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Irsolav Methodology 2013

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Irsolav Methodology 2013

Irsolav Methodology 2013

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  • 1. SUMMARY OF IRSOLAV METHODOLOGY Wednesday, January 16, 2013
  • 2. IRSOLAV METHODOLOGY PAGE 2 OF 28IrSOLaV - Investigaciones y Recursos Solares AvanzadosCalle Santiago Grisolía, 2 (PTM) – 28760, Tres Cantos (Madrid), EspañaTel.: +34 91 126 36 12info@irsolav.comwww.irsolav.comwww.solarexplorer.infoNIF B85148807 Date: Wednesday, January 16, 2013AUTHOR: REVISED:LUIS MARTIN (luis.martin@irsolav.com) DIEGO BERMEJO (diego.bp@irsolav.com)
  • 3. IRSOLAV METHODOLOGY PAGE 3 OF 28 INDEX1 DATA NEEDED ....................................................................................................................................... 52 SOLAR RADIATION DERIVED FROM SATELLITE IMAGES ................................................................ 52.1 Brief summary of IrSOLaV methodology to estimate solar radiation from satellite images .................... 72.2 Validation of hourly values of GHI data ................................................................................................. 103 SATELLITE COVERAGE ...................................................................................................................... 114 METEOROLOGICAL DATA FROM REANALYSIS MODEL ................................................................. 144.1 Validation of solar radiatione estimates from satellite images............................................................... 145 CORRECTION OF ESTIMATED DATA USING GROUND MEASURED DATA................................... 156 TYPICAL METEOROLOGICAL DATA (TMY2) ..................................................................................... 247 REFERENCES ...................................................................................................................................... 26
  • 4. IRSOLAV METHODOLOGY PAGE 4 OF 28
  • 5. IRSOLAV METHODOLOGY PAGE 5 OF 281 DATA NEEDEDThe solar radiation is a meteorological variable measured only in few measurement stations and duringshort and, on most occasions, discontinuous periods of times. The lack of reliable information on solarradiation, together with the spatial variability that it presents, leads to the fact that developers do notfind 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 powersystems.Among the possible different approaches to characterize the solar resource of a given specific site theycan 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 thebest information on the spatial distribution of the solar radiation and it is a methodology clearlyaccepted by the scientific community and with a high degree of maturity [McArthur, 1998]. In thisregard, it is worth to mention that BSRN (Baseline Surface Radiation Network) has among its objectivesthe improvement of methods for deriving solar radiation from satellite images, and also the ExpertsWorking Group of Task 36 of the Solar Heating and Cooling Implement Agreement of IEA (InternationalEnergy Agency) focuses on solar radiation knowledge from satellite images.2 SOLAR RADIATION DERIVED FROM SATELLITE IMAGESSolar radiation derived from satellite images is based upon the establishment of a functionalrelationship between the solar irradiance at the Earth’s surface and the cloud index estimated from thesatellite images. This relationship has been previously fitted by using high quality ground data, in such amanner that the solar irradiance-cloud index correlation can be extrapolated to any location of interestand solar radiation components can be calculated from the satellite observations for that point.
  • 6. IRSOLAV METHODOLOGY PAGE 6 OF 28It 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 accounttemporal and spatial distribution, availability of representative time series, to estimate solar resource atlocations where no previous ground historic radiometric records are available. The use of estimationsfrom satellites is considered better than nearby ground measurements when they are separated bymore than 3km from the location where the solar plant is planned.GEO satellites orbit in the earths equatorial plane at a mean height of 36,000 km. At this height, thesatellites orbital period matches the rotation of the Earth, so the satellite seems to stay stationary overthe same point on the equator. Since the field of view of a satellite in geostationary orbit is fixed, italways views the same geographical area, day and night. This is ideal for making regular sequentialobservations of cloud patterns over a region with visible and infrared radiometers. High temporalresolution and constant viewing angles are the defining features of geostationary imagery. Currently,IrSOLaV uses GEO satellites images from Meteosat First Generation (MFG-IODC), Meteosat SecondGeneration (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.
