Solar global horizontal and direct normal irradiation maps in Spain derived from geostationary satellites

Author’s Accepted Manuscript Solar global Horizontal and direct normal irradiation Maps in Spain derived from geostationary Satellites J. Polo www.elsevier.com/locate/jastp

PII: DOI: Reference:

S1364-6826(15)00114-5 http://dx.doi.org/10.1016/j.jastp.2015.05.015 ATP4208

To appear in: Journal of Atmospheric and Solar-Terrestrial Physics Received date: 4 February 2015 Revised date: 22 May 2015 Accepted date: 23 May 2015 Cite this article as: J. Polo, Solar global Horizontal and direct normal irradiation Maps in Spain derived from geostationary Satellites, Journal of Atmospheric and Solar-Terrestrial Physics, http://dx.doi.org/10.1016/j.jastp.2015.05.015 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Solar Global Horizontal and Direct Normal Irradiation Maps in Spain derived from Geostationary Satellites J. Polo Renewable Energy Division (Energy Department) CIEMAT Avda. Complutense 40, 28040 Madrid (Spain)

Corresponding Author: Jesús Polo, email: [email protected] , Phone: +34 913466043, Pax : +34 913466037

Keywords : solar resource assessment, solar radiation derived from satellites, solar irradiation mapping

Abstract Solar radiation derived from satellite imagery is a powerful and highly accurate technique for solar resource assessment due to its maturity and to the long term database of observation images available. This work presents the methodology developed at CIEMAT for mapping solar radiation from geostationary satellite information and it also shows solar irradiation maps of global horizontal and direct normal components elaborated for Spain. The maps presented here have been developed from daily solar irradiation estimated for eleven years of satellite images (2001-2011). An attempt to evaluate the uncertainty of the presented maps is made using ground measurements from 27 meteorological stations available in Spain for global horizontal irradiation obtained from the World Radiation Data Centre. In the case of direct normal irradiation the ground measurement database was scarce, having available only six ground stations with measurements for a period of 4 years. Yearly values of global horizontal irradiation are around 1800 kWh m-2 in most of the country and around 1950-2000 kWh m-2 for annual direct normal irradiation. Root mean square errors in monthly means were of 11% and of 29% for global horizontal and direct normal irradiation, respectively.

1

Introduction

2

Accurate knowledge of solar irradiance components at the Earth’s surface is an important issue in many scientific and technology branches related to energy, environment, climate, architecture and agriculture. In the specific case of solar energy systems to produce electricity the accurate solar resource analysis is a first step in every project since it is required for design, power output estimations, and decision making. Delivering site-specific solar resource information for a particular solar energy application in every step of a solar energy deployment project is generally denoted as Solar Resource Assessment. Solar irradiation mapping is an important issue towards exploring the spatial variability of solar radiation, analysing the solar energy potential in a country and helping the decision making for deployment of solar energy systems (Kanters et al., 2014).

Global hemispherical irradiance on horizontal surface is widely recorded through many ground stations worldwide, the World Radiation Data Centre has available data from a large number of stations organized by countries. However, in most of the cases such measuring networks do not offer enough density to provide information on the spatial variability of the solar radiation. In consequence, solar radiation derived from satellite raises as a useful and accurate tool for solar resource analysis and for supplying solar irradiance time series as well (Hoyer-Klick et al., 2009; Vignola et al., 2007; Zelenka et al., 1999). Since its origins in the early 80’s, the methods for computing solar radiation components from geostationary satellites have evolved a lot incorporating experience, improvements and new developments; many studies are available on this topic from the first ones developed in the 80’s (Cano et al., 1986; Gautier et al., 1980; Moser and Raschke, 1983) until nowadays (Mueller et al., 2004; Perez et al., 2002; Rigollier et al., 2004; Schillings et al., 2004). An overview of the fundamentals of some of the most currently used methods can be found in (Polo et al., 2008). The geostationary satellites network can deliver images at least every hour for complete observation of the atmosphere-earth system since the early 90’s.

Satellites observing the earth-atmosphere system receive part of the incident radiation coming from the Sun. The interaction of solar radiation with the atmospheric

3 constituents occurs through two mechanisms, absorption and scattering, yielding to the attenuation of part of the incident solar radiation. As a consequence of such interaction the components of solar radiation can be divided in two groups: the incident components formed by diffuse and direct normal irradiance, and the emergent components formed, within the spectral range of the incident solar radiation, by the backscattered radiation from atmosphere and the reflected by ground or clouds.

