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Long term satellite hourly, daily and monthly
global, beam and diffuse irradiance valida-
tion. Interannual variability analysis.
Pierre Ineichen
University of Geneva
December 2013
SolarGIS map
Long term satellite hourly, daily and monthly
global, beam and diffuse irradiance valida-
tion. Interannual variability analysis.
Pierre Ineichen
University of Geneva
Abstract
Satellite derived solar radiation is nowadays a good alternative to ground
measurements for renewable energy applications. It has the advantage to provide
data with a good accuracy, the best time and space granularity, in term of real time
series and average year such as TMY.
This report presents results of a long term validation in the European and Mediteranean
region of six nowcast satellite products in hourly, daily and monthly values, and six
average products on an annual basis. The performance of all the products is put
forward with the natural interannual variability; for comparison purpose, the SatelLight
model is also included in the results.
The main results are:
- the accuracy of the derived global irradiance reaches 17% with no bias, and
34% for the beam component with a negligible bias,
- even with some high discrepancies for specific sites and models, on the ave-
rage, all the products provide the annual global irradiation within one standard
deviation of the interannual variability, with a bias standard deviation from 2%
to 5%.
- eight of the nine models provide beam irradiance within one standard deviation,
the best bias standard deviation is 6%.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Table of content
Nomenclature
1. Introduction p. 1
2. Ground data p. 1
3. Satellite data: nowcasting or «real time» models p. 2
3.1 SolarGis p. 2
3.2 Helioclim-3 p. 3
3.3 Solemi p. 4
3.4 IrSOLaV p. 4
3.5 EnMetSol p. 5
3.6 Heliomont p. 5
3.7 Satellight p. 6
4. Satellite data: average and typical years p. 7
4.1 PVGIS p. 7
4.2 WRDC p. 7
4.3 RetScreen p. 8
4.5 Meteonorm p. 8
4.6 ESRA p. 8
4.7 Averaged nowcast models p. 8
5. Data quality control p. 8
5.1 Time stamp p. 9
5.2 Sensor calibration p. 10
5.3 Components consistency p. 14
6. Hourly, daily and monthly comparison indicators p. 15
6.1 First order statistics p. 15
6.2 Second order statistics p. 16
6.3 Model-measurements difference distribution p. 17
7. Interannual variability analysis method p. 18
8. Ground data validity assessment, calibration and stability p. 19
8.1 Comparison with Aeronet network p. 19
8.2 Long term stability p. 20
8.3 Components coherence p. 21
8.4 Data validation for the interannual variability p. 23
9. Validation results p. 24
9.1 Hourly, daily and monthly validation p. 24
9.2 Frequency distribution p. 29
9.3 Interannual variability p. 30
10. Conclusions p. 32
11. Acknowledgements p. 32
12. Bibliography p. 33
Annex p. 37
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Nomenclature
G
h
or GHI global horizontal solar irradiance or irradiation
G
hc
clear sky global horizontal solar irradiance or irradiation
B
n
or DNI normal beam (or direct) solar irradiance or irradiation
D
h
or DIF diffuse horizontal solar irradiance or irradiation
B
nc
clear sky normal beam solar irradiance or irradiation
G
sat
modeled solar irradiance or irradiation
G
mes
measured solar irradiance or irradiation
I
o
extra-atmospheric solar irradiance
K clearness or clear sky index
K
t
global clearness index (normalized by I
o
)
K
t’
modified global clearness index
K
c
global clear sky index (G
h
normalized by G
hc
)
K
d
diffuse clearness index
K
b
beam clearness index
K
bc
beam clear sky index (B
n
normalized by B
nc
)
T
L
Linke turbidity coefficient
T
Lam2
Linke turbidity coefficient at air mass = 2
aod atmospheric aerosol optical depth
w atmospheric water vapor content or column
cda
aerosol optical depth of a clean and dry atmosphere
w
water vapor atmospheric optical depth
T
a
ambiant temperature at 2m
RH relative humidity at 2m
h solar elevation angle
AM atmospheric air mass
n cloud index
planetary albedo
g
overcast sky planetary albedo
c
clear sky planetary albedo
mbd mean bias difference
rmsd root mean square difference
sd standard deviation
bsd bias standard deviation (standard deviation of the bias)
R correlation coefficient
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
1. Introduction
Meteorological satellite images as data sources to evaluate the ground irradiance
components become the state of the art in the field of solar energy systems. The
strongest argument is the high spatial coverage, and the fifteen minutes temporal
granularity. They also have the advantage to provide nowcast data used for example
to assess the proper operation of a solar plant. On the other hand, long term ground
data are very scarce concerning the beam irradiance. The use of secondary inputs
such as polar satellite data and ground information increases significantly the precision
of the algorithms, mainly for the beam component. Following a paper from Zelenka
(1998) concerning the nuggets effect, the interpolation distance to the nearest ground
measurement site is limited to 10 to 30 km, depending on the irradiance parameter;
this strengths the satellite derived data argument.
Many Universities and private companies provide satellite derived data, freely or for
pay, averaged over 8 years (Meteosat second generation is operational since April
2004) or in real time (nowcasting), and integrated over different time ranges. We
choose six European data providers to conduct a long term validation (2004 - 2011)
against ground measurements, for both the global and the beam components, and
based on hourly, daily and monthly values.
2. Ground data
Data acquired at eighteen ground sites are used for the validation, with up to 16
years of continuous measurements; for the validation itself, due to the satellite
variability, only data from 2004 to 2011 are used. The data acquired before 2004 are
Table I List of the ground sites with the latitude, longitude, altitude, climate, the acquired parameters
and the origin of the data
Site Gh Bn Dh latitude longitude altitude climate origin
Almeria (Spain) x x x 37.092 2.364 491 dry, hot summer PSA
Bratislava (Slovakia) x x 48.166 17.083 195 semicontinental CIE
Carpentras (France) x x x 44.083 5.059 100 mediternean BSRN
Davos (Switzerland) x x x 46.813 9.844 1586 alpine PMO/SLF
Geneva (Switzerland) x x 46.199 6.131 420 semicontinental CIE
Kassel (Germany) x x x 51.312 9.478 173 temperate humide FhG
Lerwick (Great Britain) x x x 60.133 1.183 82 cold oceanic GAW
Lindenberg (Germany) x x x 52.210 14.122 125 moderate maritim BSRN
Madrid (Spain) x x x 40.450 3.730 650 semiarid UMP
Nantes (France) x x 47.254 1.553 30 oceanic CSTB
Payerne (Switzerland) x x x 46.815 6.944 490 semicontinental BSRN
Sede Boqer (Israel) x x x 30.905 34.782 457 dry steppe BSRN
Tamanrasset (Algeria) x x x 22.780 5.510 1400 hot, desert BSRN
Toravere (Estonia) x x x 58.254 26.462 70 cold humid BSRN
Valentia (Ireland) x x 51.938 10.248 14 oceanic GAW
VaulxenVelin (France) x x x 45.778 4.923 170 semicontinental ENTPE
Wien (Austria) x x 48.250 16.367 203 continental GAW
Zilani (Letonia) x x x 56.310 25.550 107 cold humid GAW
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
used to illustrate the interannual variability. The list of the stations is given in Table I,
with their characteristics. The climate range covers desert to oceanic, the latitude
from 20°N to 60°N, and the altitudes from sea level to 1580 meters.
The concerned parameters are the global irradiance on a horizontal plane G
h
(or
GHI), the normal beam irradiance B
n
(or DNI) and the horizontal diffuse irradiance D
h
(or DIF). For some sites, only the beam or the diffuse component is acquired.
The ground data are kindly provided by the Baseline Surface Radiation Network (BSRN),
the Global Aerosol Watch project (GAW), the CIE International Daylight Measurements
Program (Commission internationale de l’éclairage IDMP), the Centre Scientifique et
Technique du Bâtiment (CSTB) in Nantes, the Universidad Politécnica de Madrid (UMP),
the Ecole National des Travaux Publiques (ENTPE) of Lyon, the Deutsches Zentrum
für Luft und Raumfahrt (DLR), the Frauhenhofer Institute in Freiburg, the Institute of
Construction and Architecture of the Slovak Academy of Sciences, the Institut für
Schnee- und Lawinenforschung (SLF) and the Physikalisch-Meteorologisches
Observatorium Davos (PMOD).
High precision instruments (WMO standards) such as Kipp+Zonen CM10, Eppley PSP
pyranometers, and Eppley NIP pyrheliometers, are used to acquire the data. A stringent
calibration, characterization and quality control was applied on all the data by the
person in charge of the measurements; the coherence of the data for all the stations
was verified by the author and is described in section 5.
3. Satellite data: nowcasting or «real time» models
For the «real time» comparison, six different products are validated in the present
study. The methodology and the input parameters are described in the following
section. DLR provide data derived with three different climatologies for Solemi,
University of Oldenburg provided data EnMetSol based on two different clear sky
models. Only the best results are kept in this report.
3.1 SolarGis
In SolarGis (GeModel Solar), the irradiance components are the results of a five
steps process: a multi-spectral analysis classifies the pixels, the lower boundary (LB)
evaluation is done for each time slot, a spatial variability is introduced for the upper
boundary (UB) and the cloud index definition, the Solis clear sky model is used as
normalization, and a terrain disaggregation is finally applied.
Four MSG spectral channels are used in a classification scheme to distinguish clouds
from snow and no-snow cloud-free situations. Prior to the classification, calibrated
pixel values were transformed to three indices: normalized difference snow index
(Ruyter 2007), cloud index (Derrien 2005), and temporal variability index. Exploiting
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
the potential of MSG spectral data for snow classification removed the need of additional
ancillary snow data and allowed using spectral cloud index information in cases of
complex conditions such as clouds over high albedo snow areas.
In the original approach by Perez (2002), the identification of surface pseudo-albedo
is based on the use of a lower bound (LB), representing cloudless situations. This
approach neglects diurnal variability of LB that is later corrected by statistical approach.
Instead of identifying one value per day, LB is represented by smooth 2- dimensional
surface (in day and time slot dimensions) that reflects diurnal and seasonal changes
in LB and reduces probability of no cloudless situation.
Overcast conditions represented in the original Perez model by a fixed Upper Bound
(UB) value were updated to account for spatial variability which is important especially
in the higher latitudes. Calculation of cloud index was extended by incorporation of
snow classification results.
The broadband simplified version of Solis model (Ineichen 2008a) was implemented.
As input of this model, the water vapor is derived from CFSR and GFS databases
from NOAA NCEP, and the atmospheric optical depth is calculated from MACC database
from ECMWF (Cebecauer 2011)
Simplified Solis model was also implemented into the global to beam Dirindex algorithms
to calculate Direct Normal Irradiance component (Perez 1992, Ineichen 2008c). Dif-
fuse irradiance for inclined surfaces is calculated by updated Perez model (1987).
Processing chain of the model includes post-processing terrain disaggregation algorithm
based on the approach by Ruiz-Arias (2010). The disaggregation is limited to
shadowing effect only, as it represents most significant local effect of terrain. The
algorithm uses local terrain horizon information with spatial resolution of 100 m.
Direct and circumsolar diffuse components of global irradiance were corrected for
terrain shadowing. Snow cover is taken from GFS and CSFR (NOAA).
3.2 Helioclim-3
The Helioclim 3 data bank is produced with the Heliosat-2 method that converts
observations made by geostationary meteorological satellites into estimates of the
global irradiation at ground level. This version integrates the knowledge gained by
various exploitations of the original Heliosat method and its varieties in a coherent
and thorough way.
It is based upon the same physical principles but the inputs to the method are calibrated
radiances, instead of the digital counts output from the sensor. This change opens
the possibilities of using known models of the physical processes in atmospheric
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
optics, thus removing the need for empirically defined parameters and of pyranometric
measurements to tune them. The ESRA models (ESRA 2000, Rigollier 2000 and
2004) are used for modeling the clear-sky irradiation. The assessment of the ground
albedo and the cloud albedo is based upon explicit formulations of the path radiance
and the transmittance of the atmosphere. The turbidity is based on climatic monthly
Linke Turbidity coefficients data banks.
The Liu and Jordan (1960) model is used to split the global irradiance into the diffuse
and beam components.
3.3 Solemi
The transfer of the extraterrestrial solar irradiance to the Earth's surface is influenced
by various constituents of the atmosphere. Ozone, aerosol and water vapor are
modelled with long time climatological and reanalysis data sets. Clouds which have
the largest influence and highest variability are determined from the half hourly
Meteosat images. Both visible and infrared channels are used to improve the detection
at sunrise and sunset and of high cirrus clouds.
For the global irradiance G
h
, an algorithm based on the Heliosat method (Cano et al.
(1986, Hammer et al. 2000 and 2003) is implemented. Contrary to the majority of
the other schemes, the beam component is directly derived from the satellite images
by the method of Schillings et al. (2003). Instead of using a general turbidity index
like most other procedures, each important constituent is treated separately with the
help of the Bird clear sky model (1984).
The atmospheric water vapor w is taken from the NOAA-NCEP (National Oceanic and
Atmospheric Administration - National Centers for Environmental Prediction) NCDC
data (National Climatic Data Center), and the impact of aerosols (aod) is taken from
NASA-GISS (National Aeronautics and Space Administration – Goddard Institute for
Space Studies) GACP-data (Global Aerosol Climatological Project). From these data
sets the transmission of the cloud-free atmosphere is calculated.
The cloud parameterization scheme is a two-channel procedure, which uses the visible
channel of Meteosat (0.45 µm to 1 µm) and the infrared channel (10.5 µm to
12.5 µm.).
3.4 IrSOLaV
In the IrSolAv irradiance derivation scheme, the cloud index n is derived using the
methodology developed by Dagestad and Olseth (Dagestad and Olseth, 2007) with
some modifications in the ground albedo determination. The ground albedo is
computed from a forward and backward moving window of 14 days taking into
account its evolution during the day, as function of the co-scattering angle.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
The global horizontal irradiance G
h
is then evaluated from the cloud index with the
model proposed by Zarzalejo (Zarzalejo et al., 2009); it uses as independent variables
the cloud index, the 50-percentile of the cloud index for a given place, and the air
mass AM. The normal beam irradiance B
n
is calculated from the global irradiance with
the help of Louche correlation (Louche et al., 1991).
In a second step, the clear sky conditions are identified with the algorithm proposed
by Polo (Polo et al., 2009a; Polo et al., 2009b); for these clear conditions, the
irradiances are evaluated with the ESRA clear sky model (Rigollier 2000), using the
aerosol optical depth aod taken from Soda, MODIS or from a method proposed by
Polo (Polo et al., 2009a) depending on their availability.
3.5 EnMetSol
The EnMetSol method is a technique for determining the global radiation at ground by
the use of data from a geostationary satellite (Beyer 1996, Hammer 2003). It is
used in combination with a clear sky model to evaluate the 3 irradiance parameters
G
h
, D
h
and B
n
. The key parameter of the method is the cloud index n, which is
estimated from the satellite measurements and related to the transmissivity of the
atmosphere via
K
c
= 1 – n
where the transmissivity is expressed by the clear sky index K
c
defined as the ratio of
global irradiance G
h
and the corresponding clear sky irradiance G
hc
:
hc
h
c
G
G
K
Two sets of data produced with the EnMetSol algorithm will be analyzed, corresponding
to two different clear sky irradiance models:
the model of Dumortier (Fontoynont 1998, Dumortier 1998) with the Remund
(2009) Meteonorm HR high resolution data base for the turbidity input,
and the original Solis clear sky model (Mueller 2004) with monthly averages of
aod (Kinne 2005) and water vapour content (Kalnay 1996) as input parameters.
For the Dumortier clearsky, a diffuse fraction model (Lorenz 2007) is used to calculate
the all sky diffuse horizontal irradiance (via G
h
- D
h
). A recently developed beam
fraction model (Hammer 2009) is used to calculate the B
n
for all sky conditions with
the Solis model.
3.6 Heliomont
In the Heliomont MeteoSwiss process (Stoeckli 2013), the all sky incident surface
solar radiation fluxes at the earth’s surface are calculated by combining the clear sky
surface radiation fluxes from a radiative transfer model with the radiative cloud forcing
derived from satellite data. This method is commonly referred to as “Heliosat” (Cano
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
et al., 1986), and was successively adapted for the EUMETSAT MFG satellite (Möser
and Raschke, 1984). It relies on the so-called “cloud index” which exploits the radiative
properties of clouds in the visible solar spectrum. When viewed from space, the
cloud transmittance is inversely related to the cloud reflectance. It empirically accounts
for the absorption, reflection and scattering of solar radiation in clouds under the
assumption that clouds are substantially brighter than the underlying surface.
For bright surfaces, the infrared cloud index replaces the visible (classical) cloud
index. Often a slot-wise approach is used to composite the clear sky reflectance for
each time step from e.g. the last month’s reflectance values. In “HelioMont” the
clear sky compositing calculates a diurnal course of the clear sky reflectance and the
clear sky brightness temperature from cloud masked reflectance and brightness
temperature values of the previous days. This guarantees that the clear sky reflectance
and brightness temperature values have consistency on the diurnal time scale. This
also enables to account for short-term changes in surface reflectance, such as during
green-up or during periods of snow fall. In “HelioMont” the maximum cloud reflectance
depends on solar- and view-geometry. Maximum cloud reflectance fields are calculated
with a radiative transfer model simulating the radiative properties of ice and water
clouds with an optical thickness of 128 at each time step (Mayer and Kylling, 2005).
In “HelioMont” the maximum cloud reflectance is thus spatially distributed and relies
on inter-calibrated radiances.
A single atmospheric turbidity parameter like the Linke turbidity cannot account for
the molecular absorption and scattering effects of the individual atmospheric
constituents on global radiation and its components (Müller et al., 2004). Also, the
climatological state of the atmosphere can strongly deviate from its instantaneous
state with adverse effects on the quality of the calculated clear sky solar radiation
fluxes. We thus make use of a radiative transfer model (Mayer and Kylling, 2005),
parameterized by the so-called Modified Lambert-Beer (MLB) set of equations in
combination with a look-up table (LUT) for efficient processing (Müller et al., 2009).
Specifically, a modified version of the publicly available gnu-MAGIC algorithm (http:/
/sourceforge.net/projects/gnu-magic/) with a re-calculated LUT is used. It is
constrained by 6-hourly total column water vapor and ozone data from the European
Centre for Medium-range Weather Forecast (ECMWF) and by use of monthly aerosol
climatology (Kinne, 2008).
3.7 Satellight
The algorithms for retrieving global irradiance from satellite data are based on the
Heliosat method (Cano et al.,1986) which has been enhanced in several domains
(Beyer et al., 1996, Hammer et al., 1998 and 1999). The method is basically driven
by the strong complementarity between the planetary albedo recorded by the satellite’s
radiometer and the surface shortwave radiant flux. The planetary albedo
increases
with increasing atmospheric turbidity and cloud cover. Therefore a cloud index n is
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
introduced as a measure of the cloud cover derived from Meteosat visible counts:
g
c g
n
where
g
and
c
are respectively the relative reflectivity in the clear sky and overcast
case. The clear sky index K
is defined as the ratio of surface global horizontal irradiance
to the corresponding clear sky irradiance as derived by Page (1996) and Dumortier
(1995) for respectively the beam and the diffuse components. The global irradiance
is then derived from the cloud index n following Fontoynont (1998).
In the cloudless case the diffuse irradiance can be derived from Dumortier (1995).
Skartveit and Olseth (1987) suggested an all sky model for the diffuse fraction of
hourly global radiation, assuming that the diffuse fraction depends on the clearness
index and the solar elevation. For a clearness index K
t
below a certain threshold the
hourly radiation is expected to be completely diffuse. With increasing clearness index
the diffuse fraction decreases. For high clearness index values it increases again due
to cloud reflection effects. The position of the minimum of diffuse fraction depends on
the solar elevation. An improved version of this model also accounts for the hour-to-
hour variability of the clearness index (Skartveit et al., 1998).
4. Satellite data: average and typical years
Depending on the application, average and typical years are used as input to
simulations. These are generally obtained from 10 to 20 years of measurements,
averaged and partially interpolated between stations. Some of them are corrected
with the help of meteorological and polar satellite data and/or ground information.
The data included in the comparison are derived within networks, programs or
software described below.
4.1 PVGIS
Photovoltaic Geographical Information System provides a map-based inventory of
solar energy resource and assessment of the electricity generation from photovoltaic
systems in Europe, Africa, and South-West Asia (available from http://
re.jrc.ec.europa.eu/pvgis/index.htm). For Europe, a new data set is available in version
4, evaluated by Eumetsat climate satellite facilities (CMSAF, DWD).
4.2 WRDC
The World Radiation Data Centre Online Archive contains international solar radiation
data stored at the WRDC, which is a central depository for data collected at over
one thousand measurement sites throughout the world (available from http://wrdc-
mgo.nrel.gov/).
