ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-7391-2015Comparison of OMI UV observations with ground-based measurements at
high northern latitudesBernhardG.bernhard@biospherical.comhttps://orcid.org/0000-0002-1264-0756ArolaA.https://orcid.org/0000-0002-9220-0194DahlbackA.FioletovV.HeikkiläA.JohnsenB.https://orcid.org/0000-0001-5711-8198KoskelaT.LakkalaK.https://orcid.org/0000-0003-2840-1132SvendbyT.TamminenJ.https://orcid.org/0000-0003-3095-0069Biospherical Instruments Inc., San Diego, California,
USAFinnish Meteorological Institute, Kuopio,
FinlandDepartment of Physics, University of Oslo, Oslo,
NorwayEnvironment Canada, Toronto, Ontario,
CanadaFinnish Meteorological Institute, Helsinki, FinlandNorwegian Radiation Protection Authority, Østerås,
NorwayFinnish Meteorological Institute, Arctic Research Centre,
Sodankylä, FinlandNorwegian Institute for Air Research, Kjeller,
NorwayG. Bernhard (bernhard@biospherical.com)09July201515137391741204February201525March201506June201517June2015This 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://acp.copernicus.org/articles/15/7391/2015/acp-15-7391-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/7391/2015/acp-15-7391-2015.pdf
The Dutch–Finnish Ozone Monitoring Instrument (OMI) on board NASA's Aura
spacecraft provides estimates of erythemal (sunburning) ultraviolet (UV) dose
rates and erythemal daily doses. These data were compared with ground-based
measurements at 13 stations located throughout the Arctic and Scandinavia
from 60 to 83∘ N. The study corroborates results from earlier work,
but is based on a longer time series (8 versus 2 years) and considers
additional data products, such as the erythemal dose rate at the time of the
satellite overpass. Furthermore, systematic errors in satellite UV data
resulting from inaccuracies in the surface albedo climatology used in the OMI
UV algorithm are systematically assessed. At times when the surface albedo is
correctly known, OMI data typically exceed ground-based measurements by
0–11 %. When the OMI albedo climatology exceeds the actual albedo, OMI
data may be biased high by as much as 55 %. In turn, when the OMI albedo
climatology is too low, OMI data can be biased low by up to 59 %. Such
large negative biases may occur when reflections from snow and ice, which
increase downwelling UV irradiance, are misinterpreted as reflections from
clouds, which decrease the UV flux at the surface. Results suggest that a
better OMI albedo climatology would greatly improve the accuracy of OMI UV
data products even if year-to-year differences of the actual albedo cannot be
accounted for. A pathway for improving the OMI albedo climatology is
discussed. Results also demonstrate that ground-based measurements from the
center of Greenland, where high, homogenous surface albedo is observed year
round, are ideally suited to detect systematic problems or temporal drifts in
estimates of surface UV irradiance from space.
Introduction
The Dutch–Finnish Ozone Monitoring Instrument (OMI) on board the NASA EOS
Aura spacecraft is a nadir viewing spectrometer that measures solar reflected
and backscattered radiation in a selected range of the ultraviolet and
visible spectrum. The Finnish Meteorological Institute in collaboration with
the NASA Goddard Space Flight Center have developed a surface ultraviolet
irradiance algorithm for OMI that produces noontime surface spectral UV
irradiance estimates at four wavelengths, noontime erythemal dose rate or the
UV index (UVI), and the erythemal daily dose. Tanskanen et al. (2007)
(hereinafter referred to as T07) have compared erythemal daily doses derived
from OMI observations with doses calculated from ground-based measurements of
18 reference instruments ranging in latitude from 72.6∘ N to
77.8∘ S. The present paper presents a similar comparison with focus
on Arctic locations. Ground stations include 13 instruments located in
Alaska, Canada, Greenland, Norway, Svalbard, and Finland (Fig. 1). These data
sets are identical to those used by Bernhard et al. (2013), hereinafter
referred to as B13.
Locations of instruments operated by Environment Canada (pink), Biospherical
Instruments (blue), the Norwegian Radiation Protection Authority and the
Norwegian Institute of Air Research (red), and the Finnish Meteorological
Institute (black).
Surface albedo from snow and ice covering the ground can enhance the
clear-sky UVI by up to 58 % (Fig. 2). The effect
is caused by photons that are reflected upward, and subsequently
Rayleigh-scattered downward by the overlying atmosphere toward the surface
(Lenoble, 1998). Fresh snow can have an albedo as high as 0.98 (Grenfell et
al., 1994). Albedo decreases with snow depth but even a thin layer of fresh
snow has a higher albedo than any other natural surface. According to
Feister and Grewe (1995), the albedo of fresh snow at 310 nm is 0.62 for a
snow depth of 2 cm and 0.76 for a depth of 5 cm. Calculations of the UVI
from space-based measurements therefore require accurate knowledge of the
surface albedo. Because OMI cannot distinguish between snow and clouds, an
albedo climatology (Tanskanen, 2004) is used by the OMI UV algorithm. This
climatology has unrealistic values at some locations and also does not take
changes in albedo from year to year into account. According to T07,
systematic errors in OMI UV data can be large (up 50 %) for polar regions
because the OMI UV algorithm sometimes uses unrealistically small surface
albedo that leads to misinterpretation of the observed bright scene as
clouds. An important goal of the present paper is to quantify these
systematic errors and their causes in greater detail, and to provide
recommendations on how these errors could be reduced.
Enhancement of the clear-sky UVI as a function of albedo. The
plot is based on radiative transfer calculations with the libRadtran model
(Mayer and Kylling, 2005) for sea level, a TOC of 400 DU, and SZAs of
60, 70 and 80∘ as indicated in the legend.
T07 only considered daily erythemal doses. OMI data files also provide the
UVI at the time of the satellite overpass and at local solar noon, and these
data are also evaluated in the present paper. For estimating the daily dose,
the OMI UV algorithm assumes that total ozone column (TOC) and cloud optical
depth (COD) remain constant throughout the day, which is unrealistic in most
cases. It may therefore be expected that differences between OMI and
ground-based measurements assessed for the time of the satellite overpass are
smaller than for the daily dose data set. It is a secondary objective of the
present paper to determine whether this is indeed the case.
The study by T07 is based on OMI data of the period September 2004–March 2006.
The present study considers data measured between September 2004 and
December 2012.
Data sets
The present paper focuses on the validation of the UVI and the daily
erythemal dose. The UVI is a dimensionless number and calculated by
weighting the spectral UV irradiance from Sun and sky that is received on a
horizontal surface, Eλ(λ), with the action spectrum for
erythema, ser(λ), integrating the weighted spectrum over
the wavelength range 250–400 nm, and multiplying the result by the constant
ker, which is equal to 40 m2 W-1 (WHO, 2002):
UVI=ker×∫250nm400nmEλ(λ)ser(λ)dλ=ker×Eer,
where Eer is called the “erythemally weighted irradiance”.
Both ground-based and OMI data are based on the action spectrum for erythema
defined by the Commission Internationale de l'Éclairage (CIE) in 1987
(McKinlay and Diffey, 1987). The spectrum has been slightly modified in 1998
(CIE, 1998; ISO, 1999). For solar zenith angles (SZAs) smaller than
60∘, UVI values calculated with the new norm are approximately
0.5–1.0 % larger than corresponding values calculated with the original
standard (Webb et al., 2011). Differences for SZAs between 60 and 90∘
are between 1 and 2 %.
Ground-based data
Ground-based data are identical with those used by B13 and are from thirteen
Arctic and Scandinavian locations (Fig. 1). Sorted by decreasing latitude,
the thirteen sites are Alert, Eureka, Ny-Ålesund, Resolute, Barrow,
Summit, Andøya, Sodankylä, Trondheim, Finse, Jokioinen,
Østerås, and Blindern. Sites that are italicized use multi-channel
filter radiometers, while the other sites use scanning spectroradiometers.
