Irsolav Methodology 2013

SUMMARY OF IRSOLAV METHODOLOGY

Wednesday, January 16, 2013

IRSOLAV METHODOLOGY PAGE 2 OF 28

IrSOLaV - Investigaciones y Recursos Solares Avanzados
Calle Santiago Grisolía, 2 (PTM) – 28760, Tres Cantos (Madrid), España
Tel.: +34 91 126 36 12
info@irsolav.com
www.irsolav.com
www.solarexplorer.info
NIF B85148807

Date: Wednesday, January 16, 2013

AUTHOR: REVISED:
LUIS MARTIN (luis.martin@irsolav.com) DIEGO BERMEJO (diego.bp@irsolav.com)


INDEX

1 DATA NEEDED ....................................................................................................................................... 5
2 SOLAR RADIATION DERIVED FROM SATELLITE IMAGES ................................................................ 5
2.1 Brief summary of IrSOLaV methodology to estimate solar radiation from satellite images .................... 7
2.2 Validation of hourly values of GHI data ................................................................................................. 10
3 SATELLITE COVERAGE ...................................................................................................................... 11
4 METEOROLOGICAL DATA FROM REANALYSIS MODEL ................................................................. 14
4.1 Validation of solar radiatione estimates from satellite images............................................................... 14
5 CORRECTION OF ESTIMATED DATA USING GROUND MEASURED DATA................................... 15
6 TYPICAL METEOROLOGICAL DATA (TMY2) ..................................................................................... 24
7 REFERENCES ...................................................................................................................................... 26


1 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.

2 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.


It has been generally accepted by the international scientific community that solar radiation estimation
(SRE) from Geostationary Earth Orbiting Satellite (GEO) images is a suitable tool, taking into account
temporal and spatial distribution, availability of representative time series, to estimate solar resource at
locations where no previous ground historic radiometric records are available. The use of estimations
from satellites is considered better than nearby ground measurements when they are separated by
more than 3km from the location where the solar plant is planned.

GEO satellites orbit in the earth's equatorial plane at a mean height of 36,000 km. At this height, the
satellite's orbital period matches the rotation of the Earth, so the satellite seems to stay stationary over
the same point on the equator. Since the field of view of a satellite in geostationary orbit is fixed, it
always views the same geographical area, day and night. This is ideal for making regular sequential
observations of cloud patterns over a region with visible and infrared radiometers. High temporal
resolution and constant viewing angles are the defining features of geostationary imagery. Currently,
IrSOLaV uses GEO satellites images from Meteosat First Generation (MFG-IODC), Meteosat Second
Generation (MSG-PRIME), Geostationary Operational Environmental Satellite (GOES) and Multi-
functional Transport Satellite (MTSAT-PACIFIC).

Figure 1. Global coverage of geostationary satellites around the Earth.

The main advantages in the use of images from GEO satellites are the following:

 The GEO satellite sees simultaneously large areas of terrain, allowing it to know the spatial
distribution of the information, as well as, determine the relative differences between one zone
to the other.
 When the information available (satellite images) belongs to the same area, it is possible to
study the evolution of the values in one pixel of the image, or in a specific geographic zone.

It is possible to know past situations when there are satellites images recorded and stored previously.


2.1 Brief summary of IrSOLaV methodology to estimate solar radiation from satellite images
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.

Various methods for deriving solar radiation from satellite images were developed during ’80. One of
them was the method Heliosat-1 (Cano, 1982; Cano et al., 1986; Diabaté et al., 1988) which could be one
of the most accurate (Grüter et al., 1986; Raschke et al., 1991). The method Heliosat-2 (Rigollier et al.,
2001; Rigollier et al., 2004) integrates the knowledge gained by these various exploitations of the
original method and its varieties in a coherent and thorough way.

Both versions are based in the computation of a cloud index (n) from the comparison between the
reflectance, or apparent albedo, observed by the spaceborne sensor (ρ), the apparent albedo of the
brightest clouds (ρc) and the apparent albedo of the ground under clear skies (ρg):

n      g   c   g 
1
(1)

For the estimation of radiation at ground level the method Heliosat-1 uses an empirical adjusted
relation between the cloud index and the clearness index (KT). The new Heliosat-2 method uses a
relation between the cloud index and the clear sky index (KC) defined as the ratio of the global
irradiance (G) to the global irradiance under clear sky (Gclear).

