MACC-II Deliverable D_122.7
Final report on
QC/validation procedures
for the UV chain
Date: 07/2014
Lead Beneficiary: FMI (#15)
Nature: R
Dissemination level: PP
Grant agreement n°283576
File: MACCII_RAD_D122_7_FMI.doc/.pdf
Work-package
Deliverable
Title
Nature
Dissemination
Lead Beneficiary
Date
Status
Authors
Approved by
Contact
122 (RAD, Radiation Service)
D_122.7
Final report on QC/validation procedures for the UV
chain
R
PP
FMI (#15)
07/2014
Draft version
Antti Arola (FMI), Mikko Pitkänen (FMI), Vaida Cesnulyte
(FMI), Anders V. Lindfors (FMI), Alessio Bozzo (ECMWF)
Marion Schroedter-Homscheidt (DLR)
info@gmes-atmosphere.eu
This document has been produced in the context of the MACC-II project (Monitoring Atmospheric
Composition and Climate - Interim Implementation). The research leading to these results has received
funding from the European Community's Seventh Framework Programme (FP7 THEME [SPA.2011.1.5-02])
under grant agreement n° 283576. All information in this document is provided "as is" and no guarantee or
warranty is given that the information is fit for any particular purpose. The user thereof uses the information
at its sole risk and liability. For the avoidance of all doubts, the European Commission has no liability in
respect of this document, which is merely representing the authors view.
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Executive Summary / Abstract
This report is a deliverable D122.7: “Final report on QC/validation procedures for the UV
chain”.
In this report we summarize the work that has been done during the period 10 2010 to 07
2014. We present three major study areas regarding the validation of the UV processor and
prognostic aerosols using ground-based spectral data, validation of UV processor using near
real time Broadband COST UV-I data. Moreover, we present a summary about our effort to
find the areas where to improve the UV processor, using a stand-alone version of it.
The UV processor and prognostic aerosol validation results show a good agreement
between the ECMWF model and the measurements, especially, for biomass burning case
(Alta Floresta and Mongu), where the model represents the burning seasons correctly. In
addition, a major feature of the model-measurements comparison is that the relative mean
bias is always smaller at 340 nm than at 500 nm, indicating a rather strong wavelengthdependent feature of the performance of AOD in the MACC system.
The current understanding of the UV processor, based on the validation against groundbased measurements, is that it is able to estimate the UVA wavelengths relatively well,
while it clearly overestimates the shortest wavelengths of UVB, particularly for the cases of
lowest solar zenith angles. We have attempted to find a reason for this bias in the surface
UV irradiance, by using a stand-alone version of the UV processor. This stand-alone version
can reproduce the pattern that has been seen also in the ground-based comparisons.
Currently the very reason for the large overestimation in the short UVB wavelengths is not
known, however we have checked several candidates and so far, as a result, ruled out that
they would cause such a large bias.
The NRT broadband UV comparison reveals that the daily cycle and seasonality of UV-I can
be reproduced well by the model. There is a good model-measurements agreement
considering clear-sky cases, where the timing and UV-I peak values are represented
correctly. However, for cloudy cases, UV-I variation in the model is rather significant.
We have published an article (Cesnulyte et al., 2014) based on validation of MACC
prognostic aerosols at UV and mid-visible wavelengths, which provides summarizes the
overall performance of the model in terms of various aerosol types. Main results and
conclusions of the manuscript are presented in Section 4.