  • 7. IRSOLAV METHODOLOGY PAGE 7 OF 282.1 Brief summary of IrSOLaV methodology to estimate solar radiation from satellite imagesThe methodology of IrSOLaV uses two main inputs to compute hourly solar irradiance: thegeostationary 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 covercharacteristics. The latter is basically information on the daily Linke turbidity which is a veryrepresentative parameter to model the attenuating processes which affects solar radiation on its paththrough the atmosphere, mainly the aerosol optical depth and water vapor column.The methodology applied has undoubtedly been accepted by the scientific community and its mainusefulness is in the estimation of the spatial distribution of solar radiation over a region. Its maturity isguaranteed 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 solarradiation network BSRN (Baseline Surface Radiation Network) promoted by WMO (WorldMeteorological Organization) has as its main objectives for the improvement of solar radiationestimation from satellite images models.Various methods for deriving solar radiation from satellite images were developed during ’80. One ofthem was the method Heliosat-1 (Cano, 1982; Cano et al., 1986; Diabaté et al., 1988) which could be oneof 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 theoriginal 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 thereflectance, or apparent albedo, observed by the spaceborne sensor (ρ), the apparent albedo of thebrightest 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 adjustedrelation between the cloud index and the clearness index (KT). The new Heliosat-2 method uses arelation between the cloud index and the clear sky index (KC) defined as the ratio of the globalirradiance (G) to the global irradiance under clear sky (Gclear). G KC  (2) Gclear
  • 8. IRSOLAV METHODOLOGY PAGE 8 OF 28The Heliosat method deals with atmospheric and cloud extinction separately. As a first step theirradiance under clear skies is calculated by using the ESRA/SOLIS/REST2 clear sky model (Rigollier etal., 2000), where daily values of Linke turbidity factor, AOD at 550nm and Water vapor content of theatmosphere are the parameters required for the composition of the atmosphere. The followingrelationship between the cloud index and the clear sky index is then used for the global solar radiationdetermination (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  0.05Solar radiation estimation from satellite images offered is made from a modified version of therenowned model Heliosat-3, developed and validated by CIEMAT with more than thirty radiometricstations in the Iberian Peninsula. Over this first development, IrSOLaV has generated a tool fullyoperational which is applied on a database of satellite images available with IrSOLaV (temporal andspatial resolution of the data depends on the satellite covering the region under study). It is worthwhileto point out that tuning-up and fitting of the original methodology in different locations of the Worldhave been performed and validated with local data from radiometric stations installed in the region ofinterest. This way, it may be considered that the treatment of the information from satellite imagesoffered by IrSOLaV is an exclusive service.Even though the different research groups working in this field are making use of the same coremethodologies, there are several characteristics that differ depending on the specific objectivespursued. Therefore, the main differences between the IrSOLaV/CIEMAT and others, like the onesapplied 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).
  • 9. IRSOLAV METHODOLOGY PAGE 9 OF 28  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 isexpressed in terms of the relative root mean squared error (RMSE). Different assessments andbenchmarking 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 forhourly values is estimated to be around 20-25% RMSE and in a daily basis the uncertainty of the modelsused is around 13-17%. It is important to mention here the contribution given by Zelenka in terms ofdistributing the origin of this error, concluding that 12-13% is produced by the methodology itselfconverting satellite information into radiation data and a relevant fraction of 7-10% because of theuncertainty of the ground measurements used for the comparison. In addition Zelenka estimates thatthe error of using nearby ground stations beyond 5 km reaches 15%. Because of that his conclusion isthat the use of hourly data from satellite images is more accurate than using information from nearbystations located more than 5 km far from the site.
