a collocation distance
between the center of an OMI ground pixel and the ground observational site
and/or the averaging time period around OMI overpass time and local solar noon
on the evaluation results. For example, Weihs et al. (2008) found that the
variability, defined as the absolute sum of the difference between the
average mean bias between OMI- and ground-measured UV index at any station
and the average mean bias from all stations divided by the total number of
measurements, increases with increasing collocation distance but decreases
with increasing averaging time period. Zempila et al. (2016) compared
OMI spectral irradiances at 305, 310, 324 and 380 nm with ground
observations considering spatial collocation and temporal averaging windows.
It was shown that the choice of collocation distance (10, 25 or 50 km)
plays a negligible role in the comparison in terms of the correlation
coefficient and mean bias. However, the selection of a longer averaging time
period (from ±1 to ±30 min) results in a
significant improvement under full-sky conditions for both OMI overpass and
solar noon time comparison. Chubarova et al. (2002) evaluated
the difference between TOMS overpass surface UV and ground data taken over
different time windows around TOMS overpass time. The results showed that
the calculated correlation coefficient of these two datasets nonlinearly
increases with the increasing averaging windows (from ±1 to
±60 min) and stays nearly constant from ±60 to ±90 min.
In this work, we will examine the separate effects of spatial collocation
and temporal averaging on evaluation results. Firstly, for each ground site,
its observation is paired with the OMI data at the pixel level if the center of
that pixel is within the distance (D) of 50 km from that ground site. Then
the ground observational data at each site are taken within (ΔT of)
±5 min around the OMI overpass time or the local solar noon time
at that pixel. Correspondingly, there will be two to three ground data found, the
temporal mean of which will be paired up with the OMI data from that pixel
for subsequent comparison. Further evaluation is conducted by changing
different D values to 10 and 25 km and/or ΔT values of ±10,
±30 and ±60 min around OMI overpass time and local
solar noon time. Consequently, a total of 12 sets of paired data are
generated for the evaluation, as a result of a different combination of
three D values and four ΔT values used for spatially and temporally
collocating OMI and ground data. For a given ΔT, there
are ∼100000, ∼67000 and ∼17000 data
pairs at all of the ground sites for D values of 50, 25 and 10 km, respectively.
Validation statistics
First, we present several commonly used validation statistics (Table 2):
mean bias (MB) calculated in Eq. (1), normalized mean bias (NMB) in Eq. (2),
the root-mean-square error (RMSE) in Eq. (3) and correlation coefficient (R).
We also show the overall evaluation of OMI surface UV data against
ground observation in the form of a Taylor diagram (Taylor, 2001) (see
Fig. 3a). A Taylor diagram provides a statistic summary of OMI data
evaluated against ground observation in terms of correlation coefficient R
(the cosine of polar angles); the ratio of standard deviations between OMI
and ground observational data (the normalized standard deviation – NSD)
shown in the x and y axis, respectively; and the normalized centered
root-mean-square difference (NRMSD) in Eq. (4), shown as the radius from the
expected point, which is located at the point where R and NSD are unity.
MB=1N∑i=1NEDR(OMI,i)-EDR(Ground,i),NMB=∑i=1NEDR(OMI,i)-EDR(Ground,i)∑i=1NEDR(Ground,i),RMSE=1N∑i=1NEDR(OMI,i)-EDR(Ground,i)2,
NRMSD=1N∑i=1NEDR(OMI,i)-EDR‾OMI-EDR(Ground,i)-EDR‾Ground21N∑i=1NEDR(Ground,i)-EDR‾Ground2,
where i is the ith paired (OMI–ground) data point, N is the total number of
paired data points, and EDR(OMI,i) and
EDR(Ground,i) are the ith EDR from OMI and
ground observation, respectively. EDR‾OMI and
EDR‾Ground are the mean of N number of OMI
and ground data, respectively. Both correlation coefficients in the Taylor
diagram and the scatter plot are obtained from the ordinary linear least squares method.
To determine whether the calculated MB or NMB are statistically significant,
a t test for differences of mean under serial dependence is applied
(Wilks, 2011). This two-sample t test assumes a first-order
autoregression in the data. The computed two-tailed p value of less
than 0.025 indicates that the difference between the means for the paired data
(OMI and ground EDR) would be statistically significant at the 95 %
confidence level. In addition, we calculate the PDF and CDF of OMI and
ground observational data. A Kolmogorov–Smirnov (K–S) test
(Wilks, 2011) is performed to compare the CDFs of the OMI and
ground datasets. The K–S test is represented by the following formula:
D=maxCDFOMI-CDFGround.