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Table of Contents
1. Introduction ........................................................................................................................ 5
2. UV processor ....................................................................................................................... 6
3. Ground-based UV and aerosol data used for the evaluation ............................................. 7
4. Evaluation of UV processor and prognostic aerosols at UV wavelengths .......................... 8
4.1. Comparison of the modeled UV irradiances with available observations ...................... 8
4.2. Comparison of modeled aerosol types with available observations .............................. 9
4.3. Conclusions.................................................................................................................... 12
5. Development state of the UV processor .......................................................................... 14
5.1. Current status of UV Processor ..................................................................................... 14
5.2. Outlook for the future development work ................................................................... 17
6. Plan for the QC/Validation procedure during the operational phase ................................. 19
7. Validation of a Near Real Time (NRT) Broadband UV-I data ............................................... 22
7.1. Methodology ................................................................................................................. 22
7.2. Results ........................................................................................................................... 23
7.3. Conclusions.................................................................................................................... 24
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1. Introduction
During the GEMS project UV monitoring and forecasting capabilities were developed within
the ECMWF Integrated Forecasting System (IFS). This work has continued during MACC and
now in MACC-II. Each step in the consolidation and improvement of the system has been
accompanied by comparison against both ground-based and satellite-based UV data. During
GEMS AER sub-project, prognostic aerosol model was developed for IFS, which has since
been coupled with the UV processor. One of the special focuses has been on the aerosol
effect on UV through the prognostic aerosols. Therefore satellite-UV, which does not
account properly for absorbing aerosols, has not been anymore included in the comparisons
recently. In addition to UV comparisons, we also compared aerosol optical properties from
the processor against measurements from the Aerosol Robotic Network (AERONET).
In this final report we summarize the work that has been done during MACC-II project. We
present three major study areas regarding the validation of the UV processor and prognostic
aerosols using ground-based spectral data, validation of UV processor using near real time
Broadband COST UV-I data. Moreover, we present a summary about our effort to find the
areas where to improve the UV processor, using a stand-alone version of it.
We have published an article (Cesnulyte et al., 2014) based on validation of MACC
prognostic aerosols at UV and mid-visible wavelengths.
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2. UV processor
The ECMWF shortwave radiative transfer scheme (Morcrette, 1991; Morcrette, 2002) has
been extended to cover UV wavelengths with a choice of 0.2, 1, or 5 nm spectral resolution.
Its inputs are the distribution of temperature, ozone, cloud fraction and cloud liquid and ice
water over 60 vertical levels between the surface and 0.1 hPa, the surface pressure and the
surface albedo. Currently this UV processor is applied to the ECMWF forecast fields in a postprocessing mode, including the prognosed ozone and cloud fraction and water loading.
During the first steps of the development of UV processor, aerosol climatology was used.
And in the latest version, prognostic aerosols have been coupled with the UV processor.
More information about the UV processor can be found from Morcrette and Arola (2007),
while the prognostic aerosol scheme is described in earlier project reports.
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3. Ground-based UV and aerosol data used for the evaluation
For the evaluation of the UV processor against ground-based UV data, we have so far used
mainly data from the following two databases.
1) EUVDB: http://www.ozone.fmi.fi/uvdb/
2) NSF: http://uv.biospherical.com/
Both databases provide spectral UV measurements. We compared separately UVB (290320nm) and UVA (320-400nm) integrated broadband fluxes against those modeled by
ECMWF UV processor.
For aerosol data we used the measurements from the AERONET network (Holben, 1998).
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4. Evaluation of UV processor and prognostic aerosols at UV wavelengths
This section presents the results and main conclusions regarding to UV processor validation
and results from a published paper on a comparison of the modeled-measured AOD at UV
and mid-visible wavelengths (Cesnulyte et al., 2014). The manuscript combines the results
presented in previous deliverables.
4.1. Comparison of the modeled UV irradiances with available observations
Validation of UV irradiance has been carried out in MACC project and further in MACC-II.
Here we show a good overall agreement between modelled and measured UV irradiances
(Figure 4.1.1). The agreement is better for UVA wavelengths than for UVB with a significant
difference in winter and summer months. Clear overestimation can be seen in San Diego at
both UVA (320-400nm) and UVB (290-320nm) while underestimation in the rest of the UV
sites.
Figure 4.1.1. Summary of UV irradiance validation statistics for UVB (290-320 nm – dark
colour) and UVA (320-400 nm – light colour) compared to measured values from NSF and
EUVDB. Irradiances are calculated for summer (green circles) and winter (red circles) periods
taking into account that the of Oct-May represents winter in Barrow, Jokioinen, Sodankylä,
Thessaloniki, San Diego and summer at the same period in Palmer and McMurdo, whereas
Jun-Sep represents winter in Palmer and McMurdo and summer in the rest of the months.
Each point corresponds to a particular validation site: J – Jokioinen, M – McMurdo, P –
Palmer, B – Barrow, S – Sodankylä, D – San Diego, T – Thessaloniki.
