Technical report site assessment of solar resource for a csp plant. corrections with ground measured data

TECHNICAL REPORT
SOLAR RESOURCE ASSESSMENT
Update: IrSOLaV data corrected with ground
measurements
XXXXX CSP SOLAR PROJECT
(COUNTRY)
(Morocco)
April 25, 2014

SOLAR RESOURCE ASSESSMENT: XXXXX CSP SOLAR PROJECT (COUNTRY) Page 3 of 66
TECHNICAL REPORT:
SOLAR RESOURCE ASSESSMENT: XXX CSP SOLAR PROJECT (Country)
DATE:
April 25, 2014
AUTHORS:
IrSOLaV S. L. (INVESTIGACIONES Y RECURSOS SOLARES AVANZADOS).
CUSTOMER:
Customer

INDEX
1 INTRODUCTION........................................................................................................................................... 7
1.1 Main objective.......................................................................................................................................... 7
1.2 Data needed ............................................................................................................................................ 7
1.3 Methodology for solar radiation derived from satellite images ................................................................ 7
1.4 Brief summary of IrSOLaV methodology................................................................................................. 8
2 AVAILABLE DATA FOR SOLAR RESOURCE ASSESSMENT ............................................................... 11
2.1 Measurements at the site of the project ................................................................................................ 11
2.2 METEONORM....................................................................................................................................... 12
2.3 NASA ..................................................................................................................................................... 13
2.4 SOLAR-MED-ATLAS............................................................................................................................. 14
2.5 PV-GIS................................................................................................................................................... 15
2.6 CM SAF-CLIMATE-DWD ...................................................................................................................... 16
2.7 SoDa Database (HELIOCLIM-3V4)....................................................................................................... 17
2.8 IrSOLaV raw estimations....................................................................................................................... 18
3 CORRECCTION OF SATELLITE ESTIMATIONS WITH GROUND MEASUREMENTS.......................... 20
4 IRSOLAV DATABASE ............................................................................................................................... 30
4.1 Uncertainties of IrSOLaV corrected estimations ................................................................................... 30
4.2 Global horizontal irradiance (GHI) in LOCATION PROJECT................................................................ 30
4.3 Direct normal irradiance (DNI) in LOCATION PROJECT...................................................................... 36
5 CORRECTED IRSOLAV DATABASE COMPARED TO MEASUREMENTS............................................ 43
6 METEOROLOGICAL PARAMETERS........................................................................................................ 49
7 INTER-ANNUAL VARIABILITY OF SOLAR RADIATION......................................................................... 53
8 UNCERTAINTY OF ESTIMATES............................................................................................................... 55
9 TYPICAL SOLAR RADIATION YEAR ....................................................................................................... 56
9.1 GHI and DNI radiation values ............................................................................................................. 59
10 STATISTICAL ANALYSIS OF THE LONG TERM..................................................................................... 61
11 CONCLUSIONS AND COMMENTS........................................................................................................... 62
12 REFERENCES............................................................................................................................................ 64

1 INTRODUCTION
1.1 Main objective
The main objective of this report is to analyze the solar resource available and to produce the
corresponding typical solar radiation year for a specific site in Country, selected to host a solar
thermal power plant with a capacity of XXX MW. The solar resource analysis applies to a site with
geographic coordinates: Latitude XX.XX N, Longitude XX.XX W, 1260 meters of altitude, hereinafter
referred to as LOCATION.
1.2 Data needed
The solar radiation is a meteorological variable measured only in few measurement stations and
during short and, on most occasions, discontinuous periods of times. The lack of reliable information
on solar radiation, together with the spatial variability that it presents, leads to the fact that
developers do not find appropriate historical databases with information available on solar resource
for concrete sites. This lack provokes in turn serious difficulties at the moment of projecting or
evaluating solar power systems.
Among the possible different approaches to characterize the solar resource of a given specific site
they can be pointed out the following:
 Data from nearby stations. This option can be useful for relatively flat terrains and when
distances are less than 10 km far from the site. In the case of complex terrain or longer
distances the use of radiation data from other geographical points is absolutely
inappropriate.
 Interpolation of surrounding measurements. This approach can be only used for areas with a
high density of stations and for average distances between stations of about 20-50 km
[Pérez et al., 1997; Zelenka et al., 1999].
Solar radiation estimation from satellite images is currently the most suitable approach. It supplies
the best information on the spatial distribution of the solar radiation and it is a methodology clearly
accepted by the scientific community and with a high degree of maturity [McArthur, 1998]. In this
regard, it is worth to mention that BSRN (Baseline Surface Radiation Network) has among its
objectives the improvement of methods for deriving solar radiation from satellite images, and also
the Experts Working Group of Task 36 of the Solar Heating and Cooling Implement Agreement of
IEA (International Energy Agency) focuses on solar radiation knowledge from satellite images.
1.3 Methodology for solar radiation derived from satellite images
Solar radiation derived from satellite images is based upon the establishment of a functional
relationship between the solar irradiance at the Earth’s surface and the cloud index estimated from

the satellite images. This relationship has been previously fitted by using high quality ground data, in
such a manner that the solar irradiance-cloud index correlation can be extrapolated to any location
of interest and solar radiation components can be calculated from the satellite observations for that
point.
1.4 Brief summary of IrSOLaV methodology
The methodology of IrSOLaV uses two main inputs to compute hourly solar irradiance: the
geostationary satellite images and the information about the attenuating properties of the
atmosphere. The former consists of one image per hour offering information related with the cloud
cover characteristics. The latter is basically information on the daily Linke turbidity which is a very
representative parameter to model the attenuating processes which affects solar radiation on its
path through the atmosphere, mainly the aerosol optical depth and water vapor column.
The methodology applied has undoubtedly been accepted by the scientific community and its main
usefulness is in the estimation of the spatial distribution of solar radiation over a region. Its maturity
is guaranteed by initiatives like the establishment in 2004 of a new IEA (International Energy
Agency) task known as “Solar Radiation Knowledge from Satellite Images” or the fact that the
measuring solar radiation network BSRN (Baseline Surface Radiation Network) promoted by WMO
(World Meteorological Organization) has as its main objectives for the improvement of solar
radiation estimation from satellite images models.
Solar radiation estimation from satellite images offered is made from a modified version of the
renowned model Heliosat-3, developed and validated by CIEMAT with more than thirty radiometric
stations in the Iberian Peninsula. Over this first development, IrSOLaV has generated a tool fully
operational which is applied on a database of satellite images available with IrSOLaV (temporal and
spatial resolution of the data depends on the satellite covering the region under study). It is
worthwhile to point out that tuning-up and fitting of the original methodology in different locations of
the World have been performed and validated with local data from radiometric stations installed in
the region of interest. This way, it may be considered that the treatment of the information from
satellite images offered by IrSOLaV is an exclusive service.
Even though the different research groups working in this field are making use of the same core
methodologies, there are several characteristics that differ depending on the specific objectives
pursued. Therefore, the main differences between the IrSOLaV/CIEMAT and others, like the ones
applied by PVGis or Helioclim are:
 Filtering of images and terrestrial data. Images and data used for the fitting and relations are
thoroughly filtered with procedures developed specifically for this purpose.