Essential information such as the sites' latitude and longitude is provided
in Table 1 of B13. Climatic conditions at the 13 sites are summarized by B13
and discussed in more detail in Sect. 5.1. Detailed information on
instrumentation, data processing, and measurement uncertainties are also
provided by B13. For all instruments but those installed at Sodankylä and
Jokioinen, the expanded uncertainty (coverage factor k=2) of UVI data
ranges between 5.8 and 6.2 %. For the two Brewer spectrophotometers
installed at Sodankylä and Jokioinen, a rigorous uncertainty budget has
not been developed. However, the two instruments have participated in several
intercomparision campaigns and were also regularly compared with the QASUME
(Quality Assurance of Spectral UV Measurements in Europe) reference
spectroradiometer (Bais et al., 2003). Measurements were consistently high by
1–6 % compared to measurements of the QASUME instrument. Data have not
been adjusted to the irradiance scale of the QASUME instrument because the
difference of 1–6 % is within the uncertainty of UV measurements of the
QASUME instrument (Gröbner et al., 2005) and that from other ground
stations.
The erythemal daily dose was calculated by integrating measurements over 24 h periods centered at local solar noon. Methods to fill data gaps have been
described by B13.
OMI data
Details of the OMI surface UV algorithm have been discussed in detail by T07
and references therein. In brief, the algorithm first estimates the
clear-sky surface irradiance using the OMI-measured total column ozone,
climatological surface albedo (Tanskanen, 2004), elevation, solar
zenith angle (SZA), and latitude-dependent climatological ozone and
temperature profiles. Next, the clear-sky irradiance is multiplied by a
cloud modification factor (CMF) that accounts for the attenuation of UV
radiation (UVR) by clouds and non-absorbing aerosols. The CMFs are derived
from the measured reflectance at 360 nm, assuming that clouds are
non-absorbing and their optical depth is independent of wavelength. Estimate
of UVR are corrected for the effects of absorbing aerosols by applying a
correction factor Ca as described by Arola et al. (2009). Ca
typically ranges between 0.96 and 1.00 for the locations considered here.
OMI UV data were downloaded on 18 July 2014 from
http://avdc.gsfc.nasa.gov/index.php?site=595385375&id=79. According to
the files' header, the data set is referenced as “EOS Aura OMI OMUVB
(Collection 3, PGE v1.3; for ascending orbit only with SZA < 88)”. These
“overpass” data are provided by NASA's Aura Validation Data Center (AVDC)
by filtering Level 2 OMUVB data for over 250 ground stations where regular
surface UV measurements are performed. Additional OMI UV products are
available from the website http://omi.fmi.fi/products.html but these
were not used for this study.
The OMI data files provide both Eer (in units of mW m-2)
and the UVI. Because the numerical precision of Eer is larger
than that of the UVI (which is rounded to one decimal place), we used
Eer, and divided the ground-based UVI measurements with
ker before comparing with the OMI data sets. The low precision
of the native OMI UVI data is a particular problem for Arctic locations
where the UVI is frequently smaller than 1.
Data products in parenthesis were not directly assessed in the
present paper because of their poor numerical precision compared to the
corresponding erythemally weighted irradiance data sets. Data product names
and acronyms are identical to those used in the OMI data files.
OMI overpass files contain several UV data products (Table 1). Data products
(DP) assessed in the present paper include (1) the “Overpass Erythemal Dose
Rate”; the (2) “Erythemal Daily Dose Rate”; (3) the “Clear Sky Erythemal
Daily Dose Rate”; (4) the “Erythemal Daily Dose”; and (5) the “Clear Sky
Erythemal Daily Dose”.
DP (1) is the erythemally weighted irradiance at the time of the satellite
overpass. DP (2) is the erythemally weighted irradiance at local solar noon
that is calculated from DP (1) by taking the difference of the SZA between
the time of local solar noon and the time of the satellite overpass into
account. The calculations assume that TOC and COD remain constant between
the two times. DP (3) equals DP (2) without the CMF being applied. DP (4) is
determined from the measured TOC and COD at the time of the overpass and the
assumption that TOC and COD remain constant throughout the day. DP (5)
equals DP (4) without the CMF being applied.
Data files contain additional information on data quality; SZA; viewing
zenith angle (VZA); horizontal distance between the center of the OMI pixel
(defined by the OMI Cross Track Position or CTP) and the nominal location
(Dis); the value of the OMI surface albedo climatology used in the retrieval
algorithm (SufAlbedo); Lambertian equivalent reflectivity (LambEquRef);
terrain height (TerrHgt); and the COD estimated by the OMI UV algorithm
(CldOpt). Some of these parameters were used for filtering the data sets when
comparing with ground-based data. Because of the challenges to distinguish
between high surface albedo and clouds from space, the method of selecting
clear-sky data by filtering for CldOpt = 0 may not be accurate.
At low latitudes, OMI measurements are nominally made once a day in the
afternoon around 13:45 local solar time. At high latitudes, there is more
than one satellite overpass per day. In these cases, the daily values of DPs
(2)–(5) were averaged before comparing with ground-based data. When
satellite data were filtered using some of the parameters mentioned above
the number of data records contributing to the daily average is reduced to
one in most cases.
OMI overpass data files include data for Dis < 180 km. In particular for
stations that are located close to the coast or situated on a mountain, the
actual albedo as well as the albedo value SufAlbedo used in the OMI surface
UV algorithm can change greatly over this distance. Figure 3 shows SufAlbedo
for all ground stations extracted from the OMI data files. SufAlbedo is
plotted for all data (black symbols) and data where Dis is either smaller
than 12 km (blue symbols) or 5 km (red symbols). As can be seen from
Fig. 3, values of SufAlbedo close to the station can differ substantially
(e.g., by up to 0.65 during winter and spring at Finse and Ny-Ålesund)
from values farther away. At Eureka, the albedo away from the station is
biased high compared to values in close proximity. When the data set is
filtered for Dis < 12 km, values of SufAlbedo for a given day of the
year are clustered to within ±0.05 for all sites but Finse. This site
exhibits a bimodal distribution that even persist when the maximum distance
is reduced to 5 km because adjacent pixels of the OMI albedo climatology
have greatly different albedo values. For validating OMI, ideally only data
should be used where the center of the OMI pixel is close to the ground
station. However, by choosing a small value, the number of match-up data
points is greatly reduced and the statistics of the comparison become less
certain. Based on the results shown in Fig. 3, data were filtered for a
maximum distance of 12 km, which we believe to be a good compromise.
Surface albedo (SufAlbedo) of the OMI albedo climatology for
each site, extracted from the OMI data files. Black symbols indicate all
available data. Blue symbols indicate data where the distance (parameter
“Dis”) between the location of the stations and the center of the OMI
pixel is smaller than 12 km. For red symbols, Dis is smaller than 5 km.
Validation method
Ground-based data were linearly interpolated to either the time of the
satellite overpass (DP 1) or local solar noon (DP 2 and 3). Daily dose data
(DP 4 and 5) did not require interpolation. Data were not used when the time
between ground and satellite data was larger than the “maximum time”
tm. Sites that use multi-filter instruments typically provide a
UVI measurement every minute. The maximum time difference for these sites is
usually 30 s and tm was set to 5 min. Sites equipped with
spectroradiometers provide measurements with a frequency ranging from one to
four scans per hour. Typical time differences between ground and satellite
data for these sites therefore range between 7.5 (Barrow and Summit) and
30 min (Sodankylä and Jokioinen). tm was set to 30 min for
Alert, Eureka, Resolute, Barrow, and Summit, and to 60 min for Sodankylä
and Jokioinen.
To allow a comparison of results from this study to those by T07, similar
metrics were used to quantify differences between the OMI and ground-based
data sets. These are
ρi=Es,iEs,iEg,iEg,i:
ratio of satellite-derived data Es,i and ground-based data
Eg,i, where the index i indicates the data product (i=1,2,3,4,5).
