ASRAdvances in Science and ResearchASRAdv. Sci. Res.1992-0636Copernicus PublicationsGöttingen, Germany10.5194/asr-14-7-2017Evaluating meso-scale change in performance of several databases of hourly
surface irradiation in South-eastern Arabic PensinsulaMarchandMathildemathilde.marchand@transvalor.comAl-AzriNasserOmbe-NdeffotsingArmelWeyEtienneWaldLucienhttps://orcid.org/0000-0002-2916-2391Transvalor, Mougins, 06255, FranceSultan Qaboos University, Al Khod, 123, OmanTotal Energies Nouvelles, Paris La Défense, 92069, FranceMINES ParisTech, PSL Research University, Sophia Antipolis, 06904,
FranceMathilde Marchand (mathilde.marchand@transvalor.com)8February20171471511November201627January201731January2017This 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://asr.copernicus.org/articles/14/7/2017/asr-14-7-2017.htmlThe full text article is available as a PDF file from https://asr.copernicus.org/articles/14/7/2017/asr-14-7-2017.pdf
The solar hourly global irradiation received at ground level estimated by
the databases HelioClim-3v4, HelioClim-3v5 and CAMS Radiation Service are
compared to coincident measurements made in five stations in Oman and Abu
Dhabi. CAMS is an abbreviation of Copernicus Atmosphere Monitoring Service.
Each database describes the hour-to-hour changes in irradiation very well
with correlation coefficients greater than 0.97 for all stations. Each
database exhibits a tendency to underestimate the irradiation in this area;
the bias is small and less than 5 % of the average of the measurements in
absolute value. The RMSE ranges between 70 and 90 Wh m-2 (11 to
16 %). This validation of the three databases for this arid region on the
edge of the Meteosat coverage reveals satisfactory results. Each database
captures accurately the temporal and spatial variability of the irradiance
field. It is found that the three databases do not exhibit noticeable
geographical changes in performances and are reliable sources to assess the
SSI in this region.
Introduction
The energy received from the sun on a horizontal surface at ground level and
per unit surface is called the surface solar downward irradiation (SSI).
Hourly SSI is the irradiation received during one hour. The SSI plays a
major role in many domains such as weather and climate (Abdel Wahab et al.,
2009; Blanc et al., 2011, 2015; Lefèvre et al., 2007), ocean (Bell et
al., 2009), agriculture (Bois et al., 2008a), forestry (Colombo et al.,
2009), ecology (Wagner et al., 2011), oenology (Bois et al., 2008b), human
health (Juzeniene et al., 2011), energy (Kenfack et al., 2009; Szabó et
al., 2011), or architecture (Leloux et al., 2012; Rotar and Badescu, 2011).
Current means for SSI assessment are ground-based instruments which may
offer high to good quality measurements if well attended but are too few to
offer the coverage needed in space and time. Global meteorological
reanalyses exist that go several decades back in time. It has, however, been
shown that the SSI data from these have significantly lower quality than the
SSI data from satellite-based databases (Boilley and Wald, 2015). Accurate
assessments of the SSI can be made from satellite images which may
supplement radiometric measurements (Lefèvre et al., 2014; Wagner et
al., 2011).
Several databases have been constructed from satellite images that contain
15 min SSI. Among them, are the HelioClim-3 databases (version 4 and
version 5) and the CAMS Radiation Service (CAMS-Rad) detailed hereafter, where CAMS
stands for Copernicus Atmosphere Monitoring Service. These databases are
accessed on-line by several hundreds of users each year (Thomas et al.,
2016a). Many validations were performed and are still being performed to
supply users with the most complete knowledge of the accuracy of each
database. Such validations are made by comparing satellite-derived estimates
against coincident qualified ground measurements for various climates to
better understand the strengths and weaknesses of the databases. For
example, joint validations of these three databases were performed for nine
stations belonging to the Baseline Surface Radiation Network (BSRN) and
located in various climates in Europe (Thomas et al., 2016b), for nine
stations in Egypt (Eissa et al., 2015a), and in Brazil by Thomas et al. (2016a).