 Selection of albedo for clear sky days. The algorithm used to select albedos for clear sky
days provides a daily sequence that is different for every year; however the other
methodologies use a unique monthly value.
 Introduction of characteristic variables. The relation developed by IrSOLaV/CIEMAT includes
new variables characterizing the climatology of the site and the geographical location, with a
significant improvement of the results obtained for global and direct solar radiation.
 Global horizontal irradiance is estimated by relating the clear sky index with the cloud index,
the cloud index distribution and the air mass (Zarzalejo et al., 2009).
 Ground albedo is estimated by a moving window of about 20 days that comprises images of
the central instants in terms of co-scattering angle (Zarzalejo, 2005). This method allows the
daily computation of the ground albedo.
 Direct normal irradiance for non-clear sky situations is calculated using the Louche
conversion function (Louche et al., 1991) and DirIndex model which takes into account daily
values of AOD at 550nm and water vapour column obtained from MODIS satellite and MACC
database.
 Clear sky days are identified (Polo et al., 2009) and estimated separately by the ESRA
transmittance model (Rigollier et al., 2000). Besides, as some clear sky models behave
better in some locations and other depending in local climatic conditions of the sites, SOLIS
and REST2 clear sky models are also tested.
 Input of daily of values of Aerosol optical depth (AOD) 500nm and column water vapor
content estimated from MODIS satellite for the period from 2000 to 2012. The resolution of
the dataset is 1º by 1º and it has a global coverage.
 Daily Linke turbidity factor is estimated by the Ineichen correlation from AOD at 550 nm
obtained from MODIS Aqua and Terra satellite (Ineichen, 2008), MISR satellite and MACC
model. Water vapour obtained from NCEP and MODIS satellite.
Application of a method to fit the angular dependence of the sun and satellite and the ground albedo
estimations {Polo, 2012 1000423 /id}. In classical Heliosat-3 method the potential overestimation of
cloud index under some situations for high reflective (deserted regions mainly) sites could lead to
noticeable underestimation of the surface solar irradiance.
The uncertainty of the estimation comparing with hourly ground pyranometric measurements is
expressed in terms of the relative root mean squared deviation (RMSD). Different assessments and
benchmarking tests can been found at the available literature concerning the use of satellite images
(Meteosat and GOES) on different geographic sites and using different models [Pinker y Ewing,
1985; Zelenka et al., 1999; Pereira et al., 2003; Rigollier et al., 2004; Lefevre et al., 2007]. The
uncertainty for hourly values is estimated around 20-25% RMSD and in a daily basis the uncertainty
of the models used to be about 13-17%. It is important to mention here the contribution given by

Zelenka in terms of distributing the origin of this error, concluding that 12-13% is produced by the
methodology itself converting satellite information into radiation data and a relevant fraction of 7-
10% because of the uncertainty of the ground measurements used for the comparison. In addition
Zelenka estimates that the error of using nearby ground stations beyond 5 km reaches 15%.
Because of that his conclusion is that the use of hourly data from satellite images is more accurate
than using information from nearby stations located more than 5 km far from the site.
The IrSOLaV methodology is based on the work developed in CIEMAT by the group of Solar
Radiation Studies. The model has been assessed for about 30 Spanish sites with the following
uncertainty data for global horizontal irradiance:
 About 12% RMSD for hourly values
 Less than 10% for daily values
 Less than 5% for annual and monthly means.
The model has been modified for a better estimation of solar radiation with clear sky, leading to an
important improvement in the accuracy of the model [Polo, 2009; Polo et al., 2009b]. The
uncertainties in the raw estimated values of GHI and DNI have been calculated by comparison with
a set of ground measurements located in region XXXX, in terms of root mean squared difference
(RMSD), and the deviations of the estimations with respect to the measurements are quantified by
the mean bias difference (MBD). Uncertainties and deviations are summarized in Table 1 and Table
2, respectively.
Table 1. Relative uncertainties (RMSD) [%] of GHI and DNI by IrSOLaV methodology.
Hourly Daily Monthly Yearly
GHI 18 12 5 4
DNI 40 25 15 9
Table 2. Relative deviations (MBD) [%] of GHI and DNI by IrSOLaV methodology.
GHI 1 0 1 0
DNI -1 0 0 0
This improved model is the one applied to the LOCATION site in this report.
IrSOLaV also provides data online through its web portal SOLAR EXPLORER at
http://www.solarexplorer.info/.

2 AVAILABLE DATA FOR SOLAR RESOURCE ASSESSMENT
For the site of this study, various sources of solar radiation data are discussed, both from databases
of terrestrial measurements and from satellite image processing.
2.1 Measurements at the site of the project
Three components of the solar radiation (global horizontal (GHI), diffuse horizontal (DIF) and direct
normal irradiance (DNI)) are measured in the station of LOCATION using two pyranometers and
one pyrheliometer. The time step of the measurements is 10 minutes and the temporal reference is
Coordinated Universal Time (UTC + 0h, Longitude of reference 0º).
Global solar radiation is defined as the solar radiation received on a horizontal surface in a solid
angle of 2π steradians. As a result of the interaction of sunlight with the atmosphere, the global
horizontal irradiation (GHI) is the sum of the direct normal irradiance (DNI) (which has not interacted
with atmospheric components and therefore has not changed in its angle of incidence) and the
diffuse component (DIF) (result of atmospheric dispersion processes and it can be assumed to
come from all points of the sky and has no predominant direction). These three components, (GHI,
DNI and DIF) are related to each other by using the following expression, where ϴ is the zenith
angle.
cosDNIDIFGHI  (1)
The monthly and yearly measurements of GHI, DNI and DIF are presented in Table 3, Table 4 and
Table 5, respectively.
Table 3. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) GHI METEOROLOGICAL-
STATION.
METEOROLOGICAL-STATION
GLOBAL HORIZONTAL IRRADIANCE (kWh m-2
day-1
)
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Yearly
2011
2012
2013
AVG

Table 4. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) DNI METEOROLOGICAL-
STATION.
DIRECT NORMAL IRRADIANCE (kWh m-2
day-1
)
2011
2012
2013
AVG
Table 5. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) DIF METEOROLOGICAL-
STATION.
DIFFUSE HORIZONTAL IRRADIANCE (kWh m-2
day-1
)
2011
2012
2013
AVG
With nearly three years of measurements in the station, average annual global irradiance (GHI) is
xxxxx kWh m-2
year-1
and direct normal irradiance (DNI) is xxxxx kWh m-2
year-1
.
Ground data measurements will be used below to adapt the satellite time-series estimations to the
site climatic conditions.
2.2 METEONORM
METEONORM is a comprehensive meteorological reference, 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 the expertise acquired over 23 years in the development
of meteorological databases for energy applications.
The most important features of METEONORM are climatological data of more than 8055 weather
stations, measured parameters (monthly means of global radiation, temperature, humidity,
precipitation, days with precipitation, wind speed and direction, sunshine duration), interpolation
models to calculate mean values for any site in the world, calculation of radiation for inclined
surfaces with updated models, etc. In this report we have used METEONORM version 6.
The results from METEONORM for LOCATION (Table 6) show an average annual global irradiance
(GHI) of xxxxxx kWh m-2
year-1
.

Table 6. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated GHI and DNI on
horizontal surface. Radiometric data from METEONORM.
LOCATION PROJECT
TMY from METEONORM
Month GHI
(kWh m-2
day-1
)
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Annual xxxxxxx
2.3 NASA
NASA, through its Science Mission Directorate, has long supported satellite systems and research
providing data important to the study of climate and climate processes. NASA Surface meteorology
and Solar Energy (SSE) dataset include long-term estimates of meteorological quantities and
surface solar energy fluxes. These satellite and modeled based products have been shown to be
accurate to provide reliable solar and meteorological resource data over regions where surface
measurements are sparse or nonexistent, and offer two unique features - the data is global and, in
general, contiguous in time. Monthly and yearly values of GHI provided by NASA are summarized in
the Table 7.

Table 7. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated GHI (NASA).
LOCATION PROJECT (NASA)
day-1
)
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
AVG
The results of NASA for LOCATION show an average annual global radiation of xxxxx kWh m-2
day-1
. NASA also provides the yearly DNI which is in the range xxxx-xxxxx kWh m-2
year-1
.
2.4 SOLAR-MED-ATLAS
SOLAR-MED-ATLAS gives freely values of GHI and DNI on their web portal http://www.solar-med-
atlas.org. The results of SOLARGIS for LOCATION show an average annual global horizontal
irradiation of xxxx kWh m-2
year-1
and direct normal irradiation of xxxxx kWh m-2
year-1
. For the
period from 1992 to 2010. Table 8 shows single yearly values for each year from 1992 to 2010 and
long-term yearly average.