G
KC  (2)
Gclear


The Heliosat method deals with atmospheric and cloud extinction separately. As a first step the
irradiance under clear skies is calculated by using the ESRA/SOLIS/REST2 clear sky model (Rigollier et
al., 2000), where daily values of Linke turbidity factor, AOD at 550nm and Water vapor content of the
atmosphere are the parameters required for the composition of the atmosphere. The following
relationship between the cloud index and the clear sky index is then used for the global solar radiation
determination (Rigollier and Wald, 1998; Fontoynont et al., 1998):

n   0.2 , KC  1.2
 0.2  n  0.8 , KC  1  n
(3)
0.8  n  1.1 , KC  2.0667  3.6667 n  1.6667 n 2
1.1  n , KC  0.05

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 {Perez, 1992 1000439 /id} 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 and
water vapour obtained from MODIS Aqua and Terra satellite (Ineichen, 2008) for ESRA model.
 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 error (RMSE). 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 to be around 20-25% RMSE and in a daily basis the uncertainty of the models
used is around 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 30 Spanish sites with the following uncertainty results for
global horizontal irradiance:

 About 12% RMSE for hourly values
 Less than 10% for daily values
 Less than 5% for annual and monthly means

2.2 Validation of hourly values of GHI data

This validation section belongs to the scientific publication {Zarzalejo, 2009 1000137 /id}. Simultaneous
data of satellite derived cloud index and hourly global irradiance on ground-based stations are used for
model development and assessment for 28 locations in Spain. The geographic information of the
radiometric stations is listed in Table 1. The time period covered is from January 1994 to December
2004. In the cloud index estimations the HRI-VIS channel images of Meteosat are used. The spatial
resolution is 2.5 x 2.5 km at nadir and the temporal resolution is 30 minutes (EUMETSAT, 2001).

After an exhaustive quality analysis of the simultaneous data around 370000 hourly data pairs are
available for fitting and assessment the new models (Zarzalejo, 2006). The whole data set is randomly
separated into two groups, 80% for fitting the models and 20% for assessment.

Table 1. Geographic information of the Spanish radiometric stations

# Station Latitude Longitude Height # Station Latitude Longitude Height
(m) (m)
1 Cádiz 36.50 ºN 6.27 ºW 15 15 Barcelona 41.38 ºN 2.20 ºE 25
2 Málaga 36.72 ºN 4.48 ºW 61 16 Soria 41.60 ºN 2.50 ºW 1090
3 Almería (CMT) 36.85 ºN 2.38 ºW 29 17 Zaragoza 41.63 ºN 0.92 ºW 250
4 Huelva 37.28 ºN 6.92 ºW 19 18 Lérida 41.63 ºN 0.60 ºE 202
5 Murcia 38.00 ºN 1.17 ºW 69 19 Valladolid 41.65 ºN 4.77 ºW 740
6 Badajoz 38.88 ºN 7.02 ºW 190 20 La Rioja 42.43 ºN 2.38 ºW 365
7 Ciudad Real 38.98 ºN 3.92 ºW 628 21 Pontevedra 42.58 ºN 8.80 ºW 15
8 Albacete 39.00 ºN 1.87 ºW 674 22 León 42.58 ºN 5.65 ºW 914
9 Cáceres 39.47 ºN 6.33 ºW 405 23 Álava 42.85 ºN 2.65 ºW 508
10 Valencia 39.48 ºN 0.38 ºW 23 24 Vizcaya 43.30 ºN 2.93 ºW 41
11 Toledo 39.88 ºN 4.05 ºW 516 25 Guipúzcoa 43.30 ºN 2.03 ºW 259
12 Madrid 40.45 ºN 3.72 ºW 680 26 Asturias 43.35 ºN 5.87 ºW 348
13 Tarragona 40.82 ºN 0.48 ºE 44 27 La Coruña 43.37 ºN 8.42 ºW 67
14 Salamanca 40.95 ºN 5.92 ºW 803 28 Cantabria 43.48 ºN 3.80 ºW 79


Relative mean bias error and root mean squared error of IrSOLaV/CIEMAT is 0.31% MBE and 17.21%
RMSE.