  • 10. IRSOLAV METHODOLOGY PAGE 10 OF 28The IrSOLaV methodology is based on the work developed in CIEMAT by the group of Solar RadiationStudies. The model has been assessed for 30 Spanish sites with the following uncertainty results forglobal horizontal irradiance:  About 12% RMSE for hourly values  Less than 10% for daily values  Less than 5% for annual and monthly means2.2 Validation of hourly values of GHI dataThis validation section belongs to the scientific publication {Zarzalejo, 2009 1000137 /id}. Simultaneousdata of satellite derived cloud index and hourly global irradiance on ground-based stations are used formodel development and assessment for 28 locations in Spain. The geographic information of theradiometric stations is listed in Table 1. The time period covered is from January 1994 to December2004. In the cloud index estimations the HRI-VIS channel images of Meteosat are used. The spatialresolution 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 areavailable for fitting and assessment the new models (Zarzalejo, 2006). The whole data set is randomlyseparated 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 50810 Valencia 39.48 ºN 0.38 ºW 23 24 Vizcaya 43.30 ºN 2.93 ºW 4111 Toledo 39.88 ºN 4.05 ºW 516 25 Guipúzcoa 43.30 ºN 2.03 ºW 25912 Madrid 40.45 ºN 3.72 ºW 680 26 Asturias 43.35 ºN 5.87 ºW 34813 Tarragona 40.82 ºN 0.48 ºE 44 27 La Coruña 43.37 ºN 8.42 ºW 6714 Salamanca 40.95 ºN 5.92 ºW 803 28 Cantabria 43.48 ºN 3.80 ºW 79
  • 11. IRSOLAV METHODOLOGY PAGE 11 OF 28Relative 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 18.853 SATELLITE COVERAGEThere are two main satellite orbits: Geostationary Earth Orbiting Satellites (GEO) and Low EarthOrbiting Satellites (LEO). GEO satellites hover over a single point at an altitude of about 36,000kilometers and to maintain constant height and momentum, a geostationary satellite must be locatedover the equator. LEO satellites travel in a circular orbit moving from pole to pole, collecting data in a
  • 12. IRSOLAV METHODOLOGY PAGE 12 OF 28swath beneath them as the earth rotates on its axis. In this way, a polar orbiting satellite can “see” theentire planet twice in a 24 hour period.GEO satellites orbit in the earths equatorial plane at a mean height of 36,000 km. At this height, thesatellites orbital period matches the rotation of the Earth, so the satellite seems to stay stationary overthe same point on the equator. Since the field of view of a satellite in geostationary orbit is fixed, italways views the same geographical area, day and night. This is ideal for making regular sequentialobservations of cloud patterns over a region with visible and infrared radiometers. High temporalresolution and constant viewing angles are the defining features of geostationary imagery. Currently,IrSOLaV uses GEO satellites images from Meteosat First Generation (Meteosat-7), Meteosat SecondGeneration (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. Thenew images received are filtered before its storage in a fully automatic process. The data warehouse ofIrSOLaV 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 DataCoverage (IODC) service covering the region shown in the centered image further down. These set ofsatellites were previously located over the position 0º of latitude covering Europe, Africa, ArabianPeninsula and some parts of Brazil (see figure further down on the right). The current near real-timedata are rectified to 57.50 E and it provides imagery data 24 hours a day from the three spectralchannels 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 thelatitude 0 degrees (previous position) for the period from 1994 to 2005.
  • 13. IRSOLAV METHODOLOGY PAGE 13 OF 28MSG: The Meteosat Second Generation satellite is a significantly enhanced system to the previousversion of Meteosat (MFG). MSG consists of a series of four geostationary meteorological satellites thatoperate consecutively. The MSG system provides accurate weather monitoring data through its primaryinstrument the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which has the capacity toobserve the Earth in 12 spectral channels. The temporal resolution of the satellite is 15 minutes and thespatial 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 anymistakes in the acquisition from the sensor. The data are accompanied with the appropriate ancillaryinformation that allows the user to calculate the geographical position and radiance of any pixel. TheIrSOLaV-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 operatestwo meteorological satellites in geostationary orbit over the equator. Each satellite views almost a thirdof the Earths surface: one monitors North and South America and most of the Atlantic Ocean, the otherNorth America and the Pacific Ocean basin. GOES-12 (or GOES-East) is positioned at 75º W longitude onthe equator, while GOES-11 (or GOES-West) is positioned at 135º W longitude on the equator. Bothoperate together to produce a full-face picture of the Earth, day and night. Coverage extendsapproximately from 20º W longitude to 165º E longitude. The GOES satellites are able to observe theEarth disk with five spectral channels. The IrSOLaV-CIEMAT database contain GOES images from 2000to 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 passesfrom North to South across the equator in the morning, while Aqua passes from South to North over theequator in the afternoon. Terra and Aqua view the entire Earths surface with a frequency from 1 to 2days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specificationson NASA web). These data improve our understanding of global dynamics and processes occurring onthe 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 policymakers in making sound decisions concerning the protection of our environment.