Basically, a satellite image, in the visible channel, is a measure of the upwelling shortwave radiance travelling from the earth-atmosphere system at a specific time and over a spatial window (the emergent components of solar radiation). By using the proper calibration constant the information contained in the image can be transformed into radiance values. The radiance values at the image are strongly correlated with the state of the atmosphere (from clear sky situations to complete overcast), and in a lesser extent with the reflectance of the ground surface. In this sense, satellite images give spatial information on the cloud distribution and thickness. Thus they are able to reproduce most of the variability associated to cloud attenuation of solar radiation by establishing a relationship between the cloud index (as estimator of cloudiness) and clear sky index (as estimator of surface solar irradiance). Meteorological satellites can be separated into two main groups according to their orbit: polar orbiting and geostationary satellites (Grüter et al., 1986; Hay, 1993a; Hay, 1993b). Polar orbiting satellites observe the earth from about 800 km, and they have high spatial resolution but limited temporal coverage (they cover a specific viewing region one or two times every day). Geostationary satellites, having an orbit of 36000 km, can achieve temporal resolutions of up to 15 minutes for full disk High-resolution visible images and even every 5 minutes for MSG images over parts of Europe, Africa and adjacent seas. On the other hand the spatial resolution is up to 1 km. Most of the methods for deriving solar radiation from satellite information use geostationary satellite images, since they allow for continuous determination of cloud cover information. Polar satellites are frequently used to derive daily information of land characteristics and atmospheric components (ozone, aerosol optical depth, water vapour, etc) (Bilal et al., 2013; Chesters and Neuendorffer, 1991; Kern et al., 2008; McPeters and Stolarski, 2015).

4 This document describes the methodology developed at CIEMAT (Centre for Research on Energy, Environment and Technology) for deriving solar radiation incident components from geostationary satellites, and for mapping the global horizontal (GHI) and direct normal (DNI) irradiation components. In particular, the application of this methodology to Meteosat first and second generation satellites images is exposed and solar radiation maps of Spain computed from satellite information for the period of 2001-2011 are presented.

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2.1

Methodology

Cloud index and GHI computation

Hourly time series of global horizontal and direct normal irradiances were computed with the CIEMAT satellite model (Polo, 2009; Zarzalejo et al., 2009; Zarzalejo, 2005) which use the Heliosat-2 (Rigollier et al., 2004) and the Heliosat-3 (Dagestad and Olseth, 2007) as reference, and including several new proposals in the ground albedo determination and in the aerosol information that need the clear sky models as input.

For each satellite image, i.e. at every hour, global horizontal irradiance is estimated by using a semi-empirical relationship for the clear sky index (defined as the global irradiance normalized to the clear sky global irradiance) as a function of the cloud index, cloud index median and air mass (Zarzalejo et al., 2009),

kc 

Gh  1.010  0.789 n  0.153 n50  0.025 m Ghclear

(1)

where Gh and Ghclear are the global horizontal irradiance and the global horizontal irradiance for clear sky conditions, respectively, n is the cloud index, n50 is the median of the cloud index whole time series (50th percentile) and m is the relative air mass.

The cloud index is estimated by normalizing the instantaneous planetary albedo with the dynamic range,

5 n

  g c   g

(2)

being ρ the instantaneous planetary albedo (that is the reflectance recorded at the sensor on board), and ρg and ρc the ground and cloud albedo, respectively.

Instantaneous planetary albedo and ground albedo are computed taking as reference the formulation of Heliosat-3, where the backscattered radiation from the atmosphere, ratm, is computed so that it can be removed from the measured albedo (Dagestad and Olseth, 2007),



L  I  cos 



 ratm I 0 cos 

(3)

where L is the radiance measured on the satellite sensor, ε is the eccentricity correction factor of earth, Iμ is solar irradiance of Meteosat visible channel at the top of atmosphere, I0 is the solar constant, and θ is the solar zenith angle. The ground albedo is computed in Heliosat-3 approach as the 4-percentile of reflectivity after normalizing it by a shape function that accounts for the ground albedo dependence on the co-scattering angle (Dagestad and Olseth, 2007). However, in the CIEMAT satellite approach the ground albedo is computed in a slightly different way, since the function that governs the dependence with co-scattering angle is dynamically and locally estimated for each pixel allowing the determination of the different angular dependence for the terrain as a function of its reflectivity (Polo et al., 2012). A local shape function is defined, at every pixel, by fitting a third order polynomial normalized with the independent coefficient to ensure that f’(0)=1.

p  p  p  f '( )   1   3   2   2   3    1  p4   p4   p4 

(4)

