Category Archives: EuroSun2008-13

Simplified global irradiance model

We have shown in earlier work how a simplified model can be used to achieve a good fit to the global irrad — iance data [2]. The following expression shows that the global irradiance on a horizontal surface IG as the sum of two terms: the first term expresses the direct solar beam irradiance, and the second term expressed the diffuse irradiance due to Rayleigh and Mie scattering from molecules and aerosols in the sky and from clouds.

Подпись:Ic = U0 F, a1 slnV — I,

I0 = 1367 W/m2 is the solar constant. FJ takes account of the yearly variation of the solar irradiance due to the elliptical orbit of the earth around the sun. A practical equation for FJ is available in reference [6]. The factor aL accounts for the attenuation of direct beam irradiance due to absorption and scattering, where L is the Rayleigh air mass. Finally, the factor sin V takes the geometry of the situation into account for a solar elevation angle V. The solar elevation angle can be computed with knowledge of the latitude, the solar declination angle and the local time. The equation required is widely available in the literature of solar energy design [6].

The air mass L through which the direct rays of the sun must pass depends of course on the angle V between the horizontal and a line from the observer to the center of the sun. For angles V > 250 a simple drawing will show that the air mass L = 1/sin V, for in this case it is reasonable to assume that the earth is a flat surface with a thin layer of atmosphere. However, for angles less than 250 with the sun low on the horizon it is essential to take the curvature of the earth and temperature gradients into account. Fritz Kasten and Andrew

Estimation of solar resource map at Galicia by using self-learning techniques

Alberto Pettazzi, Jose Antonio Souto Gonzalez*

Department of Chemical Engineering, University of Santiago de Compostela, Lope Gomez de Marzoa St.,

Campus Sur. 15782 Santiago de Compostela, Spain
Corresponding author, ja. souto@usc. es


A basic target of the Renewable Energies and Energetic Sustainability MSc Degree organized by the University of Santiago de Compostela is to provide to the students an adequate knowledge about the estimation of solar resource. Students of this MSc in 2007-08 academic year were involved in analysing and processing solar radiation measurements provided by the Galician weather stations network (www. meteogalicia. es). By the combination of their results, a solar resource map may be obtained and, in addition, it will be compared to other solar resource estimations at Galicia.

Apart from the good results of this work in terms of self-learning teaching, comparison of solar irradiation distribution to other results obtained processing satellite data shows the necessity to consider local effects in the estimation of the solar resource at this region. Keywords: self-learning, solar resource, ground-satellite comparison

1. Introduction

The development of technologies for renewable energies is a priority target in the policy from different fields. To reach this goal, the education on these issues is of primary importance.

A basic target of the Renewable Energies and Energetic Sustainability MSc Degree organized by the University of Santiago de Compostela is to provide the students an adequate knowledge about the estimation of solar resource.

Several works dealing with solar climatology may be found; solar radiation atlases are obtained by different techniques, as satellite measurements ([1], [2], [3]), ground measurements ([4], [5]) or combining ground data with radiative transfer models [6]. In Galicia (NW Spain), Vazquez et al. [7] elaborated a solar radiation atlas using Meteosat-6 satellite measurements [8] over the years 2002-2004. In this work, atlas data were compared against two pyranometers located in Vigo and A Coruna (see Fig. 1).

Following the methodology used by Pettazzi et al. [9], 34 students of the Solar Radiation subject of the MSc above mentioned during 2007-08 academic year were involved in analysing and processing solar radiation measurements provided by the Galician weather stations network [10], from September 2006 to August 2007. The task committed to the students included annual and seasonal analysis of the following parameters: global irradiation, sunshine hours and clearness index, KT. Additional analysis of other climatologically relevant parameters — temperature, relative humidity and precipitations — was also undertaken.

By the combination of their results, solar resource maps have been obtained and, in addition, were compared to results achieved by Vazquez et al. [7].