Both Es,i and Eg,i indicate “match-up” data for a particular record of the OMI data file. The quantity ρi
defines a distribution, which in most cases cannot be well represented by a normal distribution. The statistics defined below were calculated both from
monthly and annual distributions of ρi. These monthly and annual statistics include all years when data are available. Potential
temporal drifts of the OMI data set were assessed with data from Summit, the site with the least cloud influence. A linear regression fitted
to a time series of the ratio of OMI and ground overpass data (DP 1) revealed a statistically insignificant drift of 0.07 ± 0.11 % (±2σ)
per year. The absence of drifts was further confirmed by analyzing monthly average data.
Ni: the number of ρi contributing to the statistics of a given month or the year.
ρ¯i: the average of ρi.
ρ̃i: the median of ρi.
Mini and Maxi: the minimum and maximum values of ρi.
pf,i: the ratio at the fth-percentile with f= 5, 25, 75, and 95. For example,
p25,2 is the ratio at the 25th percentile of the ρ2 distribution pertaining to DP (2).
The difference between p25,i and p75,i is called the “interquartile range.”
W10,i,W20,i,W30,i: percentage of satellite-derived data that agree to within 10, 20, and 30 %, respectively, with ground-based data.
As an alternative approach to quantifying the difference between OMI and
ground data, we also calculated the monthly average from both data sets, and
ratioed these averages:
Ri(y,m)≡∑Es,i(y,m)∑Eg,i(y,m),
where the summations are over all data within a given year y and month m, provided
that both satellite and ground-based measurements are available. For each
month, ratios Ri(y,m) of all years were averaged and the resulting
average is denoted R¯i. When at least 5 years of data were
available, also the standard deviation σi was calculated from
the 5–9 annual values, allowing to quantify the variability of Ri(y,m)
from year to year. To avoid artifacts caused by data gaps when calculating
monthly averages, only months with at least 20 days of data were considered.
Despite this restriction, there could still be a bias in the monthly average
if periods with missing days are not equally distributed in every year. For
example, solar radiation tends to increase during months in the spring
because the noontime SZA decreases. If measurements are missing at the
beginning of a month, the monthly average will be biased high. To correct
for this effect, the method developed by Bernhard (2011) was applied.
Results
As part of the analysis, the ratio and difference of OMI and ground UVI data
were plotted for each site as functions of time, the UVI measured at the
ground, and the day of the year. Furthermore, correlations between OMI and
ground-based data were calculated and frequency distributions of OMI/ground
ratios were plotted for each month. This analysis was repeated for the five
data products discussed in Sect. 3. The resulting wealth of information
exceeds the space of this paper; however, the resulting plots and statistics
are available as supplements: for each site and data product, a PDF page in a
standardized format is provided. An annotated example of such a page is
provided in Appendix A.
Ratio of the erythemal daily dose (DP 4) measured by OMI and ground
stations for each site. The box-whisker plots indicate for each month the 5th
and 95th percentiles (whisker), the interquartile range (box), median (line),
and average (red dot). Statistics based on annual data are indicated as the
13th month. Match-up data were filtered for SZA < 84∘ and
Dis < 12 km.
Because the values of ρi are not normal distributed and change
greatly from month to month at some locations, box-whisker plots were chosen
to visualize the results. Figure 4 shows these plots for DP (4). Data were
filtered for SZA < 84∘ and Dis < 12 km. (The SZA was
restricted to avoid that data affected by instrument noise skew the
statistics. For SZA > 84∘, the UVI is typically smaller than 0.2
and systematic errors at this low intensity are of little relevance.)
Figure 4 indicates for each site and month the statistics ρ¯4, ρ̃4, p5,4, p25,4, p75,4, and
p95,4. Statistics for the entire year are indicated as the 13th month.
Table 2 shows the comparison in tabular form. Two months were chosen for each
site for this table: a month in spring when the surface is covered by snow
and a month in summer when it is snow free. These months were selected based
on the albedo climatology of Fig. 3. The OMI albedo climatology is invariant
from year to year and therefore does not capture variability caused by the
timing of snow melt. It can therefore be expected that ρi shows the
highest variability in the “transition” months when snow melt occurs. On
the other hand, for the “high winter” and “mid-summer” months chosen for
Table 2, a static albedo climatology is conceivably sufficient for accurate
UVI retrievals from space-based observations.
Validation statisticsa for daily erythemal dose (DP 4).
aMatch-up data were filtered for SZA < 84∘ and Dis;
12 km.
b SC = snow cover, SF = snow-free, PSC = permanent snow cover.
Figure 4 and Table 2 indicate large systematic differences between OMI and
ground data at some sites and for some months. For example, ρ̃4 is 0.60 between March and May at Ny-Ålesund, 1.55 in February
and March at Trondheim, and smaller than 0.5 between January and April at
Finse. On the other hand, the agreement between the two data sets is
excellent at Summit and Sondakylä for all months. Good agreement is also
observed during spring at Alert, Eureka, Resolute, and Barrow, and during
summer at Ny-Ålesund, Finse, Jokioinen and Blindern. In Andøya and
southern Scandinavian sites, the variability of the difference between OMI
and ground daily doses is large as evidenced by the large interquartile range
(e.g., Andøya in summer) and large whiskers (e.g., Blindern in
fall). The possible reasons for the observed
systematic differences and variations between space- and ground-based
observations are discussed in Sect. 5.
Same as Fig. 4 but for overpass
erythemal dose rate (DP 1).
Figure 5 shows box-whisker plots and validation
statistics for overpass erythemal dose rate (DP 1). A table similar to
Table 2 but for DP (1) instead of DP (4) is
available in the Supplement. These data were again filtered for SZA < 84∘ and Dis < 12 km. By comparing
Fig. 4 with Fig. 5 it can
be seen that the distributions for DP (1) (as indicated by the interquartile
range and the length of the whiskers) are generally much wider than those
for DP (4) discussed earlier.
We will show in the following that the different results for DP (1) and DP (4) are a consequence of the different sampling and averaging schemes of
ground and satellite data.
Ground measurements are a point measurement, whereas OMI provides the mean
surface UV over a large area (13 × 24 km2
(along × across track) in nadir direction and increasing to
13 × 128 km2 at the most outer swath-angle of 57∘
(http://www.knmi.nl/omi/research/instrument/characteristics.php)). The
variability of the erythemal dose rate over the area of the OMI pixel is
averaged in OMI data, while ground measurements capture these fluctuations.
Hence, the ratio of OMI/ground is also affected by this variability, leading
to the wide distributions evident in Fig. 5. The effect is largest at sites
with high cloud variability and smallest at sites or seasons where clouds are
either infrequent (e.g., Resolute in July) or where the attenuation of UVR by
clouds is reduced by high surface albedo (e.g., Alert in spring, Summit all
year). This reduction is the result of multiple scattering between the
surface and cloud ceiling, which effectively traps light (e.g., Nichol et
al., 2003).
As discussed in Sect. 1, the daily dose of ground measurements is calculated
from the individual measurements performed throughout the day, while the OMI
UV algorithm assumes that the TOC and COD remain constant. The difference in
sampling will result in variability in the ratio of the two data sets. The
comparison of Fig. 4 with Fig. 5 suggests that the uncertainty of the
OMI-derived erythemal daily dose introduced by the assumption of constant TOC
and COD is smaller than the uncertainty in the OMI overpass erythemal dose
rate applicable to a specific location that is caused by the variability of
this dose rate over the area of the OMI pixel.
The comparison of OMI and ground overpass erythemal dose rate data was
repeated without filtering these data for SZA < 84∘ and
Dis < 12 km. As expected, distributions calculated without the
filter were considerably larger than those obtained with the filter. These
data are part of the Supplement.
Comparison of ρ¯4 (red lines), ρ̃4 (green lines), and R¯4 (open circles). The error bars
indicate ±σ4. Data used for this figure were not filtered for
SZA and Dis because such filtering would have reduced the number of data
points of R¯4 substantially. Values of ρ¯4 and
ρ̃4 are therefore slightly different from those indicated
in Fig. 4.