Table 8. Yearly sum (kWh m-2 year-1) GHI and DNI estimated from satellite (Solar Med Atlas).
Year GHI DNI
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
MEAN
2.5 PV-GIS
PVGIS is a geographical information system which offers modeled values of solar radiation and
temperature in Europe and Africa, based on ten-year average data from a large number of
measuring stations. Within PVGIS a relation for the conversion efficiency of crystalline silicon PV
modules at varying irradiance and temperature is used to obtain values for the loss of efficiency at
any location.
The computational approach used in PVGIS to construct the solar radiation database is based on a
solar radiation model r.sun, and the spline interpolation techniques s.surf.rst and s.vol.rst that are
implemented within the open-source GIS software GRASS. The r.sun model algorithm uses the
equations published in the European Solar Radiation Atlas. The model estimates beam, diffuse and
reflected components of the clear-sky and real-sky global irradiance/irradiation on horizontal and
inclined surfaces. The total daily irradiation (Wh m-2
) is computed by the integration of the irradiance

values (W m-2
) that are calculated at a time step of 15 minutes from sunrise to sunset. The data from
566 meteorological stations are monthly averages of measurements over the period 1981-1990.
The results of PVGIS for LOCATION shows an average annual global horizontal irradiation of xxxx
kWh m-2
day-1
and average annual direct normal irradiation of xxxx kWh m-2
day-1
. Table 9 shows
the results for yearly and monthly averages from PVGIS:
Table 9. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated global irradiation on
horizontal surface (GHI) and direct normal irradiation (DNI). Data from PVGIS.
LOCATION PROJECT (PVGIS)
Month GHI
(kWh m-2
day-1
)
DNI
(kWh m-2
day-1
)
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Annual
2.6 CM SAF-CLIMATE-DWD
CM SAF aims at the provision of satellite-derived geophysical parameter data sets suitable for
climate monitoring. CM SAF provides climatologies for Essential Climate Variables (ECV), as
required by the Global Climate Observing System (GCOS) implementation plan in support of the
United Nations Framework Convention on Climate Change (UNFCCC).
The CM SAF data products are categorized in monitoring data obtained in near real time and data
sets based on carefully intersensor calibrated radiances. The homogenous sets of high-quality data
help scientists to investigate climate variability and long-term changes in the climate mean state.
The products are derived from several instruments on-board meteorological operational satellites in
geostationary and polar orbit as the Meteosat and EUMETSAT Polar System satellites, respectively.

The results of CMSAF-CLIMATE for LOCATION shows an average annual global horizontal
irradiation of xxxx kWh m-2
day-1
and average annual direct normal irradiation of xxxx kWh m-2
day-1
. Table 10 shows the results for yearly and monthly averages from CMSAF-CLIMATE.
Table 10. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated global irradiation on
horizontal surface (GHI) and direct normal irradiation (DNI). Data from CM SAF-CLIMATE-DWD.
LOCATION PROJECT (CM SAF-CLIMATE-DWD)
Month GHI
(kWh m-2
day-1
)
DNI
(kWh m-2
day-1
)
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Annual
2.7 SoDa Database (HELIOCLIM-3V4)
The project SoDa (Solar Radiation Data) answers the needs of industry and research for information
on solar radiation parameters with a satisfactory quality.
A prototype service has been developed that integrate and efficiently exploits diverse networked
information sources to supply value-added information in a selected number of environmental
applications.
The project began on January 1st, 2000 and ended in March 2003. The project SoDa aims at
responding to the strong multi-disciplinary needs for information on solar radiation. It represents a
real innovation. Advanced information and communication technologies have been used to supply
high quality value-added information that match the actual customer needs. The methodology was
user-driven with a large involvement of users in the project. A WWW-based service has been
developed and demonstrated which realize the integration of information sources of different natures
within a smart network. These sources include databases containing solar radiation parameters and
other relevant information, including algorithms and end-users applications; some of them may

originate from an advanced processing of remote sensing images. Before the SoDa service was
made available, these resources were available separately. Table 11 and Table 12 show single
yearly values of GHI and DNI for year 2004 and 2005 and yearly average.
Table 11. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated global radiation on
horizontal surface (GHI). Data from SoDa. HELIOCLIM-3V4
LOCATION PROJECT (SoDa)
day-1
)
2004
2005
AVG
Table 12. Monthly average (kWh m-2 day-1) and yearly sum (kWh m-2 year-1) estimated direct normal irradiation
(DNI). Data from SoDa. HELIOCLIM-3V4.
LOCATION PROJECT (SoDa)
day-1
)
2004
2005
AVG
The results of SoDa for LOCATION PROJECT show an average annual global radiation of xxxx
kWh m-2
year-1
and average annual direct normal irradiation of xxx kWh m-2
year-1
.
2.8 IrSOLaV raw estimations
GHI and DNI as estimated by IrSOLaV methodology has been discussed in a previous version of
this report. Nevertheless, monthly and yearly values for the period 1994 to 2013 are shown again in
Table 13 and
Table 14. Notice that years 2003 and 2004 have been removed due to the fact of the transition from
Meteosat First Generation to Meteosat Second Generation satellites, which caused a decrease in
available images and therefore produced non-realistic low irradiances.
Table 13. Monthly averages (kWh m-2 day-1) and yearly sums (kWh m-2 year-1) for estimated global irradiance.
Averages over the 20-year period is also shown.

LOCATION PROJECT (IrSOLaV Database)
day-1
)
YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Annual
1994
1995
1996
1997
1998
1999
2000
2001
2002
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
AVG
Table 14. Monthly average (kWh m-2 day-1) and yearly sums (kWh m-2 year-1) for estimated direct normal
irradiance. Averages over the 20-year period are also shown.
LOCATION PROJECT (IrSOLaV)

day-1
)
1994
1995
1996
1997
1998
1999
2000
2001
2002
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
AVG
3 CORRECCTION OF SATELLITE ESTIMATIONS WITH GROUND MEASUREMENTS
The strength of satellite estimations is its capability to provide long-term time series of a solar
component. However, the signal received is representative for an extended region, and it means a
loss of sensitivity to variability of cloud information, aerosols, water vapor and shading, which affects

strongly to DNI estimations. Hence, deviations are expected when comparing estimations with
pinpoint measurements. Besides, the coarse spatial resolution of aerosol and water vapor
databases used for satellite estimations makes difficult to describe the special characteristics of a
specific site. GHI estimations are less affected by this lack of sensitivity.
IrSOLaV estimations are based on Heliosat-3 methodology which has been developed and validated
using ground measurements from stations spread along the European geography. When
estimations are performed in regions where the methodology has not been validated, it is essential
to have ground measurements that provides knowledge on the spectral response of the satellite in
the region. Hence, in order to improve the quality of IrSOLaV data, the estimations are correlated to
ground measurements, allowing the decrease of the bias and deviations of the estimations with
respect to the measurements and a better agreement between frequency distribution functions.
From this correlation, a correction for the aerosols and ground reflectivity is obtained, which better
represent the local conditions of the site.
The correlation has taken into account a dedicated filtering of the ground measurements, and only
daytime values have been used.
The improvement of the correction is analyzed by comparing with the measurements. This
comparison is made in two ways:
- Standard:
The validation of the estimations ˆy against measurements y for N hours (ti=1..N) are done mainly with
bias difference (MBD) and the root mean squared difference (RMSD). The mean bias difference
(MBD) is defined by:
 
1
1
ˆMBD ( ) ( )
N
i i
i
y t y t
N 
 
 
2
1
1
ˆ(t ) (t )
N
i i
i
RMSD y y
N 
 
The corresponding relative differences are MBD(%) and rRMSD(%):

1
MBD
MBD(%) 100
( )
/
RMSD
RMSD(%) 100
N
i
i
y y t
y
N
y

 
  
 

 
  
 

Another parameter used in the validation is the correlation coefficient (R), giving information on the
linear relation between measurements and estimations.
- Distributions:
Additional measures for model quality based on the analysis of cumulative distribution function
(CDF) are used.
A comprehensive approach to an analysis of the deviations of measured and modelled CDFs which
is based on the Kolmogorov-Smirnov (KS) test and defines new parameters to quantify the similarity
of the two CDFs. Although there are several statistical tests and ways of evaluating the goodness of
a model, the KS test has the advantage of making no assumption about the data distribution, and is
thus a non-parametric, distribution-free test.
The KS test tries to determine if two datasets differ significantly. The test consists of comparing the
distribution of a dataset to a reference distribution. This can be done by converting the list of data
points to an unbiased estimator S(xi) of the CDF, i = 1..N, N is the population size. The KS statistic D
is defined as the maximum value of the absolute difference between the two CDFs:
 max (x ) Ri iD S x 
Where R(xi) is the CDF of the reference dataset. Thus, if the D statistic is lower than the threshold
value VC, the null hypothesis that the two datasets come from the same distribution cannot be
rejected. The critical value depends on N and is calculated for a 99% level of confidence as:
1.63
, 35cV N
N
 