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Long term satellite global, beam and diffuse irradiance validation
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4.3 RetScreen
The RETScreen Clean Energy Project Analysis Software is a unique decision support
tool developed with the contribution of numerous experts from government, industry,
and academia. The software, provided free-of-charge, can be used worldwide to
evaluate the energy production and savings, costs, emission reductions, financial
viability and risk for various types of Renewable-energy and Energy-efficient
Technologies (RETs, available from http://www.retscreen.net).
4.4 NASA SSE
is a renewable energy resource web site of global meteorology and surface solar
energy climatology from NASA satellite data on 1 by 1 degree resolution (available
from http://eosweb.larc.nasa.gov/sse/).
4.5 Meteonorm
Meteonorm (v7) is a comprehensive meteorological reference software, incorporating
a catalogue of meteorological data and calculation procedures for solar applications
and system design at any desired location in the world. It is based on over 23 years
of experience in the development of meteorological databases for energy applications
(see http://www.meteonorm.com).
4.6 ESRA
The European Solar Radiation Atlas is oriented towards the needs of the users like
solar architects and engineers, respecting the state of the art of their working field
and their need of precise input data. From best available measured solar data
complemented with other meteorological data necessary for solar engineering, digital
maps for the European continents are produced. Satellite-derived maps help in
improving accuracy in spatial interpolation (see http://www.helioclim.com).
4.7 Averaged nowcast models
For the six nowcasting modelled data sets described section 3, the data are either
aggregated into monthly values, or directly retrieved from the provider in monthly
values.
5. Data quality control
Sensor calibration is the key point for precise data acquisition in the field of solar
radiation. The radiation sensors should be calibrated by comparison against a sub-
standard before the beginning of the acquisition period, and then every year. Due to
possible errors and inaccuracies, a post-calibration is difficult to conduct.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
The validity of the results obtained from the use of measured data is highly correlated
with the quality of the data bank used as reference. Controlling data quality is therefore
the first step to perform in the process of validating models against ground data. This
essential step should be devised properly and automated in order to rapidly detect
significant instrumental problems like sensor failure or errors in calibration, orienta-
tion, leveling, tracking, consistency, etc. Normally, this quality control process should
be done by the institution responsible for the measurements. Unfortunately, it is not
the case at many stations. Even if some quality control procedures have been
implemented, it might not be sufficient to catch all errors, or the data points might
not be flagged to indicate the source of the problem. A stringent control quality
procedure must therefore be adopted in the present context, and its various elements
are described in what follows.
If the three solar irradiance components—beam, diffuse and global—are available, a
consistency test can be applied, based on the closure equation that link them:
sin( )
h h
n
G D
B
h
where B
n
, G
h
and D
h
are respectively the normal beam irradiance, the horizontal
global and diffuse irradiance, and h the solar elevation angle over the horizon.
An a posteriori automatic quality control cannot detect all acquisition problems that
could have happened, however. The remaining elements to be assessed are threefold:
the measurement’s time stamp (needed to compute the solar geometry),
the sensors’ calibration coefficient used to convert the acquired data into
physical values,
the coherence between the parameters.
5.1 Time stamp
To detect a possible time shift in the data, the symmetry (with respect to solar noon)
of the irradiance for very clear days is visually checked. The global horizontal and
direct normal irradiances are plotted versus the sine of the solar elevation angle for
specific clear days. If the time stamp is correct, the afternoon curve should normally
lay over the morning curve as visualized in Figure 1. Exceptions do occur, however, at
sites where the atmospheric turbidity changes during the day, due for example to
topography-induced effects, where the clear-sky irradiance can be significantly different
in the afternoon than in the morning. As the global irradiance is less sensitive to
turbidity, the accordance morning/afternoon is of more importance for the global
component.
If this test is positive, verification can be done with the help of the global clearness
indices K
t
and K
b
defined respectively as:
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
)sin(hI
G
K
o
h
t
o
n
b
I
B
K
where I
o
is the extraterrestrial solar irradiance (i.e., the solar constant corrected for
the actual sun-earth distance). The clearness index is then plotted for the morning
and afternoon data separately, e.g. using different colors. The upper limit,
representative of clear-sky conditions, should lay over for the morning and the
afternoon data as represented in Figure 2 for one year of global irradiance data
acquired at Carpentras (France). Ideal hourly clear-sky values, calculated with the
Solis model, are plotted in blue on the same graph. This test is very sensitive since a
time shift of only a few minutes will conduct to a visible asymmetry.
When these two conditions (symmetry around solar noon and consistency of
envelope) are fulfilled, the time stamp of the data bank can be considered correct,
and the solar geometry can be precisely calculated.
5.2 Sensor calibration
The sensors’ calibration can be verified for clear sky conditions by comparison against
data from a nearby station or with the help of additional measurements. To conduct
this test, for each day, the highest hourly value of G
h
and B
n
is selected from the
measurements and plotted against the day of the year as illustrated in Figure 3.
0
200
400
600
800
1000
0.0 0.2 0.4 0.6 0.8 1.0
Global and beam irradiances
sin (h)
Carpentras
July 20, 2005
0.0
0.2
0.4
0.6
0.8
1.0
0 20 40 60 80
Global clearness index K
t
Solar elevation angle h
Carpentras (France)
0.0
0.2
0.4
0.6
0.8
1.0
0 20 40 60 80
Beam clearness index K
b
Solar elevation angle h
Carpentras (France)
Figure 1 above: G
h
and B
n
represented versus the sinus of the
solar elevation angle for a clear day.
Figure 2 right: K
t
and K
b
represented separately for the
morning (green) and the afternoon
(yellow) data, versus the solar
elevation angle for one year of
hourly values in Carpentras.
Corresponding clear sky model data
are represented in light blue.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
These points are representative of the clearest daily conditions. As the highest value
for each day is selected, the upper limit normally represents clear-sky conditions (for
G
h
, it happens that higher-than-clear-sky values are obtained under partly cloudy or
scattered clouds, high-sun conditions, this is why this test should not be applied for
data with time granularity lower than hourly). On such graphs, data from nearby
sites, or from different years for the same site can be compared.
The G
h
graphs can be augmented by superimposing the modified clearness index K
t
’,
which was defined by Perez and Ineichen (1990) as:
1.0/4.99.0/4.1exp031.1
'
AM
K
K
t
t
where AM is the optical air mass as defined by Kasten (1980). This modified clearness
index has the advantage of being relatively more independent from the solar elevation
angle than K
t
. Therefore, it is possible to delineate three K
t
’ zones to characterize the
sky condition (Ineichen 2009):
clear-sky conditions 0.65 < K
t
’ 1.00
intermediate sky conditions 0.30 < K
t
’ 0.65
cloudy sky conditions 0.00 < K
t
’ 0.30
In the upper part of Figure 3, only values with K
t
’ > 0.65 are represented.
For B
n
, the clear-sky index is defined as:
)(
wcda
MA
o
n
bc
eI
B
K
where
cda
is the broadband clean and dry atmosphere optical depth, and
w
is the
water vapor optical depth. These two broadband optical depths can be evaluated
following Molineaux (1998) with simplified expressions:
0.16 0.55 0.34
0.101 0.235 0.112
cda w
AM AM w
Figure 3 Daily highest value of respectively the global and the beam irradiances reported versus
the day of the year for the station of Carpentras. The corresponding modified clearness index
and clear sky index are also represented.
0
200
400
600
800
1000
1200
0 30 61 91 122 152 182 213 243 274 304 334 365
0
1
2
3
Carpentras irradiance
Carpentras clearness index
selection limit
Day of the year
Highest daily beam irradiance value [W/m2]
Clear sky index
Year 2006
-200
0
200
400
600
800
1000
1200
0 30 61 91 122 152 182 213 243 274 304 334 365
0
1
2
3
Carpentras irradiance
Carpentras clearness index
selection limit
Highest daily global irradiance value [W/m2]
Day of the year
Modified clearness index Kt'
Year 2006
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
The denominator of K
bc
is representative of the beam irradiance transmitted by a
clean atmosphere. K
bc
is represented on the right graph of Figure 3. On the upper
part, only values for K
bc
higher than 0.8 are represented.
The sensor calibration’s correctness can then be assessed by comparison if data
from a nearby site are available. If not, this can alternatively be done with the help of
a clear-sky radiative model when the atmospheric aerosol optical depth (aod) and
the water vapor column (w) are known. The long term stability of the calibration can
also be assessed with this method by plotting on the same graph several years of
data.
In the first case, it can be assumed that clear conditions result in similar irradiances if
the sites are not too far one from the other, and are in similar climate situations. The
upper limits of the compared plots should therefore coincide. This is illustrated on
Figure 4 for the sites of Toravere and Zilani, situated at 200 km one from the other
and under similar climates.
In the second case, aod and w data may be retrieved from independent ground-
based sunphotometer networks such as Aeronet, if a station is close to that of the
radiometric station being investigated. These quantities are measured automatically
at 15-minute intervals by Aeronet stations. Since only data acquired under direct sun
conditions are valid, the original data stream (Level-1) is analyzed to filter out non-
sun conditions (Level-1.5). Further corrections are applied to reflect any change in
calibration or quality-control issues (Level-2). Level-2 data should be used whenever
possible since they are of the best possible quality. Individual values are then averaged
to obtain a daily value. The same can be done for w. In case it is not measured by a
nearby Aeronet or similar network, it can be evaluated from the ground ambient
temperature T
a
and relative humidity RH by the use of an empirical model, such as
Atwater’s model (Atwater 1976). The latter method is approximate, but spatially
extrapolating actual measurements also introduces errors, so that there is no perfect
method in most cases. When temperature and/or humidity data are missing, the
data from a neighboring station can be used, or as a last resort (and much larger
errors), monthly average from climatic data banks. These aod and w values are then
used with a clear sky model (Solis clear-sky radiative model (Muller 2004, Ineichen
2008), or CPCR2 (Gueymard 1989) to evaluate the clear-sky hourly G
h
and B
n
values. These are plotted on the same graphs than above, as shown in Figure 5 for
measurements from Carpentras. On these graphs, the upper limits of the irradiance
values and of the clearness indices obtained with the two methods should be similar.
To quantitatively assess the correctness of the calibration factor, a linear regression is
applied on the clear condition selected hourly values, between the two sets to be
compared. This is illustrated in Figure 6 for the two components. The slope of the
regression line is also shown for each case.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Figure 4 Comparison of measurements from two nearby (200km) stations: daily highest value of the
global irradiance reported versus the day of the year for the station of Toravere and Zilani, for the
irradiance components and the corresponding modified clearness index.
Figure 7 Comparison between two differents years of BSRN measurements: same representations
as above
Figure 6 Scatter plots of the two data sets for the global and the beam components. The slope given
on the graph is representative of the calibration coefficient difference.
Figure 5 Comparison of the clear sky irradiance obtained from BSRN measurements and evaluated
with the solis model with aeronet data
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
In the latter case, the irradiance components can be compared year by year for the
same site in order to assess the stability of the measurements. Here again, the upper
boundary should not change from one year to the other, if there are no significant
changes in the turbidity and/or the humidity. An example is given on Figure 7 for
measurements acquired in 2006 and 2010 at Carpentras. Considering the whole
period used in the validation, a calibration coefficient shift can be pointed out by this
method.
5.3 Components consistency
The consistency test between the G
h
and B
n
components can be verified with the help
of the global and beam clearness indices.
The hourly beam clearness index is plotted versus the corresponding global index as
illustrated for the site of Carpentras in Figure 8. On the same graph, the clear-sky
predictions from the Solis radiative model are represented for four different a priori
values of aod. The corresponding Linke turbidity coefficient T
Lam2
is then calculated
from the B
n
thus obtained:
2cda Lam
T AM
n o
B I e
T
Lam2
is evaluated for AM = 2 and its correspondence with aod is also indicated on the
graph. Any important deviation between the predicted and measured clear-sky va-
lues indicates calibration uncertainties, pyrheliometer misalignment, soiled or shaded
sensors, or miscategorization of clear-sky conditions.
When the three components, global, diffuse and beam, are available, the closure
equation can be applied. Due to the measurement methods for each of the
components, the strict equality cannot be verified for all the values and acceptability
limits are to be defined. For example:
Figure 8 The beam clearness index is plotted against the global clearness index for Lerwick (mari-
time) and Carpentras (rural). On the same graph, clear sky modelled values are represented for 4
different aerosol loads. The corresponding Linke turbidity coefficients are also indicated
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
the BSRN quality control is the following:
sin( )
h
h n
G
D B h
should be within 8% for h > 15° and 15% for h 15°
for
2
sin( ) 50
h n
W
D B h
m
the SERI quality control for the closure is defined as follow
K
t
= K
d
+ K
b
± 0.03
closure equation applied in this report:
if
sin( )
h h
ncalc
G D
B
h
and
lim
2
1.1 50
it n
W
B B
m
then B
n
+ abs(B
n
- B
ncalc
) < B
limit
These different quality controls are illustrated in the Figure 9 where the selected
hourly values are represented in blue.
6. Hourly, daily and monthly comparison indicators
6.1 First order statistics
The most conventional comparison indicators are the mean bias difference (mbd),
Figure 9 Quality control applied on the beam
irradiance when the three components are
available. The selected hourly values are
represented in blue. For the QC applied in
this report, the limit is also reported on the
graph.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
the root mean square difference (rmsd), the standard deviation (sd) and the
determination coefficient (R); they represent a quantification of the model dispersion
and are defined as follow:
2
sat mes sat mes
G G G G
mbd rmsd
N N
2
2 2
sat mes sat sat mes mes
sat sat mes mes
G G G G G G
sd R
N
G G G G
where G
mes
and G
sat
represent respectively the measured and the modeled irradiance.
The mbd gives an indication of the systematic bias of a model. Even if the average
bias over all the sites is small, it can be highly variable from one site to the other.
Therefore, the standard deviation of the biases (bias standard deviation bsd) is
evaluated, it give an indication of the spatial stability of the model.
Comparison can also be done in terms of frequency of occurrence and cumulated
frequency of occurrence: for the irradiance, it gives an indication of the repartition for
each level of radiation, and for the clearness index, it assess that the modeled level of
radiation occurs at the right time during the day. The obtained graph is a line (or a bar
chart) representative of the relative frequency of occurrence of the considered
parameter. This is illustrated on Figure 10 (left) for the clearness index K
t
. On the
same graph, the frequency of occurrence of the ground measurements is represented
as grey bars, and the different models in color lines.
6.2 Second order statistics
A second order statistic, the Kolmogorov-Smirnov test (Massey 1951, Espinar 2009),
is also applied to the data. It represents the capability of the model to reproduce the
frequency of occurrence at each of the irradiance level. In order to avoid a peak at
Figure 10 Relative frequency of occurrence of the clearness index (left) and the cumulated frequency
of occurrence of the beam irradiance for the measurements (grey) and the different models (right).
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
the zero level of the beam irradiance, these values are excluded from the statistic. A
visualization is given on Figure 10 (right) where the irradiance cumulated frequency
of occurrence is represented against the irradiance for the same site than above. The
quantitative value representative of the Kolmogorov Smirnov test Integral (KSI) is
defined as:
max
min
G
c sat c mes mes
G
KSI F G F G dG
where F
c
(G
mes
) and F
c
(G
sat
) are respectively the irradiance ground measurements
and the corresponding modelled cumulated frequencies of occurrence. KSI% is then
obtained by normalizing KSI by a critical value depending on the number of events.
These statistical parameters include dispersions introduced by:
the retrieval procedure,
the comparison of point measurements (ground data) with area
measurements (pixels),
the comparison of the average of four instantaneous measurements with
60 minutes integrated values.
6.3 Model-measurements difference distribution
In terms of validation, when evaluating satellite derived parameters with the same
time step, the comparison can be done by means of scatter plots; these give a visual
evaluation of the capability of the model to reproduce the measurements. On these
graphs, the diagonal line is representative of an ideal model, and the points should lay
around this line. An illustration is given on Figure 11 for hourly and daily values.
On Figure 12, the distribution of the difference between the model and the
measurements around the 1:1 axis for hourly values is represented in term of
frequency of occurrence for the whole period, and, in the Annex, for the months of
April and August. On the same graph, the cumulated frequency of occurrence is also
Figure 11 Hourly and daily modelled against measured global irradiance at the site of Carpentras for
the whole considered period.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
represented. For a good result, this curve should be as steep as possible, and cross
the 50% value on the zero axis of the horizontal scale. The cumulated frequency is
given every two months on a separate graph (Figure 12, right). The same
representations are done for the three radiation components.
7. Interannual variability analysis method
The annual global and beam irradiation values are analyzed year by year. A reference
period covering the years 2004 to 2010 will guide the evaluation of the different
products. The yearly total determined by the average over this reference period is
used as normalization value for all the annual totals. This normalized average (= 100%)
is represented on the Figure 13 by the first blue bar from the left and labelled 2004-
2010 average. A standard deviation is calculated over the 8 years reference period,
it is represented on the graph by the light orange zone surrounding the 100% line.
-200 -150 -100 -50 0 50 100 150 200
Cumulated frequency of occurrence
Global irradiance [W/m2]
Bias frequency of occurrence
Carpentras 2004-2011 MeteoSwiss Global irradiance
-200 -150 -100 -50 0 50 100 150 200
February
April
June
August
Oktober
December
Year
Global irradiance [W/m2]
Bias frequency of occurrence
Carpentras 2004-2011 MeteoSwiss Global irradiance
Figure 12 Difference distribution (left) between model and measurements for the MeteoSwiss model
and the site of Carpentras. On the right graph, the cumulated frequency of occurrence is given for 6
months, MeteoSwiss model and Carpentras site.
Figure 13 Total annual irradiation normalized to the average annual value over the reference period
(2004-2010) for the site of Carpentras.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
The following values are represented on Figure 13:
the year per year annual ground irradiation measurements are represented
by blue bars,
the average or typical data banks, satellite based or ground measurements,
are represented by the yellow bars on the left part of the graph,
the year per year SolarGis data are represented by the orange bars,
the year per year Helioclim 3 data are represented by the green bars,
the year per year Solemi data are represented by the light yellow bars,
the year per year IrSOLaV data are represented by the dark violet bars,
the year per year EnMetSol data are represented by the light violet bars,
the year per year MeteoSwiss data are represented by the dark blue bars,
the year per year Satel-Light data are represented by the light blue bars for
the years 1996 to 2000,
the annual deviation from the 100% reference period average is represented
by the red bars for the typical year data,
the ground measurements annual deviation from the reference period are
represented by the brown bars.
This method implies that there are no missing values in the evaluation of the yearly
total. As this is not always the case, and to circumvent the elimination of too many
data, a correction has to be applied as described in section 8.4.
8. Ground data validity assessment, calibration and stability
The first step in a model validation procedure is to assess the validity of the ground
measurements. This can be done by applying a stringent quality control, but if some
simple errors like a time shift in the data can be corrected, the suspicious data should
be discarded. After having confirmed the time step in the data banks, the following
tests are applied on the time series.
8.1 Comparison with Aeronet network
For 6 of the 18 ground measurements sites, data from a nearby Aeronet station are
used to assess the calibration coefficient of the instruments. These are Carpentras
(France), Davos (Switzerland), Madrid (Spain, only for 2012), Sede Boqer (Israel),
Tamanrasset (Algeria) and Toravere (Estonia). The Solis clear sky model (Müller 2004,
Ineichen 2008) is used to evaluate the global and beam irradiance from the aerosol
optical depth aod and the water vapor content w of the atmosphere.
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Long term satellite global, beam and diffuse irradiance validation
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To validate the calibration coefficient applied during the measurements, the clear sky
values are selected from the measured data (see section 5.2), and compared with
the corresponding values derived from the Aeronet data and the Solis model.
For all the sites except Davos, Table II shows small differences between the sets of
data, less than 2% in the average, these effects are not significative enough to
consider a calibration adaptation (see for example, Figure 7 for the site of Carpen-
tras).
The site of Davos shows differences of the order of 5% between spring and autumn,
probably due to snow effects.
8.2 Long term stability
For the long term stability test, one can assume that the highest global and beam
irradiance values do not vary significantly from one year to the other, and that a
steep variation should be an issue of a calibration drift, more particularly when the
effect disappears abruptly, i.e. in the case of a re-calibration of the sensors.