The present work contributes to this continuous effort and supports the
gathering of evidence of site-specific strengths and weaknesses of the
databases. It contributes to addressing the issue of the spatial consistency
of errors in the retrieval of hourly global irradiation. It focuses on the
South-eastern part of the Arabic Peninsula. The climate is BWh in the
Köppen-Geiger climate classification, i.e. an arid area with few
precipitations and hot air temperatures. Additionally, the proximity of the
Arabian Gulf affects the climate because of large relative humidity
encountered close to the coastline.
Description of measurements used for comparison and quality control
Measurements of hourly global SSI performed at five stations were collected
from the Directorate General of Meteorology of Oman for the sites Sunainah,
Muscat Airport, Sur and Adam Airport, and the National Center for
Meteorology and Seismology of the United Arab Emirates (UAE) for Madinat
Zayed, in the period from 2004 to 2016 (Fig. 1). The sites have below 150 m above average sea level, except Sunainah (257 m) and Adam Airport (328 m).
Geographical locations and coordinates of the five stations.
Measurements were quality-checked following the WMO procedure (1981). The
procedure applied to flag suspicious or erroneous measurements is described
in detail in Korany et al. (2016). Non plausible values, i.e. values
exceeding extremely rare limits and physically possible limits, were
rejected. The lowest values can be noise and are therefore insignificant. In
order to remove them, a threshold was set to 1.5 times the relative
uncertainty given by WMO (2008) for measurements of good to moderate quality
(13 %), i.e. approximately 50 Wh m-2. Only measurements exceeding
this threshold were kept; in this way, there was 0.3 % chance of having
insignificant very low values. Eventually, a visual analysis was performed
to remove outliers.
The family of Heliosat methods, the HelioClim databases and the CAMS
Radiation Service
The Heliosat family is a series of methods whose aim is to convert images
acquired by satellites into fields of SSI. The original method was published
by Cano et al. (1986). It assumes that the appearance of a cloud over a
pixel results in an increase of radiance in visible imagery. It examines the
change between the radiance that should be observed in cloud-free conditions
and that currently observed; this change is quantified by a cloud index. The
extinction of the downwelling radiation by the atmosphere is related to this
cloud index. Of interest here, is the follow-up Heliosat-2 (Rigollier et
al., 2004) which yields more accurate results and is easier to implement.
Espinar et al. (2009) analysed the influences of uncertainties of inputs on
the outcomes of Heliosat-2.
The Meteosat series of geostationary meteorological satellites have been
initiated by the European Space Agency and are currently operated by
Eumetsat, a European agency comprising the national weather offices. They
provide synoptic views of Europe, Africa and Atlantic Ocean. Images are
acquired in several spectral channels from which the presence of clouds and
their effects on radiation may be deduced.
Within the HelioClim project (Blanc et al., 2011), images of the first
generation of the Meteosat series were processed with the Heliosat-2 method
to create and update the HelioClim-1 database containing daily SSI
(Lefèvre et al., 2007). The HelioClim-1 database has been promoted to
Data Collection of Open Resources for Everyone (Data-CORE) by the GEOSS
(Global Earth Observation System of Systems) (Lefèvre et al., 2014). The
GEOSS Data-CORE is a distributed pool of documented data sets with full,
open and unrestricted access. Heliosat-2 was further adapted to processing
of images from the second generation of the Meteosat satellites (Cros et
al., 2006). Since 2004, Meteosat images are acquired every 15 min and
routinely processed to update the HelioClim-3 database containing 15 min
SSI, abbreviated hereafter in HC3. Access to the HelioClim databases is
given by the SoDa Service (www.soda-pro.com) (Gschwind et al.,
2006).