Figure 6 is based on DP (4) and compares the average ρ¯4 and
median ρ̃4 of the match-up statistics discussed earlier
with the average ratio R¯4 derived from the monthly average
daily doses. The median ρ̃4 agrees well with
R¯4 for all sites and months, suggesting that ρ̃4 is an appropriate statistical quantity to assess systematic biases
between OMI and ground data. The average ρ¯4 is less
appropriate for this assessment because it is more affected by the skewness
of ρ4 distributions. As explained in Sect. 3, the year-to-year
variability of the OMI/ground ratios is quantified with σ4 and
this standard deviation is indicated by error bars in Fig. 6. At some sites
(e.g., Summit, Sondankylä), the error bars are smaller than the size of
the symbol, highlighting that the bias between OMI and ground data is nearly
constant over time. At high-Arctic sites, σ4 is typically small in
March and April when the ground is covered by snow in all years. Similarly,
σ4 is small during summer at Scandinavian sites when the ground is
snow free. As can be expected, σ4 is largest in the transition
months when the surface becomes snow free (e.g., June at Alert and Barrow,
April at Finse) or when snow starts to accumulate again after the summer
(e.g., September at Alert, October at Barrow).
All results presented above were based on the ratio of OMI and ground data.
For the large SZAs prevailing at high latitudes early in spring or late in
fall, even large relative differences between the two data sets have only a
small effect (with arguably negligible consequences) on absolute UVR levels.
To emphasize this point, Fig. 7 shows box-whisker plots of the difference
of OMI and ground UVI measurements for the time of the satellite overpass.
Statistics (i.e., whiskers, interquartile range, median, and average) were
calculated the same way as for the analysis of ratios shown in Fig. 5. With
few exceptions, the 25th and 75th percentiles of the difference do not exceed
±1 UVI unit. Exceptions include June at Resolute (median bias of 1.0 UVI
units), and April and May at Trondheim (bias of 1.2) and Finse (bias of
-2.1).
Difference of OMI and ground UVI data, calculated from overpass
erythemal dose rate data (DP 1). The box-whisker plots indicate for each
month the 5th and 95th percentiles (whisker), the interquartile range (box),
median (line), and average (red dot). Statistics based on annual data are
indicated as the 13th month. Match-up data were filtered for
SZA < 84∘ and Dis < 12 km.
Discussion
The effect of unrealistic albedo can either lead to a positive or negative
bias of OMI UV data because the albedo is a key parameter when calculating
the CMF. When the OMI parameter SufAlbedo exceeds the actual albedo (“Case
1”), the OMI UV algorithm interprets reflectance from clouds as reflectance
from the surface and sets CldOpt to 0, resulting in CMF = 1. This has two
effects, which both lead to a positive bias of OMI data. First, a high
value of SufAlbedo leads to a high value of the derived clear-sky irradiance
(e.g., Fig. 2). Second, since CMF = 1, the irradiance returned by the OMI
UV algorithm is not reduced by cloud attenuation, in contrast to the
irradiance seen by the instrument at the surface. High values of SufAlbedo
lead to an inconsistency when there are no clouds: in this case, the
reflectance measured by the satellite is lower than that expected from the
high value of SufAlbedo. This inconsistency could be exploited to improve the
OMI albedo climatology. For example, data records with a large difference
between the measured (low) reflectance and that expected from the high value
of SufAlbedo could be selected for each grid point, and the albedo
climatology could be adjusted until the difference disappears.
If SufAlbedo greatly underestimates the actual albedo (“Case 2”),
reflectance from the surface is assumed to be caused by clouds, and the cloud
optical depth is set to a value larger than 0, resulting in CMF < 1. This
has two effects, which both lead to a negative bias of OMI data. First, a
low value of SufAlbedo leads to a low value of the derived clear-sky
irradiance. Second, since CMF is smaller than 1, the irradiance returned by
the OMI UV algorithm is further reduced. In contrast to Case 1, no
inconsistencies can occur because high reflectance from snow measured during
clear skies can always (albeit incorrectly) be interpreted as cloud
reflectance.
Examples of Cases 1 and 2 are provided in Sect. 5.1 when discussing results from the various sites.
During periods of scattered clouds, the UV irradiance at the surface can
exceed the clear-sky irradiance (e.g., Mims III and Frederick, 1994). Such
enhancements occur when the solar disk is not obstructed, while clouds in the
vicinity of the Sun increase the diffuse component over the value for clear
skies. High surface albedo may increase this effect further (Bernhard et al.,
2010). The OMI UV algorithm does not account for this effect and this
omission may contribute to negative biases for overpass data (DP 1) when
scattered clouds are present. The magnitude of the effect is modest, however,
because cloud enhancements of the UVI by more than 10 % are very rare in
the Arctic (e.g., Bernhard et al., 2007, 2008), and also the frequency of
enhancements between 0 and 10 % is typically small (e.g., less than 12 %
of all measurements at Summit (Bernhard et al., 2008) and even less at sites
where overcast skies are the norm, such as Barrow in the fall; Bernhard et
al., 2007).
It was anticipated that comparisons for overpass data show the least
variability because this data product provides the best temporal match
between satellite- and ground-based observations. Our results refute this
hypothesis. The least variation was instead observed for the daily erythemal
dose. The reason for this finding is likely due to ergodicity: for
space-based observations, the variation introduced by clouds is spatially
averaged over the area of the pixel, while the temporal integration of
ground-based measurements performed over the course of the day “smoothes”
out cloud effects. The effects of spatial and temporal averaging seem to be
similar.
Discussion by site
Results from each site are briefly discussed below, with the exception of
Summit, Barrow, and Trondheim, for which more elaborate analyses are
presented. Measurements from Summit and Barrow are completed with radiative
transfer calculations, which are used for the interpretation of the
difference of ground and satellite data. For Barrow, measurements of surface
albedo and COD are also available and were used for interpretation. For
other sites, the actual surface albedo was estimated from snow depth
information. Measurements from Trondheim are used to study the Case 1
mechanism in more detail. If not otherwise noted, systematic differences or
“biases” discussed below refer to ρ̃4 and are expressed
in percent (e.g., ρ̃4=1.05 corresponds to a bias of
+5 %).
Alert, Canada
Alert is located close to the northernmost point of Canada. The bias for
April and May (when SufAlbedo is about 0.8; Fig. 3)
is less than 2 %. According to Canadian Climate Normals (CCN;
http://climate.weather.gc.ca/climate_normals/), the ground at
Alert is covered by more than 10 cm of snow at all days during these months.
Results from Barrow (Sect. 5.1.6), which is an Arctic coastal site like
Alert, indicate that an albedo of 0.8 is a reasonable value for these
conditions. In June and July, the bias is about 15 %. SufAlbedo decreases
from 0.75 to 0.25 during this period, which is likely too large considering
that less than two days in July have a snow depth of 2 cm or larger.
Variability of ρ4 is relatively high in the summer and
fall when the surface is snow free. For example, the interquartile range is
0.99–1.05 in May, but 0.95–1.34 in July.
Eureka, Canada
Eureka is about 480 km southwest of Alert. OMI data are biased high by about
11 % between March and May when SufAlbedo is about 0.75. According to CCN,
not all days during this period have snow cover in excess of 5 cm. The
albedo value used by the OMI UV algorithm is therefore likely too large,
which may explain the positive bias. The ground in July and August is
virtually snow free (suggesting an albedo of less than 0.05 (Blumthaler and
Ambach, 1988)), while SufAlbedo is between 0.1 and 0.2. Figure 2 suggest that
up to 10 % of the of the bias of 12–19 % observed during these months
could be caused by the relatively large values of SufAlbedo applied during
these month.