Although the overall agreement is good for most of the sites, the spectral results for
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Thessaloniki shows that there is a strong overestimation for wavelengths close to 300nm
(Figure 4.1.2). The observed overestimation led to further investigations of possible reasons
that causes it and will be discussed in more details in Chapter 5.
Figure 4.1.2. Comparison between EUVDB Brewer and ECMWF UV irradiance in
Thessaloniki, 2003-2004, for all sky situations and solar zenith angles larger than 17 degrees.
Plot (a) is for UVA, (b) for UVB and (c) is for irradiance at 305-310 nm, which contributes
most efficiently to erythemally weighted UV irradiance. (d) Represents the ratio of ECMWF
to Brewer irradiance in spectral sense for the same data, for averages of different solar
zenith angle ranges.
4.2. Comparison of modeled aerosol types with available observations
In this section we present the performance of the MACC AOD as compared to ground-based
AERONET observations for the period of 2003–2006. The comparison of total AOD was done
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at 340 and 500 nm and the AOD of different components at 550 nm. Twelve AOD sites (Fig.
4.2.1) were selected for the comparison. We chose sites where both wavelengths (340 and
500nm) are available that have data for the period 2003–2006. The selected sites were
categorized in three groups: urban/anthropogenic, biomass burning and dust. This was done
in order to examine the model-measurements performance for different aerosol types.
Figure 4.2.1. AERONET stations included in the study. Sites are color-coded according to
expected dominant aerosol type: urban/anthropogenic (green), biomass burning (brown),
and dust (red).
Figure 4.2.2. ECMWF AOD compared to AERONET AOD in Ispra. (a) Monthly mean AOD550
for the period 2003–2006. The modeled total AOD550 consists of five components: sea salt,
(SS) dust (DU), organic carbon (OC), black carbon (BC), and sulfate (SO4). The corresponding
monthly mean AOD550 from AERONET is shown with a black line (extrapolated using
Ångström exponent at wavelength range of 440–870 nm). (b) Modeled AOD with respect to
measured AOD at 340 nm (blue dots) and 500 nm (red dots). Data points include only
observations, when AOD of both wavelengths are available. Black line represents 1:1 line. (c)
Ratio (AODec / AODaer, green line) and absolute difference (AODec − AODaer, blue line)
between modeled and measured AOD at 340 nm.
Figure 4.2.2 shows one example of comparison between MACC re-analysis (fdmj) and
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AERONET AODs at 550 nm for 4-year period in Ispra. The figure part (a) shows the
contribution by different aerosol components according to the MACC data. Both the model
and AERONET show seasonality with higher AOD550 values in the spring and summer. The
model captures the general AOD550 variation fairly well, although occasional peak values, for
example, March 2003 and October 2004, are clearly underestimated. The ECMWF model
indicates, that SO4 is the main aerosol component in Ispra. The model furthermore shows a
clear contribution by DU in the summer months.
Figure 4.2.2 (b) presents the performance of the MACC AOD340,500 versus AERONET
AOD340,500. The figure shows a large scatter and clear underestimation, which is stronger at
340 nm than at 500 nm (MB340 = −0.21, MB500 = −0.09). There is a reasonable correlation
that is similar at both wavelengths (CC340 = 0.61, CC500 = 0.60). A major part of the AOD340,500
values are below 2.5, with few exceptionally large values that go up to 4.
Figure 4.2.2 (c) shows the ratio and absolute difference between modeled and measured
AOD at 340nm in Ispra over the course of the year. Both, the ratio and the difference, show
underestimation of AOD340 for all months. Although the absolute difference (blue line) stays
rather constant throughout the year, the ratio (green line) is higher in the summer months.
Figure 4.2.3. Summary of aerosol validation statistics (rMB vs. CC) for modeled coarse
(SS+DU) and fine (OC+BC+SO4) mode AOD550 compared to coarse and fine mode AOD550
from AERONET SDA. Filled shapes indicate coarse mode AOD550, whereas empty shapes
indicate fine mode AOD550. Each point corresponds to a particular validation site: I – Ispra, M
– Mongu, R – Ilorin, P – La Parguera, C – Capo Verde, K – Kampur, E – El Arenosillo, S – Solar
Village, A – Alta Floresta, L – La Jolla, T – Thessaloniki, X – Xianghe.