Data basis and verification

The performance of the NWP model ARPS and the proposed MOS procedure is verified for the site Florianopolis, localized in the south of Brazil with 48° 3ri5’’W longitude and 27° 36’ 76’’S latitude. The measured global horizontal radiation within the period from January 2000 to June 2006 was used to calculate the daily mean values. For daily mean values, the utilized pyranometer CM11 has a measurement uncertainty of 1 % for 95 % confidence as stated in [31]. For the quality control [32], [33] the measured radiation vales have to appear in the measurement range of (0 to 1367) W/m2. If this criterion was not fulfilled for a time interval larger than 10 min, the daily mean value was rejected as recommended in [33]. From the training-validation of 6.5 years, 119 days were excluded by the quality criterion, leading to the remaining data, which appears in 53 consistent time series. To facilitate the implementation, the data blocks of the training data set were chained, rather than is accomplished a specific DWT of each block, obtaining three equal length vectors with synchronized day numbers. The vector of residuals {sA} is obtained by subtraction of the measured {H} from the forecasted daily solar radiation means {HA}. The obtained predictors sAsi_1 to sAsi-k and predictands sAsi (eqn. 3), selected from the partially reconstructed sub-signals (eqn. 2, {sAS}), have to consider the limitations of each of the data blocks to avoid uncharacteristic modification of the ANN input pattern. As to see in eqn. 3, the data of the first k days of each block are used exclusively as predictors, thus a time series with the length nj provide (n-k) training samples for the ANN. Each sample has an input vector with the pattern length k, the predictors, and one output variable, the observed predictand, of the forecast at the considered time scale. The total number of training-validation samples ntv is obtained with expression (5).

ntv = YU (nj — k ) (5)

Where j = 1…nb defines the number of data blocks obtained by the data qualification, with block individual number of training samples nj. The resulting data set is subdivided in two subsets, the training and the validation set. As recommended in Kaastra [34], the validation set, which is independent from the training set has to represent (10 … 30) % of the data. This set may be selected randomly from the data or it follows immediately the training set [34]. From the data the last year, representing a validation set of 18 % was separated. Due to hardware improvements of the measurement system [35], the validation set was not exposed to system outages, which leads to its consistence.


4. Results

The average of the selected daily mean values of the measured solar radiation is 182.36 W/m2. For ARPS model simulations, based on the reanalysis dada, was obtained a RMSE of 70 W/m2 that corresponds to 38.4% of the measured average value. The maximal error of the daily mean solar radiation simulation is with 264.3 W/m2 higher than the measured average value (compare figure 3 — third chart and figure 4). If the correction is build up with data of twelve subsequent previous days (k = 12), a set of 1356 predictor vectors (sAs, (i-1) … sAs, (i-k) ) were selected. With the proposed MOS method the RMSE of the ARPS model reduces to 18.92 W/m2 for the training data set, which corresponds to 10.37 % of the measured average value of 182.36 W/m2. For the independent validation data set was obtained 9.06 % (see figure 3, fourth chart and figure 5). Worst performances were observed for the sub-signal di which contains the details of the higher frequency band (RMSE = 9.08 W/m2) and for the approximation sub-signal a1 (RMSE = 6.82 W/m2). The generalization performance of the ANN was verified with the validation data set. By arbitrary configured number of neurons in each layer with (k = 12), the best performance of the d1 sub-signal correction was observed, for 22 neurons at the first, and 12 neurons at the second hidden layer. This configuration of the neurons was used also for the other three ANN, whereby the one used for the approximation signal was configured as RNN, due to slight improvement in its performance. To access the probably higher boundary uncertainties under operation of the prediction model, it is necessary to accomplish ntv times (equation 5) the DWT for the data set having ntv predictor/predictand samples (see discussion in section 3.2). Avoiding numerical effort, the present article release only the results based on a single DWT of the data set as accomplished in

[9] .

Figure 3 — Daily mean values of the solar radiation — charts from the top to the bottom: (1) measured solar radiation H; (2) forecasted solar radiation with the ARPS model HA; (3) (H — HA); (4) (H — HA, corr), where HA, con — is the corrected ARPS forecast. The validation set appears from 2000 to 2500 days.