This test detects smaller deviations in cumulative distributions than the χ2
test does. However,
instead of using the original one, an extended KS test is used, in which the distances between the
CDFs are calculated over the whole range of the variable x, i.e. the solar radiation. A discretization
in n = 1…m levels is applied here. In the following m = 100 intervals as a reasonable choice are

used. Greater order of magnitude for m is not recommended since it implies more computational
cost for no improvement in the accuracy of the result. The interval distance p is thus defined as:
max min
, 100
x x
p m
m

 
where xmax and xmin are the extreme values of the independent variable. Then, the distances
between the CDFs are defined, for each interval, as:
     min min, 1 ,n j j jD S x R x x x n p x np       
The representation of the values Dn, along with the critical value, gives the complete testing
behaviour of the CDF with respect to the reference over the whole range. However, although
application of the KS test contributes valuable information, it only materializes in the acceptance or
rejection of the null hypothesis. In the next sections new parameters are proposed, which, based on
the estimation of the distance between the two CDFs for the sets compared, define quantitative
measures that can be used to rank models.
Kolmogorov – Smirnov test Integral, parameter KSI
The KSI parameter (Kolmogorov-Smirnov test Integral) is defined as the integral of the area
between the CDFs for the two sets. The unit of this index is the same for the corresponding
magnitude, the value of which depends on it.
The KSI is defined as the integral:
max
min
x
nx
KSI D dx 
As Dn is a discrete variable and the number of integration intervals is identical in all cases,
trapezoidal integration is possible over the whole range of the independent variable x. A percentage
of KSI is obtained by normalizing the critical area, acritical:
max
min
% *100
x
nx
critical
D dx
KSI
a


where acritical is calculated as:

acritical = Vc* (xmax-xmin)
and Vc is the critical value for the level of confidence selected and (xmax, xmin) are the extreme values
of the independent variable. Normalization to the critical area enables the comparison of different
KSI values from different tests. The minimum value of the KSI index is zero, in which case, it can be
said that the CDFs of the two sets compared are the same.
Figure 1 shows the distribution of estimated (left panel) and corrected (right panel) hourly GHI
versus measured. Notice the improvement at low values, while it remains nearly unchanged for
larger GHI. Analogously, Figure 2 shows the distributions obtained for hourly DNI where it can be
seen the reduction of the deviations and a better agreement below 500Wh. In all cases, black line
represents the perfect correlation.
0 200 400 600 800 1000 1200
0
200
400
600
800
1000
1200
HOURLY GHI SCATTER PLOT: IRSOLAV RAW VS GROUND
HOURLY GROUND MEASURED DATA
HOURLYIRSOLAVRAWDATA
0 200 400 600 800 1000 1200
0
200
400
600
800
1000
1200
HOURLY GHI SCATTER PLOT: IRSOLAV CORRECTED VS GROUND
HOURLYIRSOLAVCORRECTEDDATA
Figure 1. Hourly GHI (Wh/m2) raw estimated (left) and corrected (right) versus measured.

0 200 400 600 800 1000 1200
0
200
400
600
800
1000
1200
HOURLY DNI SCATTER PLOT: IRSOLAV RAW VS GROUND
HOURLYIRSOLAVRAWDATA
0 200 400 600 800 1000 1200
0
200
400
600
800
1000
1200
HOURLY DNI SCATTER PLOT: IRSOLAV CORRECTED VS GROUND
HOURLYIRSOLAVCORRECTEDDATA
Figure 2. Hourly DNI (Wh/m2) raw estimated (left) and corrected (right) versus measured.
If we focus on the agreement between measured data distribution and estimated and corrected, we
see more clearly the improvement of the correction. Figure 3 shows the Kolmogorov-Smirnov test
for GHI and DNI. The right panels show the clear improvement of the corrections with respect the
raw estimations, except for largest values of GHI. The statistic Dn is below the threshold for almost
all DNI values range except for some values around 1000Wh, where Dn is slightly larger. This way,
we can conclude that the similarities between measured and satellite corrected DNI are high.

0 200 400 600 800 1000 1200
0
0.005
0.01
0.015
0.02
0.025
GHI Wh/m2
Dn
Statistical
Kolmogorov Smirnov Test for hourly GHI
GHI Raw
0 200 400 600 800 1000 1200
0
0.005
0.01
0.015
0.02
0.025
GHI Wh/m2
Dn
Statistical
Kolmogorov Smirnov Test for hourly GHI
GHI Corrected
0 200 400 600 800 1000 1200
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
DNI Wh/m2
Dn
Statistical
Kolmogorov Smirnov Test for hourly DNI
DNI Raw
0 200 400 600 800 1000 1200
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
DNI Wh/m2
Dn
Statistical
Kolmogorov Smirnov Test for hourly DNI
DNI Corrected
Figure 3. Kolmogorov-Smirnoff test for GHI (upper panels) and DNI (lower panels). Raw estimations are
plotted in left panels and corrections are shown in right panels.
Figure 4 shows the distribution of a comparison of estimated, corrected and measured radiation
values, where it can be observed that the distribution for raw GHI can hardly be improved, but the
results for DNI indicate a better agreement between corrected and measured DNI.

0 100 200 300 400 500 600 700 800 900 1000 1100 1200
0
20
40
60
80
100
120
140
160
180
200
220
GHI Wh/m2
Frequencyofocurrence
Hourly GHI ground
Hourly GHI IrSOLaV Raw
0 100 200 300 400 500 600 700 800 900 1000 1100 1200
0
20
40
60
80
100
120
140
160
180
200
220
GHI Wh/m2
Hourly GHI ground
Hourly GHI IrSOLaV Corrected
0 100 200 300 400 500 600 700 800 900 1000 1100 1200
0
50
100
150
200
250
300
DNI Wh/m2
Hourly DNI ground
Hourly DNI IrSOLaV Raw
0 100 200 300 400 500 600 700 800 900 1000 1100 1200
0
50
100
150
200
250
300
DNI Wh/m2
Hourly DNI ground
Hourly DNI IrSOLaV Corrected
Figure 4. Histograms for GHI (upper panels) and DNI (lower panels), raw estimations (left) and corrections
(right) are shown. Red lines represent IrSOLaV data and ground measurements are shown in blue bars.
To conclude with the comparison between raw estimations, corrections and measurements, Figure 5
shows the corresponding CDFs. Again, it is clear the overall improvement achieved for DNI, while
for GHI the improvement is obtained for values below 900Wh/m2
, getting a light deviation over
100Wh/m2
.

0 200 400 600 800 1000 1200
0
0.2
0.4
0.6
0.8
1
GHI Wh/m2
F(x)
CDF GHI
Hourly GHI ground measurements
Hourly GHI IrSOLaV Raw
0 200 400 600 800 1000 1200
0
0.2
0.4
0.6
0.8
1
GHI Wh/m2
F(x)
CDF GHI
Hourly GHI ground measurements
Hourly GHI IrSOLaV corrected
0 200 400 600 800 1000 1200
0
0.2
0.4
0.6
0.8
1
DNI Wh/m2
F(x)
CDF DNI
Hourly DNI ground measurements
Hourly DNI IrSOLaV Raw
0 200 400 600 800 1000 1200
0
0.2
0.4
0.6
0.8
1
DNI Wh/m2
F(x)
CDF DNI
Hourly DNI ground measurements
Hourly DNI IrSOLaV corrected
Figure 5. CDF for GHI (upper panels) and DNI (lower panels), raw estimations (left) and corrections (right) are
shown. Red lines represent ground measurements and IrSOLaV data is shown in blue lines.
The achieved improvement in the satellite estimations that the correction has brought can be well
represented by the calculation of the correlation index with respect to the measurements, the mean
bias and root mean squares for hourly, daily and monthly values (Table 15, Table 16) and the KSI
parameter (Table 17).