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Figure 4.2.3 presents a comparison between the MACC coarse and fine AOD at 550 nm and
AERONET SDA retrievals by showing the relative mean bias (rMB) and the correlation
coefficient (CC) for all stations included in the study. The modeled coarse mode AOD 550 is
defined as SS+DU and fine mode AOD550 as OC+BC+SO4 and compared with measured
coarse and fine mode AOD550. For most of the urban sites (blue squares), there is a clear
underestimation in fine mode (empty squares) and overestimation in coarse mode AOD 550
(color-filled squares). The rMB for fine mode AOD550 averaged over all urban sites (rMBF,urb)
is -0.22; rMB for coarse mode AOD550 averaged over all urban sites (rMBC,urb) is 0.38. This
indicates that there is too much coarse mode and too little fine mode particles in the MACC
system.
The dust particles (green triangles), in particular, play a significant role due to a higher
contribution compare to sea-salt (as visually seen from difference between light and dark
blue color bars in Fig. 1-13 a in Cesnulyte et al. 2014).
Similar behaviour with an even larger difference between coarse and fine mode AOD 550 is
seen for biomass burning (red circles), where rMBs for fine and coarse mode AOD 550 values
averaged over all biomass burning sites are rMBF,biom = -0.22 and rMBC,biom = 0.64,
respectively. The dust sites exhibit a mixed behaviour. For sites located close to dust sources
(Capo Verde, El Arenosillo, Solar Village), the rMB is smaller for coarse mode than for fine
mode AOD550. However, the difference between coarse and fine mode is smaller than that
seen for urban and biomass burning sites (seen from the length of the lines connecting an
empty and color-filled shapes). La Parguera and Ilorin, on the other hand, show the opposite
pattern with a larger rMB for fine mode. The overall underestimation in Ilorin is most likely
due to the model parameterization of the dust sources included in the MACC system, since
there is no aerosol retrieval from the MODIS radiances over all Sahara, and also from the
fact that the MACC system might miss localized sources of anthropogenic aerosols.
For the sites where fine mode aerosols dominate (most of urban and biomass burning) the
correlation averaged over all sites representing different areas is higher for fine mode
AOD550 (CCF,urb = 0.59, CCF,biom = 0.89) than for coarse mode (CCC,urb = 0.51, CCC,biom = 0.33).
For the dust sites, the pattern is opposite with a higher correlation averaged over all dust
sites being for coarse mode AOD550 (CCC,dust = 0.81, CCF,dust = 0.57). Generally, the MACC
system tends to overestimate the coarse mode AOD550 for most of the sites.
4.3. Conclusions
For monthly AOD, the ECMWF model tends to follow the AERONET measurements rather
well, also representing the yearly cycle correctly for each of the sites. Hourly values,
however, exhibit a larger spread. In terms of correlation coefficient and relative mean bias,
the best agreement between modeled and measured AOD 340,500 values is seen in biomass
burning sites (Alta Floresta and Mongu). For these both sites, the ECMWF model is able to
capture the burning season correctly, however, some occasional peaks are underestimated.
The AOD for the dust sites included in our study also show rather good agreement with the
AERONET observations, and the ECMWF model follows the seasonal pattern in the observed
AOD fairly well. The urban sites have the lowest correlation and largest bias.
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Generally, a major feature of the model-measurements comparison is that the rMB is
always smaller at 340 nm than at 500 nm, and the difference between rMB at 340 and 500
nm averaged over all stations is approximately 0.2. Thus, that indicates a rather strong
wavelength-dependent feature of the performance of AOD in the MACC system.
Among all urban sites included in the comparison, Thessaloniki shows the largest difference
between the performance of AOD340 and AOD500. This is seen also in the Ångström
exponent, which is unrealistically low in the model, thus indicating this might have
something to do with the overall combination of too little fine mode particles and too many
coarse mode particles in the MACC system. This pattern is seen for almost all urban sites.
We also analyzed the behaviour of the Ångström exponent for the rest of the sites. The
results show that the Ångström exponent in the MACC system is too low for all sites
included in this study.