If D is greater than the critical value, 0.841/m (m is the total
number of data points), then the null hypothesis that the two datasets were
drawn from the same distribution will be rejected at the 99 % confidence level.
Trend analysis
Following the work of Weatherhead et al. (1997, 1998), the trend of surface
UV irradiance from OMI and ground observation can be estimated using the
following linear model:
Yt=C+St+ωXt+Ntt=1…T,
where T is the total number of months considered and t is the month index,
starting from January 2005 to December 2017. Yt is the monthly mean
surface UV irradiance either from OMI or the ground observation in the US
and C is a constant. Xt=t/12 represents the linear trend function
and ω is the magnitude of the trend per year. St is a seasonal
component, represented in the following form:
St=∑j=14β1,jsin(2πjt/12)+β2,jcos(2πjt/12).
Nt is the noise not represented by the linear model and is often assumed
to be a first-order autoregressive model, which can be expressed as
Nt=ϕNt-1+εt,
where Nt-1 is the noise from month (t-1); ϕ is the
autocorrelation between Nt and Nt-1; and εt is the
white noise which should be approximately independent, normally distributed
with zero mean and common variance σε2.
As described in Weatherhead et al. (1998), generalized least squares (GLS)
regression was applied to Eq. (6) to derive the approximation of ω
and its standard deviation σω as
σω=σNn3/21+ϕ1-ϕ,
where n=T/12 is the number of years of the data used in the analysis
and σN is the standard deviation of Nt. We
will consider the trend significant at the 95 % confidence level if
|ω/σω|>2. Such linear models have
been widely used to study the various environmental monthly time series data
in the previous studies (Boys et al., 2014; Zhang and Reid, 2010; Weatherhead et al., 2000).
Scatter plots of OMI EDR data with ground observations from year 2005
to 2017. (a, b) show the comparisons of OMI OP_FS and Noon_FS EDR
with measurements at all of the 31 ground observational sites, respectively,
while (c, d) only show the comparisons of OMI EDR with ground
measurements at Homestead, Florida (FL01). In each scatter plot, also shown is
the correlation coefficient (R), the root-mean-square error (RMSE), the number
of collocated data points (N), the density of points (the color bar), the
best-fit linear regression line (the dashed black line) and the 1:1 line (the
solid black line).
![]()
Results
Spatial and temporal inter-comparison
Figure 1 shows the map of OMI level 3 EDR at solar noon time under full-sky
conditions averaged from 2005 to 2017, overlaid with 31 ground observational
sites of EDR averaged from the same local noon time. First, we find that OMI
data show a meridional gradient with the dose rate increasing from
∼80 mW m-2 in the northern US to ∼203 mW m-2 in the
southern US. At higher-elevation regions such as in
Colorado, OMI-derived EDRs are larger than other areas of the same latitude
zone. In comparison, the ground sites range from ∼73 mW m-2 in
the northern US to a maximum of ∼190 mW m-2 for site
NM01 in the southern US, generally capturing the OMI meridional
gradient well. At most sites, OMI data overestimate the ground observation
by more than 5 %, with sites in Steamboat Springs, Colorado (CO11);
Burlington, Vermont (VT01); and Homestead, Florida (FL01), showing the highest
bias of more than 15 %.
Taylor diagrams for evaluating OMI OP_FS EDR (a) and
Noon_FS EDR (b) against 31 ground observational sites matched with
D=50 km and ΔT=±5 min, respectively. The circles represent the
ground sites and the color at each circle represents the NMB (%).
(c, d) are the zoomed-in plot for the boxes in (a, b),
respectively. Also, the squares in (c, d) represent sites that have
significant NMB at the 95 % confidence level. (e) is the zoomed-in
plot for OMI OP_FS EDR evaluation with D=50 km and ΔT=±60 min.
(f) shows the evaluation of OMI OP_FS EDR (triangles) and Noon_FS
EDR (circles) with D=50 km and ΔT=±5, 10, 30 and 60 min against
the ensemble of 31 ground observational sites.