Ny-Ålesund, Svalbard
Ny-Ålesund is at the western side of the Svalbard archipelago. Despite
its high northern latitude, the climate is relatively mild because of the
influence of the Gulf Stream. The bias at Ny-Ålesund between March and
May is -40 %. SufAlbedo decreases from 0.35 to 0.20 during this period,
which is likely far too low considering that snow cover at this time
typically exceeds 50 cm. The underestimate is an example of the Case 2
mechanism discussed above. During July and August, when SufAlbedo is less
than 0.15 and the ground is snow free, the bias is less than 6 %,
confirming that OMI data are quite accurate when the albedo is accurately
specified.
Resolute, Canada
Resolute is located about 600 km south of Eureka. Complete years of
ground-based measurements at Resolute are only available in 2007, 2009,
2010, and 2011. Large data gaps at this site make statistics less robust
(e.g., σ4 could not be calculated for this site). In March and
April, when SufAlbedo is 0.85 and snow cover exceeds 10 cm during more than
28 days per month according to CCN, the bias is 9 %, suggesting that the
OMI albedo climatology is appropriate. On the other hand, there is a large
bias of 48 % and large variability in June, when SufAlbedo drops from 0.85
to 0.5. CCN data indicate that snow disappears in June and the albedo values
used by the OMI UV algorithm are therefore likely too large, explaining the
large positive bias (Case 1).
Summit, Greenland
Summit is located near the top of the Greenland ice cap and has a very high
surface albedo of about 0.97 year round (Bernhard et al., 2008). Because of
this high albedo, the influence of clouds is limited: the average attenuation
of spectral irradiance at 345 nm is 3.5 % in spring and 5.8 % in summer
(Bernhard et al., 2008). Because of the small cloud effect and constant
albedo, the scatter between OMI and ground observations is extremely small.
For sites located above 2500 m such as Summit, the OMI surface UV algorithm
does not apply a cloud correction; i.e., clear-sky conditions are assumed for
these altitudes at all times. This has to be taken into consideration when
comparing OMI and ground data at Summit.
Comparison of OMI and ground data at Summit. Panel a: median ratios
ρ̃1, ρ̃2, and ρ̃4 of
DP (1), DP (2), and DP (4), respectively. Panel b: comparison of median
ratios ρ̃1 of OMI and ground overpass measurements (solid
symbols) with median ratios of modeled and measured data (open symbols).
Results for data filtered for SZA < 84∘ and Dis; 12 km are
indicated in red. Results for data that were additionally filtered for
clear-sky (CS) conditions are indicated in blue. The two data sets indicated
by red solid symbols in Panels a and b are identical.
Figure 8a compares the medians ρ̃1, ρ̃2, and ρ̃4 of DP (1), DP (2), and DP (4), respectively.
The median ρ̃1 for DP (1) (which was already shown in
Fig. 5) is relatively constant and varies between 1.04 (equal to a bias of
4 %) in February and March and 1.10 (bias of 10 %) in August. The median
ρ̃2 and ρ̃4 for DPs (2) and (4) exhibit
increasing tendencies with ρ̃2 ranging from 0.98 (bias of
-2 %) in February to 1.14 (bias of 14 %) in August. The medians
ρ̃2 and ρ̃4 are rather similar, except
for February when ρ̃4 is 0.90.
Ground-based measurements at Summit are part of the Version 2 data set of the
NSF UV monitoring network (http://uv.biospherical.com/Version2/),
referred to as “V2 data set” in the following. This data set includes
clear-sky model data for every measurement. The availability of these model
data presents the opportunity to better understand the reasons of the
difference between OMI and ground-based measurements shown in Fig. 8a.
Model data were calculated with the radiative transfer model
UVSPEC/libRadtran (Mayer and Kylling, 2005). Model input parameters are
described in detail by Bernhard et al. (2008). In brief, parameters include
SZA; the extraterrestrial spectrum; atmospheric profiles of air density,
temperature, ozone, and aerosol extinction; TOC; surface albedo; atmospheric
pressure at station level; aerosol optical depth (τa); and
single scattering albedo for aerosols. The TOC used for modeling was
calculated from measured UV spectra according to the method by Bernhard et
al. (2003). Surface albedo was set to 0.97 in accordance with measurements by
Grenfell et al. (1994). The spectral dependence of τa was
parameterized with Ångström's formula: τa=βλ-α. Aerosol optical depth data for Summit are currently not
available, and calculations were performed for stratospheric background
aerosol conditions by setting α=1.0 and β=0.008. This
translates to τa=0.027 at 300 nm. Actual values of
τa are likely larger, in particular during spring when Summit
may be affected by Arctic haze (VanCuren et al., 2012). Bernhard et al. (2008) suggest that
aerosols may reduce spectral irradiance at 345 nm by about 1–3 % at
Summit. Model data are therefore likely too large by this amount.
Figure 8b compares ρ̃1 (solid red symbols) with the median
calculated from the ratio of the model results and the ground-based
measurements (open red symbols). The two data sets agree with each other to
within ±1.5 % for all months, but are biased high by 4–10 %. A bias
of this magnitude is not surprising because neither the OMI UV algorithm nor
the model take cloud attenuation into account. As mentioned earlier, clouds
attenuate on average by 3.5 % between 01 March and 21 June and by 5.8 %
between 22 June and 12 October (Bernhard et al., 2008).
Measurements performed during clear skies are flagged in the V2 data set.
Clear-sky periods are determined based on temporal variability of measured
spectral irradiance at 600 nm as described by Bernhard et al. (2008).
Ground-based, OMI, and model data were filtered for clear-sky periods, the
comparisons between the three data sets were repeated, and results are
indicated with blue symbols in Fig. 8b. The median ratio of OMI and ground
overpass data (solid blue symbols in Fig. 8b) and the median ratios between
model and ground data (open blue symbols) agree to within ±3 %, but are
both biased high by 2–6 %, depending on month. If measurements from ground
and space as well as the model results were without error, the bias would be
0. The small bias that was actually observed is likely caused by a
combination of several factors. First, attenuation by aerosols is not
considered by either OMI or the model. Adjustment for this effect would
reduce the bias by about 2–3 % in spring (when Arctic haze is potentially
present) and 1 % in fall. Second, the OMI albedo climatology for Summit is
0.9 in February and October, and 0.95 at the summer solstice. The albedo used
by the model is 0.97 year round. Model results should therefore exceed OMI
data by about 2 % most of the year. Third, ground-based data are traceable
to the scale of spectral irradiance established in 1990 by the US National
Institute of Standards and Technology (NIST). The current (and presumably
more accurate) NIST scale of 2000 is about 1.3 % higher in the UV-B than
the 1990 scale (Yoon et al., 2002). If ground-based measurements were
recalibrated to the NIST 2000 scale, the bias would be further reduced by
about 1 %. Forth, the bias is within the expanded uncertainty of 6 % of
the ground-based measurements (Bernhard et al., 2008) and some discrepancies
can therefore be expected.
As noted earlier and illustrated in Fig. 8a, the bias for the erythemal daily
dose rate (DPs 2) and that of the daily dose (DP 4), increase from about
-1 % in March to 14 % in September. Several hypotheses were
investigated and ultimately rejected to explain this increase. For example,
the TOC is larger in spring than fall. If the OMI algorithm used to convert
the measurements at the time of the overpass to the time of local solar noon
does not take the TOC correctly into account, this could conceivably result
in a bias. When the ratio of EDRate / OPEDRate (see Table 1 for acronyms)
was plotted versus TOC, a strong correlation was indeed observed. However,
when data were filtered by month, the correlation disappeared. For example,
the ratio of EDRate/OPEDRate was similar for spring of 2010, when TOC was
abnormally low, and spring of 2011, when it was abnormally high (B13). We
therefore conclude that TOC cannot be the cause of the effect. Instead, the
correlation with TOC only exists because TOC is effectively a proxy for time.
Ratio of EDRate / OPEDRate from the OMI data file (red, left
axis) and the hour of the OMI overpass (blue, right axis) derived from the
Summit data set. Data were filtered for VZA < 20∘. The ticks on
the x axis indicate the start of a given month.