In addition, the wavelength dependent difference between MACC AOD and AERONET-based
AOD may be partly, but to a smaller extent, explained by the wavelength-independent
optical properties of different aerosol types assumed in the model. For instance, the same
refractive index was assumed for SO4 and OC. This assumption means that OC is not
absorbing. However, recently there has been growing evidence that some of the organic
species are strongly absorbing at UV wavelengths.
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5. Development state of the UV processor
5.1. Current status of UV Processor
The current understanding of the UV processor, based on the validation against groundbased measurements, is that it is able to estimate the UVA wavelengths relatively well,
while it clearly overestimates the shortest wavelengths of UVB, particularly for the cases of
lowest solar zenith angles (Figure 4.1.2d). It is therefore important to investigate the reason
and correction for this overestimation, since those wavelengths are very crucial in the
erythemal weighting, which in turn determines the UV index. Figure 4.1.2 shows first UVA
and UVB (panels a) and b), respectively) comparisons for Thessaloniki. On the other hand,
panel c) shows only 305-310nm part of UVB and the overestimation at the local noon (when
the solar zenith angle is smallest and thus the surface UV values are the highest) becomes
more visible than what one can interpret from the entire UVB comparison. Panel d) shows
the ratio spectrally, pointing out that the overestimation increases as the wavelength
decreases towards 300nm (as the solar zenith angle decreases). Therefore, the most
effective wavelengths that affect UV index get overestimated rather significantly.
This same pattern has been also confirmed by model-to-model comparisons, e.g. in the
comparisons shown in the Figures 5.1 and 5.2. They show the model comparisons between
the UV processor and uvspec of LibRadtran radiative transfer (Mayer et al., 1997). Both RT
models are run with the same input and assuming as similar model setup as possible, so
uvspec is run assuming two-stream solver, since two-stream approximation is also applied in
the UV processor. First of the figures shows the comparison for two solar zenith angles,
including only the ozone absorption (so without molecular scattering). On the other hand,
figure 5.2 shows the results when also Rayleigh scattering (molecular scattering) is included.
These figures confirm the overestimation and suggest that the overall pattern of systematic
spectral biases, as a function of solar zenith angle (seen also in the Figure 4.1.2), are due to a
combined effect of Rayleigh scattering and ozone absorption in the UV processor.
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Figure 5.1. Simulation with O3 absorption only. (a) UV surface irradiance for MACC (red line)
and UVSPEC (blue line) at SZA 62o and SZA 24o; (b) MACC/UVSPEC irradiance ratio at two
different solar zenith angles.
Figure 5.2. Same as figure 5.1, only simulations with O3 and molecular scattering (Rayleigh
scattering) combined.
We have attempted to find a reason for this bias in the surface UV irradiance, by using a
stand-alone version of the UV processor. This stand-alone version can reproduce the
pattern that has been seen also in the ground-based comparisons (e.g. in the Figures 5.1
and 5.2). Currently the very reason for the large overestimation in the short UVB
wavelengths is not known, however we have checked several candidates and so far, as a
result, ruled out that they would cause such a large bias. We have compared ozone crosssections, solar extraterrestrial spectrum, and different radiative transfer solvers among
other things to confirm compatibility of the two models, which is discussed shown next.
As our earlier analysis shows, the most considerable bias in the surface UV irradiances
appears to be below 320 nm, in the same region where UV absorption by ozone is most
effective. To investigate whether the ozone absorption in the UV-processor could influence
the UV bias; we compared the wavelength dependent ozone absorption cross sections with
corresponding values in literature. As the reference ozone absorption cross sections we use
the ones reported by Molina and Molina (JGR, vol. 91, pp 14501-14508, 1986) and provided
with libRadtran radiative transfer package. The ozone absorption cross sections in the UV
processor are based on the Molina and Molina too, however their temperature dependence
is parameterized differently than in the LibRadtran package. Therefore, by this comparison
we wanted to confirm that the ozone cross sections are correctly incorporated also in the
UV processor. The temperature dependence of the absorption cross sections was
investigated assuming different values of temperature, and the cross sections of the UV
Processor show the same consistency compared to Molina and Molina (1986) as the results
below in the temperature range from 220 K to 270 K.