![]()
Scatter plots of OMI OP_FS and Noon_FS EDR
with all 31 ground observational sites are shown in Fig. 2a and b.
Overall, the comparison for OMI OP_FS EDR shows better
agreements with the ground data than the comparison for OMI
Noon_FS EDR. In both cases, a good linear relationship is
found with a correlation coefficient (R) of 0.9 and 0.88 for OMI
OP_FS and Noon_FS. This statistically
significant correlation (with p<0.01) can also be found at most
individual sites, as shown in the Taylor diagrams (Fig. 3a and b). The
high correlation found here in the US is consistent with previous work
that evaluated OMI EDR in Europe (Buchard et al., 2008; Ialongo et al.,
2008). In addition, both OMI OP_FS and Noon_FS
EDR were found to overestimate the ground counterparts, with MB of
8 (∼7 %) and 8.9 (∼7 %) mW m-2,
respectively. Furthermore, the respective RMSEs are 34.9 and 41.5 mW m-2.
The better performance found for OMI OP_FS EDR
indicates the uncertainty caused by the assumption of constant atmospheric
conditions between OMI overpass time and local solar noon time in the
current OMI surface UV algorithm, which has also been highlighted by
previous work (Buntoung and Webb, 2010) and will be discussed more in
details in Sect. 4.3.
Taylor diagrams in Fig. 3 further illustrate the comparison of OMI
OP_FS and Noon_FS EDR with ground measurements
at each site. Most individual sites show better performances for OMI
overpass time evaluation than local solar noon time evaluation, as expected.
For both cases, the performance at each site shows large variation. Site CO11
is located above 3 km and therefore the cloud effects are not
corrected, which very likely results in the high bias found in both data
comparisons. Thus, CO11 will be excluded in the following discussion. For
evaluating OMI OP_FS EDR, the correlation varies from 0.74 (FL01) to
0.95 (CA01), the normalized mean bias varies from -0.54 % (NC01) to
24.5 % (FL01) and the mean bias changes from -0.66 mW m-2 (NC01) to 33.1 mW m-2 (FL01),
with 22 sites being statistically significant at the 95 % confidence level. For the OMI
Noon_FS EDR comparison, the correlation changes from 0.66 (FL01) to
0.94 (CA01), the NMB increases from -0.39 % (AZ01) to 19.3 % (FL01) and the
MB increases from -0.66 mW m-2 (AZ01) to 33.0 mW m-2 (FL01),
with 21 sites showing statistical significance at the 95 %
confidence level. Also, generally larger standard deviation in the ratio
between OMI and ground EDR data is found in the solar noon time comparison
(Table S3). Overall, the site at Florida (Fig. 2c and d) shows the worst
performance while the site at Davis, CA, shows the best performance.
The various degrees of biases in evaluating OMI EDR reflect the influence of
the regional and local differences of air pollution such as aerosol loadings
and meteorology across the US. We will use the OMI
Noon_FS EDR comparison to discuss the potential regional
influence. In the southeast, sites (FL01, LA01, GA01, MS01) show smaller
correlation (0.66–0.85) and larger biases – higher than 10 %. The southeast US
is characterized by heavy air pollution and high humidity, which would
affect clouds and aerosol loadings. Some sites (ME01, MD01, ON01, VT01) in
the northeast also shows higher bias above 7 %. The northeast region is
also subject to heavy local air pollution. Two sites (IN01, MN01) in the
Midwest also show higher bias above 7 %, which could be due to the
regional air pollution. A few sites (AZ01, NM01, CA01) in the southwest show
smaller bias, which is partially attributed to the dry and less cloudy
conditions. In addition, AZ01 and NM01 are located at higher altitude with
much cleaner air. As a result, smaller negative biases are found in these
two sites. CA21, TX21 and TX41 have biases of 11 %, 7 % and 15 %, which
is very likely driven by the local air pollution and possible pollution
transport from Mexico. Sites such as UT01, MT01, WA01 and OK01 located in
the Pacific Northwest, Rocky Mountains and the central Great Plains region
generally have a smaller bias of less than 5 % except for NE01. The spatial
variability of OMI EDR biases found in our work is also similar to the work
of Xu et al. (2010) which evaluated TOMS spectral UV irradiance with
ground measurements at 27 climatological sites from UVMRP in the
continental US. These discrepancies can be related to several factors such as
the method of collocating OMI data with ground observation spatially and
temporally, clouds in the atmosphere, and the assumption of constant
atmospheric conditions between OMI overpass time and local solar noon time,
which are discussed in the following sections.