Table 2. Statistical errors of hourly time series estimated from meteosat satellite against ground
measured data

# Station MBE(%) RMSE(%)
1 Cádiz -0.06 12.24
2 Málaga 1.40 12.60
3 Almería (CMT) 1.20 13.11
4 Huelva -1.04 14.59
5 Murcia 13.69 30.08
6 Badajoz 3.51 15.03
7 Ciudad Real 0.63 13.89
8 Albacete -0.24 16.85
9 Cáceres 1.08 16.39
10 Valencia 0.88 18.04
11 Toledo 0.61 15.16
12 Madrid 1.17 13.65
13 Tarragona 1.33 15.09
14 Salamanca -0.04 15.17
15 Barcelona 5.62 24.22
16 Soria 0.17 22.07
17 Zaragoza 0.25 13.47
18 Lérida -0.42 26.18
19 Valladolid 1.52 14.11
20 La Rioja 0.59 13.84
21 Pontevedra 0.21 16.68
22 León -0.53 20.93
23 Álava -0.66 21.37
24 Vizcaya 0.42 18.35
25 Guipúzcoa -0.37 27.04
26 Asturias -0.12 24.63
27 La Coruña -1.94 25.64
28 Cantabria -0.21 28.75
MEAN 0.93 18.85

3 SATELLITE COVERAGE

There are two main satellite orbits: Geostationary Earth Orbiting Satellites (GEO) and Low Earth
Orbiting Satellites (LEO). GEO satellites hover over a single point at an altitude of about 36,000
kilometers and to maintain constant height and momentum, a geostationary satellite must be located
over the equator. LEO satellites travel in a circular orbit moving from pole to pole, collecting data in a


swath beneath them as the earth rotates on its axis. In this way, a polar orbiting satellite can “see” the
entire planet twice in a 24 hour period.

GEO satellites orbit in the earth's equatorial plane at a mean height of 36,000 km. At this height, the
satellite's orbital period matches the rotation of the Earth, so the satellite seems to stay stationary over
the same point on the equator. Since the field of view of a satellite in geostationary orbit is fixed, it
always views the same geographical area, day and night. This is ideal for making regular sequential
observations of cloud patterns over a region with visible and infrared radiometers. High temporal
resolution and constant viewing angles are the defining features of geostationary imagery. Currently,
IrSOLaV uses GEO satellites images from Meteosat First Generation (Meteosat-7), Meteosat Second
Generation (MSG) and GOES as well as atmospheric data from Terra and Aqua Polar (LEO) satellites.

The main advantages in the use of images from GEO satellites are the following:

• The GEO satellite sees simultaneously large areas of terrain, allowing it to know the spatial
distribution of the information, as well as, determine the relative differences between one zone
to the other
• When the information available (satellite images) belongs to the same area, it is possible to
study the evolution of the values in one pixel of the image, or in a specific geographic zone.
• It is possible to know past situations when there are satellite images recorded and stored
previously.

IrSOLaV has a database of satellite images of excellent quality and updated by a receiving station. The
new images received are filtered before its storage in a fully automatic process. The data warehouse of
IrSOLaV is composed of the following satellite images which covers different regions of the planet:

MFG: The Meteosat First Generation (MFG) are a set of satellites which provides the Indian Ocean Data
Coverage (IODC) service covering the region shown in the centered image further down. These set of
satellites were previously located over the position 0º of latitude covering Europe, Africa, Arabian
Peninsula and some parts of Brazil (see figure further down on the right). The current near real-time
data are rectified to 57.50 E and it provides imagery data 24 hours a day from the three spectral
channels of the main instrument, the Meteosat Visible and InfraRed Imager (MVIRI), every 30 minutes.
The three channels are in the visible, infrared, and water vapor regions of the electromagnetic spectrum.
The IrSOLaV-CIEMAT database stores MFG images for IODC from 1999 to the present and also for the
latitude 0 degrees (previous position) for the period from 1994 to 2005.


MSG: The Meteosat Second Generation satellite is a significantly enhanced system to the previous
version of Meteosat (MFG). MSG consists of a series of four geostationary meteorological satellites that
operate consecutively. The MSG system provides accurate weather monitoring data through its primary
instrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity to
observe the Earth in 12 spectral channels. The temporal resolution of the satellite is 15 minutes and the
spatial resolution is 1km at Nadir Position (over latitude 0 and longitude 0).

The radiometric and geometric non-linearity errors of the imagery data are corrected to solve any
mistakes in the acquisition from the sensor. The data are accompanied with the appropriate ancillary
information that allows the user to calculate the geographical position and radiance of any pixel. The
IrSOLaV-CIEMAT database stores MSG images from 2006 to the current period (latitude 0 deg).