The long term stability is verified by comparing year by year the highest values of the
hourly global and beam irradiance (see Figure 7). This analysis pointed out some
significative drifts given in Table II for the following data:
Davos: the beam irradiance is 20% too high from December 1999 to February
aeronet comparison
global irradiance beam irradiance G
h
and B
n
Gh Bn
Almeria (Spain) 20012011 20012011 n/a none 93% 100% 100%
Bratislava (Slovakia) 19942007 19942007 n/a none n/a 100% 100%
Carpentras (France) 19952011 19952011 20032011
G
h
(aero/solis) > G
h
(bsrn) [around 2%]
Gh(aero/cpcr2) Gh(bsrn)
83% 99% 98%
Davos (Switzerland) 19992011 19992011 20062011
Bn 20% to high from Dec. 1999 to Feb 2000
Aeronet Gh 5% higher in spring than in autumn
68% 91% 97%
Geneva (Switzerland) 19952011 19952011 n/a
Gh compatible with Payerne
and VaulxenVelin
n/a 99% 94%
Kassel (Germany) 20032011 20032011 n/a none 91% 98% 98%
Lerwick (Great Britain) 20012009 20012009 n/a none 98% 94% 89%
Lindenberg (Germany) 19952006 19952006 n/a none 91% 100% 100%
Madrid (Spain) 20042011 20042011 2012
Gh and Bn within 23% with aeronet
Bn: too many missing data for interannual validation
84% 99% 86%
Nantes (France) 19952010 19952010 n/a none n/a 97% 94%
Payerne (Switzerland) 19942009 19942009 n/a Gh compatible with Geneva 81% 100% 97%
Sede Boqer (Israel) 20032011 20032011 19962010
Bn(aero)<Bn(bsrn) [2% summer]
Gh 2% to high from 20052008
90% 100% 94%
Tamanrasset (Algeria) 19952010 19952010 20062009
Gh, very clear conditions, at noon,
5% underestimation by aeronet/solis
1% overestimation by aeronet/cpcr2
90% 100% 99%
Toravere (Estonia) 19992011 19992011 20022009 none 88% 100% 98%
Valentia (Ireland) 19962009 19962009 n/a none n/a 97% 94%
VaulxenVelin (France) 19952011 19952011 n/a 19952004 Gh and Bn to high (59%) 90% 96% 95%
Wien (Austria) 19942010 19942010 n/a none n/a 100% 97%
Zilani (Letonia) 19932009 19932009 n/a
Gh 10% to low in 1999
Gh 15% to high in 2003
91% 99% 98%
year per year comparison
Site Remark
Closure
equation
Interannual
Table II List of the ground sites with year by year period of comparison, the aeronet convergent
period, the results of the closure test, and the percentage of aqcuired monthly values considered for
the interannual variability.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
2000. This period is not included in the validation, but is integrated in the
interannual variability analysis,
Vaulx-en-Velin: the irradiance is 5% to 9% too high from 1995 to 2004,
Zilani: the global irradiance is 10% too low in 1999, and the beam is 15% too
high in 2000. Here also, it is not included in the validation period.
After applying the corresponding coefficients on the data, the quality control show
normal behavior.
8.3 Components coherence
The component coherence is verified according to section 5.3 with the help of the
closure equation when the three components are available. The results of the
coherence test are given in Table II in term of a percentage of data kept after the
test. This percentage stays between 68% and 93%.
The lowest value, 68%, is the result for Davos, where the beam component is not
acquired in the same location than the global and the diffuse; a slight difference in the
time stamp is also possible. In addition, the distance between two sites is about 300
meters, Davos is in a valley and the two horizons are slightly different as shown on
Figure 14. If the effect on the global irradiance is not visible, the sky view factors are
similar, the beam component can be influenced by this difference.
The closure equation is applied on the normal beam component which is very sensi-
tive for low solar elevations, and therefore, values of 90% are satisfactory.
The site of Leerwick shows a 98% even if the three components are stamped as
separate measurements at the WRDC server. It is probable that the third component
is retrieved from the two others.
For the sites where only two components are available, the closure equation is not
applicable, and 100% of the data are kept.
Figure 14 Horizon of the two sites of Davos PMOD and Davos SLF. The sky view factors do not differ,
but slight differences can occur on the beam component.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Figure 15 Components coherence test. Site of Lerwick: (A) K
b
versus K
t
, low beam component compared
to the Solis clear sky, (C) diffuse fraction versus K
t
, high diffuse. The closure equation (D) gives good
results. (B) Almeria is given for comparison purpose.
(E to G) Site of Madrid: (E and G) it is the only site with these specific shapes. Slight improvement
when representing the beam evaluated from the global and the diffuse (F). The closure equation (H)
shows high discrepances.
A B
C D
E F
G H
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
The second coherence test is done on only the global and the beam component by
comparing the corresponding clearness indices (Figure 8 in section 5.3). This test is
applied indifferently on beam measurements, or beam evaluated from global and
diffuse irradiances. For this test, only two sites show singularities:
as shown on Figure 15, the beam clearness index in Lerwick (A) shows values
that seems too low compared with the clear sky model Solis which is also
represented on the figure for four different aerosol optical depths. For comparison,
the same graph is given for the site of Almeria (B). This is confirmed with the
diffuse fraction test given on (C) where the fraction is high compared to the
clear sky model. This test is applied on only the global and the diffuse components
and is independent from the beam. This can be a result of very high permanent
turbidity. The closure test is coherent.
for the site of Madrid, the shape of Figure 16E is not common, it is the only site
showing such a behavior. Replacing the beam component in the clearness index
by the global/diffuse difference improves slightly the shape, but it is still not
common (F). Another issue can be a levelling default of the global sensor. A
sensitivity test is conducted on the data of Almeria and given on Figure 16. The
reference graphs drawn from the measurements are given in (A and B). When
an levelling error of 3° is artificially introduced in the measurements, the result
given on (C and D) show a similar behavior compared to the corresponding
graphs for Madrid (E and F). This could be an explanation of this singularity.
8.4 Data validation for the interannual variability
The aim of the analysis of the interannual variability is to take into account the natural
variation of the irradiation from one year to the other in the model uncertainty. To
conduct a significant interannual variability analysis, a long period of data is needed.
These long time series have to be continuous and with no missing data. As the
majority of the ground measurements time series are not complete and as it is not
possible the fill the gaps, a strategy has to be developed to circumvent the problem.
The following corrections are applied on the data: to obtain a yearly total, the data
are taken month by month and added. For each month, the missing share of ground
measurements is evaluated in term of a number of missing data percentage. When
the gaps’ length represents less than 5% of the month, a linear extrapolation is
applied on the monthly values based on the normalized number of hourly values
aggregated in the considered month. When more than 5% of the data are missing,
the monthly value is replaced by the average of all the corresponding months of the
considered time series. The missing share statistics are given in Table II.
In Lerwick, the 11% missing data for the beam component occur mainly in 2011. For
the site of Madrid too many data are missing for the beam component, so that the
interannual variability analysis is not significative.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Due to these corrections, the results given in the interannual variability bar charts do
not correspond exactly to the hourly validation results. As the hourly comparison is
restricted to validated values, some differences may also occur depending on the
length of the comparison period. Nevertheless, the results are significative when
considered as a general overview of the tendency of a model to reproduce the data.
9. Validation results
9.1 Hourly, daily and monthly validation
The total amount of points included in the comparison and the corresponding irradiance
A B
D
E F
Figure 16 Components coherence test. Global sensor levelling sensitivity study on the data from
Almeria. (A and B) Diffuse fraction and clearness index tests applied on the measurements. (C and D)
effect on the tests for a 3° sloped global sensor. The shape on (D) looks similar to (F).
C
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
and irradiation averages are the following:
• 475’000 hourly values G
h
= 340 B
n
= 350 D
h
= 135 [W/m
2
]
• 43’000 daily values G
h
= 3.7 B
n
= 3.8 D
h
= 1.45 [Wh/m
2
.day]
• 1’500 monthly values G
h
= 110 B
n
= 110 D
h
= 43 [Wh/m
2
.month]
The number of ground or satellite derived values differ from one site to the other, and
the covered periods are not of the same length for all the sites (see Table II).
Table III and Figure 17 give the main results of the validation (the complete results,
site by site, model by model, component by component, in hourly, daily and monthly
vues, and in absolute and relative values are given in the annex, Tables a-In, a-IIn and
a-IIIn, n=g, b or d for global, beam or diffuse). A general observation is that the
hourly global irradiance is retrieved with a negligible bias and a standard deviation
ranging from 17% to 24% (57 to 81[W/m
2
]), the beam component from 34% to
49% (119 to 174[W/m
2
]) with a -11% to +6% bias, and the diffuse from 35% to
60% (46 to 80 [W/m
2
]) with a bias from -10% to +25%. If the overall bias for the
global irradiance models is near of zero, it can be highly variable from one site to the
other. This is highlighted by the standard deviation of the mean bias deviation bsd; it
varies from 2.1% to 5.1% for the global component. For the beam component (and
a fortiori for the diffuse irradiance), the bias varies from site to site and model to
model. It has to be noted that the beam irradiance bias for Solemi is always negative
(opposite bias for the diffuse component, see Table a-Ib to a-IIIb and a-Id to a-IIId).
In a general way, for the global component and all the models, the bias distribution
around the 1:1 axis follows a un- or slightly skewed normal distribution, so that the
standard deviation indicator is significative (see Figures 11 & 12, a-1g to a-8g). This
is not the case for the normal beam irradiance bias, where bimodal, skewed or not
normal distributions can occur depending on the model and the site. No common rule
Table III Results of the hourly, daily and monthly validation.
The standard devi ation calculated on the mean bias
differences over all the 18 sites.
Figure 17 Corresponding graphical
representration of the results.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
can be drawn from the Figures a-1b to a-8b, the shape of the distribution depend on
the clear sky model used and the specificities of the input parameters. For some sites
with sunny conditions like Tamanrasset or Sede Boqer, the dispersion of the hourly
bias is so high that the distribution cannot be considered as normal. In this case, the
standard deviation has to be considered with precaution.
The observation of the bias versus the modified clearness index K
t
’ (or the sky type,
see Figures a-9g and Fig. a-9b) shows the same general tendency for all the models
and both components: a slight overestimation for cloudy conditions and an
underestimation for clear skies. The highest effect is a beam component overestimation
for intermediate conditions. This is illustrated on Figure 18. For clear conditions, the
dispersion is due to an approximate knowledge of turbidity. In the case of intermediate
cloud cover, the models do not identify with enough precision the type and thickness
of the clouds.
An altitude effect can be pointed out on the graphs from the sites of Tamanrasset and
Davos. It indicates an underestimation of the clear sky model for these high altitude
sites (respectively 1400m and 1586m); the effect is sharper on the beam component.
An illustration is given on Figure 19 where the beam clearness index K
b
is represented
against the solar elevation angle. On these graphs, the upper limit represents the
Figure 18 Model bias versus the clearness K
t
(or sky conditions) for the the global and beam
components.
Figure 19 Beam clearness index K
b
plotted versus the solar elevation angle for the two high altitude
sites.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
clearest sky conditions; this limit (blue dots for the model values) is too low when
compared to the measurements (in yellow). As it was already noted in (Ineichen
2011), a similar pattern is observed for the Helioclim 3 model, where an
underestimation is present for all the sites, but is not related to the altitude.
Nevertheless, the complete Helioclim 3 algorithm chain takes this into account and
the results are better than waited.
Moreover, for the site of Davos, the snow cover during the winter period has a
significant effect on four of the six models. If the snow cover is correctly taken into
account by SolarGis and Heliomont, the scatter plots for Solemi and IrSOLaV show a
high dispersion for both the components. EnMetSol gives good results in no-snow
situations (point aggregation on the 1:1 axis on Figure 20a). For this model, the
snow effect is illustrated on Figure 20c and 20d, where the distribution of the differences
is quasi-normal for the month of August, but not for the month of April. For Helioclim 3,
the snow correction seems to be too important (cf. Figures a-1g and a-1b).
On the graph for the site of Tamanrasset (Figure 19 right), another effect is visible:
the time resolution of the input parameters for the clear sky model aod and w.
Indeed, aggregates of points appear clearly on the graph depending on the season
(highlighted by the different range of solar elevation angles). A visualization is given
on Figure 21, where three different methodologies are illustrated: on the top graphs,
both aerosol optical depths aod and water vapor w are retrieved from daily data
Figure 20 Illustration of the snow effect for the site of Davos and EnMetSol model. a) model-
measurements scatter plot, b), c) and d) distribution of the model-measurement differences around
the 1:1 axis for the complete period, and for the months of April and August.
a
b
c
d
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Figure 21 Beam clearness index K
b
plotted versus the solar elevation angle for winter and summer
conditions, and three different methodologies: daily aerosol optical depths (aod) and water vapour
(w) on top, monthly aod and w in the middle, and daily w and monthly aod at the bottom.
Figure 22 Diffuse irradiance scatter plot with the correspondig model-measurement difference distri-
bution around the 1:1 axis.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
banks, in the middle graphs, both are monthly climatic values, and on the bottom
graphs, aod monthly and w daily values are used. For high clearness indices,
representative of clear sky values, the result of the use of monthly turbidity values is
highlighted by “discrete” point’s aggregations.
For the diffuse component, EnMetSol gives slightly better results with an overall
negligible bias mbd, followed by SolarGis and Heliomont. For these models, the distri-
bution of the hourly bias present unskewed normal distributions as illustrated on
Figure 22. This is an interesting option when diffuse irradiance is specifically needed,
as for example when evaluating the UV erythema (Vernez 2013). The other models
present often skewed distributions, sometimes not even normal distributions. For
example, the results for Solemi on sunny sites like Sede Boqer, Tamanrasset and
Almeria (see Figures a-1d) show a high dispersion, probably in correlation with the
use of low time and space resolution climatological data banks for the aod and w.
Figure 23 is a graphical illustration of the monthly validation. On the left graph, monthly
values surrounded by ± one standard deviation for the SolarGis model and the
measurements are represented. On the right graph, all the nowcasting models are
shown; the measurements are in red, the dashed red lines represent ± one standard
deviation around the monthly value.
Figures a-11, a-18 and a-19 in the annex give a graphical representation for all the
sites and models. For the global component, and for either the average or the
nowcasting models, 94% of the monthly modeled values are situated between the
two dashed lines. For the beam component and the average models, 83% of the
values are between the dashed lines, and for the nowcasting models, the 88% of the
monthly values are within ± one standard deviation limits. Except for Madrid where
all the models present a significative positive bias for the spring months, no particular
pattern can be pointed out, the deviation depend on the model and the site.
Figure 23 Left: monthly values surrounded by ± one standard deviation for the SolarGis model.
Right: monthly values for all the models. In red, the measurements, the red dashed lines represent
± one standard deviation.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
9.2 Frequency distribution
The correspondence between the frequency distribution of the modeled values and
the measurements is as important as a low bias and standard deviation. It is the
guaranteeing of a realist representation of the solar ressource by the satellite models.
The general observation is that all the models for all the sites have a coherent frequency
distribution representation of the global irradiance level (with the exception of Davos
for the snow periods, Figures a-12g and a-13g). This is not the case when considering
the global clearness index frequency distribution. For sites with high insolation levels
like Madrid, Sede Boqer and Tamanrasset, the number of occurrence for high clearness
indices is overestimated (Figures a-14g and a-15g).
For the beam component, the frequency distributions, for both the irradiance/irradia-
tion and the clear sky index are more randomly distributed, depending on the site and
the model, without specific patterns. The only thing which one can point out is higher
discrepancy with the measurements for sunny sites such as Sede Boqer or Taman-
rasset, and clear conditions. But here again, the bias is very different from one model
to the other (see annex Figures a-14b and a-15b). An illustration is given on Fi-
gure 24 where the site of Tamanrasset is represented.
9.3 Interannual variability
Beside the visual analysis of Figure a-20g and a-20b, it is interesting to compare the
bias of the models with the interannual variability expressed by the standard deviation
around the annual irradiation average for both the global and the beam components.
The comparison results are given in Table IV. The blue columns represent the annual
average for each site and the corresponding standard deviation over the reference
period 2004-2010. The results for the different products are expressed as mean bias
differences; if the mbd is less than one standard deviation sd, the cell background is
represented in green. These mbd are highly variable from site to site and from model
Figure 24 Frequency distribution of occurrence versus the hourly irradiance value and the beam
clearness index for the site of Tamanrasset and all models.
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
to model, even if the combined results for all sites are relatively good. On the last
lines, the absolute bias and the standard deviation of the bias bsd is given for all
models. These values express the spatial «smoothness» of the model.
From Table III, the following points can be underlined for the global component:
� the overall bias for PVGIS-CM SAF and Meteonorm 7 is situated within ± one
standard deviation of the interannual variability, with a bias standard deviation
bsd around 4%,
� all the nowcasting models have a low bias, within ± one standard deviation of
the interannual variability. The sbd varies from 2.1% to 5.1% (Satellight covers
not the same period and is given for comparison purpose),
� considering the site by site results, 60% of the site-model have a bias within
± one standard deviation of the interannual variability, 24% within ± two stan-
dard deviation, and 16% with a higher bias,
and for the beam component:
� the bias for NASA-SSE and Meteonorm 7 are within ± one standard deviation of
the interannual variability with a sbd of respectively 9% and 12%,
� except Solemi, all the nowcasting models have an overall relatively low bias,
within ± one standard deviation of the interannual variability. The corresponding
sbd varies from 6% to 14%,
� considering the site by site results, 46% of the site-model have a bias within
± one standard deviation of the interannual variability, 29% within ± two stan-
dard deviation, and 25% with a higher bias.
Table IV Results of the yearly validation and interannual variability analysis.
PVGISCM SAF
WRDC (19811993)
RetScreen (19611990)
NASASSE (19832005)
MN 7 (1980-2000)
ESRA (19811990)
Satellight (19962000)
SolarGis (20042011)
Helioclim (20042011)
Solemi (2004-2011)
Heliomont (20062011)
EnMetSol (2004-2011)
IrSOLaV (2006-2011)
NASASSE (19832005)
MN 7 (1980-2000)
Satellight (19962000)
SolarGis (20042011)
Helioclim (20042011)
Solemi (20042011)
Heliomont (20062011)
EnMetSol (20042011)
IrSOLaV (20062011)
Sites
Almeria 1850 2.5%
1.8% 8.1% 8.1% 3.0% 4.9% 0.4% 6.1% 3.0% 3.2% 1.9% 0.3%
2126 5.5%
3.8% 11.1% 15.1% 1.9% 12.1% 3.0% 6.3% 3.9% 3.9%
Bratislava 1176 2.9%
3.2% 1.0% 1.1% 1.0% 1.7% 4.3% 3.5% 3.2% 0.2% 5.4% 2.8% 3.9% 4.4%
1191 7.4%
4.0% 7.5% 11.9% 9.6% 2.1% 21.8% 15.3% 2.7% 9.1%
Carpentras 1587 2.1%
2.5% 4.8% 15.0% 6.0% 2.8% 5.4% 0.4% 0.3% 0.6% 2.7% 1.6% 2.2% 1.2%
1884 4.1%
0.4% 10.1% 4.9% 1.8% 0.3% 4.2% 3.0% 4.5% 1.6%
Davos 1383 1.3%
0.8% 2.7% 7.9% 2.1% 2.9% 17.5% 4.2% 11.5% 13.2% 4.4% 8.3% 7.1%
1420 8.4%
8.0% 18.1% 26.2% 2.8% 21.9% 33.6% 9.5% 13.7% 36.9%
Geneva 1282 2.3%
3.5% 6.3% 0.1% 0.1% 4.9% 5.5% 0.6% 4.2% 0.1% 6.4% 3.5% 5.2% 5.3%
1274 3.3%
4.3% 9.8% 0.9% 7.0% 1.9% 4.2% 8.6% 10.9% 4.0%
Kassel 1048 2.7%
0.6% 5.6% 5.6% 5.8% 6.6% 5.9% 0.1% 3.4% 0.8% 2.9% 1.6% 4.0%
874 6.4%
1.0% 7.9% 5.1% 2.0% 10.2% 19.4% 4.4% 11.0% 21.8%
Lerwick 810 4.7%
4.3% 9.2% 9.1% 3.5% 4.4% 2.5% 0.7% 5.4% 3.8% 3.2% 4.4%
580 13.3%
55.5% 18.2% 0.8% 6.9% 50.4% 11.5% 21.6% 14.4%
Lindenberg 1120 3.8%
3.7% 3.8% 3.8% 9.8% 3.9% 12.3% 4.5% 3.1% 2.1% 3.5% 4.8% 0.4% 0.4%
1026 9.6%
8.1% 1.4% 0.4% 6.4% 5.8% 27.9% 15.1% 3.5% 30.5%
Madrid 1697 4.9%
3.5% 5.2% 5.2% 3.1% 2.5% 1.7% 1.4% 4.4% 5.7% 5.6% 1.4% 1.9%
1798 5.2%
10.0% 0.8% 14.1% 5.4% 8.6% 4.8% 16.4% 4.4% 2.3%
Nantes 1266 3.4%
1.5% 5.2% 3.4% 6.7% 2.2% 0.9% 2.7% 3.3% 0.4% 3.3% 0.7% 3.8% 1.3%
1307 6.7%
12.1% 9.6% 8.8% 8.4% 2.7% 11.1% 4.2% 9.7% 0.7%
Payerne 1278 2.4%
1.7% 8.4% 2.5% 0.4% 1.9% 8.3% 2.8% 0.7% 6.4% 1.8% 0.1% 2.1% 4.8%
1191 4.4%
11.1% 5.9% 2.0% 7.0% 3.4% 5.8% 6.1% 12.2% 12.5%
Sede Boqer 2114 1.2%
9.2% 0.5% 6.7% 3.9% 4.0% 0.6% 6.1% 3.4% 4.7% 3.9% 1.7%
2382 3.6%
4.6% 5.4% 4.6% 16.9% 8.7% 3.1% 11.3% 7.8%
Tamanrasset 2345 1.8%
2.8% 0.8% 2.6% 8.1% 0.9% 1.2% 2.1% 1.8% 1.0% 0.8%
2355 4.0%
6.1% 18.1% 2.5% 14.7% 10.5% 9.2% 10.8%
Toravere 981 3.8%
3.1% 3.1% 0.1% 4.6% 2.3% 2.1% 1.5% 4.4% 5.4% 0.8%
1028 8.8%
8.4% 2.4% 7.2% 6.5% 7.2% 28.4% 14.3% 8.7% 1.9%
Valentia 1021 4.6%
9.4% 3.9% 4.8% 8.0% 5.3% 4.7% 4.2% 3.6% 4.1% 3.2% 1.8% 1.6% 1.4%
992 13.4%
10.7% 21.5% 21.3% 21.6% 3.3% 25.1% 2.3% 11.6% 1.9%
VaulxenVelin 1304 4.4%
3.4% 7.8% 4.0% 3.0% 6.3% 3.3% 0.4% 3.1% 2.6% 7.3% 5.9% 2.4% 3.6%
1359 5.3%
2.1% 11.6% 0.5% 0.9% 4.2% 4.7% 10.1% 0.1% 0.5%
Wien 1175 2.7%
0.5% 6.8% 6.0% 0.8% 1.0% 7.0% 1.4% 0.3% 3.0% 3.4% 0.4% 0.4% 0.7%
1112 8.0%
2.9% 3.1% 2.5% 2.3% 4.0% 12.7% 3.5% 5.9% 15.0%
Zilani 1024 3.3%
6.1% 3.2% 2.5% 2.6% 6.0% 1.4% 10.9% 3.2% 2.6% 6.3% 17.6%
1000 9.1%
13.4% 0.1% 20.5% 0.2% 31.9% 26.5% 7.3% 5.3% 13.6%
All sites 1359 2.9%
0.1% 3.5% 3.5% 3.3% 2.3% 4.5% 1.6% 0.1% 1.4% 1.8% 1.0% 0.8% 0.5%
1383 6.3%
3.8% 1.6% 0.1% 1.6% 5.9% 11.3% 0.1% 0.1% 0.4%
3.4% 4.0% 5.1% 5.1% 3.0% 5.1% 3.9%
1.7% 3.9% 3.9% 2.9% 2.9% 3.0%
7.5% 9.3% 9.5%
4.8% 10.0% 12.0% 7.8% 7.9% 7.8%
4.6% 4.6% 6.5% 6.3% 3.4% 5.7% 5.9%
2.1% 5.1% 4.8% 3.6% 3.6% 4.2%
9.0% 11.9% 13.2%
5.9% 13.9% 14.5% 9.3% 9.4% 12.0%
mbd
higher than two standard deviations
mbd
within one standard deviation
Beam irradiation, mean bias difference mbd
Yearly total [kWh/m2]
average over 20042010
standard deviation
over 20042010
Yearly total [kWh/m2]
average over 20042010
standard deviation
over 20042010
Standard deviation of mbd
All sites absolute mean bias
Global irradiation, mean bias difference mbd
mbd
within two standard deviations
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Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
10. Conclusions
The first conclusion is that the quality control is a key point in any model validation.