Models evaluation

The ordinary and residual kriging models were obtained based on the training dataset. After that, the validation dataset were used to evaluate these models. Particularly, model estimates were evaluates in terms of the ME, MAE, RMSE and the correlation coefficient.

Reference values for ground reflectivity

In order to calculate the ground reflectivity pg, in an iterative procedure values larger than the mean value of the distribution plus ag are filtered out, until convergence is achieved. The mean value of the new distribution is assigned to pg.

In the original Heliosat method a constant value ag was used, ag = 27 is a suitable value for MSG. But an approach that accounts for the dependency of ag on the sun-satellite geometry leads to even better results. The bias of the clear sky irradiance is used as a measure to quantify the influence of ag. For clear sky situations, the quality of the ground reflectivity can be evaluated directly and there is little super imposition with other effects. If ag is chosen too small, the corresponding values of the ground reflectivity will be too small as well. This results in an underestimation of the irradiance. On the other hand, if CTg is too high, an overestimation of the irradiance is the consequence. Best results were found for 15<=CTg<= 50 for different classes of solar and satellite zenith angles and the azimuth angle between sun and satellite.

Parameters influencing SSI

Clouds regularly cover about 50 % of the earth and represent the most important modulators of radiation in the earth-atmosphere system (Liou 1976). A cloud in libRadtran is characterized by its optical thickness (tc), type (cloud water or ice cloud), height of cloud top (ztop) and bottom (zbot) and the effective radius (ref) particles.

Many radiative transfer codes contain the same six atmospheric profiles corresponding to geographical and seasonal averages (Mayer, Kylling 2005; Vermote et al. 1997): Midlatitude Summer (afglms), Midlatitude Winter (afglmw), Subarctic Summer (afglss), Subarctic Winter (afglsw), Tropical (afglt) and U. S. Standard (afglus). “afgl” means Air Force Geophysics Laboratory.

The gas whose variation have a major influence on the SSI are water vapour (H2O), ozone (O3), carbon dioxide (CO2), oxygen (O2), methane (CH4), and nitrous oxide (N2O) (Vermote et al. 1997).

The attenuation of radiation by aerosols varies with its nature, density and size distribution. Following Shettle (1989), required parameters in libRadtran are: aerosol type from 0 km to 2 km altitude (haze), aerosol type above 2 km altitude (vulcan), season, and visibility (vis). The visibility is closely linked to the aerosol optical thickness (iaer) (Vermote et al. 1997). Taer represents the total extinction induced by aerosols of the medium for a given wavelength. It is sensitive to micro-physical properties of aerosols. Because these properties are difficult to assess accurately, the spectral variation of the aerosol optical thickness is usually calculated using a simplified method:

Taer A = в (Я/ Ям/" (1)

where AM = 1000 nm, в is the aerosol optical thickness at the wavelength 1000 nm and a is the Angstrom coefficient (Perrin de Brichambaut and Vauge, 1982).

When radiation reaches the earth’s surface, it can be absorbed or reflected. The intensity of the reflected radiation varies with the value of the incident radiation and the reflectance of the receiving surface. The ground reflectance is the function of the illuminating and emitting angles; the albedo is its hemispherical average. Both change with soil type and wavelength. A portion of this reflected radiation is then backscattered by the atmosphere and increases the value of the diffuse component of the SSI.

Unifying Access

The second major objective of the MESoR project is to unify and ease access to solar resource information. This builds upon experiences made within the SoDa portal, adopting mapping features from PVGIS web system. SoDa was build with proprietary software and communication protocols. As the World Wide Web evolved over recent years, the new MESoR portal builds upon open source software with a larger development community and standard web services. This will make the new portal more sustainable in terms of software development and the connection to the portal more easy and open as only widely accepted standards have to be followed.

The portal will serve as a broker to solar resource information and services. It does not contain and maintain data for itself. It just links data bases and services with a single point of entry and a common user interface. Databases and services have to be hosted by the providers. They keep control over their data and applications.