Frequency of the surface EDR at the solar noon time for OMI (a)
and 31 ground observational sites (b) for year 2005–2017. All the data
pairs are matched with D=50 km and ΔT=±5 min.
(c) shows the cumulative distribution functions (CDFs) of surface EDR from both OMI and
31 ground observational sites over 2005–2017. The maximum differences between
OMI and ground observational CDFs are shown in the horizontal dashed lines and
their values are shown as the labels. (d–f) are contour plots of
normalized frequency of surface EDR from OMI and ground Noon_FS EDR as well as
ground peak for 31 ground sites. The ground peak refers to the
highest dose rate found in a day at each site. The normalized frequency is
calculated as follows: first, the surface EDRs from both OMI and ground
observation are binned by 25 mW m-2 for each year and then normalized
by the total number of data points for each year. A smooth effect at the contour
line was also performed.
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To further show how well OMI surface EDR represents the ground observational
EDR, the frequency of both OMI and ground EDR is shown (Fig. 4). First, we
find that the distribution of surface EDR at solar noon time from both OMI and
ground observational data shows two peaks, one around 20 mW m-2 and the
other one around 200 mW m-2. A similar distribution with two peaks is
also found for OMI and ground EDR at overpass time, which are not shown here.
These two peaks are largely due to the SZA effects (Wang and Christopher,
2006). Figure 4c shows the calculated CDFs for OMI and ground
OP_FS and Noon_FS EDR as well as the maximum
difference between EDRs at the corresponding time. The critical value for
both comparisons is 0.087 to verify that the two CDFs show a good fit at
the 99 % confidence level. From Fig. 4c, we can see that both of the
maximum differences are smaller than the critical values at the 99 %
confidence level. Therefore, the null hypothesis (OMI surface EDR and ground-observed EDR were drawn from the same distribution) will not be rejected.
This good fit between OMI and ground EDR distribution for both solar noon
time and overpass time again confirms the good correlation found between
these two datasets.
In order to better understand the variability of surface UV, we also study
the peak UV frequency inferred from ground observation along with OMI and
ground Noon_FS EDR frequency. The peak UV is calculated as
the highest dose rate found in a day at each site. As seen in Fig. 4d–f,
all of the OMI Noon_FS, ground Noon_FS and
ground peak data show a high frequency at the lower end of surface EDR
(<100 mW m-2), which also reflects the smaller peak found in
Fig. 4a and b. Moreover, this high frequency of occurrence persisted
from 2005 to 2017 for all datasets. In addition, both OMI and ground
Noon_FS EDR show another high frequency of surface EDR
around 200 mW m-2 corresponding to the other peak in Fig. 4a and b.
However, the OMI Noon_FS data show a stronger and more
persistent frequency than that of ground Noon_FS data.
Additionally, the ground peak values find a high frequency around
∼220 mW m-2 (Fig. 4f). The high-frequency occurrence
of ∼220 mW m-2 in ground measurements prevailed until 2015
and, at the same time, we find the frequency of higher surface EDR from
ground peak of ∼300 mW m-2 starts to increase around 2014.
This increase in the occurrence of peak UV irradiance could have
potential implications for human exposure and subsequent health effects,
which is beyond the scope of this study. The contrast between Fig. 4e
and f suggests that the peak of surface UV irradiance may not always occur
during the solar noon time, reflecting the change of meteorology during the
day and suggesting the need for multiple observations per day.
Impacts of spatial collocation and temporal averaging
Table S2 summarizes the regression statistics and other validation
statistics of evaluating OMI OP_FS and Noon_FS
EDR with different spatial collocation distances (D) and temporal averaging
windows (ΔT), respectively. We find that the length of temporal
averaging windows seems to play a more important role in the overall
comparison results than the spatial colocation distance. Figure 3c–e
shows that most of the dots representing the OMI OP_FS EDR
evaluation on the Taylor diagram move closer to the expected point as
ΔT increases from ±5 to ±60 min. The same
progression is also found for the OMI Noon_FS EDR evaluation,
which is not shown here. Figure 3f further shows that the
correlation of the OMI OP_FS and Noon_FS EDR evaluation increases as
ΔT changes from ±5 to ±60 min. In addition, the
RMSE decreases by 12 % for both data comparisons when ΔT increases
from ±5 to ±60 min. The improvement with a longer temporal
averaging window for overpass time under full sky is also found by
Zempila et al. (2016).