A clear-sky model is a model that predicts the SSI that should be observed
at a given site any time in cloud-free conditions. The Heliosat-2 method
combines the cloud index with the ESRA clear sky model (European Solar
Radiation Atlas, Page et al., 2001). This model is detailed in Rigollier et al. (2000) with corrections in Geiger et al. (2002). Main inputs are the
reflectance of the ground that originates from the Meteosat images
themselves (Moussu et al., 1989; Rigollier et al., 2004) and the Linke
turbidity factor which accounts for the optical state of the cloudless
atmosphere (Remund et al., 2003). Post-processing layers are applied when a
request for a time-series of SSI is launched to bring improvements and
corrections to the original HC3 database. For example, the SSI stored in HC3
is modulated to account for the actual elevation of the required location or
the shadowing effect of the horizon. In order to avoid several
re-processings of the whole set of images dating back to 2004, improvements
in HC3 are made by changes in the post-processing layers, leaving the
original database unchanged. HC3v4 is the most advanced version of HC3 using
the ESRA model.
Building on the approximation of Oumbe et al. (2014) for decoupling the
effects of the clouds from those of the cloudless atmosphere, Qu et al. (2014) have proposed to combine HC3v4 and the recent McClear clear sky model
(Lefèvre et al., 2013) in an attempt to overcome the fact that the
Remund's database of the Linke turbidity factor is not updated and does not
take into account temporal changes in the atmosphere turbidity. McClear
takes as input properties of the cloud-free atmosphere updated every 3 h
supplied by CAMS and provides estimates of the SSI that should be observed
in cloud-free conditions for any site in the world since 2004. McClear has
been validated against measurements performed in the BSRN all over the world
(Lefèvre et al., 2013) and in more restricted areas such as Egypt (Eissa
et al., 2015a), Israel (Lefèvre and Wald, 2016) and the UAE (Eissa et al.,
2015b). This combination of McClear and HC3v4 yields HC3v5. In brief, the SSI
is firstly computed as in HC3v4, then divided by the ESRA clear-sky SSI and
eventually multiplied by the McClear clear-sky SSI.
The new Heliosat-4 method differs from the previous Heliosat methods (Qu et
al., 2016) in its concept. It adopts the approximation of Oumbe et al. (2014) where the SSI is approximated by the product of the McClear SSI by a
modification factor which depends on solar zenith angle, cloud properties and
ground albedo. Cloud properties are provided by the German Aerospace Center
(DLR), and result from the processing of the different channels of the
Meteosat images by the APOLLO method (Qu et al., 2016). The ground albedo is
that from Blanc et al. (2014). CAMS-Rad exploits the Heliosat-4 method to
provide time series of 15 min SSI, starting in 2004. It can be accessed by
the Copernicus web site (https://atmosphere.copernicus.eu) or the SoDa web site.
Validation against the measurements and results
Coincident HC3v4, HC3v5 and CAMS-Rad estimates of hourly global SSI
G were obtained from the web sites for the five stations and the
same periods. Also provided were the corresponding time-series of the
irradiance at the top of atmosphere on horizontal: E0, from which one
may compute the clearness index: KT:
KT=G/E0,
Validation follows the ISO (International Organization for Standardization)
standard (1995) where the deviations are computed by subtracting measurements
for each instant from the databases estimates. Deviations were summarized by
the bias, the root mean square error (RMSE), and the correlation
coefficient. Relative values are expressed with respect to the average of
the measurements. The validation of KT was also
included.
The 2-D histograms of measured and estimated values are presented for Adam
Airport (Figs. 2, 3 and 4). Red, respectively dark blue, dots correspond to
regions with great, respectively very low, densities of samples. The plots
also present the number of samples, the mean observed value, the bias, the
RMSE, the correlation coefficient (CC) and the 1:1 line (y=x). Points
are aligned along the 1:1 line with a limited scattering. The bias is 7 Wh m-2 and the RMSE is 61 Wh m-2. The temporal changes in
G are well reproduced by HC3v4 as demonstrated by the very large
correlation coefficient: 0.976. One may note an overestimation of the
greatest G.
2-D histogram between in situ measurements and HC3v4. Mean observed
value, bias, standard-deviation and correlation coefficient are reported.
2-D histogram between in situ measurements and HC3v5. Mean observed
value, bias, standard-deviation and correlation coefficient are reported.