Table 15. Mean bias difference (MBD) of the comparison of raw and corrected estimations with measured
data.
MBD
Solar
Component
GHI raw
xx Wh/m2
xx Wh/m2
xx kWh/m2
xx kWh/ m2
xx % xx % xx % xx %
GHI corrected
xx Wh/m2
xx Wh/m2
xx kWh/m2
xx kWh/m2
xx % xx % xx % xx %
DNI raw
xx Wh/m2
xx Wh/m2
xx kWh/m2
xx kWh/m2
xx % xx % xx % xx %
DNI corrected
xx Wh/m2
xx Wh/m2
xx kWh/m2
xx k Wh/m2
Xx % Xx % Xx % Xx %
Table 16. Root mean square (RMSD) of the comparison of raw and corrected estimations with measured data.
RMSD
Solar
Component
GHI raw
xx Wh/m2
xx Wh/m2
xx kWh/m2
xx kWh/m2
xx % xx % xx % xx %
GHI corrected
xx Wh/m2
xx Wh/m2
xx kWh/m2
xx kWh/m2
xx % xx % xx % xx %
DNI raw
xx Wh/m2
xx Wh/m2
xx kWh/m2
xx kWh/m2
xx % xx % xx % xx %
DNI corrected
xx Wh/m2
xx Wh/m2
xx kWh/m2
xx kWh/m2
xx % xx % xx % xx %
Table 17. Correlation index (R) and KSI index of the comparisons of raw estimations and corrected values with
measured data.
R KSI
Solar Component Hourly Hourly
GHI raw 0.xx xx.xx
GHI corrected 0.xx xx.xx
DNI raw 0.xx xx.xx
DNI corrected 0.xx xx.xx

4 IRSOLAV DATABASE
This section shows the results of applying the methodology developed by IRSOLAV/CIEMAT, for the
specific location called LOCATION. The methodology makes use of Meteosat satellite images for
the period 1994-2014, generating cloudiness index in the site, and then estimates global radiation
and direct normal radiation. After the raw estimation, the correlation with ground measurements is
performed, and a correction for the estimations is obtained.
A number of statistically representative parameters are obtained, such as monthly and annual
averages of global and direct solar radiation, which summarize the availability of solar energy
resource on the LOCATION site.
4.1 Uncertainties of IrSOLaV corrected estimations
The uncertainties of the estimated GHI and DNI from satellite images have no single source. On the
one hand, there are systematic uncertainties due to the estimation methodology itself, i.e., spectral
response of the satellite’s radiometer, radiative transfer model and input atmospheric data. On the
other hand, there are uncertainties related to the differences between estimations and ground
measurements, which are considered as the true values of radiation (Table 15, Table 16 and Table
17). Uncertainties related to the WMO classification of the pyranometers used in the measurement
has been taken into account.
For the specific site of LOCATION, the uncertainties related to GHI and DNI, for long-term annual
values are of 2.5% and 3%, respectively. Stochastic effects such as volcanic eruptions or climatic
change effects on the radiation that reaches the Earth surface have not been taken into account.
4.2 Global horizontal irradiance (GHI) in LOCATION PROJECT
The yearly average of GHI resource obtained from the treatment of satellite images over the period
1994-2013 is xxxx kWh m-2
yr-1
. Figure 6 shows the monthly averages of global radiation on
horizontal surface at the site of LOCATION. Monthly and yearly values are summarized in Table 18.

Table 18. Monthly average (kWh m-2 day-1) and yearly sums (kWh m-2 year-1) for estimated global horizontal
irradiance as corrected by ground measurements. Averages over the 20-year period are also shown.
LOCATION PROJECT (IrSOLaV Database)
day-1
)
1994
1995
1996
1997
1998
1999
2000
2001
2002
2005
2006
2007
2008
2009
2010
2011
2012
2013
AVG
The distribution of the monthly means of GHI is plotted in Figure 6. The Box-Whisker diagram in
Figure 7 shows the main moments of the distribution of daily global radiation for all the period
analyzed (median, 25th
and 75th
percentile). The value corresponds to an annual-average daily
radiation of xxxx Wh m-2
day-1
. The good behavior of the radiation is observed during the year 2007,
that qualifies as the best year of the series of 18 years studied and that agrees with the information
coming from the previously analyzed databases.

Monthly mean of daily global horizontal irradiance (GHI) (kWh/m
2
day)
Month
Year
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
3
4
5
6
7
8
9
Figure 6. Monthly average of the global radiation on horizontal surface from the database of satellite.
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
years
kWh/m2
Figure 7. Year-on-year distribution of the global daily radiation (GDR). Box-Whisker diagram.

Regarding the analysis of the information in hourly base, Figure 8 shows the hourly contours of
radiation on average over the 20 years. It is noted that the hourly average over the 18 years,
between 11 and 14 hours in April and May, exceeds 800 Wh m-2
. Figure 9 presents the inverse
cumulative distribution of hours for different levels of GHI.
Month
Hour
100
100
200
200
300
300
400
400
500
500
600
700
800
900
1e+003
6
8
10
12
14
16
18
Figure 8. Monthly distribution of the average of global hourly radiation.

0 200 400 600 800 1000 1200
0
10
20
30
40
50
60
70
80
90
100
Wh/m2
[%]
Figure 9. Inverse cumulative distribution of the percentage of hours for global horizontal radiation levels.
Figure 10 shows Box-Whisker diagrams of monthly global radiation year on year for each month.
Notice the low values obtained for 2003 and 2004 (years 10 and 11, respectively), which have been
eventually removed from the study.

1000
2000
3000
4000
5000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
JAN
kW/m2
/day
1000
2000
3000
4000
5000
6000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
FEB
kW/m2
/day
2000
4000
6000
8000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
MAR
kW/m2
/day
0
2000
4000
6000
8000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
APR
kW/m2
/day
0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
MAY
kW/m2
/day
0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
JUN
kW/m2
/day

0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
JUL
kW/m2
/day
2000
4000
6000
8000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
AUG
kW/m2
/day
0
2000
4000
6000
8000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
SEP
kW/m2
/day
1000
2000
3000
4000
5000
6000
7000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
OCT
kW/m2
/day
0
1000
2000
3000
4000
5000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
NOV
kW/m2
/day
0
1000
2000
3000
4000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
DEC
kW/m2
/day
Figure 10. Year-on-year distribution of the monthly global radiation (DDR). Box-Whisker diagram.
4.3 Direct normal irradiance (DNI) in LOCATION PROJECT
This section presents the results obtained from the analysis of direct normal solar radiation at
LOCATION site.

Table 19 shows the yearly and monthly average values of direct normal radiation at LOCATION.
The annual average irradiance potential during the period 1994-2013 is xxxx kWh m-2
yr-1
.
LOCATION. For the 18 years studied, the irradiances are reaching always approximate values in
the range from xxxx to xxxx kWh m-2
yr-1
. The cumulated average value corresponds to a typical
average day of xxxxx Wh m-2
day-1
. This value can be considered as representative for the whole
period 1994-2013.
Table 19. Monthly average (kWh m-2 day-1) and yearly sums (kWh m-2 year-1) for estimated direct solar
radiation as corrected by ground measurements. Averages over the 20-year period are also shown.
day-1
)
1994
1995
1996
1997
1998
1999
2000
2001
2002
2005
2006
2007
2008
2009
2010
2011
2012
2013
AVG
The distribution of the monthly means of DNI is plotted in Figure 11, and the Box & Whisker plot is
shown in Figure 12, where despite the noticeable variable of the annual DNI through the years the
quartile size appearing in the Box & Whisker shows a general homogeneous behaviour. Figure 13
shows the hourly contours of radiation on average over the 20 years. Figure 14 presents the inverse
cumulative distribution of hours for different levels of DNI, suggesting the convenience of designing
a Solar Thermal Plant of 900 W m-2
, since this value is an inflection point. The percentage of
operational hours with a DNI level beyond 900 Wm-2
is below 10%. Figure 15 shows year-on-year
distribution of the monthly DNI. Notice the anomalous values for years 2003 and 2004, which have
been finally removed from the study.

