Atmospheric Research 143 (2014) 73–84

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Atmospheric Research

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Turbidity coefficients from normal direct solar irradiance inCentral Spain

J. Bilbao *, R. Román, A. MiguelUniversity of Valladolid, Energy and Meteorology Laboratory, Faculty of Sciences, Spain

a r t i c l e i n f o

⁎ Corresponding author at: University of Valladolid.E-mail address: juliab@fa1.uva.es (J. Bilbao).

http://dx.doi.org/10.1016/j.atmosres.2014.02.0070169-8095/© 2014 Published by Elsevier B.V.

a b s t r a c t

Article history:Received 29 June 2013Received in revised form 7 February 2014Accepted 9 February 2014Available online 18 February 2014

Atmospheric turbidity causes attenuation of solar radiation reaching the earth's surface under acloudless sky. The Ångström turbidity coefficient and the aerosol optical thickness, AOD550, wereobtained from10-minute direct normal solar irradiancemeasurements recorded in a rural area ofCastilla y León region, Spain, from July 2010 to December 2012. During the study period, thediurnal variation of themeanmonthly 10-minute turbidity coefficient increased in earlymorning,remainedwith fluctuations around noon, and increased or diminished in the evening, near sunset.The monthly turbidity coefficient shows an annual cycle with minimum values in winter andmaximum values in summer, varying between 0.04 inwinter and 0.16 in summer. The frequencydistribution of 10-minÅngström turbidity coefficient on cloudless days shows that 0.65% of valuesare below 0.02, 84.50% between 0.02 and 0.15, and 14.85% above 0.15. Comparing at solar noonAOD550nm retrieved from MODIS (MODerate resolution Imaging Spectroradiometer on-boardthe Terra satellite) with those estimated from direct normal solar radiationmeasurements showsa good correlation coefficient of 0.78, althoughMODIS values are lower than estimated ones. Highturbidity situations were investigated depending on the season and air-mass origin; the resultsshow that they might be attributed to aerosol dust from the Sahara desert.The most significanthigh turbidity situations were investigated on base of wind at 700 mb and air-mass origin; theresult shows that this might be attributed to aerosol dust from the Sahara desert.

© 2014 Published by Elsevier B.V.

Keywords:Atmospheric turbidityAerosol optical depthPrecipitable waterAir mass trayectories

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742. Data collection and analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

2.1. Ground-based data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 752.2. Satellite data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 752.3. Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 763.1. Frequency distribution of the Ångström turbidity coefficient and precipitable water-vapour . . . . . . . . . . . . . . 773.2. Analysis of the hourly, daily and monthly Ångström turbidity coefficient values . . . . . . . . . . . . . . . . . . . . . 773.3. Measured and estimated AOD550 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783.4. Turbidity and precipitable water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783.5. Air mass back trajectories, case study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Faculty of Sciences. Valladolid, Spain.

74 J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

1. Introduction

Solar radiation is attenuated by absorption and scatteringprocesses when passing through the earth's atmosphere; themain gaseous absorbers are ozone, oxygen, water vapour, andcarbon dioxide. All atmospheric gases and aerosols scatter solarradiation at all wavelengths although absorption by aerosols issmaller than by scattering. It is known that under cloudlessconditions, ozone, water vapour, and aerosols are the mostvariable atmospheric components although aerosols show adiverse composition, size, and distribution that make thembeing the greatest attenuators of solar radiation in the visibleand near-IR wavelengths on cloudless days.

Aerosols are small solid or liquid particles suspended in theair that follows air mass motion within certain broad limits.These particles are either of terrestrial origin (industrial smoke,pollen, volcanic eruptions, sandstorms, forest fires, as well asagricultural and slash burning), or of marine origin, and rangein size from 10−3 μm to several tens of microns (López andBatlles, 2004). There are two dominant aerosol layers in theatmosphere, one near the earth's surface (0–4 km), which isaffected by natural dust storms and anthropogenic activities,and another stratospheric dust layer (15–25 km) above sealevel (Hess et al., 1998) affected by volcanic action and cosmicsources.