Metadata are essential to exchange knowledge between applications. They describe objects to be exchanged (e. g. a time series of irradiance, a geographical location, a date…). After a series of consultations with several bodies involved in standards, such as ISO, GEOSS (Global Earth Observation System of Systems), INSPIRE (Infrastructure for Spatial Information in Europe) and national meteorological offices, a thesaurus has been defined which is specific to solar resource. A thesaurus is a set of terms that describe the solar resource.

A prototype of the broker will be set up during the project. A new user interface has been designed. It utilises the API (application programming interface) of Google Maps. Users can therefore use the full capabilities (geographical search, maps and images) of Google to identify their sites and select the right locations or regions. As this interface is easy to use and applied already in many other applications it the user feels familiar with it. The front page of a service gives the site selection window and some descriptive information of the service, as a general description, property rights and credits, inputs and outputs descriptions. The results can be written to the browser window or saved in a specific format (e. g. spreadsheet-compatible). The available data bases can be selected by the menu on top of the page. Fig. 2 shows two sample screenshots of the current prototype (see http://project. mesor. net).


The project MESoR together with the IEA Task 36 “Solar Resource Knowledge Management” aim at developing better guidance about the energy application of solar resource information. The benchmarking exercise will ease the comparison of different data sources and the standardised rules will make accuracy evaluations more transparent and comparable. The guide will help users in the choice of data sources and how to use the data for their specific needs.

The new broker portal aims to ease the access to solar resource data and online applications by serving as single point of entry to a variety of data sources all with a similar look and feel and common data formats. More information about the project can be found at http://www. mesor. net.


[1] L. Wald, (2006). Available databases, products and services. In Dunlop E. D., Wald L., Suri M. (Eds.), Solar Energy Resource Management for Electricity Generation from Local to Global Scale. Nova Science Publishers, New York, pp. 29-41.

[2] M. Suri (2007). Solar resource data and tools for an assessment of photovoltaic systems. In Jager-Waldau A. (editor), Status Report 2006, Office for Official Publications of the European Communities, Luxembourg, pp. 96-102.

[3] B. McArthur, (2004). Baseline Surface Radiation Network. Operations Manual Version 2.1.

[4] Iqbal, M., 1983. An Introduction to Solar Radiation. Academic Press, New York

[5] C. N. Long, E. G. Dutton (without year). BSRN Global Network recommended QC tests, V2.0 (available at BSRN website http://bsrn. eth. ch).

[6] B. Espinar, L. Ramirez, A. Drews, H. G. Beyer, L. F. Zarzalejo, J. Polo, L. Martin. Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations, Solar Energy (2008) In Press.

WRF Analysis Results

The Weather Research and Forecasting (WRF) system was developed by the National Oceanic and Atmospheric Adminstration (NOAA) National Centers for Environmental Prediction (NCEP). The current release is Version 2.2. The WRF modelling system software is in the public domain and is freely available for community use. The WRF is designed to be a flexible, state-of-the-art atmospheric simulation system that is portable and efficient on available parallel computing platforms. The WRF is suitable for use in a broad range of applications across scales ranging from meters to thousands of kilometres, including:

• Real-time NWP

• Forecast research

• Parameterization research

• Coupled-model applications

• Teaching

The WRF model is run over Europe with 3 nesting levels and using NCEP boundary conditions data. The variable validated is global solar radiation which is a direct output from WRF model. The period of data studied is the year 2005. The spatial resolution of the domain over Spain is 27km.

WRF model has been executed in analysis mode for the domain of the whole Europe with special resolution of 27km approximately and hourly temporal resolution. Three nested domain where used and the model was fed with data from NCEP global model. The period of simulation goes from 1/1/2005 to 28/2/2005.

The validation is done comparing with ground measurements from 40 stations from AEMet and variable direct output downward global solar radiation of WRF model. Normalized results of MBD and RMSD for hourly and daily resolution are presented in Fig. 6-4. The graphic shows all the stations ordered from lower latitude to higher latitude. Stations with lower latitude present a predominance of clear sky situations which results in small errors.