GOES (The Geostationary Operational Environmental Satellite): The United States of America operates
two meteorological satellites in geostationary orbit over the equator. Each satellite views almost a third
of the Earth's surface: one monitors North and South America and most of the Atlantic Ocean, the other
North America and the Pacific Ocean basin. GOES-12 (or GOES-East) is positioned at 75º W longitude on
the equator, while GOES-11 (or GOES-West) is positioned at 135º W longitude on the equator. Both
operate together to produce a full-face picture of the Earth, day and night. Coverage extends
approximately from 20º W longitude to 165º E longitude. The GOES satellites are able to observe the
Earth disk with five spectral channels. The IrSOLaV-CIEMAT database contain GOES images from 2000
to the present.

MODIS: The Moderate Resolution Imaging Spectroradiometer is a key instrument aboard of the Terra
(EOS AM) and Aqua (EOS PM) satellites. The orbit of Terra around the Earth is timed so that it passes
from North to South across the equator in the morning, while Aqua passes from South to North over the
equator in the afternoon. Terra and Aqua view the entire Earth's surface with a frequency from 1 to 2
days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications
on NASA web). These data improve our understanding of global dynamics and processes occurring on
the ground, oceans, and lower atmosphere. MODIS is playing a vital role in the development of validated,
global, interactive Earth system models able to predict global change accurately enough to assist policy
makers in making sound decisions concerning the protection of our environment.


The effect of the atmospheric turbidity on solar radiation is applied in IrSOLaV-CIEMAT model by using
the daily values of Linke Turbidity factor from MODIS Terra and Aqua satellites and daily values of AOD
(Aerosol Optical Depth) at 550 nm and of water vapour column.

4 METEOROLOGICAL DATA FROM REANALYSIS MODEL

Meteorological data is an important parameter to simulate correctly solar energy systems to produce
electricity. IrSOLaV uses NCEP Climate Forecast System Reanalysis (CFSR) and Climate Forecast System
Version 2 (CFSV2) datasets.

4.1 Validation of solar radiatione estimates from satellite images

The National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR)
as initially completed over the 31-year period from 1979 to 2009 and has been extended to March 2011.
Selected CFSR time series products are available at 0.3, 0.5, 1.0, and 2.5 degree horizontal resolutions at
hourly intervals by combining either 1) the analysis and one- through five-hour forecasts, or 2) the one-
through six-hour forecasts, for each initialization time.

For data to extend CFSR beyond March 2011, IrSOLaV will use the Climate Forecast System Version 2
(CFSV2) datasets. The National Centers for Environmental Prediction (NCEP) Climate Forecast System
(CFS) is initialized four times per day (00Z, 06Z, 12Z, and 18Z). NCEP upgraded CFS to version 2 on
March 30, 2011. This is the same model that was used to create the NCEP Climate Forecast System
Reanalysis (CFSR). Selected CFS time series products are available at 0.2, 0.5, 1.0, and 2.5 degree
horizontal resolutions at hourly intervals by combining either 1) the analysis and one- through five-
hour forecasts, or 2) the one- through six-hour forecasts, for each initialization time. Beginning with
January 1, 2011, these data are archived as an extension of CFSR.

IrSOLaV can provide the following meteorological data:

 Air Temperature 2 m height above ground (Ta)
 Relative air humidity 2 m height above ground (RH)
 Wind speed at 10 m height above ground (WS)
 Wind direction at 10 m height above ground (WD)
 Barometric Pressure at/near ground level (BP)
 Precipitation (R).


5 CORRECTION OF ESTIMATED DATA USING GROUND MEASURED DATA

Due to particular behavior of each one of the meteorological variables, the correction will be done with
ad-hoc physical or statistical methods which treat in a better way the dynamic of the variable. To correct
values of solar radiation estimated from satellite with ground measured radiometric data the turbidity
of the site will be characterized. The rest of meteorological variables will be corrected using statistical
methods. The methodologies which will be applied are explained in the next paragraphs.

Linke Turbidity (TL) establishes a relationship between the real and theoretical optical depth of the
atmosphere and represents the degree of transparency of the atmosphere. It is an adequate
approximation when quantifying the effects of absorption and dispersion on solar radiation when
trespassing the atmosphere. It can be obtained directly from measurements; however, due to the lack of
them, it is generally obtained from empirical adjustments. We will obtain the Linke Turbidity from
measurements registered. After this selection, we will obtain the values of TL using the inverse of a clear
sky model {Ineichen, 2002 1000401 /id}.