Even if the data are highly qualified by the organization in charge of the acquisition,
uncertainties can remain in the data and influence the validation. The best case is
when independent data such as aerosol optical depth are also available.
The conclusions of the present study are the following:
� for latitude from 20° to 60°, altitude from sea level to 1600 m and various
climate, the hourly global irradiance is retrieved with a negligible bias and an
average standard deviation around 17% for the best algorithm. For the beam
irradiance, the bias is around several percents, and the standard deviation around
34%,
� the standard deviation of the bias vary from 2% to 5% for the global irradiance,
and from 6% to 14% for the beam component,
� as expected, the main dependence comes from the clear sky model and the
knowledge of the aerosol optical depth. Better results are obtained with daily
turbidity instead of climatic monthly values. A lower dependence with the
atmospheric water vapor column and the solar elevation angle is pointed out,
� even if the snow cover is taken into account in the algorithm, the irradiance for
sites situated in high altitude like Davos present a higher dispersion,
� for the majority of the sites, SolarGis, Heliomont and EnMetSol give the best
statistics for all of the components.
11. Acknowledgements
The ground data were kindly provided by the Baseline Surface Radiation Network
(BSRN), the Global Aerosol Watch project (GAW), the CIE International Daylight
Measurements Program (Commission internationale de l’éclairage IDMP), the Centre
Scientifique et Technique du Bâtiment (CSTB) in Nantes, the Universidad Politécnica
de Madrid (UMP), the Ecole National des Travaux Publiques (ENTPE) of Lyon, the
Deutsches Zentrum für Luft und Raumfahrt (DLR), the Frauenhofer Institute in Kas-
sel (FhG), the Institute of Construction and Architecture of the Slovak Academy of
Sciences, the Institut für Schnee- und Lawinenforschung (SLF) and the Physikalisch-
Meteorologisches Observatorium Davos World Radiation (PMOD).
The satellite data were kindly provided by Geomodel Solar (Slovakia), MineParisTech
and Transvalors SA (F), IrSOLaV (Spain), University of Oldenburg (D), DLR: Deutsches
Zentrum für Luft und Raumfahrt (D), SatelLight server (F) and MeteoSwiss (CH).
A critical review of the quality control section was kindly carried out by Christian
Gueymard.
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Long term satellite global, beam and diffuse irradiance validation
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- 37 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Long term satellite hourly, daily and monthly
global, beam and diffuse irradiance valida-
tion. Interannual variability analysis.
Annex
The following pages for the site of Almeria and the SolarGis model describe the
figures given in the different annexes for each site.
The annexes can be downloaded from:
http://www.unige.ch/energie/forel/energie/equipe/ineichen/annexes-iae.html
or from
http://www.cuepe.ch/archives/annexes-iae/annex-almeria.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-bratislava.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-carpentras.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-davos.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-kassel.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-lerwick.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-lindenberg.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-madrid.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-nantes.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-sedeboqer.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-tamanrasset.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-toravere.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-valentia.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-vaulx.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-wien.pdf
http://www.cuepe.ch/archives/annexes-iae/annex-zilani.pdf
- 38 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Figures
Figure a-1n Model versus measurements for hourly global/beam/diffuse irradiance
Figure a-2n Model versus measurements for daily global/beam/diffuse irradiance.
Units: [kWh/m
2
day]
Figure a-3n Model versus measurements for monthly global/beam/diffuse irradiance.
Units: [kWh/m
2
month]
Figure a-4n Model versus measurements for monthly average global/beam/diffuse irradiance,
surrounded by ± one standard deviation on each axis.Units: [kWh/m
2
month]
Figure a-5n Hourly values distribution of the model-measurements difference around the 1:1
axis of Figure 14 for the all the data. The corresponding cumulated curve is also
represented
Figure a-6n Cumulated frequency of occurrence of the model-measurements difference versus
the model-measurements difference
Figure a-7n same as Figure 18 for April
Figure a-8n same as Figure 18 for August
Figure a-9n Model-measurements difference for hourly global irradiance values versus the
modified clearness index K
t
’
Figure a-9n Model-measurements difference for hourly normal beam irradiance values versus
the modified clearness index K
t
’
Figure a-10n Clearness index K
t
versus the solar elevation angle for the measurements (yellow)
and the modelled (blue) hourly values
Figure a-10n Beam clearness index K
b
versus the solar elevation angle for the measurements
(yellow) and the modelled (blue) hourly values
Figure a-11n Monthly averaged values surrounded by ± one standard deviation for the modelled
and the measured values of the global/beam/diffuse irradiance.
Units: [kWh/m
2
month]
Figure a-11n Monthly averaged values surrounded by ± one standard deviation for the modelled
and the measured values of the normal beam irradiance.
Units: [kWh/m
2
month]
Figure a-11n same as Figure a-11g for the diffuse irradiance. Units: [kWh/m
2
month]
Figure a-12n Relative frequency of occurrence of the hourly global/beam irradiance versus the
corresponding irradiance. The measurements are represented in grey.
Figure a-13n Relative frequency of occurrence of the daily global/beam irradiance versus the
corresponding irradiance.
The measurements are represented in grey.
Figure a-14n Relative frequency of occurrence of the global/beam clearness index versus the
corresponding clearness index. The measurements are represented in grey.
Figure a-15n Relative frequency of occurrence of the daily global/beam clearness index versus
the corresponding clearness index. The measurements are represented in grey.
Figure a-16n Cumulated frequency of occurrence of the hourly global/beam irradiance values
versus the corresponding irradiance. The measurements are represented in grey.
Figure a-17n Cumulated frequency of occurrence of the daily global/beam irradiance values
versus the corresponding irradiance.
The measurements are represented in grey.
Figure a-18n Monthly averaged values of the global/beam irradiation for the average models.
Dashed line represent ± one sd around the measurements
Figure a-19n Monthly averaged values of the global/beam irradiation for the «real time» models.
Dashed line represent ± one sd
Figure a-20n Interannual variability of the global/beam irradiation for the measurements, the
average models, and the nowcasting products. The values are normalized to the
2004-2010 reference period average.
- 39 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Tables
Table a-In Site by site and model by model hourly global/beam/diffuse irradiance valida-
tion results expressed in relative and absolute values. The absolute values
are given in [Wh/m2h].
For all sites, the overall values, the absolute mean bias and the standard
deviation of the bias are given.
Table a-IIn Site by site and model by model daily global/beam/diffuse irradiance valida-
tion results expressed in relative and absolute values. The absolute values
are given in [kWh/m2day].
For all sites, the overall values, the absolute mean bias and the standard
deviation of the bias are given.
Table a-IIIn Site by site and model by model monthly global/beam/diffuse irradiance
validation results expressed in relative and absolute values. The absolute
values are given in [kWh/m2month].
For all sites, the overall values, the absolute mean bias and the standard
deviation of the bias are given.
Figure a-1g Model versus measurements for
hourly global irradiance
Figure a-2g Model versus measurements for
daily global irradiance
Units: [kWh/m
2
day]
Figure a-3g Model versus measurements for
monthly global irradiance
Units: [kWh/m
2
month]
Figure a-4g Model versus measurements for
monthly average global irradiance, surrounded
by ± one standard deviation on each axis
Units: [kWh/m
2
month]
Figure a-5g Hourly values distribution of the model-
measurements difference around the 1:1 axis of Fig
14 for the all the data. The corresponding cumulated
curve is also represented
Figure a-7g same as Fig 18 for April Figure a-8g same as Fig 18 for August
Figure a-6g Cumulated frequency of occurrence of
the model-measurements difference versus the
model-measurements difference
Figure a-1b Model versus measurements for
hourly beam irradiance
Figure a-2b Model versus measurements for
daily beam irradiance
Units: [kWh/m
2
day]
Figure a-3b Model versus measurements for
monthly beam irradiance
Units: [kWh/m
2
month]
Figure a-4b Model versus measurements for
monthly average beam irradiance, surrounded
by ± one standard deviation on each axis
Units: [kWh/m
2
month]
Figure a-5b Hourly values distribution of the model-
measurements difference around the 1:1 axis of Fig
14 for the all the data. The corresponding cumulated
curve is also represented
Figure a-7b same as Fig 18 for April Figure a-8b same as Fig 18 for August
Figure a-6b Cumulated frequency of occurrence of
the model-measurements difference versus the
model-measurements difference
Figure a-1d Model versus measurements for
hourly diffuse irradiance
Figure a-2d Model versus measurements for
daily diffuse irradiance
Units: [kWh/m
2
day]
Figure a-3d Model versus measurements for
monthly diffuse irradiance
Units: [kWh/m
2
month]
Figure a-4d Model versus measurements for
monthly average diffuse irradiance, surrounded
by ± one standard deviation on each axis
Units: [kWh/m
2
month]
Figure a-5d Hourly values distribution of the model-
measurements difference around the 1:1 axis of Fig
14 for the all the data. The corresponding cumulated
curve is also represented
Figure a-7d same as Fig 18 for April Figure a-8d same as Fig 18 for August
Figure a-6d Cumulated frequency of occurrence of
the model-measurements difference versus the
model-measurements difference
Figure a-9g Model-measurements difference for
hourly global irradiance values versus the
modified clearness index K
t
’
Figure a-9b Model-measurements difference for
hourly normal beam irradiance values versus
the modified clearness index K
t
’
Figure a-10g Clearness index K
t
versus the solar
elevation angle for the measurements (yellow)
and the modeled (blue) hourly values
Figure a-10b Beam clearness index K
b
versus
the solar elevation angle for the measurements
(yellow) and the modeled (blue) hourly values
Figure a-11g Monthly averaged values surrounded
by ± one standard deviation for the modeled and
the measured values of the global irradiance.
Units: [kWh/m
2
month]
Figure a-11d same as Fig a-11g for the diffuse
irradiance. Units: [kWh/m
2
month]
Figure a-11b Monthly averaged values surrounded
by ± one standard deviation for the modeled and
the measured values of the normal beam irradiance.
Units: [kWh/m
2
month]
Figure a-12g Relative frequency of occurrence
of the hourly global irradiance ve rsus the
corresponding irradiance. The measurements are
represented in grey.
Figure a-14g Relative frequency of occurrence of
the glob al clearness index ve rsus the
corresponding clearness index K
t
. The
measurements are represented in grey.
Figure a-16g Cumulated frequency of occurrence
of the hourly global irradiance values versus the
corresponding irradiance. The measurements are
represented in grey.
Figure a-13g Relative frequency of occurrence
of the daily global irr adiance versu s the
corresponding irradiance. The measurements
are represented in grey.
Figure a-15g Relative frequency of occurrence
of the daily global clearness index versus the
corresponding clearness index . The
measurements are represented in grey.
Figure a-17g Cumulated frequency of occur-
rence of the daily global irradiance values
versus the corresponding irradiance. The
measurements are represented in grey.
Figure a-18g Monthly averaged values of the glo-
bal irradiation for the average models. Dashed
line represent ± one sd around the measurements
Figure a-19g Monthly averaged values of the
global irradiation for the «real time» models.
Dashed line represent ± one sd
Figure a-12b Relative frequency of occurrence
of the hour ly b eam irradiance versus the
corresponding irradiance. The measurements are
represented in grey.
Figure a-14b Relative frequency of occurrence of
the beam clearness index versus the corresponding
clearness index K
t
. The measureme nts are
represented in grey.
Figure a-16b Cumulated frequency of occurrence
of the hourly beam irradiance values versus the
corresponding irradiance. The measurements are
represented in grey.
Figure a-13b Relative frequency of occurrence
of the daily beam irradiance ve rsus the
corresponding irradiance. The measurements
are represented in grey.
Figure a-15b Relative frequency of occurrence
of the daily beam clearness index versus the
corresponding clearness index . The
measurements are represented in grey.
Figure a-17b Cumulated frequency of oc-
currence of the daily beam irradiance va-
lues versus the corresponding irradiance. The
measurements are represented in grey.
Figure a-18b Monthly averaged values of the
beam irradiation for the average models. Dashed
line represent ± one sd around the measurements
Figure a-19b Monthly averaged values of the
beam irradiation for the «real time» models.
Dashed line represent ± one sd
Figure a-20g Interannual variability of the global irradiation for the measurements, the average models, and the nowcasting products. The values are
normalized to the 2004-2010 reference period average.
Figure a-20b Interannual variability of the normal beam irradiation for the measurements, the average models, and the nowcasting products. The values
are normalized to the 2004-2010 reference period average.
Table a-Ig Site by site and model by model hourly global irradiance validation results expressed in relative and absolute values. The absolute values are given in
[Wh/m
2
h]. For all sites, the overall values, the absolute mean bias and the standard deviation of the bias are given.
Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd
Almeri a 2004 - 2011 448 32562 0.980 0% 13% 449 32142 0.971 6% 15% 448 32545 0.971 3% 15% 449 32263 0.978 3% 13% 451 31554 0.985 -2% 11% 460 23554 0.965 0% 16%
Bratislava 2004 - 2007 270 17254 0.977 3% 19% 278 16421 0.970 0% 22% 273 17125 0.949 5% 29% 329 11605 0.965 3% 21% 275 16297 0.980 4% 18% 293 8010 0.966 4% 21%
Carpentras 2004 - 2011 432 26768 0.982 0% 11% 433 26485 0.977 1% 14% 434 26437 0.977 3% 13% 432 26449 0.981 2% 12% 434 26084 0.985 2% 11% 431 19802 0.967 1% 17%
Davos 2004 - 2011 371 19735 0.948 -4% 23% 373 19398 0.905 11% 31% 370 19740 0.718 -13% 53% 371 19493 0.948 -4% 23% 374 18902 0.932 -8% 26% 399 11623 0.831 -7% 40%
Geneva 2004 - 2011 304 33602 0.977 4% 18% 308 32966 0.974 0% 19% 304 33603 0.957 6% 25% 304 33263 0.974 3% 20% 307 32652 0.982 5% 16% 315 23939 0.954 5% 25%
Kassel 2004 - 2011 258 31387 0.968 0% 22% 261 30715 0.966 -3% 23% 257 31392 0.950 1% 28% 258 31148 0.960 -3% 25% 260 30566 0.979 2% 18% 265 22632 0.946 4% 28%
Lerwick 2004 - 2009 189 22355 0.958 1% 29% 211 19465 0.930 5% 35% 189 22356 0.934 4% 36% 199 20827 0.937 -3% 34% 192 21605 0.964 -4% 27%
Lindenberg 2004 - 2006 306 10430 0.969 -3% 18% 310 10117 0.960 -2% 21% 306 10432 0.942 -4% 25% 307 10308 0.954 -5% 22% 311 9773 0.974 0% 16% 319 3220 0.939 0% 25%
Madrid 2004 - 2011 422 25440 0.981 1% 13% 423 25125 0.976 4% 15% 421 25454 0.975 6% 15% 423 25227 0.977 6% 15% 424 24749 0.983 1% 13% 431 19320 0.972 2% 15%
Nantes 2004 - 2010 286 25672 0.979 -3% 17% 289 25194 0.952 0% 26% 286 25702 0.959 3% 24% 288 25418 0.973 1% 20% 288 24765 0.983 -4% 15% 299 15950 0.935 1% 28%
Payerne 2004 - 2009 350 20825 0.973 1% 17% 354 20452 0.962 -6% 19% 350 20824 0.951 2% 22% 352 20620 0.968 0% 18% 356 19919 0.977 2% 15% 293 21005 0.953 5% 26%
Sede Boqer 2004 - 2011 535 28380 0.985 1% 9% 537 28051 0.982 -6% 10% 536 28379 0.979 3% 11% 536 28135 0.983 5% 10% 537 28092 0.983 -4% 10% 533 21386 0.968 -2% 14%
Tamanrasset 2004 - 2011 594 30159 0.984 -1% 9% 597 25818 0.971 2% 13% 597 29252 0.978 -2% 11% 595 29904 0.977 -1% 11% 592 19907 0.972 -1% 12%
Toravere 2004 - 2011 252 30386 0.968 -2% 21% 272 27674 0.938 2% 29% 252 30426 0.955 -1% 26% 252 30258 0.947 -4% 28% 253 29907 0.970 -5% 21% 256 22129 0.948 -1% 27%
Valentia 2004 - 2011 237 33292 0.961 -4% 26% 235 33748 0.949 4% 30% 240 32870 0.928 3% 34% 243 32334 0.932 2% 34% 241 32081 0.949 -2% 29% 257 23218 0.925 1% 33%
Vaulx-en-Velin 2004 - 2011 300 30271 0.979 3% 17% 303 29771 0.970 3% 20% 304 29846 0.962 7% 23% 301 29920 0.974 6% 20% 303 29324 0.982 2% 16% 303 20935 0.963 4% 22%
Wien 2004 - 2011 278 33556 0.975 0% 19% 282 32754 0.970 -3% 21% 280 33344 0.957 3% 26% 278 33173 0.967 0% 23% 280 32616 0.980 0% 17% 291 23953 0.958 1% 24%
Zilani 2004 - 2009 250 22767 0.962 -1% 25% 263 21217 0.922 11% 36% 249 22852 0.941 -3% 31% 256 21918 0.940 -3% 30% 249 22312 0.940 -6% 31% 268 13730 0.892 -18% 38%
All site s 341 26381 -0.1% 17% 345 25418 1.4% 20% 342 26255 1.8% 23% 346 25682 1.0% 19% 338 25062 -0.8% 17% 340 18401 0.5% 24%
All site s absol ute bias 1.7% 3.9% 3.9% 2.9% 2.9% 3.0%
Standard dev. of the bias 2.1% 5.1% 4.8% 3.6% 3.6% 4.2%
Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd
Almeri a 2004 - 2011 448 32562 0.980 1.9 56.6 449 32142 0.971 27.2 69.1 448 32545 0.971 13.3 67.6 449 32263 0.978 14.3 59.5 451 31554 0.985 -8.3 50.3 460 23554 0.965 1.6 72.7
Bratislava 2004 - 2007 270 17254 0.977 8.7 51.6 278 16421 0.970 -0.6 61.3 273 17125 0.949 14.6 77.9 329 11605 0.965 9.2 70.5 275 16297 0.980 10.6 48.6 293 8010 0.966 12.8 61.6
Carpentras 2004 - 2011 432 26768 0.982 1.5 49.5 433 26485 0.977 2.4 59.6 434 26437 0.977 11.5 56.3 432 26449 0.981 6.7 51.2 434 26084 0.985 9.6 46.0 431 19802 0.967 5.3 74.2
Davos 2004 - 2011 371 19735 0.948 -15.6 86.1 373 19398 0.905 42.8 116.4 370 19740 0.718 -49.0 196.3 371 19493 0.948 -16.3 86.2 374 18902 0.932 -30.9 98.9 399 11623 0.831 -28.2 158.0
Geneva 2004 - 2011 304 33602 0.977 12.8 55.6 308 32966 0.974 0.2 58.6 304 33603 0.957 19.3 75.3 304 33263 0.974 10.5 59.3 307 32652 0.982 15.9 50.4 315 23939 0.954 16.6 78.5
Kassel 2004 - 2011 258 31387 0.968 -0.2 57.1 261 30715 0.966 -8.8 59.3 257 31392 0.950 2.1 71.2 258 31148 0.960 -7.5 64.5 260 30566 0.979 4.1 47.2 265 22632 0.946 10.5 74.2
Lerwick 2004 - 2009 189 22355 0.958 1.4 54.5 211 19465 0.930 11.3 73.5 189 22356 0.934 7.1 68.2 199 20827 0.937 -6.5 67.7 192 21605 0.964 -8.4 51.1
Lindenberg 2004 - 2006 306 10430 0.969 -9.4 54.9 310 10117 0.960 -6.5 64.1 306 10432 0.942 -10.8 75.8 307 10308 0.954 -14.8 67.3 311 9773 0.974 1.3 51.3 319 3220 0.939 -1.4 80.3
Madrid 2004 - 2011 422 25440 0.981 5.8 56.5 423 25125 0.976 18.4 63.2 421 25454 0.975 24.1 64.8 423 25227 0.977 23.7 62.4 424 24749 0.983 6.1 54.0 431 19320 0.972 8.3 66.6
Nantes 2004 - 2010 286 25672 0.979 -9.5 47.6 289 25194 0.952 1.2 73.9 286 25702 0.959 9.5 68.5 288 25418 0.973 2.0 56.5 288 24765 0.983 -11.0 43.7 299 15950 0.935 3.8 84.6
Payerne 2004 - 2009 350 20825 0.973 2.6 58.0 354 20452 0.962 -22.5 68.2 350 20824 0.951 6.2 78.7 352 20620 0.968 -0.3 63.8 356 19919 0.977 7.6 54.8 293 21005 0.953 14.1 76.2
Sede Boqer 2004 - 2011 535 28380 0.985 3.0 50.6 537 28051 0.982 -33.0 56.2 536 28379 0.979 18.4 60.3 536 28135 0.983 25.0 53.6 537 28092 0.983 -20.9 55.5 533 21386 0.968 -9.0 75.3
Tamanrasset 2004 - 2011 594 30159 0.984 -7.2 55.8 597 25818 0.971 12.4 75.2 597 29252 0.978 -10.7 66.9 595 29904 0.977 -6.1 65.6 592 19907 0.972 -4.5 73.1
Toravere 2004 - 2011 252 30386 0.968 -5.8 54.1 272 27674 0.938 5.8 79.1 252 30426 0.955 -3.8 65.1 252 30258 0.947 -11.2 70.2 253 29907 0.970 -13.7 52.7 256 22129 0.948 -2.0 69.7
Valentia 2004 - 2011 237 33292 0.961 -8.7 61.8 235 33748 0.949 9.7 71.1 240 32870 0.928 7.7 82.7 243 32334 0.932 4.3 81.5 241 32081 0.949 -3.9 69.8 257 23218 0.925 3.6 84.4
Vaulx-en-Velin 2004 - 2011 300 30271 0.979 9.2 51.7 303 29771 0.970 8.0 61.6 304 29846 0.962 22.2 70.7 301 29920 0.974 17.7 60.4 303 29324 0.982 7.3 48.7 303 20935 0.963 10.8 67.8
Wien 2004 - 2011 278 33556 0.975 -0.8 53.5 282 32754 0.970 -8.5 60.4 280 33344 0.957 9.5 71.4 278 33173 0.967 1.0 63.2 280 32616 0.980 1.2 48.5 291 23953 0.958 2.0 70.1
Zilani 2004 - 2009 250 22767 0.962 -3.6 61.3 263 21217 0.922 28.7 94.1 249 22852 0.941 -8.1 76.9 256 21918 0.940 -6.5 77.4 249 22312 0.940 -15.6 77.5 268 13730 0.892 -47.1 102.2
All site s 341 26381 -0.4 56.6 345 25418 4.7 70.5 342 26255 6.2 79.8 346 25682 3.5 65.5 338 25062 -2.7 57.3 340 18401 1.8 80.8
Hourly values
Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV MeteoSwissGlobal irradiance SolarGIS
Global irradiance
Hourly values
MeteoSwiss SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
- 48 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Table a-IIg Site by site and model by model daily global irradiance validation results expressed in relative and absolute values. The absolute values are given in
[kWh/m
2
day]. For all sites, the overall values, the absolute mean bias and the standard deviation of the bias are given.
Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd
Almeri a 2004 - 2011 5.01 2914 0.992 0% 5% 5.03 2872 0.980 5% 8% 5.01 2914 0.985 3% 8% 5.02 2889 0.991 3% 6% 5.05 2818 0.993 -2% 6% 4.97 2180 0.993 0% 7%
Bratislava 2004 - 2007 3.18 1459 0.996 3% 10% 3.23 1417 0.994 0% 12% 3.20 1459 0.987 5% 18% 3.18 1459 0.996 3% 10% 3.29 1364 0.996 4% 9% 3.22 729 0.995 4% 12%
Carpentras 2004 - 2011 4.49 2568 0.997 0% 5% 4.52 2532 0.992 0% 8% 4.53 2529 0.995 2% 7% 4.50 2537 0.997 1% 5% 4.53 2491 0.996 2% 6% 4.48 1901 0.996 1% 7%
Davos 2004 - 2011 3.11 2327 0.988 -4% 12% 3.13 2287 0.958 10% 23% 3.11 2327 0.884 -12% 36% 3.12 2298 0.989 -4% 12% 3.13 2236 0.974 -7% 18% 2.78 1602 0.959 -5% 26%
Geneva 2004 - 2011 3.50 2921 0.993 4% 8% 3.52 2879 0.991 0% 9% 3.50 2921 0.981 6% 13% 3.50 2888 0.993 3% 8% 3.55 2825 0.994 5% 8% 3.45 2184 0.991 4% 11%
Kassel 2004 - 2011 2.82 2869 0.992 0% 9% 2.84 2826 0.990 -3% 11% 2.82 2869 0.983 1% 14% 2.83 2842 0.989 -2% 11% 2.86 2775 0.994 2% 8% 2.80 2139 0.989 3% 13%
Lerwick 2004 - 2009 2.10 2016 0.989 1% 16% 2.63 1565 0.979 5% 22% 2.10 2015 0.981 4% 22% 2.37 1749 0.987 -3% 17% 2.15 1925 0.991 -4% 16%
Lindenberg 2004 - 2006 3.24 982 0.997 -3% 9% 3.29 951 0.995 -2% 11% 3.24 982 0.991 -4% 15% 3.27 966 0.996 -5% 10% 3.37 901 0.997 0% 9% 3.18 322 0.994 0% 13%
Madrid 2004 - 2011 4.43 2421 0.995 1% 6% 4.45 2385 0.990 4% 9% 4.42 2423 0.993 5% 8% 4.44 2402 0.995 5% 6% 4.49 2338 0.995 1% 7% 4.44 1875 0.994 1% 8%
Nantes 2004 - 2010 3.43 2142 0.996 -3% 8% 3.46 2106 0.989 0% 12% 3.43 2142 0.990 3% 12% 3.44 2125 0.996 1% 8% 3.48 2052 0.997 -4% 8% 3.35 1421 0.989 1% 15%
Payerne 2004 - 2009 3.60 2024 0.995 1% 8% 3.63 1991 0.992 -6% 11% 3.60 2024 0.989 2% 14% 3.61 2004 0.996 0% 8% 3.67 1927 0.996 2% 9% 3.44 1788 0.989 5% 14%
Sede Boqer 2004 - 2011 5.71 2663 0.995 0% 5% 5.73 2629 0.991 -5% 6% 5.71 2663 0.991 3% 6% 5.71 2641 0.993 4% 5% 5.73 2631 0.992 -3% 6% 5.72 1994 0.994 -1% 7%
Tamanrasset 2004 - 2011 6.19 2897 0.984 -1% 5% 6.18 2492 0.983 2% 9% 6.22 2805 0.983 -1% 6% 6.19 2874 0.977 -1% 6% 6.12 1924 0.993 -1% 8%
Toravere 2004 - 2011 2.77 2764 0.992 -2% 10% 3.19 2355 0.981 2% 16% 2.77 2764 0.985 -1% 15% 2.78 2740 0.988 -4% 13% 2.80 2699 0.991 -4% 11% 2.71 2089 0.988 0% 14%
Valentia 2004 - 2011 2.79 2830 0.988 -3% 11% 2.84 2788 0.973 3% 18% 2.79 2830 0.977 3% 16% 2.81 2792 0.986 2% 13% 2.83 2733 0.989 -1% 11% 2.85 2096 0.981 1% 17%
Vaulx-en-Velin 2004 - 2011 3.30 2748 0.994 3% 8% 3.33 2705 0.990 2% 10% 3.37 2685 0.986 7% 14% 3.31 2717 0.993 6% 10% 3.34 2652 0.994 2% 8% 3.16 2003 0.993 3% 11%
Wien 2004 - 2011 3.26 2859 0.992 0% 9% 3.28 2817 0.990 -2% 9% 3.30 2830 0.980 3% 15% 3.26 2825 0.990 1% 10% 3.30 2764 0.993 1% 8% 3.28 2124 0.990 1% 12%
Zilani 2004 - 2009 2.79 2037 0.990 -1% 15% 3.12 1793 0.979 11% 21% 2.79 2037 0.982 -3% 19% 2.79 2009 0.988 -3% 16% 2.87 1942 0.986 -6% 17% 2.82 1304 0.971 -18% 26%
All site s 3.73 2414 0.0% 8% 3.81 2300 1.2% 12% 3.73 2402 1.7% 13% 3.75 2376 1.0% 9% 3.72 2279 -0.6% 9% 3.60 1735 0.4% 12%
All site s absol ute bias 1.5% 3.5% 3.5% 2.6% 2.7% 2.6%
Standard dev. of the bias 1.9% 4.5% 4.2% 3.2% 3.1% 3.8%
Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd
Almeri a 2004 - 2011 5.01 2914 0.992 0.02 0.27 5.03 2872 0.980 0.26 0.42 5.01 2914 0.985 0.13 0.39 5.02 2889 0.991 0.14 0.29 5.05 2818 0.993 -0.08 0.30 4.97 2180 0.993 0.01 0.37
Bratislava 2004 - 2007 3.18 1459 0.996 0.10 0.30 3.23 1417 0.994 -0.01 0.37 3.20 1459 0.987 0.17 0.56 3.18 1459 0.996 0.10 0.30 3.29 1364 0.996 0.13 0.29 3.22 729 0.995 0.14 0.37
Carpentras 2004 - 2011 4.49 2568 0.997 0.02 0.21 4.52 2532 0.992 0.02 0.36 4.53 2529 0.995 0.11 0.31 4.50 2537 0.997 0.06 0.22 4.53 2491 0.996 0.09 0.27 4.48 1901 0.996 0.05 0.32
Davos 2004 - 2011 3.11 2327 0.988 -0.11 0.37 3.13 2287 0.958 0.31 0.71 3.11 2327 0.884 -0.36 1.13 3.12 2298 0.989 -0.12 0.36 3.13 2236 0.974 -0.22 0.55 2.78 1602 0.959 -0.15 0.71
Geneva 2004 - 2011 3.50 2921 0.993 0.13 0.27 3.52 2879 0.991 0.00 0.31 3.50 2921 0.981 0.19 0.46 3.50 2888 0.993 0.11 0.28 3.55 2825 0.994 0.16 0.29 3.45 2184 0.991 0.14 0.37
Kassel 2004 - 2011 2.82 2869 0.992 0.01 0.26 2.84 2826 0.990 -0.08 0.31 2.82 2869 0.983 0.02 0.40 2.83 2842 0.989 -0.07 0.31 2.86 2775 0.994 0.05 0.24 2.80 2139 0.989 0.09 0.36
Lerwick 2004 - 2009 2.10 2016 0.989 0.02 0.35 2.63 1565 0.979 0.14 0.59 2.10 2015 0.981 0.08 0.47 2.37 1749 0.987 -0.08 0.41 2.15 1925 0.991 -0.09 0.34
Lindenberg 2004 - 2006 3.24 982 0.997 -0.10 0.28 3.29 951 0.995 -0.07 0.35 3.24 982 0.991 -0.11 0.47 3.27 966 0.996 -0.16 0.31 3.37 901 0.997 0.01 0.29 3.18 322 0.994 -0.01 0.42
Madrid 2004 - 2011 4.43 2421 0.995 0.05 0.27 4.45 2385 0.990 0.16 0.39 4.42 2423 0.993 0.22 0.35 4.44 2402 0.995 0.21 0.29 4.49 2338 0.995 0.05 0.30 4.44 1875 0.994 0.06 0.36
Nantes 2004 - 2010 3.43 2142 0.996 -0.11 0.26 3.46 2106 0.989 0.01 0.43 3.43 2142 0.990 0.11 0.42 3.44 2125 0.996 0.02 0.29 3.48 2052 0.997 -0.13 0.27 3.35 1421 0.989 0.04 0.49
Payerne 2004 - 2009 3.60 2024 0.995 0.03 0.30 3.63 1991 0.992 -0.23 0.38 3.60 2024 0.989 0.06 0.49 3.61 2004 0.996 0.00 0.30 3.67 1927 0.996 0.08 0.31 3.44 1788 0.989 0.17 0.48
Sede Boqer 2004 - 2011 5.71 2663 0.995 0.03 0.27 5.73 2629 0.991 -0.30 0.35 5.71 2663 0.991 0.17 0.34 5.71 2641 0.993 0.24 0.29 5.73 2631 0.992 -0.19 0.34 5.72 1994 0.994 -0.08 0.40
Tamanrasset 2004 - 2011 6.19 2897 0.984 -0.05 0.28 6.18 2492 0.983 0.13 0.53 6.22 2805 0.983 -0.09 0.36 6.19 2874 0.977 -0.05 0.38 6.12 1924 0.993 -0.050.48
Toravere 2004 - 2011 2.77 2764 0.992 -0.04 0.28 3.19 2355 0.981 0.07 0.50 2.77 2764 0.985 -0.03 0.41 2.78 2740 0.988 -0.11 0.36 2.80 2699 0.991 -0.12 0.30 2.71 2089 0.988 0.00 0.39
Valentia 2004 - 2011 2.79 2830 0.988 -0.08 0.31 2.84 2788 0.973 0.10 0.51 2.79 2830 0.977 0.08 0.46 2.81 2792 0.986 0.05 0.36 2.83 2733 0.989 -0.03 0.31 2.85 2096 0.981 0.04 0.49
Vaulx-en-Velin 2004 - 2011 3.30 2748 0.994 0.10 0.27 3.33 2705 0.990 0.07 0.34 3.37 2685 0.986 0.24 0.46 3.31 2717 0.993 0.19 0.34 3.34 2652 0.994 0.08 0.28 3.16 2003 0.993 0.09 0.33
Wien 2004 - 2011 3.26 2859 0.992 0.00 0.28 3.28 2817 0.990 -0.08 0.31 3.30 2830 0.980 0.11 0.48 3.26 2825 0.990 0.02 0.32 3.30 2764 0.993 0.02 0.27 3.28 2124 0.990 0.02 0.39
Zilani 2004 - 2009 2.79 2037 0.990 -0.04 0.41 3.12 1793 0.979 0.34 0.66 2.79 2037 0.982 -0.09 0.54 2.79 2009 0.988 -0.07 0.44 2.87 1942 0.986 -0.18 0.49 2.82 1304 0.971 -0.50 0.74
All site s 3.73 2414 0.00 0.29 4 2300 0.05 0.44 3.73 2402 0.06 0.49 3.75 2376 0.04 0.33 3.72 2279 -0.02 0.33 3.60 1735 0.02 0.44
Global irradiance
Daily values
MeteoSwiss SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
Global irradiance SolarGIS
Daily values
Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV MeteoSwiss
- 49 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Table a-IIIg Site by site and model by model monthly global irradiance validation results expressed in relative and absolute values. The absolute values are given in
[kWh/m
2
month]. For all sites, the overall values, the absolute mean bias and the standard deviation of the bias are given.
Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd
Almeri a 2004 - 2011 152 96 0.999 0% 1.8% 152 95 0.996 6% 4.1% 152 96 0.997 3% 3.3% 151 96 0.999 3% 2.5% 153 93 0.999 -2% 2.8% 151 72 0.999 0% 3.0%
Bratislava 2004 - 2007 97 48 0.999 3% 2.9% 97 47 0.998 0% 5.7% 97 48 0.997 5% 7.6% 97 48 0.999 3% 2.9% 100 45 0.999 4% 3.3% 98 24 0.999 4% 4.7%
Carpentras 2004 - 2011 123 94 1.000 0% 1.6% 123 93 0.998 1% 5.3% 125 92 0.999 3% 2.9% 122 94 1.000 2% 1.7% 124 91 0.999 2% 3.4% 122 70 0.999 1% 3.4%
Davos 2004 - 2011 83 88 0.998 -4% 4.1% 83 87 0.989 11% 15.1% 83 88 0.972 -13% 13.9% 82 88 0.997 -4% 5.2% 83 85 0.977 -8% 13.3% 71 64 0.975 -7% 17.9%
Geneva 2004 - 2011 106 96 0.999 4% 2.4% 107 95 0.999 0% 3.6% 106 96 0.996 6% 5.2% 105 96 0.999 3% 2.7% 108 93 0.998 5% 4.5% 105 72 0.998 5% 4.6%
Kassel 2004 - 2011 84 96 0.999 0% 3.8% 84 95 0.997 -3% 5.3% 84 96 0.995 1% 6.9% 84 96 0.998 -3% 4.3% 85 93 0.999 2% 3.6% 83 72 0.997 4% 6.8%
Lerwick 2004 - 2009 60 70 0.997 1% 7.0% 65 63 0.994 5% 16.0% 60 70 0.995 4% 11.4% 59 70 0.997 -3% 7.1% 62 67 0.998 -4% 7.5%
Lindenberg 2004 - 2006 89 36 0.999 -3% 4.4% 89 35 0.999 -2% 5.0% 89 36 0.998 -4% 7.2% 88 36 1.000 -5% 3.9% 92 33 0.999 0% 3.8% 86 12 0.999 0% 5.6%
Madrid 2004 - 2011 123 87 0.999 1% 2.7% 124 86 0.997 4% 5.0% 123 87 0.998 6% 3.4% 123 87 0.999 6% 2.6% 125 84 0.998 1% 4.0% 122 68 0.999 2% 3.9%
Nantes 2004 - 2010 102 72 1.000 -3% 3.5% 103 71 0.999 0% 4.7% 102 72 0.998 3% 5.0% 102 72 0.999 1% 3.0% 104 69 0.999 -4% 4.6% 99 48 0.999 1% 4.3%
Payerne 2004 - 2009 101 72 0.999 1% 2.6% 102 71 0.997 -6% 5.2% 101 72 0.998 2% 5.4% 101 72 1.000 0% 2.4% 103 69 0.999 2% 4.5% 104 59 0.998 5% 5.5%
Sede Boqer 2004 - 2011 165 92 0.999 1% 2.1% 166 91 0.996 -6% 3.6% 165 92 0.998 3% 2.6% 164 92 0.999 5% 2.2% 166 91 0.997 -4% 3.6% 168 68 0.998 -2% 3.8%
Tamanrasset 2004 - 2011 187 96 0.991 -1% 2.6% 186 83 0.995 2% 4.6% 188 93 0.993 -2% 3.0% 185 96 0.988 -1% 3.1% 184 64 0.999 -1% 3.3%
Toravere 2004 - 2011 80 96 0.998 -2% 5.3% 85 88 0.993 2% 12.0% 80 96 0.996 -1% 6.9% 79 96 0.998 -4% 6.9% 81 93 0.997 -5% 7.0% 79 72 0.996 -1% 8.4%
Valentia 2004 - 2011 85 93 0.998 -4% 4.0% 86 92 0.995 4% 12.1% 85 93 0.995 3% 6.3% 84 93 0.998 2% 5.0% 86 90 0.997 -2% 5.2% 87 69 0.998 1% 5.2%
Vaulx-en-Velin 2004 - 2011 95 96 0.998 3% 4.0% 95 95 0.997 3% 5.4% 97 93 0.994 7% 7.4% 94 96 0.997 6% 6.7% 95 93 0.998 2% 4.5% 88 72 0.998 4% 4.3%
Wien 2004 - 2011 98 95 0.998 0% 4.0% 98 94 0.998 -3% 3.6% 100 93 0.995 3% 6.1% 97 95 0.998 0% 3.6% 99 92 0.998 0% 3.7% 98 71 0.997 1% 6.8%
Zilani 2004 - 2009 81 70 0.997 -1% 6.9% 86 65 0.996 11% 13.3% 81 70 0.996 -3% 8.4% 80 70 0.998 -3% 7.2% 83 67 0.998 -6% 7.2% 80 46 0.996 -18% 14.1%
All site s 108 84 -0.1% 3.3% 109 81 1.4% 6.9% 109 83 1.8% 5.7% 108 84 1.0% 3.9% 108 79 -0.8% 4.9% 104 61 0.6% 6.1%
All site s absol ute bias 1.7% 3.9% 3.9% 2.9% 2.9% 2.9%
Standard dev. of the bias 2.1% 5.0% 4.6% 3.5% 3.4% 4.1%
Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd Gh nb R mbd sd
Almeri a 2004 - 2011 152 96 0.999 0.64 2.73 152 95 0.996 9.22 6.18 152 96 0.997 4.52 5.07 151 96 0.999 4.82 3.82 153 93 0.999 -2.83 4.30 151 72 0.999 0.51 4.57
Bratislava 2004 - 2007 97 48 0.999 3.18 2.82 97 47 0.998 -0.21 5.52 97 48 0.997 5.21 7.39 97 48 0.999 3.18 2.82 100 45 0.999 3.85 3.28 98 24 0.999 4.29 4.56
Carpentras 2004 - 2011 123 94 1.000 0.43 1.91 123 93 0.998 0.68 6.51 125 92 0.999 3.32 3.59 122 94 1.000 1.89 2.04 124 91 0.999 2.76 4.21 122 70 0.999 1.49 4.11
Davos 2004 - 2011 83 88 0.998 -3.49 3.41 83 87 0.989 9.55 12.59 83 88 0.972 -10.99 11.56 82 88 0.997 -3.60 4.27 83 85 0.977 -6.87 11.09 71 64 0.975 -4.60 12.61
Geneva 2004 - 2011 106 96 0.999 4.49 2.55 107 95 0.999 0.06 3.89 106 96 0.996 6.76 5.48 105 96 0.999 3.64 2.89 108 93 0.998 5.58 4.86 105 72 0.998 5.50 4.80
Kassel 2004 - 2011 84 96 0.999 -0.05 3.22 84 95 0.997 -2.84 4.46 84 96 0.995 0.67 5.77 84 96 0.998 -2.45 3.64 85 93 0.999 1.35 3.08 83 72 0.997 3.29 5.61
Lerwick 2004 - 2009 60 70 0.997 0.45 4.25 65 63 0.994 3.50 10.47 60 70 0.995 2.27 6.88 59 70 0.997 -1.92 4.19 62 67 0.998 -2.70 4.65
Lindenberg 2004 - 2006 89 36 0.999 -2.72 3.87 89 35 0.999 -1.88 4.43 89 36 0.998 -3.12 6.34 88 36 1.000 -4.25 3.42 92 33 0.999 0.38 3.51 86 12 0.999 -0.37 4.82
Madrid 2004 - 2011 123 87 0.999 1.71 3.36 124 86 0.997 5.38 6.19 123 87 0.998 7.05 4.16 123 87 0.999 6.86 3.24 125 84 0.998 1.80 5.01 122 68 0.999 2.36 4.82
Nantes 2004 - 2010 102 72 1.000 -3.39 3.58 103 71 0.999 0.44 4.84 102 72 0.998 3.39 5.08 102 72 0.999 0.72 3.10 104 69 0.999 -3.94 4.81 99 48 0.999 1.27 4.25
Payerne 2004 - 2009 101 72 0.999 0.74 2.62 102 71 0.997 -6.48 5.33 101 72 0.998 1.78 5.43 101 72 1.000 -0.09 2.37 103 69 0.999 2.20 4.67 104 59 0.998 5.01 5.76
Sede Boqer 2004 - 2011 165 92 0.999 0.94 3.42 166 91 0.996 -10.18 5.89 165 92 0.998 5.67 4.36 164 92 0.999 7.64 3.54 166 91 0.997 -6.44 5.94 168 68 0.998 -2.83 6.40
Tamanrasset 2004 - 2011 187 96 0.991 -2.27 4.77 186 83 0.995 3.85 8.52 188 93 0.993 -3.37 5.69 185 96 0.988 -1.91 5.78 184 64 0.999 -1.41 6.15
Toravere 2004 - 2011 80 96 0.998 -1.85 4.19 85 88 0.993 1.82 10.24 80 96 0.996 -1.19 5.51 79 96 0.998 -3.52 5.50 81 93 0.997 -4.41 5.66 79 72 0.996 -0.61 6.60
Valentia 2004 - 2011 85 93 0.998 -3.10 3.43 86 92 0.995 3.56 10.41 85 93 0.995 2.71 5.32 84 93 0.998 1.49 4.24 86 90 0.997 -1.38 4.48 87 69 0.998 1.21 4.46
Vaulx-en-Velin 2004 - 2011 95 96 0.998 2.90 3.76 95 95 0.997 2.51 5.17 97 93 0.994 7.13 7.21 94 96 0.997 5.53 6.25 95 93 0.998 2.30 4.31 88 72 0.998 3.15 3.78
Wien 2004 - 2011 98 95 0.998 -0.29 3.91 98 94 0.998 -2.96 3.54 100 93 0.995 3.40 6.07 97 95 0.998 0.36 3.51 99 92 0.998 0.42 3.72 98 71 0.997 0.67 6.71
Zilani 2004 - 2009 81 70 0.997 -1.17 5.62 86 65 0.996 9.37 11.44 81 70 0.996 -2.64 6.81 80 70 0.998 -2.05 5.79 83 67 0.998 -5.20 6.02 80 46 0.996 -14.06 11.23
All site s 108 84 -0.11 3.60 109 81 1.48 7.50 109 83 1.96 6.16 108 84 1.13 4.15 108 79 -0.87 5.34 104 61 0.59 6.40
Monthl y val ues
Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV MeteoSwissGlobal irradiance SolarGIS
Global irradiance
Monthl y val ues
MeteoSwiss SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
- 50 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Table a-Ib Site by site and model by model hourly beam irradiance validation results expressed in relative and aboslute values. The absolute values are given in
[Wh/m
2
h]. For all sites, the overall values, the absolute mean bias and the standard deviation of the bias are given.
Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd
Almeri a 2004 - 2011 510 32562 0.936 -2% 24% 509 32142 0.857 12% 35% 510 32545 0.880 -3% 32% 511 32263 0.904 6% 29% 511 31554 0.929 -4% 26% 522 23554 0.871 -4% 32%
Bratislava 2004 - 2007 281 17254 0.926 -10% 38% 279 16421 0.886 -2% 47% 275 17125 0.862 -22% 53% 332 11605 0.890 -15% 39% 285 16297 0.927 -3% 37% 298 8010 0.897 9% 45%
Carpentras 2004 - 2011 522 26768 0.919 -2% 23% 523 26485 0.824 0% 33% 523 26437 0.874 -4% 30% 523 26449 0.890 3% 27% 523 26084 0.911 4% 24% 518 19802 0.852 -2% 32%
Davos 2004 - 2011 390 19735 0.919 -3% 40% 392 19398 0.788 22% 63% 389 19740 0.516 -34% 91% 390 19493 0.922 -10% 40% 393 18902 0.817 -14% 58% 431 11623 0.661 -37% 72%
Geneva 2004 - 2011 303 33602 0.928 7% 40% 307 32966 0.896 2% 47% 303 33603 0.875 -4% 53% 303 33263 0.921 9% 42% 306 32652 0.947 11% 34% 312 23939 0.877 4% 51%
Kassel 2004 - 2011 216 31387 0.929 2% 47% 219 30715 0.891 10% 57% 216 31392 0.878 -19% 61% 216 31148 0.915 -4% 52% 217 30566 0.945 11% 42% 222 22632 0.870 22% 66%
Lerwick 2004 - 2009 133 22355 0.885 7% 78% 146 19465 0.794 50% 103% 133 22356 0.812 -11% 100% 138 20827 0.868 22% 92% 134 21605 0.902 14% 73%
Lindenberg 2004 - 2006 293 10430 0.923 -6% 38% 293 10117 0.857 6% 51% 293 10432 0.860 -28% 51% 292 10308 0.882 -15% 47% 296 9773 0.927 4% 37% 297 3220 0.833 30% 62%
Madrid 2004 - 2011 463 25440 0.945 5% 24% 463 25125 0.876 9% 35% 463 25454 0.898 5% 34% 464 25227 0.920 16% 29% 464 24749 0.935 4% 26% 477 19320 0.903 -2% 30%
Nantes 2004 - 2010 282 25672 0.942 -8% 33% 284 25194 0.844 3% 53% 282 25702 0.874 -11% 52% 283 25418 0.929 4% 39% 283 24765 0.943 -10% 33% 285 15950 0.834 1%57%
Payerne 2004 - 2009 334 20825 0.918 7% 38% 337 20452 0.861 -3% 48% 334 20824 0.870 -6% 49% 336 20620 0.905 6% 41% 339 19919 0.927 12% 36% 279 21156 0.816 13%61%
Sede Boqer 2004 - 2011 617 28380 0.894 -5% 21% 619 28051 0.766 -17% 30% 617 28379 0.775 -9% 31% 617 28135 0.812 -3% 27% 618 28092 0.821 -11% 26% 611 21386 0.714 -8% 34%
Tamanrasset 2004 - 2011 604 30159 0.916 2% 21% 610 25818 0.700 15% 38% 603 29252 0.836 -11% 29% 603 29904 0.813 -9% 31% 603 19907 0.804 11% 31%
Toravere 2004 - 2011 266 30386 0.915 -6% 46% 286 27674 0.820 7% 62% 266 30426 0.851 -28% 60% 266 30258 0.882 -14% 54% 266 29907 0.911 -9% 47% 265 22129 0.842 2% 65%
Valentia 2004 - 2011 224 33292 0.914 -22% 52% 223 33748 0.846 3% 64% 227 32870 0.832 -25% 68% 228 32334 0.867 -2% 63% 226 32081 0.909 -12% 51% 246 23218 0.801 2% 73%
Vaulx-en-Velin 2004 - 2011 302 30271 0.940 -1% 35% 304 29771 0.895 4% 46% 306 29846 0.893 -5% 48% 302 29920 0.929 10% 40% 303 29324 0.952 0% 31% 300 20935 0.893 -1% 47%
Wien 2004 - 2011 254 33556 0.914 -2% 46% 258 32754 0.887 4% 52% 246 33648 0.865 -13% 59% 254 33173 0.897 -4% 50% 256 32616 0.927 6% 43% 265 23953 0.876 15% 56%
Zilani 2004 - 2009 238 22767 0.888 0% 57% 252 21217 0.794 32% 76% 237 22852 0.822 -26% 70% 238 21918 0.872 -7% 60% 239 22312 0.857 -5% 63% 254 13730 0.767 -14% 78%
All site s 351 26381 -2% 34% 354 25418 6% 47% 350 26272 -11% 49% 354 25682 0% 39% 347 25062 0% 37% 353 18411 0% 49%
All site s absol ute bias 4.8% 10.0% 12.0% 7.8% 7.9% 7.8%
Standard dev. of the bias 5.9% 13.9% 14.5% 9.3% 9.4% 12.0%
Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd
Almeri a 2004 - 2011 510 32562 0.936 -9.8 120.8 509 32142 0.857 61.7 175.7 510 32545 0.880 -15.5 165.7 511 32263 0.904 31.9 146.0 511 31554 0.929 -20.0 130.4 522 23554 0.871 -20.6 165.5
Bratislava 2004 - 2007 281 17254 0.926 -27.1 105.6 279 16421 0.886 -5.9 130.1 275 17125 0.862 -59.8 145.5 332 11605 0.890 -50.8 130.1 285 16297 0.927 -7.8 105.4 298 8010 0.897 27.2 135.1
Carpentras 2004 - 2011 522 26768 0.919 -9.4 121.6 523 26485 0.824 -1.4 174.4 523 26437 0.874 -22.0 155.4 523 26449 0.890 15.5 141.0 523 26084 0.911 23.3 127.2 518 19802 0.852 -8.4 167.4
Davos 2004 - 2011 390 19735 0.919 -10.8 156.6 392 19398 0.788 85.9 246.1 389 19740 0.516 -130.9 356.3 390 19493 0.922 -37.2 157.5 393 18902 0.817 -54.0 229.9 431 11623 0.661 -159.3 309.3
Geneva 2004 - 2011 303 33602 0.928 21.2 120.2 307 32966 0.896 5.7 144.0 303 33603 0.875 -12.8 159.6 303 33263 0.921 26.1 127.4 306 32652 0.947 33.2 104.7 312 23939 0.877 12.4 160.4
Kassel 2004 - 2011 216 31387 0.929 4.4 102.2 219 30715 0.891 22.3 125.6 216 31392 0.878 -41.8 132.1 216 31148 0.915 -9.5 111.4 217 30566 0.945 23.9 92.2 222 22632 0.870 48.4 146.8
Lerwick 2004 - 2009 133 22355 0.885 9.1 104.3 146 19465 0.794 73.7 150.5 133 22356 0.812 -15.3 133.4 138 20827 0.868 29.8 126.6 134 21605 0.902 19.3 98.1
Lindenberg 2004 - 2006 293 10430 0.923 -18.8 112.3 293 10117 0.857 17.1 150.3 293 10432 0.860 -81.6 148.6 292 10308 0.882 -44.1 137.5 296 9773 0.927 10.4 109.2 297 3220 0.833 90.6 184.9
Madrid 2004 - 2011 463 25440 0.945 25.0 110.6 463 25125 0.876 39.7 162.5 463 25454 0.898 22.3 158.4 464 25227 0.920 76.1 136.3 464 24749 0.935 20.5 119.7 477 19320 0.903 -10.9 143.5
Nantes 2004 - 2010 282 25672 0.942 -23.8 94.3 284 25194 0.844 7.7 151.8 282 25702 0.874 -31.3 145.3 283 25418 0.929 11.8 111.3 283 24765 0.943 -27.3 94.1 285 15950 0.834 2.0 161.3
Payerne 2004 - 2009 334 20825 0.918 23.3 127.4 337 20452 0.861 -11.3 163.3 334 20824 0.870 -19.2 162.7 336 20620 0.905 20.5 137.4 339 19919 0.927 41.4 120.7 279 21156 0.816 34.9 168.9
Sede Boqer 2004 - 2011 617 28380 0.894 -28.4 126.7 619 28051 0.766 -104.5 183.6 617 28379 0.775 -54.0 194.0 617 28135 0.812 -19.0 167.9 618 28092 0.821 -69.8 161.4 611 21386 0.714 -47.6 207.2
Tamanrasset 2004 - 2011 604 30159 0.916 14.9 129.5 610 25818 0.700 89.7 229.8 603 29252 0.836 -63.4 176.6 603 29904 0.813 -55.4 187.0 603 19907 0.804 65.2 188.6
Toravere 2004 - 2011 266 30386 0.915 -17.2 121.9 286 27674 0.820 20.7 176.9 266 30426 0.851 -75.4 158.9 266 30258 0.882 -38.1 142.6 266 29907 0.911 -23.1 124.5 265 22129 0.842 5.0 171.0
Valentia 2004 - 2011 224 33292 0.914 -48.3 115.5 223 33748 0.846 7.4 142.6 227 32870 0.832 -56.8 154.4 228 32334 0.867 -5.3 142.5 226 32081 0.909 -26.1 115.5 246 23218 0.801 4.7 179.7
Vaulx-en-Velin 2004 - 2011 302 30271 0.940 -2.8 105.3 304 29771 0.895 12.9 138.7 306 29846 0.893 -14.5 146.0 302 29920 0.929 30.6 120.1 303 29324 0.952 0.3 94.5 300 20935 0.893 -1.5 140.5
Wien 2004 - 2011 254 33556 0.914 -5.9 116.9 258 32754 0.887 10.2 133.6 246 33648 0.865 -31.3 145.0 254 33173 0.897 -8.9 127.7 256 32616 0.927 15.1 108.9 265 23953 0.876 39.7 148.2
Zilani 2004 - 2009 238 22767 0.888 -0.6 135.3 252 21217 0.794 80.2 190.4 237 22852 0.822 -62.8 166.2 238 21918 0.872 -17.4 143.0 239 22312 0.857 -12.7 151.1 254 13730 0.767 -34.5 198.7
All site s 351 26381 -5.6 118.6 354 25418 20.9 166.4 350 26272 -39.6 170.0 354 25682 0.5 139.9 347 25062 -0.4 128.4 353 18411 -1.3 173.7
MeteoSwiss
MeteoSwiss
Hourly values
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
Beam irradiance
Hourly values
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
Beam irradiance
- 51 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Table a-IIb Site by site and model by model daily beam irradiance validation results expressed in relative and absolute values. The absolute values are given in
[kWh/m
2
day]. For all sites, the overall values, the absolute mean bias and the standard deviation of the bias are given.
Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd
Almeri a 2004 - 2011 5.70 2914 0.967 -2% 13% 5.70 2872 0.893 11% 24% 5.70 2914 0.916 -3% 21% 5.70 2889 0.941 6% 17% 5.72 2818 0.953 -3% 18% 5.64 2180 0.959 -3% 19%
Bratislava 2004 - 2007 3.20 1459 0.982 -7% 23% 3.23 1417 0.966 -2% 31% 3.22 1459 0.950 -22% 36% 3.20 1459 0.982 -7% 23% 3.40 1364 0.983 -3% 22% 3.28 729 0.976 9% 29%
Carpentras 2004 - 2011 5.44 2568 0.978 -1% 13% 5.46 2532 0.932 0% 24% 5.47 2529 0.954 -4% 20% 5.45 2537 0.969 3% 16% 5.48 2491 0.967 4% 17% 5.39 1901 0.975 -1% 17%
Davos 2004 - 2011 3.27 2327 0.971 -2% 24% 3.29 2287 0.873 19% 48% 3.27 2327 0.731 -28% 66% 3.27 2298 0.978 -8% 24% 3.29 2236 0.902 -11% 43% 2.94 1602 0.852 -30% 54%
Geneva 2004 - 2011 3.49 2921 0.968 7% 21% 3.52 2879 0.946 2% 28% 3.49 2921 0.930 -4% 32% 3.49 2888 0.968 8% 21% 3.53 2825 0.976 9% 19% 3.42 2184 0.963 3% 26%
Kassel 2004 - 2011 2.36 2869 0.976 3% 24% 2.38 2826 0.946 9% 36% 2.36 2869 0.940 -17% 39% 2.37 2842 0.970 -4% 29% 2.39 2775 0.977 10% 25% 2.35 2139 0.954 18%40%
Lerwick 2004 - 2009 1.47 2016 0.957 7% 45% 1.82 1565 0.897 50% 70% 1.48 2015 0.905 -11% 66% 1.64 1749 0.959 22% 48% 1.50 1925 0.961 14% 43%
Lindenberg 2004 - 2006 3.11 982 0.984 -7% 24% 3.12 951 0.960 6% 35% 3.11 982 0.956 -28% 37% 3.12 966 0.979 -15% 31% 3.21 901 0.984 3% 22% 2.97 322 0.974 30% 41%
Madrid 2004 - 2011 4.86 2421 0.983 5% 13% 4.87 2385 0.938 8% 25% 4.86 2423 0.961 4% 21% 4.87 2402 0.974 15% 16% 4.91 2338 0.974 4% 17% 4.92 1875 0.972 -2% 19%
Nantes 2004 - 2010 3.38 2142 0.981 -8% 20% 3.40 2106 0.948 3% 32% 3.38 2142 0.941 -11% 33% 3.39 2125 0.983 4% 19% 3.41 2052 0.982 -10% 21% 3.19 1421 0.952 1%35%
Payerne 2004 - 2009 3.44 2002 0.976 7% 23% 3.46 1970 0.952 -4% 34% 3.44 2002 0.949 -6% 34% 3.45 1982 0.975 6% 24% 3.50 1907 0.977 12% 23% 3.30 1811 0.917 12%49%
Sede Boqer 2004 - 2011 6.58 2663 0.968 -4% 12% 6.60 2629 0.897 -15% 21% 6.58 2663 0.896 -8% 21% 6.57 2641 0.922 -2% 18% 6.59 2631 0.927 -10% 18% 6.55 1994 0.923 -6% 24%
Tamanrasset 2004 - 2011 6.29 2897 0.940 2% 15% 6.31 2492 0.833 15% 32% 6.28 2805 0.882 -10% 23% 6.28 2874 0.858 -8% 24% 6.24 1924 0.937 11% 25%
Toravere 2004 - 2011 2.92 2764 0.971 -5% 26% 3.36 2355 0.931 7% 36% 2.92 2764 0.935 -25% 41% 2.93 2740 0.967 -12% 31% 2.94 2699 0.970 -7% 27% 2.80 2089 0.946 2% 39%
Valentia 2004 - 2011 2.63 2830 0.971 -19% 26% 2.69 2788 0.912 2% 40% 2.63 2830 0.915 -23% 40% 2.64 2792 0.967 -2% 25% 2.65 2733 0.978 -10% 23% 2.72 2096 0.920 1% 45%
Vaulx-en-Velin 2004 - 2011 3.32 2748 0.978 -1% 21% 3.35 2705 0.958 3% 30% 3.40 2685 0.946 -5% 32% 3.32 2717 0.977 10% 21% 3.35 2652 0.982 0% 19% 3.13 2003 0.967 -2% 28%
Wien 2004 - 2011 2.98 2859 0.962 -1% 26% 3.00 2817 0.948 4% 30% 2.92 2904 0.923 -10% 37% 2.98 2825 0.961 -2% 27% 3.02 2764 0.970 6% 23% 2.99 2124 0.955 14% 32%
Zilani 2004 - 2009 2.66 2037 0.957 0% 38% 2.98 1793 0.927 32% 48% 2.66 2037 0.924 -26% 50% 2.60 2009 0.962 -7% 37% 2.74 1942 0.958 -5% 37% 2.67 1304 0.920 -14% 55%
All site s 3.83 2413 -1.2% 20% 3.91 2299 5.6% 32% 3.82 2405 -10.3% 32% 3.85 2375 0.4% 23% 3.82 2278 0.2% 24% 3.73 1737 0.1% 31%
All site s absol ute bias 4.3% 9.2% 10.9% 6.8% 7.2% 6.7%
Standard dev. of the bias 5.4% 12.7% 12.7% 8.1% 8.4% 9.1%
Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd Bn nb R mbd sd
Almeri a 2004 - 2011 5.70 2914 0.967 -0.09 0.73 5.70 2872 0.893 0.60 1.35 5.70 2914 0.916 -0.14 1.18 5.70 2889 0.941 0.32 0.98 5.72 2818 0.953 -0.19 1.01 5.64 2180 0.959 -0.17 1.06
Bratislava 2004 - 2007 3.20 1459 0.982 -0.23 0.73 3.23 1417 0.966 -0.07 0.99 3.22 1459 0.950 -0.70 1.16 3.20 1459 0.982 -0.23 0.73 3.40 1364 0.983 -0.09 0.75 3.28 729 0.976 0.30 0.96
Carpentras 2004 - 2011 5.44 2568 0.978 -0.08 0.73 5.46 2532 0.932 -0.02 1.30 5.47 2529 0.954 -0.20 1.10 5.45 2537 0.969 0.15 0.89 5.48 2491 0.967 0.22 0.93 5.39 1901 0.975 -0.06 0.94
Davos 2004 - 2011 3.27 2327 0.971 -0.07 0.80 3.29 2287 0.873 0.62 1.57 3.27 2327 0.731 -0.93 2.17 3.27 2298 0.978 -0.26 0.77 3.29 2236 0.902 -0.36 1.40 2.94 1602 0.852 -0.87 1.60
Geneva 2004 - 2011 3.49 2921 0.968 0.24 0.73 3.52 2879 0.946 0.07 1.00 3.49 2921 0.930 -0.13 1.13 3.49 2888 0.968 0.26 0.73 3.53 2825 0.976 0.34 0.67 3.42 2184 0.963 0.11 0.89
Kassel 2004 - 2011 2.36 2869 0.976 0.06 0.56 2.38 2826 0.946 0.22 0.86 2.36 2869 0.940 -0.40 0.91 2.37 2842 0.970 -0.09 0.69 2.39 2775 0.977 0.24 0.59 2.35 2139 0.954 0.43 0.95
Lerwick 2004 - 2009 1.47 2016 0.957 0.10 0.67 1.82 1565 0.897 0.92 1.28 1.48 2015 0.905 -0.17 0.98 1.64 1749 0.959 0.36 0.80 1.50 1925 0.961 0.22 0.65
Lindenberg 2004 - 2006 3.11 982 0.984 -0.20 0.73 3.12 951 0.960 0.18 1.10 3.11 982 0.956 -0.87 1.16 3.12 966 0.979 -0.47 0.98 3.21 901 0.984 0.11 0.72 2.97 322 0.974 0.90 1.20
Madrid 2004 - 2011 4.86 2421 0.983 0.25 0.63 4.87 2385 0.938 0.39 1.22 4.86 2423 0.961 0.21 1.03 4.87 2402 0.974 0.71 0.78 4.91 2338 0.974 0.20 0.83 4.92 1875 0.972 -0.09 0.92
Nantes 2004 - 2010 3.38 2142 0.981 -0.29 0.66 3.40 2106 0.948 0.09 1.08 3.38 2142 0.941 -0.38 1.12 3.39 2125 0.983 0.14 0.63 3.41 2052 0.982 -0.33 0.72 3.19 1421 0.952 0.02 1.11
Payerne 2004 - 2009 3.44 2002 0.976 0.24 0.80 3.46 1970 0.952 -0.12 1.18 3.44 2002 0.949 -0.20 1.17 3.45 1982 0.975 0.21 0.82 3.50 1907 0.977 0.43 0.80 3.30 1811 0.917 0.41 1.60
Sede Boqer 2004 - 2011 6.58 2663 0.968 -0.28 0.76 6.60 2629 0.897 -0.96 1.41 6.58 2663 0.896 -0.51 1.37 6.57 2641 0.922 -0.16 1.22 6.59 2631 0.927 -0.65 1.19 6.55 1994 0.923 -0.41 1.59
Tamanrasset 2004 - 2011 6.29 2897 0.940 0.14 0.91 6.31 2492 0.833 0.93 2.03 6.28 2805 0.882 -0.62 1.41 6.28 2874 0.858 -0.53 1.52 6.24 1924 0.937 0.67 1.58
Toravere 2004 - 2011 2.92 2764 0.971 -0.15 0.76 3.36 2355 0.931 0.22 1.22 2.92 2764 0.935 -0.72 1.19 2.93 2740 0.967 -0.36 0.90 2.94 2699 0.970 -0.21 0.80 2.80 2089 0.946 0.07 1.09
Valentia 2004 - 2011 2.63 2830 0.971 -0.50 0.67 2.69 2788 0.912 0.06 1.08 2.63 2830 0.915 -0.60 1.06 2.64 2792 0.967 -0.06 0.66 2.65 2733 0.978 -0.27 0.61 2.72 2096 0.920 0.04 1.23
Vaulx-en-Velin 2004 - 2011 3.32 2748 0.978 -0.05 0.69 3.35 2705 0.958 0.10 1.01 3.40 2685 0.946 -0.17 1.08 3.32 2717 0.977 0.32 0.70 3.35 2652 0.982 -0.02 0.63 3.13 2003 0.967 -0.05 0.89
Wien 2004 - 2011 2.98 2859 0.962 -0.03 0.77 3.00 2817 0.948 0.13 0.89 2.92 2904 0.923 -0.29 1.08 2.98 2825 0.961 -0.07 0.82 3.02 2764 0.970 0.18 0.70 2.99 2124 0.955 0.40 0.96
Zilani 2004 - 2009 2.66 2037 0.957 -0.01 1.00 2.98 1793 0.927 0.95 1.42 2.66 2037 0.924 -0.70 1.34 2.60 2009 0.962 -0.19 0.97 2.74 1942 0.958 -0.15 1.02 2.67 1304 0.920 -0.36 1.46
All site s 3.83 2413 -0.05 0.75 4 2299 0.22 1.25 3.82 2405 -0.39 1.23 3.85 2375 0.02 0.90 3.82 2278 0.01 0.90 3.73 1737 0.00 1.17
Beam irradiance
Beam irradiance
Daily values
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
MeteoSwiss
Daily values
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
- 52 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Table a-Id Site by site and model by model hourly diffuse irradiance validation results expressed in relative and absolute values. The absolute values are
given in [Wh/m
2
h]. For all sites, the overall values, the absolute mean bias and the standard deviation of the bias are given.
Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd
Almeri a 2004 - 2011 137 32562 0.866 8% 35% 138 32142 0.746 -11% 47% 137 32545 0.752 21% 58% 137 32263 0.838 -2% 39% 138 31554 0.874 4% 35% 141 23554 0.794 11% 42%
Bratislava 2004 - 2007 120 17254 0.899 16% 35% 123 16421 0.859 -6% 37% 121 17125 0.768 38% 67% 137 11605 0.860 17% 37% 121 16297 0.921 11% 30% 125 8010 0.838 -5% 38%
Carpentras 2004 - 2011 131 26768 0.869 7% 35% 132 26485 0.798 -7% 44% 132 26437 0.781 23% 54% 132 26449 0.860 -2% 37% 131 26084 0.896 -3% 32% 132 19802 0.825 4% 41%
Davos 2004 - 2011 144 19735 0.825 -3% 46% 145 19398 0.712 -6% 57% 144 19740 0.458 23% 89% 145 19493 0.833 8% 45% 144 18902 0.826 -2% 46% 143 11623 0.599 44% 76%
Geneva 2004 - 2011 128 33602 0.887 6% 36% 129 32966 0.839 -6% 43% 128 33603 0.780 27% 60% 128 33263 0.879 -1% 37% 129 32652 0.914 2% 32% 132 23939 0.795 12% 49%
Kassel 2004 - 2011 128 31387 0.913 9% 31% 129 30715 0.879 -10% 35% 128 31392 0.816 30% 55% 128 31148 0.869 7% 38% 128 30566 0.932 4% 27% 130 22632 0.824 -1% 41%
Lerwick 2004 - 2009 124 22355 0.916 0% 34% 137 19465 0.822 -22% 44% 124 22356 0.856 12% 50% 130 20827 0.857 -15% 42% 125 21605 0.923 -10% 35%
Lindenberg 2004 - 2006 150 10430 0.900 2% 29% 153 10117 0.866 -16% 33% 150 10432 0.776 22% 53% 151 10308 0.842 3% 36% 152 9773 0.923 -2% 26% 158 3220 0.749 -28% 44%
Madrid 2004 - 2011 123 25440 0.880 9% 36% 124 25125 0.778 3% 47% 123 25454 0.777 28% 65% 123 25227 0.861 -2% 38% 123 24749 0.890 11% 34% 125 19320 0.826 24% 43%
Nantes 2004 - 2010 136 25672 0.921 2% 29% 138 25194 0.880 -10% 34% 136 25702 0.808 21% 57% 137 25418 0.914 -4% 30% 137 24765 0.937 5% 26% 145 15950 0.853 2% 38%
Payerne 2004 - 2009 156 20825 0.890 -3% 29% 158 20452 0.831 -14% 36% 156 20824 0.757 14% 52% 157 20620 0.872 -6% 31% 158 19919 0.906 -7% 27% 139 21156 0.831-3% 37%
Sede Boqer 2004 - 2011 138 28380 0.838 10% 37% 138 28051 0.613 9% 54% 138 28379 0.597 31% 64% 138 28135 0.684 19% 49% 138 28092 0.762 10% 44% 138 21386 0.502 6% 60%
Tamanrasset 2004 - 2011 182 30159 0.878 -4% 36% 182 25818 0.654 -32% 58% 183 29252 0.723 19% 52% 182 29904 0.739 17% 49% 182 19907 0.745 -25% 50%
Toravere 2004 - 2011 115 30386 0.889 8% 37% 123 27674 0.812 -9% 41% 115 30426 0.815 31% 62% 115 30258 0.823 8% 44% 115 29907 0.889 3% 35% 116 22129 0.811 -4%45%
Valentia 2004 - 2011 122 33292 0.918 12% 32% 121 33748 0.884 -5% 37% 124 32870 0.824 30% 58% 125 32334 0.870 4% 40% 124 32081 0.917 10% 32% 130 23218 0.821 0% 43%
Vaulx-en-Velin 2004 - 2011 127 30271 0.901 11% 32% 128 29771 0.858 -3% 38% 128 29846 0.789 27% 60% 128 29920 0.896 1% 33% 128 29324 0.923 8% 28% 129 20935 0.817 9% 44%
Wien 2004 - 2011 133 33556 0.877 5% 36% 135 32754 0.845 -14% 39% 129 33648 0.765 26% 59% 133 33173 0.846 5% 40% 134 32616 0.896 0% 33% 137 23953 0.806 -12% 42%
Zilani 2004 - 2009 120 22767 0.891 4% 36% 125 21217 0.789 -13% 45% 120 22852 0.790 24% 64% 123 21918 0.827 6% 43% 119 22312 0.862 -2% 40% 128 13730 0.751 -18% 49%
All site s 134 26381 5% 35% 135 25418 -10% 45% 134 26272 25% 60% 135 25682 4% 41% 133 25062 1% 35% 133 18411 4% 46%
All site s absol ute bias 6.7% 11.0% 24.8% 7.0% 6.6% 9.4%
Standard dev. of the bias 7.5% 14.2% 25.2% 9.6% 9.6% 13.8%
Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd
Almeri a 2004 - 2011 137 32562 0.866 10.9 48.6 138 32142 0.746 -14.9 65.4 137 32545 0.752 29.3 80.0 137 32263 0.838 -2.9 53.2 138 31554 0.874 5.6 47.6 141 23554 0.794 15.1 59.3
Bratislava 2004 - 2007 120 17254 0.899 19.0 42.3 123 16421 0.859 -7.4 46.0 121 17125 0.768 45.5 81.0 137 11605 0.860 23.9 51.0 121 16297 0.921 13.5 36.7 125 8010 0.838 -5.9 47.4
Carpentras 2004 - 2011 131 26768 0.869 9.6 46.0 132 26485 0.798 -9.7 57.7 132 26437 0.781 29.9 71.9 132 26449 0.860 -2.4 48.3 131 26084 0.896 -3.3 41.8 132 19802 0.825 5.6 54.4
Davos 2004 - 2011 144 19735 0.825 -3.7 65.8 145 19398 0.712 -8.5 82.1 144 19740 0.458 33.2 128.7 145 19493 0.833 11.0 64.8 144 18902 0.826 -2.4 66.7 143 11623 0.599 62.5 108.6
Geneva 2004 - 2011 128 33602 0.887 7.1 46.3 129 32966 0.839 -7.3 55.4 128 33603 0.780 35.1 76.4 128 33263 0.879 -1.2 47.9 129 32652 0.914 2.2 41.3 132 23939 0.795 15.4 64.9
Kassel 2004 - 2011 128 31387 0.913 12.0 39.4 129 30715 0.879 -12.8 45.0 128 31392 0.816 38.1 70.5 128 31148 0.869 9.3 48.5 128 30566 0.932 5.3 34.5 130 22632 0.824 -0.7 53.6
Lerwick 2004 - 2009 124 22355 0.916 -0.5 42.3 137 19465 0.822 -29.8 60.9 124 22356 0.856 15.4 62.3 130 20827 0.857 -19.7 55.1 125 21605 0.923 -12.4 43.2
Lindenberg 2004 - 2006 150 10430 0.900 3.0 44.0 153 10117 0.866 -24.2 50.4 150 10432 0.776 32.5 79.5 151 10308 0.842 5.1 54.5 152 9773 0.923 -2.6 39.0 158 3220 0.749 -44.3 69.1
Madrid 2004 - 2011 123 25440 0.880 11.5 44.2 124 25125 0.778 3.8 58.7 123 25454 0.777 34.6 79.6 123 25227 0.861 -2.0 47.3 123 24749 0.890 13.6 42.2 125 19320 0.826 29.5 53.2
Nantes 2004 - 2010 136 25672 0.921 3.3 39.1 138 25194 0.880 -14.1 47.0 136 25702 0.808 28.5 77.9 137 25418 0.914 -6.0 40.5 137 24765 0.937 6.9 35.3 145 15950 0.853 2.3 55.2
Payerne 2004 - 2009 156 20825 0.890 -4.2 45.8 158 20452 0.831 -21.8 56.5 156 20824 0.757 22.4 81.9 157 20620 0.872 -9.4 49.3 158 19919 0.906 -11.0 43.0 139 21156 0.831 -3.5 51.3
Sede Boqer 2004 - 2011 138 28380 0.838 14.1 51.0 138 28051 0.613 11.8 73.9 138 28379 0.597 42.5 88.6 138 28135 0.684 25.5 68.1 138 28092 0.762 14.3 60.6 138 21386 0.502 7.8 82.1
Tamanrasset 2004 - 2011 182 30159 0.878 -7.4 65.3 182 25818 0.654 -57.3 105.2 183 29252 0.723 35.7 94.4 182 29904 0.739 31.6 89.4 182 19907 0.745 -46.2 90.3
Toravere 2004 - 2011 115 30386 0.889 8.9 42.1 123 27674 0.812 -11.3 49.9 115 30426 0.815 35.8 71.3 115 30258 0.823 9.7 50.7 115 29907 0.889 3.2 40.3 116 22129 0.811 -4.3 52.2
Valentia 2004 - 2011 122 33292 0.918 14.5 39.4 121 33748 0.884 -6.4 44.6 124 32870 0.824 36.6 72.1 125 32334 0.870 5.6 49.6 124 32081 0.917 12.7 40.0 130 23218 0.821 0.1 56.5
Vaulx-en-Velin 2004 - 2011 127 30271 0.901 13.8 41.0 128 29771 0.858 -3.4 48.3 128 29846 0.789 35.0 76.5 128 29920 0.896 1.8 41.9 128 29324 0.923 10.4 36.3 129 20935 0.817 11.9 56.4
Wien 2004 - 2011 133 33556 0.877 6.0 47.8 135 32754 0.845 -19.2 52.4 129 33648 0.765 33.2 75.4 133 33173 0.846 6.3 53.4 134 32616 0.896 0.3 43.8 137 23953 0.806 -16.6 57.7
Zilani 2004 - 2009 120 22767 0.891 4.3 43.8 125 21217 0.789 -16.5 56.6 120 22852 0.790 28.1 76.7 123 21918 0.827 6.9 52.7 119 22312 0.862 -2.3 47.8 128 13730 0.751 -23.0 62.1
All site s 134 26381 7.3 46.9 135 25418 -13.0 60.3 134 26272 33.1 80.4 135 25682 5.2 54.9 133 25062 1.6 47.1 133 18411 5.1 61.5
MeteoSwiss
MeteoSwiss
Diffuse irradiance
Hourly values
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
Diffuse irradiance
Hourly values
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
- 53 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Table a-IId Site by site and model by model daily diffuse irradiance validation results expressed in relative and absolute values. The absolute values are given in
[kWh/m
2
day]. For all sites, the overall values, the absolute mean bias and the standard deviation of the bias are given.
Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd
Almeri a 2004 - 2011 1.53 2914 0.895 7% 22% 1.54 2872 0.788 -10% 32% 1.53 2914 0.785 19% 40% 1.53 2889 0.852 -2% 27% 1.54 2818 0.908 3% 23% 1.52 2180 0.920 8%27%
Bratislava 2004 - 2007 1.41 1459 0.974 16% 25% 1.43 1417 0.962 -6% 27% 1.42 1459 0.931 38% 50% 1.41 1459 0.974 16% 25% 1.45 1364 0.984 11% 18% 1.37 729 0.970 -5% 27%
Carpentras 2004 - 2011 1.37 2568 0.932 6% 23% 1.37 2532 0.882 -7% 31% 1.38 2529 0.880 20% 40% 1.37 2537 0.917 -2% 26% 1.37 2491 0.953 -3% 21% 1.37 1901 0.948 3% 25%
Davos 2004 - 2011 1.21 2327 0.924 -3% 30% 1.21 2287 0.872 -5% 39% 1.21 2327 0.737 19% 66% 1.21 2298 0.922 6% 31% 1.20 2236 0.922 -2% 32% 1.00 1602 0.873 36% 57%
Geneva 2004 - 2011 1.47 2921 0.919 4% 21% 1.48 2879 0.871 -5% 27% 1.47 2921 0.833 24% 40% 1.47 2888 0.919 -1% 21% 1.49 2825 0.954 2% 16% 1.44 2184 0.930 9% 27%
Kassel 2004 - 2011 1.40 2869 0.964 8% 17% 1.41 2826 0.936 -9% 23% 1.40 2869 0.902 26% 39% 1.40 2842 0.935 6% 24% 1.41 2775 0.977 4% 14% 1.38 2139 0.946 -1% 26%
Lerwick 2004 - 2009 1.37 2016 0.980 0% 20% 1.71 1565 0.954 -22% 29% 1.38 2015 0.955 12% 36% 1.55 1749 0.977 -15% 23% 1.41 1925 0.984 -10% 21%
Lindenberg 2004 - 2006 1.59 982 0.984 2% 19% 1.62 951 0.979 -16% 23% 1.59 982 0.952 22% 42% 1.61 966 0.972 3% 25% 1.64 901 0.990 -2% 15% 1.57 322 0.960 -28% 32%
Madrid 2004 - 2011 1.29 2421 0.943 7% 23% 1.30 2385 0.877 2% 34% 1.29 2423 0.890 24% 44% 1.29 2402 0.946 -2% 23% 1.30 2338 0.952 9% 22% 1.29 1875 0.944 19% 27%
Nantes 2004 - 2010 1.64 2142 0.975 2% 18% 1.65 2106 0.965 -10% 22% 1.64 2142 0.928 21% 38% 1.64 2125 0.979 -4% 16% 1.66 2052 0.982 5% 16% 1.63 1421 0.970 2% 23%
Payerne 2004 - 2009 1.60 2002 0.970 -3% 20% 1.61 1970 0.950 -14% 26% 1.60 2002 0.916 14% 40% 1.61 1982 0.968 -6% 20% 1.63 1907 0.981 -7% 16% 1.60 1811 0.941-3% 21%
Sede Boqer 2004 - 2011 1.46 2663 0.891 9% 25% 1.47 2629 0.697 7% 40% 1.46 2663 0.703 27% 46% 1.47 2641 0.750 16% 37% 1.47 2631 0.824 9% 32% 1.48 1994 0.818 4% 42%
Tamanrasset 2004 - 2011 1.89 2897 0.881 -4% 25% 1.88 2492 0.767 -32% 48% 1.91 2805 0.757 18% 38% 1.90 2874 0.700 16% 38% 1.88 1924 0.913 -25% 38%
Toravere 2004 - 2011 1.26 2764 0.959 7% 22% 1.44 2355 0.939 -8% 24% 1.26 2764 0.911 28% 47% 1.27 2740 0.933 7% 26% 1.27 2699 0.961 3% 20% 1.23 2089 0.940 -3%29%
Valentia 2004 - 2011 1.44 2830 0.962 11% 18% 1.46 2788 0.932 -4% 23% 1.44 2830 0.907 26% 39% 1.45 2792 0.961 4% 19% 1.46 2733 0.977 9% 15% 1.44 2096 0.942 0%27%
Vaulx-en-Velin 2004 - 2011 1.40 2748 0.944 10% 22% 1.41 2705 0.913 -3% 26% 1.42 2685 0.877 26% 42% 1.40 2717 0.949 1% 20% 1.41 2652 0.967 8% 17% 1.34 2003 0.941 8% 30%
Wien 2004 - 2011 1.56 2859 0.930 4% 21% 1.57 2817 0.911 -13% 24% 1.53 2904 0.860 22% 35% 1.56 2825 0.914 4% 24% 1.58 2764 0.955 0% 18% 1.55 2124 0.940 -10% 26%
Zilani 2004 - 2009 1.34 2037 0.971 4% 23% 1.48 1793 0.945 -13% 30% 1.34 2037 0.921 24% 49% 1.34 2009 0.958 6% 27% 1.37 1942 0.970 -2% 23% 1.34 1304 0.949 -18% 32%
All site s 1.46 2413 4.8% 22% 1.49 2299 -9.2% 31% 1.46 2405 22.6% 42% 1.47 2375 3.2% 27% 1.46 2278 0.9% 22% 1.41 1737 2.7% 30%
All site s absol ute bias 6.0% 10.4% 22.6% 6.4% 6.3% 7.9%
Standard dev. of the bias 6.8% 13.7% 23.0% 8.6% 9.2% 10.8%
Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd
Almeri a 2004 - 2011 1.53 2914 0.895 0.10 0.34 1.54 2872 0.788 -0.15 0.50 1.53 2914 0.785 0.29 0.61 1.53 2889 0.852 -0.03 0.41 1.54 2818 0.908 0.05 0.35 1.52 2180 0.920 0.13 0.41
Bratislava 2004 - 2007 1.41 1459 0.974 0.22 0.35 1.43 1417 0.962 -0.09 0.38 1.42 1459 0.931 0.53 0.71 1.41 1459 0.974 0.22 0.35 1.45 1364 0.984 0.16 0.26 1.37 729 0.970 -0.06 0.37
Carpentras 2004 - 2011 1.37 2568 0.932 0.08 0.32 1.37 2532 0.882 -0.09 0.43 1.38 2529 0.880 0.28 0.55 1.37 2537 0.917 -0.03 0.36 1.37 2491 0.953 -0.03 0.28 1.37 1901 0.948 0.04 0.35
Davos 2004 - 2011 1.21 2327 0.924 -0.03 0.37 1.21 2287 0.872 -0.06 0.47 1.21 2327 0.737 0.23 0.80 1.21 2298 0.922 0.07 0.37 1.20 2236 0.922 -0.03 0.38 1.00 1602 0.873 0.36 0.57
Geneva 2004 - 2011 1.47 2921 0.919 0.07 0.30 1.48 2879 0.871 -0.08 0.39 1.47 2921 0.833 0.35 0.59 1.47 2888 0.919 -0.01 0.31 1.49 2825 0.954 0.02 0.24 1.44 2184 0.930 0.13 0.39
Kassel 2004 - 2011 1.40 2869 0.964 0.11 0.24 1.41 2826 0.936 -0.12 0.33 1.40 2869 0.902 0.36 0.55 1.40 2842 0.935 0.09 0.34 1.41 2775 0.977 0.05 0.20 1.38 2139 0.946 -0.01 0.36
Lerwick 2004 - 2009 1.37 2016 0.980 -0.01 0.28 1.71 1565 0.954 -0.37 0.50 1.38 2015 0.955 0.17 0.49 1.55 1749 0.977 -0.23 0.35 1.41 1925 0.984 -0.14 0.29
Lindenberg 2004 - 2006 1.59 982 0.984 0.03 0.31 1.62 951 0.979 -0.25 0.37 1.59 982 0.952 0.35 0.67 1.61 966 0.972 0.05 0.41 1.64 901 0.990 -0.03 0.25 1.57 322 0.960 -0.44 0.50
Madrid 2004 - 2011 1.29 2421 0.943 0.10 0.30 1.30 2385 0.877 0.02 0.44 1.29 2423 0.890 0.31 0.57 1.29 2402 0.946 -0.03 0.30 1.30 2338 0.952 0.12 0.28 1.29 1875 0.944 0.24 0.35
Nantes 2004 - 2010 1.64 2142 0.975 0.04 0.29 1.65 2106 0.965 -0.17 0.35 1.64 2142 0.928 0.34 0.62 1.64 2125 0.979 -0.07 0.26 1.66 2052 0.982 0.08 0.26 1.63 1421 0.970 0.03 0.38
Payerne 2004 - 2009 1.60 2002 0.970 -0.04 0.31 1.61 1970 0.950 -0.22 0.41 1.60 2002 0.916 0.23 0.63 1.61 1982 0.968 -0.10 0.32 1.63 1907 0.981 -0.11 0.26 1.60 1811 0.941 -0.04 0.34
Sede Boqer 2004 - 2011 1.46 2663 0.891 0.14 0.36 1.47 2629 0.697 0.11 0.59 1.46 2663 0.703 0.40 0.68 1.47 2641 0.750 0.23 0.55 1.47 2631 0.824 0.13 0.47 1.48 1994 0.818 0.06 0.62
Tamanrasset 2004 - 2011 1.89 2897 0.881 -0.07 0.48 1.88 2492 0.767 -0.59 0.89 1.91 2805 0.757 0.35 0.72 1.90 2874 0.700 0.29 0.73 1.88 1924 0.913 -0.48 0.70
Toravere 2004 - 2011 1.26 2764 0.959 0.09 0.28 1.44 2355 0.939 -0.11 0.34 1.26 2764 0.911 0.35 0.59 1.27 2740 0.933 0.09 0.34 1.27 2699 0.961 0.04 0.26 1.23 2089 0.940 -0.03 0.36
Valentia 2004 - 2011 1.44 2830 0.962 0.15 0.27 1.46 2788 0.932 -0.07 0.34 1.44 2830 0.907 0.38 0.56 1.45 2792 0.961 0.06 0.27 1.46 2733 0.977 0.14 0.21 1.44 2096 0.942 0.00 0.38
Vaulx-en-Velin 2004 - 2011 1.40 2748 0.944 0.15 0.30 1.41 2705 0.913 -0.04 0.37 1.42 2685 0.877 0.37 0.60 1.40 2717 0.949 0.02 0.29 1.41 2652 0.967 0.11 0.24 1.34 2003 0.941 0.11 0.40
Wien 2004 - 2011 1.56 2859 0.930 0.06 0.33 1.57 2817 0.911 -0.20 0.37 1.53 2904 0.860 0.34 0.53 1.56 2825 0.914 0.06 0.37 1.58 2764 0.955 0.01 0.28 1.55 2124 0.940 -0.16 0.40
Zilani 2004 - 2009 1.34 2037 0.971 0.05 0.31 1.48 1793 0.945 -0.20 0.44 1.34 2037 0.921 0.32 0.65 1.34 2009 0.958 0.08 0.36 1.37 1942 0.970 -0.03 0.31 1.34 1304 0.949 -0.24 0.43
All site s 1.46 2413 0.07 0.32 1 2299 -0.14 0.46 1.46 2405 0.33 0.62 1.47 2375 0.05 0.39 1.46 2278 0.01 0.32 1.41 1737 0.04 0.41
Diffuse irradiance
Daily values
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
Diffuse irradiance
Daily values
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV MeteoSwiss
MeteoSwiss
- 54 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
Table a-IIId Site by site and model by model monthly diffuse irradiance validation results expressed in relative and absolute values. The absolute values are given in
[kWh/m
2
month]. For all sites, the overall values, the absolute mean bias and the standard deviation of the bias are given.
Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd
Almeri a 2004 - 2011 47 96 0.968 8% 11.1% 47 95 0.964 -11% 10.4% 47 96 0.924 21% 18.0% 46 96 0.953 -2% 11.2% 47 93 0.978 4% 8.0% 46 72 0.985 11% 10.3%
Bratislava 2004 - 2007 43 48 0.997 16% 12.3% 43 47 0.995 -6% 8.4% 43 48 0.985 38% 28.1% 43 48 0.997 16% 12.3% 44 45 0.997 11% 9.2% 42 24 0.993 -5% 13.4%
Carpentras 2004 - 2011 37 94 0.985 7% 12.3% 38 93 0.981 -7% 9.5% 38 92 0.958 23% 23.4% 37 94 0.974 -2% 11.8% 38 91 0.987 -3% 8.0% 37 70 0.995 4% 7.6%
Davos 2004 - 2011 32 88 0.968 -3% 17.4% 32 87 0.962 -6% 20.2% 32 88 0.919 23% 32.7% 32 88 0.985 8% 11.9% 32 85 0.979 -2% 15.6% 26 64 0.958 44% 35.7%
Geneva 2004 - 2011 45 96 0.981 6% 10.2% 45 95 0.980 -6% 8.9% 45 96 0.952 27% 20.8% 44 96 0.980 -1% 10.5% 45 93 0.987 2% 7.5% 44 72 0.983 12% 13.2%
Kassel 2004 - 2011 42 96 0.996 9% 7.6% 42 95 0.995 -10% 8.8% 42 96 0.981 30% 22.7% 42 96 0.992 7% 11.2% 42 93 0.997 4% 5.5% 41 72 0.990 -1% 11.4%
Lerwick 2004 - 2009 40 70 0.998 0% 5.6% 42 63 0.997 -22% 13.6% 40 70 0.989 12% 19.5% 39 70 0.997 -15% 13.8% 40 67 0.998 -10% 11.0%
Lindenberg 2004 - 2006 44 36 0.997 2% 8.9% 44 35 0.997 -16% 9.9% 44 36 0.989 22% 26.3% 43 36 0.995 3% 10.8% 45 33 0.998 -2% 6.9% 42 12 0.990 -28% 23.4%
Madrid 2004 - 2011 36 87 0.975 9% 13.2% 36 86 0.960 3% 14.6% 36 87 0.962 28% 22.1% 36 87 0.981 -2% 10.0% 36 84 0.980 11% 11.5% 36 68 0.987 24% 12.2%
Nantes 2004 - 2010 49 72 0.998 2% 5.1% 49 71 0.998 -10% 6.5% 49 72 0.987 21% 17.8% 48 72 0.996 -4% 5.8% 49 69 0.996 5% 7.3% 48 48 0.997 2% 7.5%
Payerne 2004 - 2009 45 72 0.993 -3% 8.6% 45 71 0.991 -14% 8.9% 45 72 0.978 14% 20.0% 45 72 0.992 -6% 8.4% 46 69 0.995 -7% 7.5% 49 60 0.968 0% 7.0%
Sede Boqer 2004 - 2011 42 92 0.941 10% 13.0% 42 91 0.865 9% 19.6% 42 92 0.875 31% 21.6% 42 92 0.865 19% 20.8% 43 91 0.963 10% 10.1% 43 68 0.962 6% 16.7%
Tamanrasset 2004 - 2011 57 96 0.973 -4% 9.1% 56 83 0.873 -32% 29.7% 58 93 0.922 19% 16.2% 57 96 0.904 17% 17.0% 57 64 0.975 -25% 20.0%
Toravere 2004 - 2011 36 96 0.989 8% 15.0% 39 88 0.990 -9% 9.5% 36 96 0.981 31% 34.1% 36 96 0.991 8% 11.3% 37 93 0.992 3% 10.3% 36 72 0.988 -4% 12.3%
Valentia 2004 - 2011 44 93 0.996 12% 7.3% 44 92 0.997 -5% 4.6% 44 93 0.979 30% 20.9% 43 93 0.996 4% 6.7% 44 90 0.998 10% 6.1% 44 69 0.996 0% 6.9%
Vaulx-en-Velin 2004 - 2011 40 96 0.987 11% 11.6% 40 95 0.986 -3% 8.8% 41 93 0.959 27% 23.5% 40 96 0.985 1% 11.2% 40 93 0.990 8% 8.7% 38 72 0.991 9% 10.0%
Wien 2004 - 2011 47 95 0.988 5% 9.2% 47 94 0.987 -14% 9.9% 46 96 0.961 25% 18.0% 46 95 0.984 5% 11.6% 47 92 0.989 0% 8.1% 46 71 0.988 -12% 15.2%
Zilani 2004 - 2009 39 70 0.993 4% 11.7% 41 65 0.995 -13% 11.5% 39 70 0.981 24% 30.2% 39 70 0.995 6% 9.1% 40 67 0.995 -2% 8.8% 38 46 0.996 -18% 12.1%
All site s 42 84 5.4% 10.7% 43 81 -9.6% 14.2% 43 84 24.7% 22.6% 42 84 3.9% 12.4% 43 79 1.2% 10.2% 41 61 4.0% 13.0%
All site s absol ute bias 6.7% 11.0% 24.7% 7.1% 6.6% 9.2%
Standard dev. of the bias 7.6% 14.2% 25.4% 9.5% 9.6% 12.3%
Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd Dh nb R mbd sd
Almeri a 2004 - 2011 47 96 0.968 3.71 5.19 47 95 0.964 -5.03 4.85 47 96 0.924 9.94 8.37 46 96 0.953 -0.97 5.19 47 93 0.978 1.90 3.72 46 72 0.985 4.93 4.74
Bratislava 2004 - 2007 43 48 0.997 6.80 5.27 43 47 0.995 -2.59 3.63 43 48 0.985 16.23 12.18 43 48 0.997 6.80 5.27 44 45 0.997 4.89 4.04 42 24 0.993 -1.97 5.59
Carpentras 2004 - 2011 37 94 0.985 2.74 4.62 38 93 0.981 -2.75 3.56 38 92 0.958 8.59 8.87 37 94 0.974 -0.68 4.38 38 91 0.987 -0.95 3.01 37 70 0.995 1.57 2.82
Davos 2004 - 2011 32 88 0.968 -0.82 5.62 32 87 0.962 -1.89 6.52 32 88 0.919 7.46 10.59 32 88 0.985 2.43 3.80 32 85 0.979 -0.54 5.00 26 64 0.958 11.26 9.18
Geneva 2004 - 2011 45 96 0.981 2.50 4.59 45 95 0.980 -2.52 4.01 45 96 0.952 12.29 9.30 44 96 0.980 -0.43 4.65 45 93 0.987 0.79 3.37 44 72 0.983 5.11 5.79
Kassel 2004 - 2011 42 96 0.996 3.91 3.18 42 95 0.995 -4.15 3.68 42 96 0.981 12.47 9.46 42 96 0.992 3.03 4.67 42 93 0.997 1.74 2.33 41 72 0.990 -0.22 4.65
Lerwick 2004 - 2009 40 70 0.998 -0.17 2.21 42 63 0.997 -9.20 5.76 40 70 0.989 4.91 7.70 39 70 0.997 -5.86 5.34 40 67 0.998 -3.99 4.43
Lindenberg 2004 - 2006 44 36 0.997 0.88 3.87 44 35 0.997 -6.99 4.36 44 36 0.989 9.42 11.45 43 36 0.995 1.47 4.68 45 33 0.998 -0.77 3.09 42 12 0.990 -11.88 9.93
Madrid 2004 - 2011 36 87 0.975 3.35 4.74 36 86 0.960 1.12 5.28 36 87 0.962 10.13 7.95 36 87 0.981 -0.57 3.57 36 84 0.980 4.01 4.15 36 68 0.987 8.38 4.35
Nantes 2004 - 2010 49 72 0.998 1.19 2.50 49 71 0.998 -5.01 3.17 49 72 0.987 10.17 8.68 48 72 0.996 -2.13 2.83 49 69 0.996 2.47 3.58 48 48 0.997 0.76 3.59
Payerne 2004 - 2009 45 72 0.993 -1.21 3.91 45 71 0.991 -6.28 4.04 45 72 0.978 6.47 9.03 45 72 0.992 -2.70 3.78 46 69 0.995 -3.18 3.41 49 60 0.968 -0.13 3.41
Sede Boqer 2004 - 2011 42 92 0.941 4.36 5.50 42 91 0.865 3.64 8.31 42 92 0.875 13.12 9.15 42 92 0.865 7.81 8.78 43 91 0.963 4.42 4.31 43 68 0.962 2.44 7.22
Tamanrasset 2004 - 2011 57 96 0.973 -2.31 5.20 56 83 0.873 -17.83 16.80 58 93 0.922 11.22 9.36 57 96 0.904 9.85 9.65 57 64 0.975 -14.37 11.27
Toravere 2004 - 2011 36 96 0.989 2.80 5.46 39 88 0.990 -3.55 3.66 36 96 0.981 11.36 12.38 36 96 0.991 3.05 4.09 37 93 0.992 1.04 3.80 36 72 0.988 -1.32 4.41
Valentia 2004 - 2011 44 93 0.996 5.20 3.21 44 92 0.997 -2.33 2.06 44 93 0.979 12.93 9.14 43 93 0.996 1.95 2.90 44 90 0.998 4.52 2.69 44 69 0.996 0.05 3.01
Vaulx-en-Velin 2004 - 2011 40 96 0.987 4.37 4.67 40 95 0.986 -1.06 3.54 41 93 0.959 11.23 9.66 40 96 0.985 0.57 4.47 40 93 0.990 3.27 3.50 38 72 0.991 3.47 3.75
Wien 2004 - 2011 47 95 0.988 2.12 4.31 47 94 0.987 -6.70 4.64 46 96 0.961 11.39 8.24 46 95 0.984 2.20 5.37 47 92 0.989 0.10 3.84 46 71 0.988 -5.62 7.02
Zilani 2004 - 2009 39 70 0.993 1.40 4.55 41 65 0.995 -5.39 4.69 39 70 0.981 9.19 11.77 39 70 0.995 2.15 3.52 40 67 0.995 -0.77 3.48 38 46 0.996 -6.87 4.61
All sites 42 84 2.31 4.53 43 81 -4.11 6.08 43 84 10.53 9.59 42 84 1.63 5.22 43 79 0.53 4.33 41 61 1.62 5.32
MeteoSwiss
MeteoSwiss
Diffuse irradiance
Monthl y val ues
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
Diffuse irradiance
Monthl y val ues
SolarGIS Helioclim 3 Solemi (aerocom) EnMetSol IrSOLaV
- 55 -
Long term satellite global, beam and diffuse irradiance validation
Pierre Ineichen