Frequency of the EDR ratio of Noon_FS / OP_FS. (a, b) are
for the OMI and ground ratio, respectively. All the data pairs are matched with
D=50 km and ΔT=±5 min for the 31 ground sites.
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Impacts of the assumption of constant atmospheric conditions
As described in Sect. 2.1, the current surface UV algorithm assumes the same
atmospheric conditions at OMI overpass time and the local solar noon time
regarding cloudiness, total column ozone and atmospheric aerosol loadings
but with different SZAs. However, this assumption may not hold all the time
for the real atmosphere. We take the ratio between Noon_FS
and OP_FS EDR (Noon_FS / OP_FS)
from both OMI and ground data as an indicator of the variation of
atmospheric conditions between these two times. Figure 5 shows the frequency
of this ratio from both OMI and ground data obtained with D=50 km and
ΔT=±5 min. Both ratios show the same median of 1.12;
however, the ground ratio shows a larger mean (1.38 vs. 1.18) and standard
deviation. The mean of 1.18 in the OMI ratio data reflects the effects of
SZAs while the larger mean of 1.38 obtained from ground data implies the
impacts from air pollution and meteorology. The scatter plot (Fig. 6a) of
the ground ratio and OMI ratio further shows the discrepancy. Overall,
approximately 95 % of the OMI data fall into the area with the ratio
greater than 1, again reflecting the large effects of SZA, while 22 % of
ground data show a ratio smaller than 1, reflecting the influence of
short-term variability of local atmospheric conditions such as clouds, which
can override the effect of SZA. The frequency of ground ratio less than 1
also varies at individual sites (Table S3). We find that the frequency at
site AZ01, CA01, CA21 and NM01 is among the smallest, below 15 %. As
mentioned in Sect. 4.1, these sites are located in the southwest with
prevailing dry climate and as a result the effects of clouds are much
smaller. Also, sites AZ01 and NM01 are located at higher altitude with
cleaner air and, subsequently, the effects from air pollution are minimal.
The frequency exceeds 15 % at the rest of the sites, with VT01 showing the
maximum of ∼32 %, which are most likely affected by air
pollution. These findings indicate the current OMI surface UV algorithm may
not fully capture the real atmosphere by assuming constant atmospheric
conditions between satellite overpass time and the local solar noon time.
We further investigate the possible seasonal effects on this ratio. As can
be seen in Fig. 6b, the mean and median ratio (Noon_FS / OP_FS) from OMI are greater than those from the ground
observational data throughout the year except for January, which again
indicates the potential overestimation of OMI Noon_FS EDR
using constant atmospheric conditions. Furthermore, the discrepancy between
these two ratios stays consistent in the spring and summer time. The smaller
SZA in the summer time would have relatively smaller effects and the
difference in these ratios could be largely affected by the varying
atmospheric conditions between local solar noon time and OMI overpass time.
However, this discrepancy becomes larger in the fall and winter time, which
could be the result of the elevated SZA towards winter time in North America
to some extent. The larger SZA (>70∘) in the
colder times could increase the radiation path in the atmosphere, which would
thereby amplify the atmospheric interaction with the solar radiation.
Besides, other seasonal variables such as the climatological albedo used in
the current OMI surface UV algorithm could potentially play a role in the
deviation between OMI and ground data. In addition, the ratio from both OMI
and ground observational data shows larger variation in the fall and winter
season than its respective summer season, implying the impacts of the SZA
seasonal variation on both OMI and observational data.
(a) is the scatter plot of the EDR ratio of Noon_FS / OP_FS between
OMI and ground measurements for 31 sites. All the data pairs are matched with
D=50 km and ΔT=±5 min. Also shown on the scatter plot are the
number of collocated data points (N), the density of points (the color bar)
and the 1:1 line (the solid black line). Note the scale difference between
x and y axis. (b) is the monthly EDR ratio of Noon_FS / OP_FS
from OMI (blue) and ground measurements (orange) for 31 sites. The box–whisker
plots show the 5th and 95th percentiles (whisker), the interquartile range (box),
the median (black line) and the mean (the dots).