In the next figures, we show some plots of hourly values of DNI for clear sky days selected manually for
a location in Spain. In the plots, measured clear sky DNI (blue), modeled clear sky DNI (green), DNI
estimated from satellite MODIS TL and DirIndex model (pink) and DNI estimated from satellite MODIS
TL and Louche model (red). In the figure we show also the values of daily TL estimated from MODIS
satellite and estimated from measurements for all hourly values and for two hours during the day at
noon hours (11:00 and 12:00 UTC). The values of TL are calculated from measurement at noon hours
because there are some days which have clear sky conditions in most of the hours of the day but not in
all.


Figure 2. TL estimated from MODIS and measurements of DNI for a clear sky day. 09/01/2010.




The next figures represent the same information as in the last one but for cloudy conditions.


Figure 9. TL estimated from MODIS and measurements of DNI for a cloudy sky day. 07/01/2011.

Figure 10. TL estimated from MODIS and measurements of DNI for a cloudy sky day. 10/01/2010.


The next figures show some examples of the relationship between daily Linke Turbidity (TL) estimated
from MODIS satellite and estimated from measurements with clear sky days for several months in a site
in Spain. TL is obtained from several years of measurements:

TL MEASUREMENTS
TL MODIS SATELLITE
3,5

3

2,5
Linke Turbidity

2

1,5

1

0,5

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Sample days for January

Figure 11. Daily values of TL estimated from MODIS and from measurements with clear sky days in January


TL MEASUREMENTS

TL MODIS SATELLITE
4

3,5

3
Linke Turbidity

2,5

2

1,5

1

0,5

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Sample days for February

Figure 12. Daily values of TL estimated from MODIS and from measurements with clear sky days in February

TL MEASUREMENTS
TL MODIS SATELLITE
7

6

5
Linke Turbidity

4

3

2

1

0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Sample days for June

Figure 13. Daily values of TL estimated from MODIS and from measurements with clear sky days in June


Figure 14. Daily values of TL estimated from MODIS and from measurements with clear sky days in July

Figure 15. Daily values of TL estimated from MODIS and from measurements with clear sky days in October


The deviations observed in the last figures are due to the fact that daily values of water vapor and AOD
at 550nm obtained from MODIS satellite are representative of an area of 1º by 1º and local effects on
constituents in the atmosphere are not taken into account. This way, the deviations between TL
estimated from MODIS and measurements will be corrected using non-linear models. After
characterization of Linke Turbidity, the correction coefficients will be applied to the whole series of
daily turbidity dataset estimated MODIS which has a period from year 2001 to the present. Finally, using
corrected input of Linke Turbidity into IrSOLaV method to estimate solar radiation from satellite images
the whole data will be reprocessed for the 12 years of data to obtain corrected characterized local
values of Global Horizontal (GHI), Direct Normal (DNI) and diffuse irradiance (DIF).

This process will be done in 4 phases: after having 3, 6 , 9 and 12 months of radiometric measured data.
This way, values of TL, and subsequently radiometric estimations, will be corrected only for the whole
period of years (12 years) and in those months where measured data are available. In conclusion, only
when one year of measurements is available the correction will be applied to the whole time series of 12
years of solar radiation (GHI, DNI and DIF) estimations from satellite images.

6 TYPICAL METEOROLOGICAL DATA (TMY2)

IrSOLaV has the methodology to offer time series of solar irradiance for:

• Europe: from 1994 to the present (MFG + MSG).

• Africa: from 2006 to the present (MSG).

• America: from 2000 to the present (GOES).

• Asia: from 1999 to the present (IODC).

The analysis of solar energy systems are based on the detailed study and simulation of solar energy
power plants to evaluate thermal and electrical production of the plant using the solar irradiance long-
term estimations from satellite.

For any specific site, the process of obtaining solar irradiance time series includes: a complete statistical
analysis of the satellite imagery database, analysis of the monthly and annual solar irradiance satellite
estimations comparing them with ground data available in the zone nearby. The time series that can be
delivered are global horizontal (GHI) and direct normal irradiances DNI (with tracking in one and two


axis if required). Besides, to characterize the long-term dynamics of solar radiation and meteorological
variables for any location we provide typical meteorological years (TMY).

Data of solar radiation for any location is provided in electronic format (Excel, ASCII, EPW, TMY2 or any
other format requested).


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