Figure 3 does not exhibit such an overestimation likely due to the better
estimation of cloud-free SSI by McClear compared to ESRA. The points are
well aligned though an overall underestimation is visible. The bias and RMSE
are respectively -20 and 66 Wh m-2. The correlation coefficient
is very large: 0.974. The bias for CAMS-Rad is very small: 1 Wh m-2
(Fig. 4) and the RMSE is similar to those for HC3v4 and v5: 68 Wh m-2. The dots are well aligned and the scattering is fairly
symmetrical with respect to the 1:1 line. Like the others, the correlation
coefficient is very large: 0.972.
Tables 1–4 show the results for G and
KT. The means of G (Table 1,
approximately 550 Wh m-2) and KT (Table 3, approximately 0.65) are large: the atmosphere is very often clear and not
turbid.
In order to assess the ability of a database to depict spatial variability,
the correlation coefficient between time-series of measurements was computed
for each pair of stations for G (Table 5). The same procedure is
applied to each database. Table 5 gives the correlation matrix of G
between stations. Upper right part of the matrix (in bold) is for
measurements, and lower left part is for databases. For a given database, the
closer the coefficients of the lower part to those of the upper part, the
more accurately this database depicts the variability in space. This was
performed only for stations covering the same period of time and for
coincident measurements: Sunainah and Adam Airport for the period 2010–2015
on the one hand, and Sur and Muscat Airport for 2004–2007 on the other hand.
The geodetic distance between these stations is approximately 200 and 170 km respectively.
2-D histogram between in situ measurements and CAMS-Rad. Mean
observed value, bias, standard-deviation and correlation coefficient are
reported.
Global hourly SSI. Number of coincident data, mean of measurements,
bias and RMSE and correlation coefficient for HC3v4. RMSE: root mean square
error.
Global hourly clearness index. Number of coincident data, mean of
measurements, bias and RMSE and correlation coefficient for HC3v4. RMSE:
root mean square error.
Relative bias, relative RMSE and correlation coefficient reported by
Eissa et al. (2015a), Thomas et al. (2016b) and this study.
HC3v4 HC3v5 CAMS-Rad BiasRMSECorrel. coeff.BiasRMSECorrel. coeff.BiasRMSECorrel. coeff.Egypt-9 to 2 %7 to 14 %0.820 to 0.948-3 to 4 %7 to 12 %0.868 to 0.948NANANASede Boqer-7 %13 %0.982-4 %12 %0.9840 %12 %0.974Oman UAE-11 to 1 %14 to 20 %0.969 to 0.972-9 to -1 %16 to 21 %0.968 to 0.975-5 to 0 %15 to 18 %0.959 to 0.983
NA: not available.
Discussion
The correlation coefficient in G is large for all stations and all
databases: it usually exceeds 0.97, meaning that the variability in time of
the measurements expressed as a variance is well reproduced by each
database. As a whole, HC3v5 exhibits the greatest correlation coefficients,
then HC3v4, then CAMS-Rad. These large correlation coefficients are partly
due to the fact that the daily course of the sun and seasonal effects are
well reproduced by models leading to a de facto correlation between
observations and estimates of SSI hiding potential weaknesses of models.
KT is a strict indicator of the ability of the
model to estimate the optical state of the atmosphere as its dependency with
the solar zenith angle is less pronounced than the SSI. Hence, the
correlation coefficient for KT is less than for
G: it ranges between 0.73 and 0.86. It can be concluded that each
database reproduces fairly well the variation from hour-to-hour of the
measured clearness index. The hierarchy in the databases is different:
CAMS-Rad performs best, then HC3v5, then HC3v4.
The bias for G is often small and less than 5 % in absolute value
(Tables 1 and 2), and 7 % in KT (Tables 3 and 4). It is negative in all cases but one, meaning that all databases
underestimate G. Though the periods of measurements are not the
same, there is sufficient overlap to conclude that there is no major change
in bias from one station to another for a given database. Madinat Zayed is
an exception for HC3v4 and HC3v5; the bias is very large -59 Wh m-2 (-11 %) and -50 Wh m-2 (-9 %). On the
contrary, the bias in Madinat Zayed for CAMS-Rad is similar to that of the
other stations. Accordingly, the cause of the large bias in HC3v4 and HC3v5
is likely related to the specifics of the method Heliosat-2. It is known
that the uncertainty on the ground albedo may have a great impact on the SSI
in Heliosat-2 (Espinar et al., 2009; Lefèvre et al., 2007). As a whole,
the surroundings of the stations in Oman are darker than the highly
reflective sand dunes surrounding Madinat Zayed site. This may partly
explain the special case of Madinat Zayed as an error here has a greater
influence than a similar error in the other stations. In addition, Oumbe et al. (2013) showed an overestimation of the aerosol optical depth by CAMS
which yields an underestimation in G. This may partly explain the
underestimation in CAMS-Rad.