Monthly mean of daily direct normal irradiance (DNI) (kWh/m
2
day)
Month
Year
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
3
4
5
6
7
8
9
10
11
Figure 11. Monthly average of the direct radiation on normal surface from the database of satellite.
0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
years
kWh/m2
Figure 12. Year-on-year distribution of the daily direct radiation (DDR). Box-Whisker diagram.

Month
Hour
100
100
200
200
300
300
400
400
500
500
600
600
700
700
700
800
6
8
10
12
14
16
18
Figure 13. Monthly distribution of the average of hourly direct radiation.
0 200 400 600 800 1000 1200
0
10
20
30
40
50
60
70
80
90
100
Wh/m2
[%]
Figure 14. Inverse cumulative distribution of the percentage of hours for direct normal radiation levels.

0
2000
4000
6000
8000
10000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
JAN
kW/m2
/day
0
2000
4000
6000
8000
10000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
FEB
kW/m2
/day
0
2000
4000
6000
8000
10000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
MAR
kW/m2
/day
0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
APR
kW/m2
/day
0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
MAY
kW/m2
/day
0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
JUN
kW/m2
/day

0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
JUL
kW/m2
/day
0
2000
4000
6000
8000
10000
12000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
AUG
kW/m2
/day
0
2000
4000
6000
8000
10000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
SEP
kW/m2
/day
0
2000
4000
6000
8000
10000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
OCT
kW/m2
/day
0
2000
4000
6000
8000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
NOV
kW/m2
/day
0
2000
4000
6000
8000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# year
DEC
kW/m2
/day
Figure 15. Year-on-year distribution of the monthly direct radiation (DDR). Box-Whisker diagram.
Figure 16 shows how the yearly sums of DNI for the period 1994-2013 are distributed. Values
around xxxxx kWh/m2
/year arise as the most probable according to the histogram.

2200 2300 2400 2500 2600 2700 2800 2900
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
DNI (kWh/m2
/year)
relativefrequency
Figure 16. Probability distribution of yearly values of satellite derived DNI (kWh/m2/year) as corrected by
measurements

5 CORRECTED IRSOLAV DATABASE COMPARED TO MEASUREMENTS
In this section, only the period of coincidences with ground measurements, i.e., 2011 January to
2013 October is considered.
The quantities to compare are based in clearness indices. The clear sky index (Kt) defined as:
sin( )
t
o
GHI
K
I h

where GHI is the horizontal global irradiance, Io is the solar constant, and h the solar elevation
angle.
Clear sky index (Kc) is the defined as:
c
CS
GHI
K
G

Where Gcs is the global horizontal irradiance for clear sky conditions.
A similar test can be done with the beam clearness index Kb defined as:
0
b
DNI
K
I

In order to use the clearness index as a reliable sky condition descriptor, Perez et al. (1990)
modified this parameter to make it independent of the solar elevation angle. The formulation is the
following:
 
*
1.41.031exp 0.1
(0.9 9.4 )
t
t
K
K
AM

    
where AM is the optical air mass as defined by Kasten (1980).
Next graphics present values of Kt, Kc, Kt
*
and Kb against solar elevation for filtered measurements
and IrSOLaV estimations. We can see that the form of the data is ok as expected.

0 10 20 30 40 50 60 70 80
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Hourly Kt=GHI/Io.sin(h) comparison
Solar Elevation
ClearnessindexKt=GHI/Io.sin(h)
Hourly Kt IrSOLaV Data
Hourly Kt from Ground Data
Figure 17. Clearness index of GHI values (Wh/m2) for LOCATION.
0 10 20 30 40 50 60 70 80
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Hourly Kt prima comparison
Solar Elevation
ClearnessindexKt*
Hourly Kt* IrSOLaV Data
Hourly Kt* from Ground Data
Figure 18. Clearness index of GHI values (Wh/m2) for LOCATION.

0 10 20 30 40 50 60 70 80
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Hourly Kc=GHI/Gcs comparison.
Solar Elevation
ClearskyindexKc=GHI/Gcs
Hourly Kc IrSOLaV Data
Hourly Kc Ground Data
Figure 19. Clear sky index of GHI values (Wh/m2) for LOCATION.
0 10 20 30 40 50 60 70 80
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Hourly Kb=DNI/Io comparison.
Solar Elevation
BeamclearnessindexKb=DNI/Io
Hourly Kb IrSOLaV Data
Hourly Kb Ground Data
Figure 20. Beam clearness index of DNI values (Wh/m2) for LOCATION.

Next graphics show monthly average values and single values for each year of measurements
(dashed line) and IrSOLaV satellite estimations (solid line) of GHI and DNI. As we can see, there is
an underestimation of satellite derived data.
Monthly GHI for each year
Month
(kWh/m2
day)
0
1
2
3
4
5
6
7
8
9
10
2011
2012
2013
Figure 21. Distribution of monthly daily-average (kWh m-2 day-1) of measurements and IrSOLaV database for
each year (2011-2013) for GHI. Solid lines are IrSOLaV corrected estimations; dashed lines are ground
measurements.

Averaged Monthly GHI: 2011-2013 period.
Month
(kWh/m2
day)
0
1
2
3
4
5
6
7
8
9
10
IrSOLaV corrected
Ground
Figure 22. Distribution of average monthly daily-average (kWh m-2 day-1) of measurements and IrSOLaV
database (2011-2013) for GHI.
Monthly DNI for each year
Month
(kWh/m2
day)
0
1
2
3
4
5
6
7
8
9
10
2011
2012
2013
Figure 23. Distribution of monthly daily-average (kWh m-2 day-1) of measurements and IrSOLaV database for
each year (2011-2013) for DNI. Solid lines are IrSOLaV corrected estimations; dashed lines are ground
measurements.

Averaged Monthly DNI: 2011-2013 period.
Month
(kWh/m2
day)
0
1
2
3
4
5
6
7
8
9
10
IrSOLaV corrected
Ground
Figure 24. Distribution of average monthly daily-average (kWh m-2 day-1) of measurements and IrSOLaV
database (2011-2013) for DNI.

6 METEOROLOGICAL PARAMETERS
This section presents the local climatic conditions of LOCATION. Table 20 shows for each month:
daily average, minimum and maximum for air temperature, daily average relative humidity, pressure,
wind speed, wind direction and monthly accumulated precipitation rate. Yearly means are presented
in the last row of the table.
Table 20. Monthly and yearly average for meteorological parameters.
Meteorological Parameters
Mean Temp
ºC
Min Temp
ºC
Max Temp
ºC
Mean RH
%
Mean Press Mean WS
m/s
Mean WD
º
Acu Precip
January
February
March
April
May
June
July
August
September
October
November
December
YEAR
Next figure presents the wind rose of hourly wind speed and wind direction. As we can see the
predominance of wind directions is in the Northeast to Southwest sector.
2%
4%
6%
WEST EAST
SOUTH
NORTH
0 - 2
2 - 4
4 - 6
6 - 8
8 - 10
10 - 12
12 - 14
14 - 16
16 - 18
Wind Speed (m/s)
Figure 25. Wind rose of hourly Wind Speed (m/s) and Wind Direction (º) in LOCATION.

The next figures present the box whisker diagram of hourly meteorological parameters for each
month of the year: air temperature, average relative humidity, pressure, wind speed, wind direction
and monthly accumulated precipitation rate.
-5
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 6 7 8 9 10 11 12
Month
ºC Box-plot diagram of hourly temperature by month
Figure 26. Box Whisker of hourly temperature (ºC) for each month of the year in LOCATION.
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12
Month
%
Box-plot diagram of hourly relative humidity by month
Figure 27. Box Whisker of hourly relative humidity (%) for each month of the year in LOCATION.

925
930
935
940
945
950
955
960
965
1 2 3 4 5 6 7 8 9 10 11 12
Month
[hPa]
Box-plot diagram of hourly barometric presure by month
Figure 28. Box Whisker of hourly barometric pressure (hPa) for each month of the year in LOCATION.
0
2
4
6
8
10
12
14
16
1 2 3 4 5 6 7 8 9 10 11 12
Month
m/s
Box-plot diagram of hourly wind speed by month
Figure 29. Box Whisker of hourly wind speed (m/s) for each month of the year in LOCATION.