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Figure 5.3. (a) Ozone absorption cross-section (solid lines) used in the UV processor and
their absolute difference (dashed lines) compared to Molina and Molina (1986). The
absolute differences (UV processor - Molina and Molina) are calculated for 0.2 nm, 1.0 and
5.0 nm wavelength resolutions. (b) Relative difference of ozone absorption cross-sections
between the UV-processor and Molina and Molina (1986). Temperature is 240 K.
Figure 5.3a shows the ozone cross sections used in the UV-processor and the magnitude of
the difference (UV processor - Molina and Molina) when compared with ozone cross
sections from Molina and Molina (1986). Importantly, the absolute differences below 320
nm are more than an order of magnitude smaller than the cross sections.
Figure 5.3b shows the relative difference for the corresponding cross sections and, again,
the region below 320 show the smallest differences of less than +-7%. On average the
relative differences are 1.4%, 2.8% and 0.3% higher in the UV-processor than in Molina and
Molina (1986) at 0.2 nm, 1 nm and 5 nm resolutions, correspondingly. Above 320 nm the
relative differences are considerably larger (Figure 5.3b), however the absorption crosssections and, also, their absolute differences are significantly smaller. Very small values of
the reference cross sections likely contribute to the high variability in relative differences.
From these results it can be concluded, that the ozone absorption cross sections in the UVprocessor are consistent with the corresponding values in the literature below 320 nm,
where the previously observed bias is most significant. Thus, if the positive bias in the
surface irradiances of the UV processor is related to ozone calculations, further
investigations should be directed into how ozone optical thickness and ozone transmission
are calculated and compare them directly with the corresponding results from uvspec.
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Figure 5.4. ET spectrum from uvspec and UV processor as a function of wavelength.
In order to investigate possible factors affecting the surface irradiance bias we compared
the solar extraterrestrial spectrum in both UV Processor and LibRadtran model. UV
processor uses ET spectrum of Kurudz (Kurucz, 1992) and it is spectrally convoluted for
different resolutions of the model, 0.2nm, 1nm, and 5nm. Therefore, we also wanted to
confirm that this convolution is done without any mistake. Figure 5.4 shows, as an example
the 1nm case, if compared to the corresponding ET spectrum from LibRadtran library of ET
spectrums and confirms that the extraterrestial spectrum also are essentially the same in
both models, thus the surface irradiance bias cannot be not caused by any significant
difference ET spectrum.
As shown in the Figures 5.1 and 5.2, significant bias can be seen when both models assume
the same RT solver, two stream, in this case. However, the choice of the solver could be a
partial explanation for the overestimation observed with ground-based comparisons. As a
test case we compared libRadtran simulations of surface UV irradiance using two different
RT solvers, one similar to the solver in UV processor (Delta-Eddington two-stream in plane
parallel geometry) and DISORT, a more advanced solver provided in libRadtran. In these
simulations DISORT showed lower surface irradiances by about 5 to 10 %, which implies,
that by choosing a more detailed RT solver, surface irradiance overestimation would be
decreased from the seen in Figure 4.1.2. It is not, however, enough to explain the total
overestimation produced by the UV processor.
5.2. Outlook for the future development work
To conclude, the UV processor has in previous analysis shown to cause an overestimation up
to 40% of surface UV irradiance below 320nm, when compared to measurements. In our
efforts to understand the source for this bias we used libRadtran radiative transfer tools to
produce surface irradiances with a similar setup to UV processor and were able to identify
and restrict a number of possible error sources. The total of overestimation by the UV
processor, however, still remains partly unexplained. These results will direct the near
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future investigations in MACC-III towards the calculation and comparison of ozone optical
thicknesses, ozone transmission and Rayleigh transmission separately between UV
processor and more advanced UV RT model (LibRadtran), in order to check and confirm
these relevant intermediate steps in the actual UV estimation.