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The SZA seasonal variation could subsequently affect the difference between
OMI and ground data, which will be analyzed in this section. Several
previous studies have investigated the effects of SZA on the difference
between OMI and ground observational irradiance. Buchard et al. (2008)
found that OMI spectral UV irradiance on clear-sky days showed a larger
discrepancy at SZA greater than 65∘. Kazadzis et al. (2009a)
found no systematic dependence of the difference between OMI and
ground observational spectral UV irradiance on SZA. By sorting data based on
cloud and aerosol conditions, Antón et al. (2010) showed that
the relative difference between OMI and ground irradiance decreases modestly
with SZA for all-sky conditions except for days with high aerosol loadings.
Zempila et al. (2016) suggested a small dependence of the ratio
(OMI / ground UV irradiance) on SZA under both clear-sky and all-sky
conditions. For the all-sky condition, the ratio increases steadily with
increasing SZA up to 50∘ and becomes larger than 1 after
50∘. Similar to these previous works, we also find that the
impacts of SZA could cause various levels of biases in evaluating OMI EDR
depending on locations (Fig. S1 in the Supplement and Fig. 7). As seen from Fig. S1, the mean
bias for the OMI Noon_FS EDR comparison is larger than the OP_FS comparison at most sites for both smaller SZAs (SZA < 50∘) and larger SZAs
(50∘ < SZA < 75∘). For some sites in higher latitudes such as ND01
and WA01, the mean biases at larger SZAs are smaller than those at smaller
SZAs because the frequency of negative bias increases at larger SZAs.
Clouds also play an important role in the difference between OMI and ground
observational UV irradiance. Buchard et al. (2008) found that the
relative difference between OMI and ground EDR was associated with COT at
360 nm retrieved from OMI and the difference is more appreciable for large
COT. Tanskanen et al. (2007) showed that the distribution of the OMI and
ground EDD ratio widens with increasing COT. Antón et al. (2010)
used OMI-retrieved LER at 360 nm as a proxy for cloudiness and showed that
the relative difference of OMI and ground EDR increased largely at higher
LER values. Here, we find that the relative bias for OMI OP_FS
EDR is more obvious at larger COT values as well (Fig. 7c). In
addition, the noise of the bias gets larger at higher COT values. One of the
reasons could be that the OMI surface UV algorithm uses the average of a pixel
to represent the cloudiness in that specific pixel. In reality, the spatial
distribution of cloudiness in that pixel could vary a lot, which could result
in the large difference in surface UV irradiance between the OMI pixel and
the ground observational site.
Scatter plots of the relative bias (%) between OMI and ground
observational EDR and the OMI overpass time SZA or COT (360 nm).
(a, c) are for OMI OP_FS bias while (b) is for Noon_FS bias.
All the data pairs are matched with D=50 km and ΔT=±5 min for
31 ground sites. The box–whisker plot of the bias on (a, b) is based
on the binned SZA using a bin size of 5∘. The box–whisker plots show
the 5th and 95th percentiles (whisker), the interquartile range (box), the
median (red line) and the mean (green dots).
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Trend analysis
EDR is the weighted solar irradiance from 300 to 400 nm which covers the UVB
range that is greatly affected by the atmospheric ozone column. In addition,
both UVA and UVB could be affected by the cloud cover and aerosol loadings
in the atmosphere. Thus, trends in surface EDR could be a result of the
combined effects of the aforementioned different factors and it would be
challenging to attribute the trend to any individual factor quantitatively.
Therefore, we focus on providing a descriptive summary of surface EDR trends
derived from both OMI and ground observation.
We first analyze the surface EDR trend using OMI level 3 data. We find that
OMI full-sky solar noon EDR data show a positive trend in most of the
places; but the only significant trend (95 % confidence level) was found
in parts of the northeastern US (Fig. 8b). A similar distribution of the
trend is found in OMI level 3 full-sky spectral irradiance at 310 nm
(Fig. 8d). We also analyzed the trend of OMI level 3 clear-sky EDR and total
column ozone amount (Fig. S2c) and found no significant trend in either
dataset. This could suggest that the contribution of ozone column to the
estimated trend of OMI full-sky EDR is minimal. Furthermore, significant
trends in OMI level 3 full-sky spectral irradiance at 380 nm are found in the
northeast (Fig. 8e) and no significant trends of OMI level 3 COT are found
(Fig. S2b), indicating the estimated trend could be largely induced by the aerosols.