6

Where Ψ is the co-scattering angle defined as

cos   cos  cos sat  sin  sin sat cos(  sat )

(5)

Thus the ground albedo is finally estimated by

 g  4th (0) f ’

(6)

Likewise, the cloud albedo can be estimated from equation (3) by

c  eff 

 ratm I 0 cos 

(7)

where ρeff is the planetary albedo for overcast conditions that is estimated by the following function of the solar elevation angle (α) (Taylor and Stowe, 1984),

eff  0.78  0.13 1 exp  4 sin 5  

2.2

(8)

Clear sky models and DNI computation

Clear sky solar irradiances (global hemispherical and direct normal) can be computed in this methodology using three possible clear sky transmittance models: European Solar Radiation Atlas ESRA (Rigollier et al., 2000), Solis model (Ineichen, 2008a; Mueller et al., 2004) and REST2 model (Gueymard, 2008). The clear sky models used here have different requirements for the input concerning the attenuation of solar radiation, and this was the main reason for implementing three different models. For modeling the atmospheric extinction ESRA model merges all the attenuants, except Rayleigh scattering, in one unique parameter requiring only the Linke turbidity factor as input. The Solis simplified model estimate the atmospheric transmittance under clear sky conditions using as input the broadband aerosol optical depth and the water vapor content. Finally, REST2 uses the Angstrom formula for the attenuation due to atmospheric aerosols in two spectral bands and it deals separately with the attenuation

7 of water vapor, ozone and NO2. The choice of the clear sky model to be used is made by the user and depends on the availability of the atmospheric information.

Direct normal irradiance is computed by a clear sky transmittance model in cloudless conditions and by the Louche conversion model (Louche et al., 1991) in cloudy and overcast situations. For selection of the clear sky days an automatic algorithm has been used that is based on the correlation between global horizontal irradiance computed from satellite images by expression (1) and global horizontal irradiance computed by the selected clear sky model (Polo et al., 2009).

2.3

Sequence of the calculations

Hourly values of GHI and DNI have been estimated from Meteosat images using this methodology for the period from 01/01/2001 to 31/12/2011 and processing 12 images per day. Meteosat First generation images were used for 2001-2005, and Meteosat Second Generation images were processed for 2006-2011. Differences between the two platforms are mainly higher spatial resolution in Meteosat second generation and higher radiometric sensitivity of 10-bit images. The solar radiation values estimated from 12 images per day correspond to the UTC time of the image plus the scanning time of the sensor; after converting them to true solar time (using the equation of time and the longitude correction) a cubic interpolation is then performed to estimating GHI and DNI hourly values at 0-23 hours in true solar time. Daily values of aerosol optical depth (AOD) at 550 nm, Angstrom exponent and water vapour column for the same period were taken from a gridded database that combines MODIS (Moderate resolution Imaging Spectroradiometer), Terra satellite and Aqua (Acker and Leptoukh, 2007), C005 data collection. The high relevance of the use of daily values of atmospheric component for estimating the atmospheric attenuation under clear sky conditions has been remarked by several authors (Cebecauer and Suri, 2010; Cebecauer and Suri, 2012; Cebecauer et al., 2011; Polo et al., 2011). The AOD at 550 nm in the C005 collection data of MODIS have been assessed by different authors by comparing to the AERONET data, and good results have been reported (Papadimas et al., 2009). The spatial resolution of the MODIS gridded data with global coverage is 1°x1°. The daily values of AOD at 550 nm and water vapour are used to determine daily values of Linke turbidity factor by applying the Ineichen expression (Ineichen, 2008b). At every hour

8 ESRA model is used to compute global horizontal irradiance for clear sky (Ghclear) and direct normal irradiance for clear sky (Bnclear) using the daily value of Linke turbidity factor.

Therefore, the sequence of computation of hourly GHI is as follows:

-

For every image n is computed using expression (2), m is also calculated, and Ghclear is estimated from ESRA model using the corresponding daily value of the Linke turbidity factor.

-

For the whole set of cloud index values n50 is computed as the 50th percentile.

-

For every hour expression (1) is used to compute the clear sky index and then the global horizontal irradiance, where the air mass is estimated by the formula of (Kasten and Young, 1989).

-

Each day the hourly values of GHI for 0 to 23 hours are calculated from the 12 original hourly values by cubic interpolation.

-

Hourly DNI values are estimated from hourly GHI values using the Louche model.

-

The specific algorithm for selection of clear sky conditions is applied to each day and if the day is selected as clear sly all the GHI and DNI values are replaced for those estimated from ESRA clear sky model.