  • 14. IRSOLAV METHODOLOGY PAGE 14 OF 28The effect of the atmospheric turbidity on solar radiation is applied in IrSOLaV-CIEMAT model by usingthe 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 MODELMeteorological data is an important parameter to simulate correctly solar energy systems to produceelectricity. IrSOLaV uses NCEP Climate Forecast System Reanalysis (CFSR) and Climate Forecast SystemVersion 2 (CFSV2) datasets.4.1 Validation of solar radiatione estimates from satellite imagesThe 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 athourly 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 onMarch 30, 2011. This is the same model that was used to create the NCEP Climate Forecast SystemReanalysis (CFSR). Selected CFS time series products are available at 0.2, 0.5, 1.0, and 2.5 degreehorizontal 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 withJanuary 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).
  • 15. IRSOLAV METHODOLOGY PAGE 15 OF 285 CORRECTION OF ESTIMATED DATA USING GROUND MEASURED DATADue to particular behavior of each one of the meteorological variables, the correction will be done withad-hoc physical or statistical methods which treat in a better way the dynamic of the variable. To correctvalues of solar radiation estimated from satellite with ground measured radiometric data the turbidityof the site will be characterized. The rest of meteorological variables will be corrected using statisticalmethods. 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 theatmosphere and represents the degree of transparency of the atmosphere. It is an adequateapproximation when quantifying the effects of absorption and dispersion on solar radiation whentrespassing the atmosphere. It can be obtained directly from measurements; however, due to the lack ofthem, it is generally obtained from empirical adjustments. We will obtain the Linke Turbidity frommeasurements registered. After this selection, we will obtain the values of TL using the inverse of a clearsky model {Ineichen, 2002 1000401 /id}.In the next figures, we show some plots of hourly values of DNI for clear sky days selected manually fora location in Spain. In the plots, measured clear sky DNI (blue), modeled clear sky DNI (green), DNIestimated from satellite MODIS TL and DirIndex model (pink) and DNI estimated from satellite MODISTL and Louche model (red). In the figure we show also the values of daily TL estimated from MODISsatellite and estimated from measurements for all hourly values and for two hours during the day atnoon hours (11:00 and 12:00 UTC). The values of TL are calculated from measurement at noon hoursbecause there are some days which have clear sky conditions in most of the hours of the day but not inall.
  • 16. IRSOLAV METHODOLOGY PAGE 16 OF 28 Figure 2. TL estimated from MODIS and measurements of DNI for a clear sky day. 09/01/2010. Figure 3. TL estimated from MODIS and measurements of DNI for a clear sky day. 29/01/2010.
  • 17. IRSOLAV METHODOLOGY PAGE 17 OF 28 Figure 4. TL estimated from MODIS and measurements of DNI for a clear sky day. 01/02/2010. Figure 5. TL estimated from MODIS and measurements of DNI for a clear sky day. 25/02/2011.
  • 18. IRSOLAV METHODOLOGY PAGE 18 OF 28 Figure 6. TL estimated from MODIS and measurements of DNI for a clear sky day. 02/04/2010. Figure 7. TL estimated from MODIS and measurements of DNI for a clear sky day. 05/05/2009.
  • 19. IRSOLAV METHODOLOGY PAGE 19 OF 28 Figure 8. TL estimated from MODIS and measurements of DNI for a clear sky day. 18/05/2009.The next figures represent the same information as in the last one but for cloudy conditions.
  • 20. IRSOLAV METHODOLOGY PAGE 20 OF 28 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.