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6. Plan for the QC/Validation procedure during the operational phase
During the operational phase, the evaluation of the UV processor is planned using both
spectral UV and broadband UV measurements. Typically there is a significantly longer delay
before the spectral data are updated in the databases, than what is the situation with the
broadband UV measurements. However, the accuracy of spectral data is better. Therefore,
by including both data sources, our aim is to obtain new validation data in a reasonable time
frequency, while still always exploiting also the best quality data to full extent. In the
following, we will briefly describe both types of data sources; the most likely stations to be
included and also the quality control and quality assurance included both in the spectral and
broadband data.
Spectroradiometers measure the spectrally resolved solar irradiance. If well maintained,
these instruments provide typically the most accurate UV measurements. However, there
are potential sources of uncertainty in these data, coming from a variety of instrument
dependent sources, thus requiring that these instruments are carefully characterized and
regularly calibrated. The quality control procedure of the spectral measurements in two
Finnish sites (Sodankylä and Jokioinen) is described in detail by Lakkala et al. 2008, and
other sites with quality-controlled data follow similar procedures. Cordero et al., 2008
estimated uncertainties between 4% and 6% in the absolute calibration of double
monochromator-based spectroradiometers. Additional uncertainties (e.g. imperfect cosine
response) result in an overall uncertainty for solar measurements of 7-9%.
In the operational validation of the UV processor, we plan to include spectrally resolved UV
data mainly from two different data bases: 1) EUVDB: http://www.ozone.fmi.fi/uvdb/
2)
NSF: http://uv.biospherical.com/
Unfortunately, EUVDB database has become less active in recent years, so there are only
four sites that provide new data there regularly enough for our purpose. Also, three sites
(Palmer, McMurdo, South Pole) that belonged previously to NSF, are now part of NOAA
Antarctic UV Monitoring Network, and they update the data record only once every 1-2 year.
We will exploit these data also whenever possible; however, the following table summarizes
those sites that will likely provide spectral data for our validation purposes in 6 month time
intervals.
Table 1. Spectrally resolved UV measurements planned to be included in the operational UV
validation.
Site name
Country
Instrument
Jokioinen
Finland
Brewer Mk-III
Sodankylä
Finland
Brewer Mk-II
Reading
UK
Bentham DM150
Uccle
Belgium
Bentham DTM300 / Jobin
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Yvon, Modified HD10
Barrow
AK, USA
SUV-100
Summit
Greenland
SUV-150B
Broadband UV instruments provide measurements over a specified waveband. The most
common type is designed to mimic the erythemal action spectrum, for instance SL501 by
Solar Light. Broadband instruments are usually calibrated by a spectroradiometer and
naturally they have then larger uncertainties than the spectral measurements. Uncertainty is
in the range 7-16% (Gröbner et al., 2007), imperfect cosine response being a major source of
error.
Multi-filter instruments measure at several narrow wavelength bands. They have typically
less than 10 bands with width of 1 nm to 10 nm, for instance GUV 541 from Biospherical
Instruments Inc, and somewhat lower uncertainty than in broadband UV measurements
(Johnsen et al., 2008).
For the validation of UV processor, we have previously included only spectral measurements.
However, for the operational validation, we plan to include also the following broadband UV
sites (that are part of COST-713 UV Index Database).
Table 2. Broadband and multi-filter measurements planned to be included in the operational
UV validation.
Site name
Country
Instrument
Florence (Lamma)
Italy
SL501A
Trondheim
Norway
GUV-541
Østerås
Norway
GUV-541
Ny-Ålesund
Norway
GUV-541
Landvik
Norway
GUV-541
Kise
Norway
GUV-541
Norway
GUV-541
Bergen
Norway
GUV-541
Blindern
Norway
GUV-511
Alomar, Andøya
Norway
GUV-541
Finse
alpine
reseach station
ecological
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Jokioinen
Finland
SL501A
Sodankylä
Finland
SL501A
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7. Validation of a Near Real Time (NRT) Broadband UV-I data
During MACC-II we have been participating also in validation activities of VAL sub-project,
using broadband UV data and a similar validation procedure that was described in the
previous section (Chapter 6). The overall 10 European sites were included in a comparison
of MACC_osuite version with respect to COST UV Index Database and so far covering the
period of January 2013 – April 2014 (Figure 7.1).
7.1. Methodology
Figure 7.1. NRT sites included in the comparison.