Except the case of Madinat Zayed, one may observe that for a given station,
the bias varies only slightly from one database to another whether for
G or for KT. This holds also for the
RMSE, with the exception of Madinat Zayed; the RMSE is the same for a given
station whatever the database. Conversely, for a given database, the RMSE
varies only slightly from one station to another. The RMSE ranges between 70
and 90 Wh m-2 and is 11 to 16 % of the mean of the
measurements, which is very good for hourly SSI estimated from satellite
imagery.
Table 6 presents the relative bias, the relative RMSE and the correlation
coefficient reported by Eissa et al. (2015a) for several stations in Egypt
and for the period 2004–2009, and Thomas et al. (2016b) for the BSRN site of
Sede Boqer in Israel for the period 2004–2012. For a given database, one may
observe a tendency to underestimation in Oman-UAE compared to Egypt and Sede
Boqer: there is a tendency for increasing absolute value of bias. This yields
a greater RMSE for Oman-UAE than for Egypt and Sede Boqer.
The difference in bias between these three areas with the same arid climate
BWh may be party explained by the systematic errors in the ground albedo as
already discussed. Another explanation may come from the fact that the sites
in Oman-UAE are farther from the nadir of the Meteosat satellites and that
the angles under which the space-borne sensor views these sites are greater
than for Egypt or Israel. This implies a greater influence of the parallax
effect and a greater size of the pixel which is approximately 5 km in Egypt,
and 8 km in Oman-UAE.
If a pixel is covered by clouds, the parallax effect shifts these clouds
westwards. The sensor aboard the satellite does not see exactly what is
happening in the atmospheric column right above a measuring station. This
contributes to the deviation between satellite-derived SSI and in situ
measurements. The effect of the parallax on G is more pronounced
when the cloud cover is fragmented, i.e. when the spatial variability in the
cloud cover is large. It is less pronounced when the cloud cover is
homogeneous or when the sky is clear, because a shift of homogeneous
conditions has a small impact. A larger pixel increases the chance to
observe clouds. Though clouds are not very frequent as shown by the large
mean value of KT, they are not absent and a
patchwork of small broken clouds such as cumuli over a large pixel may be
interpreted by the Heliosat-2 and Heliosat-4 methods as a large thin cloud.
This may contribute to the deviation. In addition, a greater chance to
observe clouds means also a greater chance to have a noticeable parallax
effect.
While the location of Brazilian sites is also on the very edge of the
Meteosat field-of-view, similarly to Oman and UAE sites but with opposite
sign in latitude and longitude, the relative bias observed for Brazil
(Thomas et al., 2016a) differs from that in Oman and UAE. It is between
-3 and 13 % for HC3v4, -3 to 7 % for HC3v5, and 2 to 16 % for
CAMS-Rad. It is most often positive and close to 0 % for HC3v4 and HC3v5,
and always positive for CAMS-Rad. There is an overestimation in Brazil and
underestimation in Oman and UAE. The difference in the influence of the
parallax effect may be due to the difference in cloud cover and in type of
clouds: there are more sunny days in Oman and UAE than in Brazil, and the
cloud cover is more scattered.
The correlation between measurements made at Sunainah and Adam Airport, and
Sur and Muscat Airport is large in both cases: 0.956 and 0.945 (Table 5).