EDRate is calculated from OPEDRate by the OMI UV algorithm, taking into
account the difference in SZA between the time of the overpass and the time
of solar noon. Figure 9 shows the annual variation of EDRate/OPEDRate for
Summit. (Additional analysis not shown here indicates a similar annual cycle
of EDRate/OPEDRate for all sites.) The ratio increases with month, similar to
ρ̃2 shown in Fig. 8a, but this change could be appropriate
if the viewing geometry of OMI is different in spring and fall. This is
likely not the case, however. Figure 9 also indicates the time of the
satellite overpass, illustrating that there is no difference between spring
and fall. Additional analyses also indicate that SZAs at the time of the
overpass are not systematically different in spring and fall, and that the
variation in the timing of local solar noon of about ±15 min over the
course of a year is too small to explain the effect. We conclude that the
time-dependent bias in DP (2) shown in Fig. 8a is caused by a problem in the
conversion from OPEDRate to EDRate applied by the OMI UV algorithm.
Additional analysis suggests that the pattern is likely due to a systematic
error of up to ±0.5∘ in the calculation of the local-noon SZA by
the algorithm. For a SZA of 80∘ (local noon SZA on 1 March and
11 October at Summit), a 0.5∘ error in SZA results in a UVI error of
about 8 %.
EDDose is calculated from EDRate by the OMI UV algorithm by applying a
SZA-dependent function. The function was validated by calculating a
corresponding ratio from the ground-based data. The result agreed with the
function applied by OMI to within 2 %, except at SZAs exceeding
75∘. At these large SZAs, the conversion function also becomes
dependent on TOC, which is not taken into account by the OMI UV algorithm.
This is the reason why ρ̃2 and ρ̃4 show
a relatively large difference of 8 % for February in Fig. 8a, while the
difference is smaller than 2 % for the other months.
Barrow, Alaska
Barrow is close to the northernmost point of Alaska. The adjacent Chukchi Sea
is typically covered by ice between November and July. Barrow is the only
site considered here where the “effective surface albedo” (denoted
aeff) is routinely derived from ground-based measurements.
aeff is defined as the albedo of a uniform Lambertian surface,
that, when used as input into a 1-D model, reproduces the measured spectrum
(Lenoble et al., 2004). aeff for Barrow is part of the V2 data
set and calculated from the spectral effect of surface albedo on the
downwelling irradiance (Bernhard et al., 2006, 2007). The uncertainty
(coverage factor k=1) is 0.11 for aeff=0.6, and 0.09 for
aeff=0.85. Figure 10 compares aeff with SufAlbedo.
Between March and mid-May, aeff roughly varies between 0.70 and
1.00, while SufAlbedo is about 0.8. There is generally little bias between
the two data sets. Snowmelt between mid-May and July leads to a sharp
decrease of aeff. While the general trend corresponds well to
that of SufAlbedo, there is a large variability, with aeff
sometimes being 0.4 smaller or larger than SufAlbedo. SufAlbedo starts to
increase again at the beginning of September, while aeff does
not increase before October. Reliable snow coverage at Barrow was typically
observed only after mid-October during the last decade (Bernhard, 2011).
SufAlbedo in September and October is therefore likely too large by up to
0.3.
Comparison of effective surface albedo aeff derived
from ground-based measurements (“V2” albedo, green marker) with SufAlbedo
(blue marker) of the OMI climatology for Dis < 12 km. aeff
data were measured between 1991 and 2013. aeff data between
September and November are sparse because of few clear-sky days during this
period. The ticks on the x axis indicate the start of a given month.
The bias of OMI daily dose data at Barrow is smaller than 9 % between
February and April. The low value is consistent with the good agreement of
aeff and SufAlbedo in that period. While the bias for June is
also small (-2 %), the scatter for this month is large (the interquartile
range is 0.84 to 1.10), reflecting the larger inter-annual variability in
aeff for this month (e.g., Fig. 4).
The bias for September and October is 38 and 62 %, respectively. This
large positive bias can likely be explained by the Case 1 mechanism and is
further investigated in the following.
Comparison of ratio EDRate / CSEDRate (grey, left axis),
SufAlbedo (blue, left axis) and CldOpt (red, right axis). All data are from
the OMI data file for Barrow and the year 2007. The ratio EDRate / CSEDRate
is equivalent to the cloud modification factor (CMF) at 360 nm.
Figure 11 compares the ratio EDRate / CSEDRate (which is equivalent to
the CMF) with SufAlbedo and CldOpt for the year 2007. All data are from the
OMI data file. Between mid-February and the end of April, CldOpt is 0 with
few exceptions, and the corresponding CMFs are 1, as expected. Between June
and September, CldOpt is frequently larger than 5, resulting in CMFs smaller
than 0.7. In October, CldOpt is 0 with few exceptions even though clouds
remain frequent during this month. The low values of CldOpt are a consequence
of the unrealistically large albedo for this month (Case 1), as discussed
below.
Figure 12 shows statistics of cloud optical depth at Barrow from OMI (CldOpt)
and ground-based observations. The box-whisker plot is based on data of all
years, filtered for SZA < 84∘ and Dis < 12 km. Ground-based
COD data are from the V2 data set and were derived by comparing measurements
of spectral irradiance at 450 nm with clear-sky model results (Supplement to
Bernhard et al., 2004). To a good approximation (e.g., Fig. 5.16 of Liou,
2002), COD is independent of wavelength between 450 nm and 360 nm, the
latter being the wavelength used by OMI to retrieve CldOpt.
Box-whisker plot of cloud optical depth retrieved from ground-based
measurements (blue, left of month marker) and the corresponding CldOpt data
set from OMI (green, right of month marker) at Barrow. The averages for both
data sets are indicated by red dots.
COD of both data sets is close to 0 for February, March, and April. There is
also very good agreement between the two data sets for July, when the surface
is snow free and SufAlbedo is 0.03. Statistics of COD data from the V2 data
set for August through November are similar. In contrast, CldOpt is 0 with
few exceptions for October and November, confirming that the low CldOpt
indicated in Fig. 11 for the year 2007 is the norm for these months. We
conclude that the high bias of 62 % of OMI EDDose data for October is a
consequence of the high value of the albedo climatology for this month, which
in turn leads to an underestimate of the COD.
Andøya, Norway
Andøya is located on the Norwegian coast north of the Arctic Circle. The
bias in March and April is less than ±6 %; SufAlbedo is about 0.25.
Winters are fairly mild due to the influence of the Gulf Stream and the
relative low value of SufAlbedo is therefore reasonable. The bias for June
through October is between 15 and 36 %, when SufAlbedo has an appropriate
value of about 0.05. The relatively large bias can therefore not be
explained by the OMI albedo climatology. When data are filtered for CldOpt = 0, the bias is reduced to 6–15 %. Hence, some portion of the bias is
due to the cloud correction.
Sodankylä, Finland
The bias at Sodankylä between February and October ranges between 5
and 13 % and tends to be larger in winter/spring than summer. SufAlbedo is
0.5 between February and April, drops to 0.03 by the beginning of June, and
remains below 0.03 for the remainder of the summer. Sodankylä is
surrounded by boreal pine forests and peatlands for which an albedo of 0.03
in the erythemal band is appropriate (Blumthaler and Ambach, 1988; Feister
and Grewe, 1995). Between June and August, a bias of 4–9 % is apparent in
DP (1), (2) and (4), both for all data and data filtered for CldOpt = 0. The
bias is therefore systematic and not related to potential errors in the CMF
applied by the OMI UV algorithm. About half of the bias is within the
uncertainty of the ground measurements.
Trondheim, Norway
Trondheim is located close to the coast of central Norway and has a
predominantly hemiboreal oceanic climate. The bias is between 55 and
69 % between February and April. SufAlbedo for this period is 0.6. The
albedo is likely too large considering that Trondheim is a city of 170 000 people and located on a fjord, about 50 km inland from the coast of central
Norway. An albedo of 0.6 enhances the clear-sky surface UV dose only by
30 % (Fig. 2). A large part of the observed bias
must therefore be caused by the Case 1 mechanism discussed earlier.