The UV processor performance against Ultraviolet radiation (UV) Index (UV-I)
measurements, taken from COST UV Index Database hosted by Finnish Meteorological
Institute (FMI) is evaluated in this report. NRT UV-I data is provided either every 30 min or
as daily maximum depending on different site. MACC_osuite has data coverage of every 3
hours.
In the ECMWF model output is represented at 5 nm spectral resolutions and knowing the
erythemal action spectrum (that is used to derive UV-I), spectral data are integrated to get
the UV Index that is further used in the comparison with ground-based measurements.
The UV index measurements are taken from two different types of instruments: multiband
filterradiometer and broadband detector that are operating among the 10 sites included in
the report. The instruments measuring spectral data are further convoluted with the
erythemal weighing function, in order to get UV-I. While, the broadband detectors account
for weighting function that mimics that of erythemal one.
The very best target for the uncertainty of broadband instruments falls within 10-15%
(Seckmeyer et al., 2006), however not all the recommended correction factors are
accounted for in these measurements, therefore, the uncertainties can be estimated to be
slightly higher, up to 20%. Norwegian SUV instruments, on the other hand, are slightly
different instruments and the network is very well maintained, so we anticipate those
uncertainties to be closer to 10-15% range.
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Most of the sites have measurements every 30 min, however, Uccle and Lindenberg provide
only maximum daily value (Uccle – max value, Lindenberg – max 30min average). Therefore,
daily UV-I cycle is not available from two later sites.
7.2. Results
Figure 7.2.1. Daily UV Index values of MACC_osuite against measurements from COST UV
Index Database (MUVI) in two Norwegian stations: Bergen and Østerås. The selected days
are 21st March 2014 and 17th March 2014. Time is given UTC.
Figure 7.2.1 shows daily UV-I cycle for two locations in Bergen and Østerås for particular
selected days. Good agreement between daily-modeled UV-I values compared to the
measured is seen in Østerås for clear-sky case with a slight overestimation at around noon.
It should be noted that, no cloud screening is introduced to the data in Figure 7.2.1. A clearsky case is selected based on visual evaluation and also from a smooth curve of measured
UV-I values (black line). UV-I increases in the beginning of the day and peaks at around 12
UTC. The selected days in both sites are very close to each other, however, the clear
difference by almost half in UV-I shows a cloud layer being present almost throughout the
day in Bergen. For most of the clear-sky cases, when a smooth measured UV-I daily curve is
observed, the model-measurements agreement is good. Cloudy days (broken black lines),
on the other hand, are not so well presented by the model (not shown). In both of the cases
(cloudy-day in Bergen and clear-sky day in Østerås), the model tends to overestimate UV
index values. Overall, for the clear-sky cases, the MACC_osuite captures daily UV-I values
well with correct UV-I peaking time. However, cloudy cases still remain challenging.
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Figure 7.2.2. Simulated monthly mean values of daily maximum UV-I for January 2013-April
2014 at ten sites along with NRT UV-I values from UV Index Database.
Figure 7.2.2 shows monthly mean values of daily maximum UV-I from January 2013 to April
2014. The maximum daily UV-I values from MACC_osuite are selected so that the time
differences between modeled and measured UV-I values around the noon are the smallest.
For all sites, UV Index gradually increases towards summer months with a peak in June-JulyAugust. MACC_osuite tend to show a rather similar pattern with a more sloped peak in July.
Finse exhibits a slight decrease in UV index in May. The best model-measurements
performance is seen for Bergen and Landvik in Southern Norway. Overall, the MACC_osuite
agrees well as compared to the measurements exhibiting a clear yearly cycle.
7.3. Conclusions
The NRT broadband UV comparison reveals that the daily cycle and seasonality of UV-I can
be reproduced well by the model. There is a good model-measurements agreement
considering clear-sky cases, where the timing and UV-I peak values are represented
correctly. However, for cloudy cases, UV-I variation in the model is rather significant. On the
yearly basis, the MACC_osuite tends to follow the measurements well. The best seasonal
representation remains for Bergen and Landvik in Norway. All sites exhibit the highest UV-I
values in summer. Overall, the model tends to underestimate monthly mean UV-I values for
most of the sites, especially during spring-summer months.
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