(a) is the distribution of the OMI level 3 solar noon time
full-sky EDR trend over 2005–2017 overlaid with the trend at 31 ground sites
calculated with D=50 km and ΔT=±5 min around local solar noon
time. (b) is the same as (a) but only showing the areas and
sites that are significant at the 95 % confidence level. (c) shows
the distribution of the trend at ground sites (significant at the 95 %
confidence level), computed with D=50 km and temporally averaging all the
data available in a day. (d, e) show the areas with significant trends
of OMI level 3 solar noon time full-sky spectral irradiance at 310 and
380 nm, respectively. (a–e) share the same color bar and the trend shown is
the percentage change (%) per year. (f) shows the significant trend
at the 95 % confidence level for OMI level 3 AAOD at 388 nm. The trend is
calculated as 100 × AAOD/year.
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In contrast to trends derived from OMI data, ground observation shows
different trend patterns using two different sampling methods. For both
methods, only months with more than 20 days of data are used for trend
analysis and considered missing values otherwise. The first method is to
average the ground observational data with D=50 km and ΔT=±5 min
around local solar noon time, denoted as once-per-day
sampling. A total of 16 of 31 sites is found to have significant trends at the
95 % confidence level (Fig. 8b). Seven sites have positive trends while
the rest of the 9 sites show negative trends. The second method averages all
the data in a day at each site, hereby referred to as all-per-day sampling.
We find that this method results in 14 sites with significant trends at the
95 % confidence level (Fig. 8c). Only 4 of the 14 sites have positive
trends, with the rest of the sites showing negative trends.
Both methods (e.g., once per day and all per day) find significant negative trends
for sites in the northeast and the Ohio River Valley region with the all-per-day
method showing smaller trends. Using the site IL01 as an example, Fig. S3
illustrates the difference between these two sampling methods. Both methods
could capture the seasonal variation of the surface EDR; however, the
magnitude of all-per-day sampling EDR is about 3 times smaller than that of
the once-per-day sampling, which is anticipated because the all-per-day
average is smaller than the once-per-day measurement around noon time. By
averaging all the daytime data, the all-per-day sampling method smooths out
the atmospheric conditions throughout the day. In contrast, the estimated
trend of OMI Noon_FS EDR at this site is not significant. In
addition, the ground measurements show increasing trends in the southern
Great Plains (Texas and Oklahoma), while we find significant increasing
trends from OMI AAOD at 388 nm (Fig. 8f) but no significant trends of OMI
AOD at 388 nm (Fig. S2a) are found in these regions. Zhang et al. (2017)
also found significant positive trends of OMI AAOD in this region,
largely caused by dust AAOD. However, the magnitude of these trends derived
from ground measurements is within the measurement uncertainty range. Given
these uncertainties in the surface measurements, no coherent and
scientifically sound trend can be drawn from OMI data products for EDR,
AOD, AAOD, COT, and column ozone amount (Fig. S2c) and ground observations.
Conclusion and discussion
In this study, we evaluated the OMI surface erythemal irradiance at overpass
time and solar noon time for the period of 2005–2017 with 31 UVMRP ground
sites in the continental US. The OMI surface Noon_FS
EDR shows a meridional gradient with the EDR increasing from
∼80 mW m-2 in the northern US to ∼203 mW m-2 in the
southern US. The ground observational data could capture
this gradient well with EDR increasing from ∼73 mW m-2
in the northern US to a maximum of ∼190 mW m-2 at the southern sites.
The evaluation for OMI overpass time EDR shows better agreement with ground
measurements than that for solar noon time comparison. Both OMI
OP_FS and Noon_FS EDR comparisons show good
correlation with the counterparts from ground-based measurements, with
R=0.90 and 0.88, respectively, when inter-comparison is matched with D=50 km
and ΔT=±5 min; the correlation further increases
as ΔT increases to 30 min or 1 h. Both OMI OP_FS
and Noon_FS EDR overestimate the ground measurements by
8.0 and 8.9 mW m-2, respectively, and their RMSEs are 34.9 and 41.5 mW m-2.