MBE Hourly Solar Radiation Forecasting



25 L______________ [_________________ [_________________ Г________________ Г________________ [_______________ [_________________ [_________________ [

36 37 38 39 40 41 42 43 44


Подпись: RMSE Hourly Solar Radiation Forecasting 0 36 37 38 39 40 41 42 43 44 Latitude

Fig. 6. Normalized MBD for hourly global solar radiation

Подпись: 20

MBE Daily Solar Radiation Forecasting


36 37 38 39 40 41 42 43 44


Fig. 8. Normaliazed MBd for daily global solar radiation

RMSE Daily Solar Radiation Forecasting




36 37 38 39 40 41 42 43 44




image111 image112

Overall the model is able to reproduce clear sky synoptic conditions. Errors are presents mainly in situations where fronts and huge cloud movements need to be reproduced. Fig. 10-13 presents a synoptic situation from the date 22/2/2005. Fig. 10. is the measurements from the satellite which represents the real situation. Fig. 12 is an instantaneous map of the global irradiance modelled. We can see as the model is missing a lot of small clouds. Fig. 11 and 12 represents measured and WRF modelled hourly global irradiance for two stations, Granada in the South of Spain and Oviedo in the north. In this date the model is getting a contradictory prediction with measurements. In Granada (lower middle part of Fig. 10.) the model is getting an overcast situation in the firs half of the day and clearsky in the other half, although measurements are the other way. In Oviedo (higher middle part of Fig. 10.) it is clearly an overcast situation and the WRF model predicts a clear sky situation.

2. Conclusion

WRF model has been validated for a whole year over Spain. The comparison has been done with ground solar global radiation measurements. Errors are from 30-98% in terms of RMSD for hourly global radiation and from 23-89% in terms of RMSD for daily global radiation. Different synoptic situations have been studied and the WRF model fed with NCEP data is not reproducing them quite well, however ECMWF model reproduce them in a much nicer way. Using ECMWF data as boundary conditions and testing other cloud parameterizations could improve results from WRF model in the future.


[1] Veziroglu TN and dot a. 21st Century’s energy: Hydrogen energy system. Energy Conversion and Management 2008; 49:1820-1831.

[2] Patlitzianas KD, Ntotas K, Doukas H, and Psarras J. Assessing the renewable energy producers’ environment in EU accession member states. Energy Conversion and Management 2007; 48:890-897.

Young have developed a good, practical formula which is well suited for use with small solar elevation angles [8]

. _ ms ga а gtnv-i (2)

_ з tк* V 4 sin^V-t (S. ffiLGS 9&a ri tT V 4 GJGGCaca?

This equation approaches the simple expression 1/sin V asymptotically for angles greater than 250. (The reader may wish to try using the angle V = 900 in the Kasten-Young equation. Note that L = 1 with the sun directly overhead.

2.4 Enhanced global irradiance model

One would expect the diffuse contribution IF to the global irradiance to increase with increasing atmo­spheric turbidity (increasing values of a) and that these two quantities are related to one another and to the solar elevation angle. This connection has been examined in earlier work, and the graphical result is shown in Figure 3. The Linke turbidity factor TL enters the analysis in the term for the direct irradiance and is equal to unity for a pure Rayleigh atmosphere (no aerosols — only molecular scattering). The term aL is in this formulation replaced by:

a1 = «ф [-0.B662 ■ Тя ■ L ■ (3)

where Dr(L) is the Rayleigh optical depth as a function of the air mass L. A very useful empirical equation for 1/Dr has been developed by Louche, Peri and Iqbal and modified by Fritz Kasten [8]:

Подпись: Figure 3: The observed diffuse irradiance on a horizontal surface depends upon the value of the Linke turbidity factor TL and the solar elevation angle.