-

The final hourly values of GHI and DNI are used to compute daily sums and monthly means values.

3

Solar radiation maps in Spain

The methodology for solar radiation estimation from Meteosat imaging have been applied to generate daily sums of GHI and DNI, for eleven years, in two raster regions with a spatial resolution of 0.1º. The first region comprises the Iberian Peninsula and the Balearic Islands (35º to 44º North latitude and -10º to 5º East longitude), and the second one covers the Canary Islands (27º to 30º North latitude and -19º to -13º East longitude). Solar Irradiation raster information has been processed in an open source geographic information system, Quantum GIS (http://www.qgis.org/), for generating the irradiation

9 maps of Spain. The additional cartographic information about maps with countries limits and other attributes were obtained from Natural Earth public domain map dataset (http://www.naturalearthdata.com/). Figures 1 and 2 show the maps of average values of annual sums for daily GHI and DNI, denoted as yearly average irradiation. Figures 3 and 4 show the 12 maps of monthly mean daily irradiation for global horizontal and direct normal, respectively. Annual GHI is placed in the range of 1750 – 1850 kWh m-2 year-1 in the south and middle of the country, while in the north is in the range of 1600 kWh m-2 year-1 and even lower in the Cantabrian coastal region. Annual DNI reaches the range of 1900 – 2000 kWh m-2 year-1 in the middle south and it exhibits the highest values in Extremadura and Andalusia regions. Monthly means maps show the profile of solar irradiation along the year, where the maximum values are found in the summer (especially June, July and August months). In July the monthly average of DNI reaches up to 10 kWh m-2 day-1 in parts of Extremadura and Andalusia regions.

4

Assessment of uncertainty

Ground data of the Spanish radiometric network operated by the Spanish Meteorology Agency (AEMet) have been used to evaluate the uncertainty of solar radiation maps. Monthly means of global horizontal and direct normal irradiation measured are compared to those values extracted from the maps for several stations. In the case of global horizontal irradiation data from 27 stations during the period 2001-2007 were used, and in the case of direct normal only 6 stations for the period 2001-2004 were available. Table 1 lists the stations involved in the assessment of monthly means of GHI and it shows also the results of the uncertainty parameters used; likewise, table 2 shows the stations and the uncertainty parameters for the monthly means of DNI. The uncertainty parameters used here are the relative mean bias error and the relative root mean squared error defined as,

∑

(

) (9)

10

√ ∑(

)

Yexp and Ymod are the measured and estimated value of the random variable, respectively, and N is the total number of points. In general, the average result of the uncertainty is around 11 % and 29 % of rRMSE for GHI and DNI, respectively. It should be noted that a low bias in the GHI estimations was found. The assessment of DNI is not very accurate because of the scarce ground data information freely available.

According to the results a general trend to overestimate the monthly GHI is observed mostly in the stations of the north and north-east, while some underestimation is observed for stations of the east. In conclusion, for GHI monthly average estimations larger errors occur in the north of the country (associated to the higher frequency of cloudy and overcast days). In the case of DNI, La Coruña station (placed in the northwest of Spain) was the one with highest errors in the computation of the monthly means. These results are in certain agreement in general terms with the uncertainty parameters of other methods for computing solar radiation from satellite imagery (Ineichen, 2014).

There are several possible sources of uncertainty in satellite-based methods for deriving solar radiation components (Cebecauer and Suri, 2010; Cebecauer et al., 2011). The method used here has employed one image every hour so the hourly values computed from that image assume that the cloud coverage of the image is static during the whole hour (which is not true obviously). In addition, satellite-derived solar radiation doesn’t take into account the three-dimensional structure of the clouds (Girodo et al., 2004). In consequence, the solar radiation estimations for cloudy situations present normally higher errors than those for clear sky prevalent conditions. However, under clear sky conditions the inaccuracy in the determination of the aerosol loads (an even other atmospheric constituents) can have an important contribution to the model retrievals (Gueymard, 2011; Gueymard, 2012; Gueymard, 2014). The use of high quality ground data in hourly basis of the measured solar radiation components can allow for improvement of the satellite-based models, by reducing the bias or even systematic

11 errors, that could result in more accurate mapping of the solar resource in a region (Cebecauer and Suri, 2012; Mieslinger et al., 2014; Polo et al., 2015).