Aerosols play a role in the earth's energy balance, incloud formation, precipitation, and in atmospheric chemicalreactions. In order to estimate aerosol climatic effects, thephysical–chemical and optical properties need to be known(Adamopoulos et al., 2007). Aerosol turbidity is an essentialatmospheric variable which conditions the magnitude andvariability of solar radiation (Gueymard, 2012). An atmo-sphere with aerosols is called turbid and the effects thatthose aerosols produce on solar radiation are known asturbidity (Pedrós et al., 1999). The presence of aerosols inthe atmosphere can be quantified by different parametersrelated to the chemical, physical and optical properties ofthe particles (D'Almeida et al., 1991; Hess et al., 1998).

The parameters that characterized the atmospheric turbidityare called turbidity coefficients. The most commonly usedcoefficients are the Ångström turbidity coefficient β, the Linketurbidity factor TL, and the Unsworth–Monteith turbiditycoefficient TUM (Kambezidis et al., 1993, 1998, 2000; Lópezand Batlles, 2004) and the Ångström turbidity coefficientrepresents the amount of aerosols in the atmosphere (Iqbal,1983). The Ångström turbidity formula incorporates thenumber of particles and particle size, and is given by thefollowing expression

τa λð Þ ¼ βλ−α ð1Þ

where λ is the wavelength (in μm), and the Ångströmexponent,α, is related to the size distribution of the aerosols(large values of α indicate a relatively high ratio of smallparticles to large particles), τa(λ) is the monochromaticaerosol attenuation coefficient, known as aerosol opticaldepth, AOD and β is the AOD at 1 μm wavelength.

Like many other climate variables, β and α can varythroughout the day due to photochemical activity, localemissions, mesoscale circulation, ventilation by wind, andchanges in temperature that cause evaporation or condensation

of moisture in the atmosphere. These changes can decrease orincrease the value of these coefficients.

The coefficients α and β can be determined by sun-photometers and spectral radiometers. Moreover β andα canbe retrieved using solar broadband sensors. Several methodsare based on broadband measurements of solar radiation.Louche et al. (1987) determined the value of β with directsolar irradiance data from Ajaccio (France), assigning a fixedvalue to α. Gueymard and Vignola (1998) evaluated turbidityfrom a proposed semi-physical method that demonstratedthe utility of diffuse broadband irradiance data for estimatingatmospheric turbidity.

In Spain, Cañada et al. (1993) estimatedβ in Valencia and theresults were compared with those from Ajaccio, Avignon, andDhahran, and they kept α equal to 1.3. López and Batlles (2004)compared different methods for evaluating turbidity and pre-cipitable water, and reported that the most exact method is theone proposed in Gueymard and Vignola (1998). Based onspectral measurements, Adamopoulos et al. (2007) determinedthe aerosol particle radii in the atmosphere over Athens fromspectral solar direct irradiance on 42 cloudless days and air-masstrajectories were used to reveal certain specific events. Power(2001) proposed amodel forÅngströmβ evaluation fromreadilyavailable surface-weather data, regardless of cloud cover.

The Aerosol Robotic Network (AERONET) was set up tomonitor global aerosols and their optical and physical proper-ties (e.g. size distribution, single scattering albedo, asymmetryfactor, and refractive index) are obtained through the Duboviket al. (2006) algorithm.

The present paper aims to analyse the Ångström turbiditycoefficient and AOD at 550 nm, AOD550, in Central Spain. Themethod proposed by Louche et al. (1987) has been run herewith the Ångström exponent input equal to themeasured valueretrieved from MODIS on board the Terra and Aqua satellites.According to Levy et al. (2013), the Ångström exponentbetween 470 and 660 nm derived over land presents somelimitations concerning its reliability. But we have consideredthat the Ångström exponent values used here are the mostpractical ones when no better values can be obtained withoutappropriated instrumentation. On the other hand it has beenobserved that the tendency of the Ångström exponent valuesused in this study is similar to the ones recorded at the nearestAeronet station Autilla (41°N, 4 W), Spain.

AOD550 values estimated by direct normal solar irradianceand satellite retrieved ones are compared. The turbidity de-pendence on atmospheric conditions is studied relating dailyandmonthly turbidity withmeteorological data and taking intoaccount air mass trajectories. High aerosol load (e.g. that on 7thApril 2011and other days) was analysed using the HYSPLIT(HYbrid Single Particle Lagrangian Integrated Trajectory)model and synoptic conditions at the measuring station.Results and analysis should contribute to increasing knowledgeof atmospheric and climatic characteristics of the geographicalarea and could be used to improve the accuracy of solarradiation modelling over regions of interest for solar applica-tions (Gueymard, 2005).