The correlation coefficients of the three databases are close: approximately
0.96. Hence, one may conclude that the spatial variability of the SSI within
each pair of stations is well reproduced by each database. One may note a
slight overestimation of the spatial correlation by each database. This can
be partly explained by the coarse spatial and temporal resolutions of
several inputs to Heliosat-2 and Heliosat-4 methods. CAMS products on
aerosols and total content in water vapour and ozone are available every 3 h. Their spatial resolution is approximately 120 km along a longitude before
2014 and 80 km after 2014. As for the Linke turbidity factor input to the
ESRA model, there is one value per calendar month and its variability in
space is fairly smooth. This spatially smooth variability of the inputs
increases the spatial correlation. A similar conclusion was reached by
Lefèvre and Wald (2016) in cloud-free conditions with stations located
approximately 100 km apart in similar arid climate.
The group composed of Sur and Muscat Airport is close to the coastline and
exhibits more negative bias than the group composed of Sunainah and Adam
Airport which are more inland. This cannot be explained by the increase of
parallax effect as the viewing angles are very similar for all stations. The
probable reason should be found in local weather due to the temperature
gradients between land and sea with effects on clouds, aerosols, and water
vapour. These effects cannot be accurately observed because of the low
spatial resolution of the inputs to the Heliosat-2 and Heliosat-4 methods,
the best resolution being 8 km.
Conclusions and perspectives
This paper presents a comparison of three satellite-derived radiation
databases against the measurements of 5 stations in Oman and Abu Dhabi. A
great deal of attention has been paid to the quality of the measurements
prior to the comparison.
It was found that the three databases reproduce the hour-to-hour changes in
SSI very well with correlation coefficients greater than 0.97 for all
stations. Each database exhibits a tendency to underestimate the SSI in this
area. With the exception of Madinat Zayed (Abu Dhabi) for the HC3 databases,
the bias is small and less than 5 % in absolute value. There is no major
change in bias from one station to another for a given database. Conversely,
and except Madinat Zayed, it is found that for a given station, the bias
varies only slightly from one database to another whether for the SSI or the
clearness index.
This is also true for the RMSE: it is the same for a given station whatever
the database, and conversely, for a given database, it varies only slightly
from one station to another. The RMSE ranges between 70 and 90 Wh m-2 and is 11 to 16 % of the mean of the measurements,
which is very good for hourly SSI estimated from satellite imagery.
Performances are still far from WMO standards: 95 % of the deviations less
than 20 % for data of moderate quality (WMO, 2008).
Despite the identified drawbacks, this validation of the three databases for
this arid region which is on the edge of the Meteosat coverage reveals
satisfactory results. The three databases capture accurately the temporal
and spatial variability of the irradiance field. It is found that the three
databases do not exhibit noticeable geographical changes in performances and
are reliable sources to assess the SSI in this region.
The present work is a contribution to the continuous effort of validation of
the three databases to evidence site-specific strengths and weaknesses of the
databases. It enables a better knowledge of the capacity of these databases
in estimating the SSI.
Data availability
Station data for surface solar irradiance were received from the Directorate
General of Meteorology of Oman and the National Center for Meteorology and
Seismology of the United Arab Emirates after writing a request. Time-series
of CAMS-Rad data may be downloaded for free after registration at the
Copernicus CAMS web site (https://atmosphere.copernicus.eu, CAMS, 2016) or the SoDa web
site (www.soda-pro.com, SoDa, 2016). The output comprises the irradiation at the top of
atmosphere. Time-series of HelioClim-3v4 or HelioClim-3v5 data may be
downloaded from the SoDa web site (www.soda-pro.com) managed by the company
Transvalor. Data is available to anyone for free for years 2004–2006 as a
GEOSS Data-CORE (GEOSS Data Collection of Open Resources for Everyone) and
for-pay for the most recent years with charge depending on requests and
requester. The output comprises the irradiation at the top of atmosphere. The
time-series for the stations listed in this article are available for free in
CSV format by request to Mathilde Marchand.
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank the Directorate General of Meteorology of Oman and the
National Center for Meteorology and Seismology of the United Arab Emirates
for providing measurements of solar radiation. The authors thank the two
anonymous reviewers whose comments greatly help in improving the quality of
the text. The research leading to these results has been partly undertaken
within the Copernicus Atmosphere Monitoring Service (CAMS) of the European
Union.
Edited by: S.-E. Gryning
Reviewed by: two anonymous referees
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