-7T = 6J&296+ 1.7 515 ■ L — 0.12Є2 ■ — (ШВ65 ■ £a — 0JD0Q13 ■ L4 <4)

The data of Figure 3 has been used to find an expression for the diffuse irradiance on a horizontal surface IF as a function of the elevation angle V and the turbidity factor TL:

£r = (4ВД4 ■ Г, — Ш2)- (1-ехр[аі — Г, — 0.P905- Y]’) (5)

2.5 Equation for finding TL from observations

In view of the foregoing remarks it is now possible to write an algorithm for the determination of the Linke turbidity factor from observations of the global solar irradiance on a horizontal surface and with knowledge of the solar elevation angle. The elevation angle is readily computed when the latitude, longitude and time of day are known.

tc = 2» ■ Щ — sinF ■ дС-азьи-*!. — l-вд + (4^4,^ _ 4^33) ■ (1 — (6)

IG is the global irradiance measured, and V can be found from the time and position data. Knowledge of V yields the air mass L which in turn permits the optical depth DR to be determined. The only unknown parameter in the equation is the Linke turbidity factor TL which can then be calculated. This program has been carried out for clear days at a wide range of locations during the voyage from the Arctic to the Antarctic.

2.6 Results


For all of the good, clear days during the eight month expedition the Linke turbidity factor was computed using the algorithm described above. For these same days the mid day temperature and relative humidity were obtained by examination of the Galathea III database. From the temperature and humidity data it is straightforward to compute the amount of water present in a cubic meter of surface air. These calculations were performed with the data shown in Figure 4 as the result. The regression shows that a moisture content close to zero should yield a Linke turbidity factor near unity as expected. The value 1,19 may reflect the fact that some aerosols will typically be present in the maritime environments from which data is available in addition to water vapor.

This analysis permits the estimation of the Linke turbidity factor in maritime environments based upon knowledge of the temperature and relative humidity. Compute the water content of a cubic meter of surface air, and apply the equation shown in Figure 4 to find TL. With TL in hand a good prediction of the global irradiance on the horizontal on a clear day, including the distribution of direct and diffuse irradiance, can be made using Equation 6. As local temperature and relative humidity are standard meteorological parameters, no special equipment or data is needed to do the calculations. Visibility conditions are also derivable from knowledge of the turbidity factor as discussed elsewhere [7]. This method does not take account of other aerosol which may be present, but it can be applied in typical maritime conditions without a modest amount of dry atmospheric aerosol particles.


[1] Galathea 3 Expedition portal in English: http://galathea3.emu. dk/eng/index. html

[2] Frank Bason; Solar Irradiance Measurements from the Danish Galathea 3 Expedition, ISES Solar World Conference Beijing 2007, Proceedings.

[3] Pierre Ineichen, Richard Perez; A new airmass independent formulation for the Linke turbidity coef­

ficient, Solar Energy 73, no. 3, pp 151-157, 2002.

[4] Linke, F.; Transmissions-Koeffizient und Trubungsfaktor, Beitr. Phys. fr. Atmos. 10, 91-103 (1922).

[5] Frank Bason; Solar Radiation Measurements from Greenland to Antarctica — Optics Table Data from the Danish Galathea III Expedition (2006-2007) , North Sun Conference 2007, Riga,

Latvia, May 2007

[6] John Duffie, William Beckman, Solar Engineering of Thermal Processes, Wiley & Sons, New York, 1991.

[7] Frank Bason; Diffuse Solar Irradiance and Atmospheric Turbidity, EuroSun 2004 Conference

Proceedings, Freiburg, Germany, June 2004.

[8] F. Kasten, A. T. Young; Revised optical air mass tables and approximation formula, Applied Optics,

28, no. 22, pp 4735-4738, 15 Nov 1989.