By means of the proposed method, it will be possibleto evaluate turbidity in areas where normal direct solarirradiance measurements are available, and by using theexponent coefficient α, precipitable water vapour, andAOD550 data from satellite measurements.

75J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

Section 2 of the article gives a detailed description of themeasuring station, the data setup used and the theoreticalbackground. Section 3 presents the results and discussionand Section 4 contains a summary of the most importantconclusions of this study.

2. Data collection and analysis

2.1. Ground-based data

The University of Valladolid Solar Radiometric Station(SRSUVA) is located in a rural area free from obstructions(41°40′N, 4°50′W, 840 m a.s.l.), about 35 km NW of the cityof Valladolid (Spain) in the autonomous region of Castilla yLeón, which covers one fifth of the whole country (Bilbao etal., 2003). The climate is not only continental, but is alsoinfluenced by the Mediterranean Sea, with dry and warmsummers (maximum temperatures of ~38 °C), and cold andwet winters (minimum temperatures of ~−7 °C). Cloudlessskies predominate in the summer months while cloudy skiesare more frequent in autumn and winter. Typical annualrainfall is about 476 mm (Oña et al., 2013).

The SRSUVA station houses many radiometric and meteo-rological sensors (Bilbao andMiguel, 2010). Direct normal solarirradiance, DNI, measurements were recorded using a solartracker (Solys-2 from Kipp & Zonen) with a CHP-1 pyrheliom-eter, which has a 2% calibration error and its response lies in thespectral range of 280–4000 nm; its sensitivity is 14 μV/Wm−2

in the above mentioned spectral range. In addition, two wellcalibrated CMP21 pyranometers (Kipp & Zonen) with spectralrange of 280–2800 nm are mounted on the same solar trackerto take global and diffuse measurements of solar irradiance onhorizontal surface (using a shadowball),with an error of 2% (deMiguel and Bilbao, 2005). The pressure data were recorded bya RPT410F Barometric Pressure sensor (Campbell Scientific)which accuracy is 1.5 mb between −20 °C and 50 °C. Allsensors were connected to two CR23X Campbell dataloggerswhich are programmed to recordmeasurements every 10 s andstore 10-minute averages of them. Data quality control testswere performed before the data were used (Bilbao et al., 2011).Thus three time series of global, DNI and diffuse solar irradiance10-minute values with 55,108 data each one were formed fromwhich hourly and daily data were then estimated for the threesolar radiation components.

The DNI, global and diffuse solar irradiance for zenith solarangles lower than 80° were considered and analysed from July2010 to December 2012. The cloud periods were identified bythe pyranometer records of global, diffuse and DNI solarradiation, since the continuous signal shows a large short-termvariability with monotonic and smooth evolutions. It is knownthat aerosol layers have smaller impact than clouds on visiblesolar radiation (Di Sarra et al., 2002). After the representationand observations of the pyranometer signals, periods classifiedas cloudless sky and no clouds close the sun were retained andused in the paper analysis. A total of 112 cloudless sky dayswereisolated for consideration.

2.2. Satellite data

Values of total ozone column (TOC), aerosol optical depth at550 nm (AOD550nm), Ångström exponent at 470–660 nm

range and precipitable water vapour have been used inthis work. The Ozone Monitoring Instrument (OMI) on-board the Aura satellite, and the Global Ozone MonitoringExperiment-2 (GOME-2) on-board the MetOp satellite takedaily measurements of TOC. TOC from OMI has beencalculated using the algorithms of TOMS and DOAS. In thiswork, the TOC from the TOMS algorithm was used first;when this was not available, the DOAS algorithm took over.As a third alternative TOC from GOME-2 was used when thisparameter could not be retrieved from OMI. All TOC dataused have been downloaded from http://avdc.gsfc.nasa.gov.TOC measurements from GOME-2 were compared withground-based measurements recorded by five Brewerspectroradiometers in the Iberian Peninsula, and the resultsshowed that GOME-2 data has a very good quality. TOCs areon average 3.05% lower than Brewer measurements (Antónet al., 2009). TOC from OMI-TOMS are on average 2% lowerthan Brewer data and for OMI-DOAS data the bias is 1.4%(Antón et al., 2009).