0
50
100
150
200
250
300
350
1 2 3 4 5 6 7 8 9 10 11 12
Month
º
Box-plot diagram of hourly wind direction by month
Figure 30. Box Whisker of hourly wind direction (º) for each month of the year in LOCATION.
0
0.5
1
1.5
2
2.5
3
x 10
-3
1 2 3 4 5 6 7 8 9 10 11 12
Month
kg/m2s
Box-plot diagram of hourly precipitation rate by month
Figure 31. Box Whisker of accumulated hourly precipitation rate (kg/m2s) for each month of the year in
LOCATION.

7 INTER-ANNUAL VARIABILITY OF SOLAR RADIATION
The weather conditions of a location changes in cycles and has also a stochastic nature (for
example. NAO oscillation, El Niño,…). This way, yearly solar radiation can deviate from the long-
term average in the range of few percent in the case of GHI and up to 30% for DNI.
This section presents the estimation of inter-annual variability. The uncertainty of DNI is highest if
only one single year is considered, but when averaged for a longer period it is averaged and
approximated to the long-term average. The inter-annual variability is calculated from the unbiased
standard deviation of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) over 18
years, considering, in the long-term, the normal distribution of the annual sums.
Figure 32 shows time series of yearly values of GHI and DNI for the period 1994-2013. Several
features are observed that deserve a brief analysis. The gap of 2003-2004 clearly distinguishes two
different behaviors of yearly values for both GHI and DNI coming from the different satellite families
MFG (1994-2002) and MSG (2005-2013). On the one hand, the inter-annual variability is larger,
more realistic, for MSG than for MFG, as a consequence of the better spatial resolution gained for
MSG. On the other hand, the DNI has, on average, larger values for MSG than for MFG. This is also
due to the coarser spatial resolution of MFG, which turns into a loss of sensitivity to cloud coverage
variability, aerosols and water vapor, but also due to the worse spatial resolution and accuracy of
the aerosols and water vapor databases for the MFG period. These factors highly influence the DNI
estimation, but do not affect the GHI, as it can be observed in the time series.
Figure 32. Yearly GHI and DNI for the period 1994-2013 including average (blue line) and standard deviation
(shaded region) (kWh/m2).
Table 21, Table 22, Table 23 and Table 24 show an expectation of GHI and DNI values that is to be
exceeded at P90 for a consecutive number of years. The variability for a number of years (n) is
calculated from the standard deviation.
stanardard deviation
variability
n

The uncertainty related to the inter-annual variability is characterized by P90, i.e. 90% probability of
excess, and this is calculated from the variability, multiplying it by 1.282, since a normal distribution
for yearly values is considered.

Table 21. Annual sum of GHI that should be exceeded with 90% probability in the period of 1 to 10 years.
Years 1 2 3 4 5 6 7 8 9 10
Variability
[±%]
2.34 1.65 1.35 1.17 1.04 0.95 0.88 0.82 0.78 0.74
Uncertainty
P(90) [±%]
3.00 2.12 1.73 1.50 1.34 1.22 1.13 1.06 1.00 0.94
Minimum
Gh at P90
[kWh/m2
]
xxxx xxxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx
Table 22. Annual sum of GHI that should be exceeded with 90% probability in the period of 11 to 20 years.
Years 11 12 13 14 15 16 17 18 19 20
Variability
[±%]
0.70 0.67 0.65 0.63 0.61 0.58 0.57 0.55 0.54 0.52
Uncertainty
P(90) [±%]
0.90 0.87 0.83 0.80 0.77 0.75 0.72 0.71 0.69 0.67
Minimum
Gh at P90
[kWh/m2
]
xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx xxxx
Table 23. Annual sum of DNI that should be exceeded with 90% probability in the period of 1 to 10 years.
Years 1 2 3 4 5 6 7 8 9 10
Variability
[±%]
6.26 4.43 3.62 2.80 2.55 2.36 2.21 2.09 1.98 1.88
Uncertainty
P(90) [±%]
8.03 5.68 4.63 4.01 3.59 3.28 3.03 2.84 2.68 2.53
Minimum
Bn at P90
[kWh/m2
]

Table 24. Annual sum of DNI that should be exceeded with 90% probability in the period of 11 to 20 years.
Years 11 12 13 14 15 16 17 18 19 20
Variability
[±%]
1.81 1.74 1.68 1.61 1.57 1.51 1.48 1.44 1.43 1.40
Uncertainty
P(90) [±%]
2.42 2.32 2.23 2.15 2.07 2.00 1.95 1.89 1.84 1.80
Minimum
Bn at P90
[kWh/m2
]
Last tables show consequences of inter-annual variability if yearly GHI and DNI for different number
of consecutive years is estimated. Following, few examples show how this information can be
interpreted:
i. GHI inter-annual variability of 2.34% has to be considered for any single year. In other words,
assuming that the long-term average is xxxx kWh/m2
, it is expected (at 90% probability)
that annual Global Horizontal Irradiation exceeds at any single year value of xxxx
kWh/m2
,
ii. Within a period of three consecutive years, it is expected at P90 that annual average of DNI
exceeds value of xxxx kWh/m2
;
iii. For a period of 20 years, it is expected at P90 that due to inter-annual variability, the
estimates of the long-term annual DNI average may be off within the range of +-1.40%.
Thus assuming that the estimate of the long-term average is xxxx kWh/m2
, it can be
expected at P90 that due to variability of weather, it should be at least xxxx kWh/m2
.
We must point out that the prediction of long-term solar radiation is based on the analysis of the
recent historical data. Future weather changes may include: man-induced or natural events such as
volcano eruptions, which may have impact on the long-term prediction.
8 UNCERTAINTY OF ESTIMATES
This section presents the cumulative uncertainty of estimations as a combination of the inter-annual
variability (Section 7) and the uncertainty of satellite estimations once they have been corrected with
ground measurements (Section 4.1). Values are presented for single year, 10 years and 20 years.at
P90 probability for GHI (Table 25) and DNI (Table 26).

Table 25. Cumulative uncertainty of the corrected GHI yearly means for 1, 10 and 20 years in LOCATION.
Uncertainty
Cumulative
uncertainty
Yearly value
[kWh/m2
]
Minimum value for 1 year assuming a
percentile 90 for the estimations
3.00% 3.91% xxx
Minimum value for 10 years assuming a
0.95% 2.67% xxxx
0.67% 2.59% xxxx
Table 26. Cumulative uncertainty of the corrected DNI yearly means for 1, 10 and 20 years in LOCATION.
Uncertainty
Cumulative
uncertainty
Yearly value
[kWh/m2
]
Minimum value for 1 year assuming a
8.03% 8.57% xxxx
2.54% 3.93% xxx
1.80% 3.50% xxxx
9 TYPICAL SOLAR RADIATION YEAR
Simulation of solar thermal power production systems is a tool of high interest in various phases of
design and development of any project. They will require, among others, climatological data to
define the climatic environment in which the site is located. The approaches to the definition of the
climatic environment have evolved in line with the requirements of the different simulation programs
and the availability of climatic data.
A first approximation may be the annual series of hourly values called Short Reference Year (Lund,
1985) available to several European countries (Lund, 1985). This type of time information cannot be
considered "sufficiently typical" versus so-called Typical Meteorological Year (TMY) due mainly to
the requirements / availability of data needed and the selection method used.
The TMY is formed by a set of hourly data including solar radiation, temperature, humidity and wind,
over a 1-year period, that is, 8760 records of the main climatic variables. It is formed, in the strict
sense, by the concatenation of selected months from specific years (i.e., January '96 + February '97
+ March '02, etc). The criterion used for the selection of these months is adopted according to their
applicability for the simulation of solar systems (concentrated solar systems, CPC, PV, buildings).
Therefore, it can be said that there is no standard method for their generation. In any case,
depending on the necessities of the end-user of the TMY and data availability, it is possible to
choose the fair method from the one suggested by different authors and published in the scientific