The biases also show large spatial variability. For both OMI
OP_FS and Noon_FS EDR comparisons, the NMB
varies from -1 % to 20 % while the OMI Noon_FS
comparison shows larger MB. This suggests that the atmospheric condition
does not stay consistent even within an hour, underscoring the importance of
geostationary satellite measurements. The relatively large bias and RMSE in
magnitude for OMI Noon_FS EDR suggest the importance of
accounting for the variation of atmospheric conditions between solar noon and
satellite overpass time, which cannot be resolved by polar-orbiting
satellite measurements but future geostationary satellites such as TEMPO
(Tropospheric Emissions: Monitoring of Pollution) (Zoogman et al.,
2017), Sentinel-4 (Ingmann et al., 2012; Veihelmann et al., 2015) and
GEMS (Geostationary Environmental Monitoring Spectrometer) should be able to
resolve this issue.
We also extended the evaluation of OMI and ground EDR by comparing the PDFs
and CDFs as well as considering the peak UV density. First, both OMI and
ground EDR distributions show two peaks, one around 20 mW m-2 and another around
200 mW m-2, mainly related to larger and smaller SZAs, respectively.
The K–S test shows that the OMI and ground EDR are from the same sample
distribution at the 99 % confidence level. Both OMI Noon_FS
EDR, ground Noon_FS EDR and ground peak show the
high-frequency occurrence of the smaller peak (∼20 mW m-2)
over the period of 2005–2017. However, the other high-frequency occurrence
of ground noontime EDR (∼200 mW m-2) is not consistent
with the high frequency found in ground daily-peak values (∼220 mW m-2),
implying that the peak UV values in a day may not always
occur at the local solar noon time, thus highlighting the necessity for
finer temporal resolution data.
Ground-based continuous measurements were used to show the effects of
atmospheric variation on surface EDR. The ratio of OMI Noon_FS / OP_FS
EDR is greater than 1 for 95 % of the data points,
while the ratio derived from the ground-based data has a Gaussian
distribution, with 22 % of the data smaller than 1 and a mean value of 1.38. This
means that the assumption of a consistent cloudiness, column ozone amount
and aerosol loadings between these two times would lead to large positive
bias in the estimates of surface UV at solar noon time, which is revealed in
this study. Furthermore, we find that the OMI OP_FS EDR bias
shows various levels of dependence on the SZAs. Additionally, the OMI
OP_FS EDR bias shows slight dependence on COT. The error
distribution of the bias gets much wider at larger COT values. This error
statistic suggests the importance of multiple scattering by aerosols and
clouds in the radiative transfer model, which is overlooked in the radiative
transfer calculation for the current OMI's lookup table approach to
estimate surface UV. Lastly, because the current work deals with erythemal
irradiance data, the comparison of satellite and ground observational
erythemal irradiance at both satellite overpass and local solar noon time
could only provide us the overall combined effects of the varying
atmospheric conditions between these two times. The limitation is that it
would not provide quantitative information of the individual effect of the
atmospheric condition such as aerosol loadings on the transferability from
satellite overpass time to the local solar noon time. Additional comparison
of spectral irradiance such as in the work of Xu et al. (2010) would
help identify the specific cause. The current work by focusing on only
erythemal irradiance still shows the short-time variability from satellite
overpass time and local solar noon time. Again, future geostationary
satellite data (TEMPO and GEMS) combined with ground observational data
would help better understand the temporal and spatial variability of surface UV irradiance.
Lastly, we investigated the surface UV trend from both OMI and ground
observational data. The trend from ground data depends on sampling method.
The once-per-day sampling at noon time shows larger spatial variability in
the magnitude and signs of the trend while the all-per-day sampling shows
less variation in the magnitude. But, over the northeastern US, both
methods yield negative trends from the surface observations, while
significant positive trends were found from OMI full-sky data during solar
noon time. Furthermore, ground measurements and OMI data show significant
trends of surface UV in the southern Great Plains. However, the values of
trends are within the surface measurement uncertainties. Overall, there are
no scientifically sound and coherent trends among OMI data for aerosols,
clouds and ozone that can explain the surface UV trends revealed either by
OMI or ground-based estimates; these data also cannot reconcile trend
differences between the two estimates. Further studies of the trends in OMI
and ground-based spectral irradiances may help reveal more information of
the effects of total ozone amount on surface UV irradiance. Also, detailed
studies of aerosols trends may provide extra insights on the effects of
aerosols on the surface UV trends.