The AOD550 values from MODIS (Moderate ResolutionImaging Spectroradiometer) are considered at solar noonand the Ångström exponent, and precipitable water vapourwere obtained also from the MODIS instrument and areconsidered as daily values. AOD550 was taken from MODISon-board Terra satellite which takes measurements overSpain between 10:00 UTC and 13:00 UTC and the valueobtained is considered as at solar noon. The error in theretrieved aerosol optical depth AOD550 from MODIS wasexpected to be: Δ(AOD550) = 0.05 ± 0.2(AOD550) (Kaufmanet al., 1997).

The Ångström exponent values were also obtained fromMODIS on-board the Terra satellite (470/660 nm range “Landconfiguration”); the sameparameter between 200 and 4000 nmcan showsmall differences for aerosolmixture optical properties(Hess et al., 1998), and we have assumed that the use of470–660 nm range is more adequate than to use a standardvalue for the area, all days are the same. On the other hand, thehigher irradiance reaching the surface is in this band. Ångströmexponent values have been comparedwith Aeronet daily data atthe nearest station (Autilla (41°N, 4 W), Spain) and we haveobtained that the two series show the same tendency.When thisdata from Terra was not feasible, the retrieved α from MODISon-board the Aqua satellite afternoonmeasurements over Spainwas used.

Finally, water-vapour data were obtained from the noonmeasurements of MODIS onboard the Terra satellite. Precipita-ble water-vapour retrieved using MODIS near-infrared chan-nels, centred near 0.905, 0.936 and 0.940 μmwith atmosphericwindow channels at 0.865 and 1.24 μmwasused. Typical errorsin the derivedwater-vapour values are in the range between 5%and 10%, (Gao and Kaufman, 2003).

The MODIS data have been downloaded from theGIOVANNI application (http://disc.sci.gsfc.nasa.gov/giovanni/overview/index.html). GIOVANNI data are given over an areaof 0.2° latitude × 0.2° longitude around each measurementsite.

2.3. Theoretical background

DNI at ground level can be expressed in terms ofthe individual transmittances of the various atmospheric

76 J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

components. For cloudless conditions and at a given instant,DNI I ˙n can be written as (Iqbal, 1983; Louche et al., 1987):

I ˙n ¼ 0:9751E0I ˙scτrτoτgτwτa ð2Þ

where 0.9751 is a factor that depends on the extraterrestrialspectrum used in the calculations. In order to see the effect ofthe different spectral ranges, a radiative transfer code has beenused: LibradTran 1.7 (Mayer and Kylling, 2005). The directirradiance reaching the surface has been estimated by the twostream solver under the atmospheric conditions averaged forthe SRSUVA station. The direct irradiance has been calculatedfor 280–2800 nm and for 200–4000 nm, and for the solarzenith angle, SZA, values of 0°, 20°, 40°, 60° and 80°. The resultsindicate that the difference of the irradiance is lower than 1%from 280–2800 nm to 200–4000 nm, for all SZA values.Therefore, we use the value of 0.9751, assuming it within 1%of accuracy. E0 is the eccentricity correction factor of the earth'sorbit and I ˙sc is the solar constant, 1367 Wm−2. Transmittancesby Rayleigh scattering τr, ozone τo, uniformly mixed gases τg,water vapour τw, and aerosols τa are given by the followingexpressions:

The transmittance for Rayleigh scattering is (Iqbal, 1983):

τr ¼ exp −0:0903m0:84a 1:01þma−m1:01

a

� �h ið3Þ

where ma is the air mass given by:

ma ¼ mrp

101325

� �ð4Þ

p is the local pressure in Pascal and mr is the relative opticalmass given by Kasten (1966)

mr ¼ cosθz þ 0:15 93:885−θzð Þ−1:253h i−1 ð5Þ

θz being the solar zenith angle in degrees.The transmittance for ozone is (Iqbal, 1983):

τ0 ¼ 1−h0:1611U3 1:0þ 139;48U3ð Þ−0:3035

−0:00271U3 1:0þ 0:044U3 þ 0:0003U23

� �−1ið6Þ

where U3 = mrl is the ozone relative optical path length andl is the total ozone column (TOC) in cm (NTP), obtained fromOMI.