journals. Whatever the method used for getting the TMY of the site, it should be representative of
the climatic evolution of the different variables included.
From the dataset available for the site of LOCATION PROJECT, hourly data of solar radiation over a
period of 12 years, a modified version of the empirical methodology proposed by the Sandia
National Laboratories (Hall et al., 1978) is applied. The basic requirements for the use of such a
method are:
 Databases with global solar irradiation over horizontal surface, dry bulb temperature and
any of the variables that define the moisture content of the atmosphere (relative humidity,
wet bulb temperature, dew temperature ,...), direction and wind speed.
 Sampling period equal to or less than one hour.
 Database with a minimum of 10 years.
In the method proposed by Hall the hourly data available are processed and a daily database is
built, consisting of 13 meteorological parameters:
 Ambient temperature (maximum, minimum, average and variation).
 Relative humidity or temperature or dew-wet temperature (maximum, minimum, average,
and oscillation).
 Wind speed (maximum, minimum, average, and oscillation).
 Cumulated global solar radiation on horizontal surface.
Each month of the year is examined separately and the TMY is formulated in the same way or
applying same methodology as TMM (Typical Meteorological Month). The process to obtain the
TMMs is done by comparing the cumulative frequency distribution function (CDF) of each parameter
for a given month (i.e. January 2000) with the CDF corresponding to the set of all similar months of
the whole period (i.e. January 2000-2010). The comparison is performed using the Finkelstein-
Schafer statistic, FS, that quantifies the discrepancy for any particular month versus the set of the
same month for all the years (Filkenstein and Schafer, 1971).
1
1 n
i
i
FS
n


  (1)
Where i is the absolute difference between the CDF for a particular month and the CDF for the set
of same months and n is the number of days of such a month.

FS equation is calculated for each month and for each of the 13 parameters. Since these
parameters are not equally important, appropriate weighting factors, wj, are applied to each of them
and a statistical value for the whole set is calculated, WS, as the following weighted sum:
13
1
j j
j
WS w FS

  (2)
The weighting factors for each one of the 13 analyzed parameters depend on the type of application
to be given to the TMY. A "typical month" is considered that one who minimizes the statistic WS.
The methodology presented is one of the most commonly used during the last twenty years
[Pissimanis et al., 1988; Zarzalejo et al., 1995; de Miguel y Bilbao, 2005; Yang et al., 2007] for all
types of systems depending on the weighting factors chosen.
For the selection of the typical year for the site of LOCATION PROJECT, only the solar radiation
variable is used. Table 27 shows the results produced by the statistical WS for the three years that
better characterize the series. From this information it is possible to select the "typical month" among
all available months.
Table 27. WS statistical standard for the three best candidates.
Year/Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

2010
2011
2012
2013
The final selection corresponds to the following sequence of months/years:
TMY= {01/200x, 02/200x, 03/201x, 04/199x, 05/200x, 06/201x, 07/200x, 08/199x, 09/200x,
10/199x, 11/199x, 12/200x}
9.1 GHI and DNI radiation values
Table 28 shows monthly daily-average values of the months selected to build the TMY and the
values corresponding to the average of 18 years. These data is plotted in Figure 33, where it can be
seen how the profiles of the TMY fit adequately to the average profiles of the whole series, in both
global radiation on horizontal surface as direct radiation on normal surface.
Table 28. Daily values of the months selected for the TMY and average of 18 years (kWh m-2 day-1). Total
values in (kWh m-2 year-1).
GHI DNI
Month Year TMY AVG TMY AVG
JAN 2000
FEB 2009
MAR 2011
APR 1996
MAY 2006
JUN 2012
JUL 2001
AUG 1995
SEP 2006
OCT 1994
NOV 1995
DEC 2000
Total xxxx xxxx xxxx xxxx

Figure 33. Distribution of monthly daily-average (kWh m-2 day-1) of TMY and IrSOLaV database (1994-2013).
for GHI and DNI.

10 STATISTICAL ANALYSIS OF THE LONG TERM
The long term analysis of the solar resource for LOCATION PROJECT is based on the estimations
of the complementary values to the percentiles. Let’s denote Pi as the i-th percentile of a given
population. Thus, P75 is the 75-percentile of the sample and it means that the probability of finding a
value equal or less than P75 is just 75%. The complementary of the percentile will be denoted as pi,
in such a way that p75 will represent the same value as P25, and the meaning is that the probability
of finding a value equal or higher than p75 is just the 75%. A statistical analysis based upon the p
values in the sense of complementary values to the percentiles is presented in this section.
The p50, p75, p95 and p99 of the monthly values for global horizontal and direct normal irradiation
are shown in Table 29.
Table 29. Monthly values of p50, p75, p90 and p99 for GHI and DNI (kWh m-2day-1) from IrSOLaV database.
IrSOLaV
Database
P50 P75 P90 P99
DIFFERENCE
P50 AND P75
DIFFERENCE
P50 AND P90
DIFFERENCE
P50 AND P99
GHI xxxx xxxx xxxx xxxx 1.82 % 2.43 % 2.61 %
DNI xxxx xxxx xxxx xxxx 1.82 % 6.37 % 6.70 %
From the yearly time series provided by SOLAR MED ATLAS, we have calculated the percentiles
p50, 090 and p99 which are shown in Table 30.
Table 30. Monthly values of p50, p75, p90 and p99 for GHI and DNI (kWh m-2day-1) from Solar Met Atlas
database.
SOLAR_MET_ATLAS
(1992-2010)
P50 P75 P90 P99
DIFFERENCE
P50 AND P75
DIFFERENCE
P50 AND P90
DIFFERENCE
P50 AND P99
GHI xxxx xxxx xxxx xxxx 1.14 % 1.46 % 2.37 %
DNI xxxx xxxx xxxx xxxx 1.26 % 2.45 % 2.85 %

11 CONCLUSIONS AND COMMENTS
The main conclusions that can be extracted from this work are summarized in the following
paragraphs.
Solar radiation has been measured in LOCATION radiometric station from MASEN and data for the
period from 1st
January 2011 to 31th
October 2013 are available. For the period measured, the
annual global irradiance (GHI) is xxxx kWh m-2
year-1
and annual direct normal irradiance (DNI) is
xxxx kWh m-2
year-1
.
The yearly values from different free radiometric database are the following:
o METEONORM: the yearly value of GHI estimated is xxxx kWh m-2
year-1
.
o NASA: the yearly value of GHI estimated is xxxx kWh m-2
year-1
and DNI estimated
is in the range xxxx-xxxx kWh m-2
year-1
.
o SOLAR-MED-ATLAS: the yearly value of GHI estimated is xxxx kWh m-2
year-1
and
DNI estimated is xxxx kWh m-2
year-1
.
o PVGIS: the yearly value of GHI estimated is xxxx kWh m-2
year-1
and DNI estimated
is xxxx kWh m-2
year-1
.
o CAMSAF-CLIMATE: the yearly value of GHI estimated is xxxx kWh m-2
year-1
and
DNI estimated is xxxx kWh m-2
year-1
.
o SODA (HELIOCLIM-3V4): the yearly value of GHI estimated is xxxx kWh m-2
year-1
and DNI estimated is xxxxx kWh m-2
year-1
.
Estimations of hourly time series of global horizontal and direct normal irradiance have been
performed with IrSOLaV methodology for LOCATION site during the period from 1994 to 2013. The
methodology has used Meteosat satellite images and daily AOD at 550nm from MISR satellite and
daily water vapor information from NCEP. The conclusions from the period of 18 years of data
studied are the following:
 The average annual value of global solar radiation is xxxx kWh m-2
year-1
. The average
annual value of direct solar radiation is xxxx kWh m-2
year-1
.
 The values of cumulative global solar radiation and direct normal for the typical
meteorological year (TMY) are xxxx kWh m-2
year-1
and xxxxx kWh m-2
year-1
respectively.
 P50, P75, P90 and P99 is estimated to be xxxx kWh m-2
year-1
, xxxxx kWh m-2
year-1
, xxxx
kWh m-2
year-1
and xxxxx kWh m-2
year-1
respectively for annual GHI.

 P50, P75, P90 and P99 is estimated to be xxxx kWh m-2
year-1
, xxxx kWh m-2
year-1
, xxxx
kWh m-2
year-1
and xxxx kWh m-2
year-1
respectively for annual DNI.

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