5

Conclusions

Solar irradiation maps of Spain for the global horizontal and direct normal components have been elaborated from raster information derived from Meteosat geostationary satellite. The methodology for mapping is based on a combination of heliosat-based approaches and some new improvements on different key aspects of the chain of algorithms for solar radiation derived from satellite imagery. The raster information obtained from the method has been processed with Quantum GIS software package for delivering the final maps of irradiation. Assessment of the uncertainty has been made by using 27 ground stations of global irradiation and only 6 with available measurements of DNI. Low bias in average was found in GHI estimation (-0.4 %) and an average error of 11% of RMSE for the monthly means; much higher error, as expected, was found in the case of DNI (29% of RMSE) although it should be remarked the scarcity of ground measurements for mapping assessment. Finally, the irradiation maps of monthly means and yearly average of GHI and DNI allows the analysis of solar energy potential in Spain and the spatial variability as well.

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15 Taylor, V. R. and Stowe, L. L., 1984. Reflectance characteristics of uniform earth and cloud surfaces derived from NIMBUS-7 ERB. Journal of Geophysical Research. 89, 4987-4996. Vignola, F., Harlan, P., Perez, R. and Kmiecik, M., 2007. Analysis of satellite derived beam and global solar radiation data. Solar Energy. 81, 768-772. Zarzalejo, L. F., Polo, J., Martín, L., Ramírez, L. and Espinar, B., 2009. A new statistical approach for deriving global solar radiation from satellite images. Solar Energy. 83, 480-484. Zarzalejo, L. F., 2005. Estimación de la irradiancia global horaria a partir de imágenes de satélite. Desarrollo de modelos empíricos. PhD presented at Universidad Complutense de Madrid. Zelenka, A., Perez, R., Seals, R. and Renne, D., 1999. Effective accuracy of satellitederived hourly irradiances. Theoretical and Applied Climatology. 62, 199-207. Table 1. List of ground stations for evaluating global irradiation maps (Period 20012007) and results of the uncertainty parameters

Station Santander La Coruña Bilbao Villanova León Logroño Zaragoza Valladolid Lleida Soria Reus Salamanca Madrid Toledo Palma Cáceres Albacete Ciudad Real Badajoz Ibiza Alicante Murcia Huelva Granada Almería Jerez Malaga

Longitude (ºE) -3.81 -8.41 -2.93 -8.75 -5.65 -2.33 -1.06 -4.76 0.60 -2.50 1.16 -5.91 -3.71 -4.05 2.61 -6.33 -1.86 -3.91 -7.01 1.36 -0.55 -1.16 -6.91 -3.63 -2.38 -6.06 -4.48

Latitude (ºN) 43.46 43.36 43.30 42.60 42.58 42.45 41.66 41.65 41.61 41.60 41.15 40.95 40.45 39.88 39.55 39.46 39.00 38.98 38.88 38.86 38.28 38.00 37.28 37.13 36.85 36.73 36.71

z (m) 64 58 40 15 914 353 298 734 201 1090 73 803 664 515 6 405 674 628 185 10 31 61 19 687 20 35 61

rMBE (%) -5.4 -3.0 -5.2 1.4 -5.9 -5.3 3.6 -1.5 4.7 -4.5 1.1 -4.4 -0.3 -0.9 1.2 -1.6 -0.5 -3.4 0.5 -0.2 4.1 4.2 0.2 1.1 2.6 0.5 5.4

rRMSE (%) 12.4 9.5 13.4 8.7 11.2 12.6 11.4 9.1 13.9 14.4 13.6 13.3 10.3 11.6 10.5 10.0 12.0 13.4 9.0 11.1 13.0 11.1 3.3 9.6 10.1 8.8 10.9

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Table 2. List of ground stations for evaluating direct normal irradiation maps (Period 2001-2004) and results of the uncertainty parameters

Station La Coruña Santander Valladolid Madrid Valencia Cáceres

Longitude (ºE) -8.41 -3.81 -4.76 -3.71 -0.38 -6.33

Latitude (ºN) 43.36 43.46 41.65 40.45 39.48 39.46

z (m) 58 64 734 664 23 405

rMBE (%) -28 5 -0.3 8 -4.9 9

Figure 1. GHI yearly average (2001-2011) in Spain (kWh m-2 year-1)

rRMSE (%) 52 25 26 21 24 29

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Figure 2. DNI yearly average (2001-2011) in Spain (kWh m-2 year-1)

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Figure 3. GHI monthly means in Spain (kWh m-2 day-1)

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Figure 4. DNI monthly means in Spain (kWh m-2 day-1)

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Solar irradiation mapping in Spain is done from satellite information 27 stations were used to evaluate the uncertainty (11% in GHI) Satellite models are today a very useful tool for solar resource mapping