To provide further evidence that the Case 1 mechanism is indeed responsible
for the large bias observed for Trondheim, we filtered the ground-based
measurements for clear-sky conditions and re-calculated the bias between OMI
overpass data (DP 1) and ground-based measurements. The clear-sky filter
exploits the temporal variation in the measurements and takes advantage of
the fact that the multi-channel radiometer used at Trondheim provides a
measurement every minute. Data were considered clear-sky when the following
two conditions were met: (1) The UVI at a given time must deviate by less
than 1 % from measurements performed 1 and 2 min before and after this
time. (2) Condition (1) must be met for consecutive 15 min before and after
the time of interest. Periods of constant cloudiness may meet condition (1),
but are removed by condition (2).
The OMI data set does not include overpass data without the CMF applied. We
therefore calculated the CMF from the EDRate and CSEDRate data products and
divided the overpass erythemal dose rate (OPEDRate) by the CMF to reconstruct
the clear-sky overpass erythemal dose rate (CSOPEDRate).
Ratio of overpass erythemal dose rate (DP 1) measured by
OMI and the radiometer at Trondheim. Box-whiskers represent the distribution
of ratios filtered for clear-sky (blue, left of month marker) and all-sky
(green, right of month marker), and indicate the 5th and 95th percentile
(whisker), the interquartile range (box), median (line), and average (red
dot). Match-up data were filtered for SZA < 84∘ and Dis < 12 km.
Figure 13 compares box-whiskers calculated from the ratio of CSOPEDRate and
the filtered clear-sky ground data (blue) with box-whiskers calculated from
OPEDRate and “all-sky” ground data. The bias and variability of the
clear-sky subset are much smaller than the corresponding values for all-sky
data. For clear-sky data, the bias ranges between 16 % in August (when
SufAlbedo has an appropriate value of 0.04) and 44 % in March and April,
when SufAlbedo is 0.62. According to Fig. 2, an albedo of 0.62 enhances the
clear-sky UVI by 30 %. This theoretical value is consistent with the albedo
effect derived from the measurements (44–16 % = 28 %), assuming that
the observed summer-time bias of 16 % – which results from unknown causes
– also applies to winter months. This analysis suggests that the actual UV
albedo at Trondheim during winter is similar to that in summer, which is not
surprising considering the location of the instrument close to the center of
a large city.
During summer months, the biases of the clear- and all-sky data sets agree to
within 5 %, while in March and April, the all-sky bias exceeds the
clear-sky bias by 15 and 28 %, respectively. Furthermore, the distributions
of ρ1 for the all-sky data set are much more skewed towards larger
values compared to those of the clear-sky data set because attenuation by
clouds is underestimated by OMI as a result of the large value of SufAlbedo
used by the OMI UV algorithm. For example, the OMI data files indicate
clear-sky conditions (i.e., CldOpt = 0) in 65 % of data records for
March and April. This percentage is far too large considering that the median
cloud cover for these months is about 87 % according to weather data from
the Trondheim airport
(https://weatherspark.com/averages/28896/Stj-rdal-Nord-Trondelag-Norway).
This analysis confirms that the Case 1 mechanism that leads to the
overestimate by OMI is indeed composed of two components, one affecting the
computation of clear-sky data and one influencing the calculation of cloud
modification factors.
Finse, Norway
The instrument at Finse is located on a mountain top, 1210 m above sea level
and about 250 m above the tree line. The site is typically snow-covered
between the months of September and June/July. Because of this location,
surface conditions within the OMI pixel are generally different from those at
the instrument site, and a large difference between satellite and ground
observations can be expected. This is particular true for winter months when
the immediate vicinity of the instrument is snow covered while the boreal
forests within the OMI pixels are not. Indeed, the bias for February through
May varies between -45 and -61 %. SufAlbedo has a bimodal distribution
(either 0.55 or 0.70), which is likely too low on many occasions. Between
July and September, when the ground is snow free, the bias is less than ±3 %. This bias is smaller than for the other Norwegian sites. One
contributing factor for this relatively small bias is potentially the
proximity of Finse to Hardangerjøkulen, a 78 km2 large glacier
located 5 km north of Finse. Because of the closeness to the glacier, the
actual effective albedo for Finse during August could be larger than the
surface albedo of 0.06 used by OMI, which would increase the ground
measurement relative to the OMI observation and reduce the bias.
Jokioinen, Finland
Jokioinen is in the southwest of Finland, on the southern edge of the boreal
forest belt, and has a temperate climate. Snow cover extends from December
to March. The bias is -20 % between January and March, when SufAlbedo is
0.30. The actual albedo measured under overcast skies in February 2012 was
0.70 ± 0.08 (±1σ) according to Meinander et al. (2012).
The negative bias is therefore likely caused by the Case 2 mechanism.
Between April and November, the bias ranges between -1 and +6 % when
SufAlbedo is 0.02. Hence, the albedo climatology used by OMI between April
and December is almost ideal for this site and CMFs are calculated
correctly.
Østerås and Blindern, Norway
Østerås and Blindern are suburbs of Oslo, about 6 km apart. Biases
for both sites agree to within ± 2 % for all months except February
and March when the bias at Østerås is 6 % smaller than at Blindern.
Averaged over the year, the daily erythemal dose measured by OMI exceeds
that measured at Østerås and Blindern by 7 and 8 %,
respectively. SufAlbedo is about 0.15 between January and March and 0.02
between June and November, which are appropriate values. The influence of
clouds at both sites is substantial and the observed biases suggest that the
CMFs applied by the OMI UV algorithm are slightly too large.
Comparison with results by T07
Comparison of results from the present paper (PP) and those
published by T07.
a SC = snow cover, SF = snow-free, PSC = permanent snow
cover.
b Data are from Table 2 of T07.
Measurements at several sites discussed above (i.e., Eureka, Summit, Barrow,
Sodankylä, and Jokioinen) have also been compared with OMI data by T07.
Table 3 compares the medians ρ̃4 of these sites with those
reported by T07. Results agree to within ± 8 % with two exceptions:
Barrow in March and Jokioinen in July. Differences of a few percent can be
expected considering that the work by T07 is based on measurements performed
between September 2004 and March 2006 only, while the present study uses data
recorded between September 2004 and December 2012. In addition, values in
Table 3 from the present study refer to months where the surface conditions
are most certain (i.e., either snow covered or snow free), while the
classification of the surface condition applied by T07 is entirely based on
the OMI albedo climatology: when albedo was higher than 0.1, snow cover was
assumed, while the rest of the data were classified as snow free. As
discussed above (and also emphasized by T07), the true snow conditions may
diverge from the OMI albedo climatology. For Barrow, ρ̃4
for March (when snow is present) is 0.99, while T07 reports a value of 1.20.
The difference may be explained by the fact that the “snow cover” value by
T07 also includes data from May, September and October, months where also the
present study indicates large positive biases. For July at Jokioinen,
ρ̃4 is 0.99 according to the present study; the
corresponding value by T07 is 1.11. SufAlbedo for this month is 0.03, which
should be an accurate value, supporting the smaller bias reported here.
Suitability of measurements at Summit to detect drifts in satellite UV data
Results presented in Sect. 5.1.5 showed that measurements at a high elevation
site located at the center of a major ice sheet, such as Summit, are
potentially very helpful for satellite validation. Because of the high,
homogenous surface albedo at this site, cloud effects are suppressed,
resulting in very small day-to-day variations when comparing data from space
and the ground. The low variability afforded the detection of systematic
problems in the satellite data set and is also helpful for detecting
potential long-term drifts in satellite UV observations. Compared to
lower-elevation sites, Summit is less affected by increases in air
temperature and their effect on albedo. For example, He et al. (2013) found
that changes in short-wave surface albedo observed in Greenland between 2000
and 2012 were most pronounced at elevations between 500 and 2500 m, ranging
between -0.025 and -0.055 per decade. In contrast, the decadal change at
elevations above 3000 m was only -0.013. Future reductions in albedo due
increased deposition of organic aerosols cannot be excluded, however. For
example, the expected increase in boreal forests fire activity (Kelly et al.,
2013) could have a significant impact on black carbon (BC) deposition. The BC
content in the Summit snowpack is currently very low with the highest value
given in the literature being 1.5–2 ng g-1 (Hagler et al., 2007;
Doherty et al., 2010). During May and June 2011, the mean BC content measured
over the first 1–3 cm of the snowpack was 0.3 ± 0.3 ng g-1 and
simulations suggest that its impact on albedo is negligible (Carmagnola et
al., 2013). By taking into account the relationship between BC and snow
albedo (Hadley and Kirchstetter, 2012), we conclude that even a 10-fold
increase in BC at Summit would not significantly affect our ability to detect
drifts in satellite UV data using ground-based measurements at this site.