  • 21. IRSOLAV METHODOLOGY PAGE 21 OF 28The next figures show some examples of the relationship between daily Linke Turbidity (TL) estimatedfrom MODIS satellite and estimated from measurements with clear sky days for several months in a sitein 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
  • 22. IRSOLAV METHODOLOGY PAGE 22 OF 28 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
  • 23. IRSOLAV METHODOLOGY PAGE 23 OF 28 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
  • 24. IRSOLAV METHODOLOGY PAGE 24 OF 28The deviations observed in the last figures are due to the fact that daily values of water vapor and AODat 550nm obtained from MODIS satellite are representative of an area of 1º by 1º and local effects onconstituents in the atmosphere are not taken into account. This way, the deviations between TLestimated from MODIS and measurements will be corrected using non-linear models. Aftercharacterization of Linke Turbidity, the correction coefficients will be applied to the whole series ofdaily turbidity dataset estimated MODIS which has a period from year 2001 to the present. Finally, usingcorrected input of Linke Turbidity into IrSOLaV method to estimate solar radiation from satellite imagesthe whole data will be reprocessed for the 12 years of data to obtain corrected characterized localvalues 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 wholeperiod of years (12 years) and in those months where measured data are available. In conclusion, onlywhen one year of measurements is available the correction will be applied to the whole time series of 12years 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 energypower 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 statisticalanalysis of the satellite imagery database, analysis of the monthly and annual solar irradiance satelliteestimations comparing them with ground data available in the zone nearby. The time series that can bedelivered are global horizontal (GHI) and direct normal irradiances DNI (with tracking in one and two
  • 25. IRSOLAV METHODOLOGY PAGE 25 OF 28axis if required). Besides, to characterize the long-term dynamics of solar radiation and meteorologicalvariables 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 anyother format requested).
  • 26. IRSOLAV METHODOLOGY PAGE 26 OF 287 REFERENCESDe Miguel, A., and Bilbao, J., 2005. Test reference year generation from meteorological and simulatedsolar radiation data. Solar Energy 78, 695-703.Cony, M., Polo, J., Martín, L. and Navarro, A.A., 2012. Analysis of solar irradiation anomalies in long termover India. Geophysical Research, 1761. AustriaCony, M., Martín, L., Polo, J., Marchante, R., and Navarro, A.A. 2011. Sensitivity of satellite derived solarradiation to the temporal variability of aerosol input. SolarPACES, Granada, Spain.Cony, M., Martin, L., Marchante, R., Polo, J., Zarzalejo, L.F., Navarro, A.A., 2011. Global horizontalirradiance and direct normal irradiance from HRV images of Meteosat Second Generation. GeophysicalResearch, 10373. Austria.Cony, M., Zarzalejo, L.F., Polo, J., Marchante, R., Martín, L., Navarro, A.A., 2010. Modelling solar irradiancefrom HRV images of Meteosat Second Generation. Geophysical Research Abstract, Vol. 12, EGU2010-4292. Vienna, Austria.Espinar, B., Ramirez, L., Drews, A., Beyer, H.G., Zarzalejo, L.F., Polo, J., Martin, L., 2009. Analysis ofdifferent comparison parameters applied to solar radiation data from satellite and German radiometricstations. Solar Energy, Vol 83, 1, 118-125.Lefevre, M., Wald, L., and Diabate, L., 2007. Using reduced data sets ISCCP-B2 from the Meteosatsatellites to assess surface solar irradiance. Solar Energy 81, 240-253.Martín, L., Cony, M., Polo, J., Zarzalejo, L.F., Navarro, A., and Marchante, R., 2011. Global Solar and DirectNormal Irradiance Forecasting Using Global Forecast System (GFS) and Statistical Techniques.SolarPACES, Granada, Spain.Martin, L., Zarzalejo, L.F., Polo, J., Navarro, A.A., Marchante, R., and Cony, M., 2010. Prediction of globalsolar irradiance based on time series analysis: Application to solar thermal power plants energyproduction planning, Solar Energy, Vol 84, 10, 1772-1781.Martín, L., Cony, M., Navarro, A.A., Zarzalejo, L.F., and Polo, J., 2010. Estación de recepción de imágenesdel satélite Meteosat Segunda Generación: Arquitectura Informática y Software de Proceso. InformeTécnico CIEMAT, Vol. 1200, 1135-9420, NIPO: 471-10-014-8.McArthur, L. J. B., 1998. Baseline Surface Radiation Network (BSRN). Operations manual V1.0. Serie:World Climate Research Programme. Secretariat of the World Meteorological Organization, Geneva(Switzerland).
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