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 [email protected] www.irsolav.com www.solarexplorer.info NIF B85148807

Date: Wednesday, January 16, 2013

AUTHOR:

LUIS MARTIN ([email protected])

REVISED:

DIEGO BERMEJO ([email protected])

mailto:[email protected]

http://www.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):

1

g c gn

(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).

C

clear

GK

G (2)



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):

2

0.2 , 1.2

0.2 0.8 , 1

0.8 1.1 , 2.0667 3.6667 1.6667

1.1 , 0.05

C

C

C

C

n K

n K n

n K n n

n K

(3)

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 (m)

# Station Latitude Longitude Height (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:

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

0

0,5

1

1,5

2

2,5

3

3,5

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Lin

ke T

urb

idit

y

Sample days for January

TL MEASUREMENTS

TL MODIS SATELLITE


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

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

0

0,5

1

1,5

2

2,5

3

3,5

4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Lin

ke T

urb

idit

y

Sample days for February

TL MEASUREMENTS

TL MODIS SATELLITE

0

1

2

3

4

5

6

7

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

Lin

ke T

urb

idit

y

Sample days for June

TL MEASUREMENTS

TL MODIS SATELLITE


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).


7 REFERENCES

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