Conclusions and outlook
UV data of the OMI instrument aboard NASA's Aura satellite were compared
with measurements at 13 ground stations. OMI data files include several data
products including the erythemal irradiance at the time of the satellite
overpass, the erythemal irradiance at local solar noon, and the daily
erythemal dose. The biases between OMI and ground-based instruments
calculated for these data products are generally consistent, with few
exceptions. For example at Summit, the bias between OMI and ground-based
data evaluated at the time of the satellite overpass is almost constant
throughout the year. In contrast, the biases for noon-time erythemal
irradiance and the daily dose at this site increase from about -1 % in
March to 14 % in November. This annual cycle was attributed to a problem
in the OMI UV algorithm, specifically the calculation of the local-noon SZA.
The problem affects other sites to a similar degree.
At times when the surface albedo is known and correctly specified by the OMI
albedo climatology, OMI data tend to exceed ground-based measurements by
0–11 %. Examples include Alert in April (OMI daily dose is biased high by
2 %), Ny-Ålesund in August (6 % bias), Barrow in July (10 % bias),
and Østerås and Blindern year round (7 % bias). These positive
biases are quantitatively consistent with systematic differences between OMI
and ground-based measurements that have been observed at unpolluted,
snow-free mid- and low-latitude locations (e.g., Antón et al., 2010; Bais et
al., 2015; Cordero et al., 2014; Buntoung and Webb, 2010; Mateos et al.,
2013). Several studies have shown that the bias in OMI UV data increases
with increasing aerosol optical depth, in particular for absorbing aerosols
(Arola et al., 2009; Cachorro et al., 2010; Ialongo et al., 2008), and can
reach over 40 % in highly polluted areas (Cabrera et al., 2012) and in
regions affected by desert dust intrusions (e.g., Anton et al., 2012). We
did not address the effect of aerosols because our study focuses on pristine
high latitude sites with generally low aerosol optical depth.
When the OMI albedo climatology exceeds the actual albedo, OMI data can be
biased high by as much as 55 % (e.g., Trondheim in February and March). The
bias is caused by two effects that go in the same direction: an
unrealistically high value of the OMI albedo climatology leads to a high
estimate of the clear-sky irradiance and to an underestimate of attenuation
by clouds. In turn, when the OMI albedo climatology is too low, OMI data can
be biased low by as much as 59 % (e.g., Ny-Ålesund in March).
Calculated biases are generally consistent with those published by T07 for
those sites considered both by T07 and the present study. While relative
differences can be large, absolute differences in terms of the UVI remain
modest at all sites (e.g., the median bias is smaller than 2 UVI units at all
sites; Fig. 7) because the large SZAs prevailing at high latitudes limit the
UVI to less than 8 at all sites considered here. The relatively small UVIs
observed in the Arctic and the resulting modest differences between OMI and
ground observations should not lead to the conclusions that UV radiation and
its accurate measurement are not important. First, the day length in the
Arctic can be as long as 24 h and organisms that cannot escape the Sun may be
exposed to similar daily UV doses than those living at lower latitudes
(Bernhard et al., 2010). Second, UV reflections from snow-covered surfaces
can lead to considerable UV exposure to a person's face (Cockell et al.,
2001), the eyes of an animal, and man-made materials used outdoors
(Heikkilä, 2014).
A better albedo climatology could greatly improve the accuracy of OMI UV data
products even if year-to-year differences in albedo are not accounted for.
One way of improving the albedo climatology is to exploit an apparent
inconsistency in OMI data: when the albedo climatology is too large,
measurement of reflectance from space during clear skies can be lower than
the reflectance that is expected from the (high) value of the albedo
climatology. For locations and times where such an inconsistency is
repeatably observed year after year, the climatological value could be
reduced until the inconsistency disappears. The alternative is to combine
measurements from OMI with data from satellites that are also sensitive in
the IR or microwave region and which are able to distinguish reflectance from
clouds and snow.
Due to rapidly changing albedo conditions, typically taking place during
spring and fall at high latitudes and in mountainous regions, surface UV
radiation products will always suffer from poorly known albedo unless
real-time data are available. Several satellite-based snow products have been
developed recently for various applications. For example, the recently
published global broadband albedo time series based on 5-day interval AVHRR
data (Riihelä et al., 2013) could potentially improve the OMI albedo
climatology. Such new albedo data sets should be considered when the next
re-processing of OMI surface UV data will take place.
In order to improve the daily surface UV products targeted for the general
public, an alternative solution would be to use daily snow information. For
example, Aqua/MODIS snow products, which are observed close in time with OMI
measurements, could be implemented.
Results presented in this study also showed that measurements at a high
elevation site located at the center of a major ice sheet, such as Summit,
are very helpful for satellite validation. Because of the high homogenous
surface albedo at this site, cloud effects are suppressed, resulting in very
small day-to-day variations when comparing data from space and the ground.
Measurements at such a site are therefore ideally suited to detect systematic
problems or drifts over time in the satellite data set.
Standardized results plots
For each site and data product, a PDF page in a standardized format is
available as supplement to this paper. Figure A1 provides an annotated
example of such a page. The page consists of five panels, labeled a–f.
Panel (a) provides comparison statistics by months, specifically: Ni,
Mini, p5,i, p25,i, ρ̃i,
ρ¯i, p75,i, p95,i, Maxi, W10,i, W20,i, and W30,i. Panel (b) shows OMI and ground-based data
plotted versus time. Panel (c) is a scatter plot of OMI versus ground data.
Also indicated in Panel (c) are results of two linear regressions to the
data, one with the intercept calculated (red line) and one with the intercept
forced through the origin (green). Dashed black lines indicate ±20 %
deviations from the ideal 1:1 relationship (solid black line). Panel (d)
consists of four sub-panels showing the ratio of OMI and ground data
plotted versus time, ground-based measurements, and day of the year, plus a
box-whisker plot of the ratio statistics. Panel (e) provides similar plots
for the difference of OMI and ground measurements. Panel (f) provides for
every month a histogram of the frequency distribution of the OMI/ground
ratio. Note that the first plot of the sequence is the distribution for the
whole year rather than January. The number of data points (Ni) that
were used to calculate the distributions as well as ρ̃i
(green, labeled “Med”) and ρ¯i (red, labeled “Avg”) are
also indicated.
Example of a standardized page summarizing the results of
the comparison of OMI and ground-based erythemal daily dose data at Barrow.
Additional pages of this type are available as a Supplement. The contents of
panels (a)–(f) are explained in the text.
The Supplement related to this article is available online at doi:10.5194/acp-15-7391-2015-supplement.
Acknowledgements
Funding for this study was provided by the US National Science Foundation's
Office of Polar Programs Arctic Sciences Section (award ARC-1203250), the
Academy of Finland through the SAARA and INQUIRE projects, and the Norwegian
Climate and Pollution Agency (KLIF). The work was also partly supported by
the Research Council of Norway through its Centres of Excellence funding
scheme, project number 223268/F50. We are grateful to the numerous dedicated
individuals who have operated UV radiometers at the thirteen locations for
many years. We also thank two anonymous reviewers and the editor for their
constructive comments.Edited by: S. Kazadzis
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