Materials and Methods

The meteorological and climatic features of Galicia are monitored by a network made up by 93 weather stations, located all over the region covering the usual meteorological parameters. Ten — minute averaged data were collected for both monitoring and climatic studies. The network is managed by MeteoGalicia, the Galician weather service (www. meteogalicia. es). Currently, 25 stations provide the solar radiation measurements with Ph Schenk first class pyranometers (Philipp Schenk GmbH Wien & Co KG, Wien, Austria) and Kipp & Zonen sunshine duration sensors (Kipp & Zonen, Delft, The Netherlands) located in the most representative sites of Galicia. Moreover, 40 silicon cell sensor pyranometres (Skye Instruments LTD, UK), are installed in as many stations. These sensors are periodically cleaned and calibrated by specialized crew, following the manufacturer’s recommendations. Data are collected by dataloggers, sent to a central data server and stored in a database specifically designed for environmental data sets. Here, several filters are applied to ensure the data quality [10, 11].


Fig. 1: Names and locations of the meteorological stations considered in this work, and location of the two pyranometers (at A Coruna and Vigo) used by Vazquez et al. [7].

As a part of the Solar Radiation subject’s programme, each student was required to choose one station between the 65 equipped with pyranometers and to analyse data between September 2006 and August 2007. The main features of these stations are listed on Table 1.

Data proceeding from every selected meteorological station have been processed to obtain monthly-averaged daily values of global irradiation, sunshine duration and clearness index. Measurements of precipitation, temperature and relative humidity have also been processed in order to complete the dataset for analysis.

Table 1: Main features of the meteorological stations applied in the analysis.





Altitude (m)




CIS Ferrol

43.49° N




Suburban, Coastal




42.82° N




Rural, Inland

N. A.


Rio do Sol

43.1° N




Rural, Inland



Marco Curra

43.34° N




Rural, Inland



EOAS Santiago

42.87° N




Urban, Inland




43.13° N




Rural, Inland



Serra Faladoira

43.59° N




Rural, Coastal




42.97° N




Rural, Inland




43.34° N




Rural, Coastal




42.6° N




Rural, Inland




43.56° N




Rural, Inland



Penedo Galo

43.66° N




Rural, Coastal




43.65° N




Rural, Coastal




42.82° N




Rural, Inland




42.65° N




Rural, Inland



Pedro Murias

43.54° N




Rural, Coastal




42.75° N




Rural, Inland




41.81° N




Rural, Inland




42.22° N




Rural, Coastal




42.23° N




Rural, Inland




42.56° N




Rural, Coastal




42.58° N




Rural, Coastal




42.63° N




Rural, Inland



Monte Aloia

42.08° N




Rural, Inland




42.41° N







Urban, Coastal



Castro Vicaludo

42° N




Rural, Coastal



Serra do Faro

42.58° N




Rural, Inland




42.47° N




Rural, Inland

N. A.



41.95° N




Rural, Inland




42.35° N




Urban, Inland




42.12° N




Rural, Inland



Monte Medo

42.23° N




Rural, Inland




42.42° N




Rural, Inland

N. A.



41.9° N




Rural, Inland


Finally, monthly and yearly values of global solar irradiation measured in the meteorological stations have been compared with the results obtained by Vazquez et al. [7].

The clearness index, KT, has been evaluated by the ratio G/G0, where G is the ground measured irradiation and G0 is the extraterrestrial irradiation. For this purpose, a solar constant value of

Подпись: C9 Подпись: C3 image062 Подпись: P3 Подпись: C7, Подпись: -7.5 Подпись: R3 Подпись: I

1376 Wm-2 was adopted, as recommended by Davies [12]. Astronomical relationships were obtained following Iqbal [13].

image068 Подпись: 3100 3000 2900 2800 2700 2600 2500 2400 2300 2200 2100 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 600 500 image070 Подпись: -9 image072 image073 Подпись: -7.5 Подпись: -7 Подпись: 7 3

Fig. 2: Annual mean values of daily global solar irradiation (kJm-2) distribution over the region. Squares show the locations of the stations selected by the students and applied in this analysis.

image077image078Fig. 3: Annual values of (a) precipitation (mm) and (b) mean relative humidity (%) over the region.

Data for every station were collected, joined together and interpolated using Kriging technique, in order to get appropriate maps for analysis. Software package SURFER (http://www. ssg- surfer. com/) provided graphical representation of these results.

2. Results