The transmittance for uniformly mixed gases is given by(Bird and Hulstrom, 1981):

τg ¼ exp −0:0127m0:26a

h i: ð7Þ

The transmittance for water vapour is (Lacis and Hansen,1974):

τw ¼ 1−2:4959 Ul 1:0þ 79:034Ulð Þ0:6828 þ 6:385Ul

h i−1 ð8Þ

where Ul = wmr is the pressure-corrected relative opticalpath length of precipitable water and w is the precipitablewater vapour, obtained from MODIS.

The transmittance for aerosols is given by (Iqbal, 1983):

τa ¼ 0:1244α−0:0162ð Þþ 1:003−0:125αð Þ exp −βma 1:089αþ 0:5123ð Þ½ �: ð9Þ

Combining Eqs. (2) to (9), an explicit expression for β canbe written as

β ¼ 1maD

lnC

A−B

� �ð10Þ

where the coefficients are as follows:

A ¼ In0:975E0Iscτrτoτgτw

ð11Þ

B ¼ 0:1244α−0:0162 ð12Þ

C ¼ 1:003−0:125α ð13Þ

D ¼ 1:089αþ 0:5123 ð14Þ

whereα is theÅngströmexponent. From theabove expressionsit can be seen that the transmittances τr, and τg depend on airmass only. The transmittance τo needs information on the totalozone column, and τw uses air mass and precipitable watervapour content as input. Values ofα, precipitable water vapour,and total ozone column have been retrieved for the purpose ofthis work from satellite databases while the temporal evolutionof TOC over SRSUVA is presented in De Miguel et al. (2011).

In addition, the error analysis of the experimental mea-surements and the indirect results obtained normally areevaluated by the following processes: a) The accuracy of thedifferent instruments and sensors has been taken as themeasurement error values (i.e. 2% for global sensors); b) Theerror of parametric expressions recommended by the authorscan be chosen and c) When scatter plots and least squaremethods were chosen for data analysis, the parameter fit slopeand the intercept value are calculated and the correspondinguncertainty can be obtained as the standard error of theregression parameters Pedrós et al. (1999).

For solar radiation values, due to the fact that the paperhas been developed using hourly, daily and average monthlydata values and that these values are the average of the10-minute data, the error has been the standard deviationbecause it is a good index for understanding and consideringthe errors associated with the estimations. For data retrievedfrom satellites, the uncertainty of the measurements given bydifferent author references has been used as it was explainedin Section 2.2.

3. Results and discussion

FromEqs. (2) to (14), the 10-minute values of theÅngströmturbidity coefficient β for cloudless days were computed. Atypical daily pattern of DNI and Ångström turbidity β for acloudless day (19 October 2010) is shown in Fig. 1. In thisfigure, it can be seen that DNI drops a little when there is anincrease in turbidity. The turbidity shows fluctuations along theday, increasing in early morning, remaining with fluctuationsduring the central part of the day, and then decreasing orincreasing in the evening. These fluctuations can be influenced

7 8 9 10 11 12 13 14 15 16 17

Time (hours)

0.03

0.04

0.05

Tur

bidi

ty, β

600

700

800

900

1000

TurbidityDNI

19th October 2010

DN

I (W

m-2

)

Fig. 1. Diurnal evolution of DNI and Ångström turbidity coefficient for acloudless day in the region of Castilla y León, Spain.

Fig. 2. Frequency distribution of 10-min Ångström turbidity coefficient β oncloudless days in the period 2010–2012 in the region of Castilla y León,Spain.

77J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

by a number of causes such as meteorological conditions, localair pollution sources, photochemical activity, biomass smokefrom seasonal forest fires, desert dust from the Sahara andindustrial activities.

3.1. Frequency distribution of the Ångström turbidity coefficientand precipitable water-vapour

An initial analysis of the Ångström turbidity coefficient andprecipitable water-vapour data was made on the basis offrequency distribution. As it can be seen in Fig. 2, β frequencydistribution in the region of Castilla and León has a maximummode close to a 0.10 and it is skewed on the right, indicatingthat themean is higher than themode and the median and themost frequent values being between 0.08 and 1.0. The statisticsfrom Fig. 2 shows that 0.65% of β values are below 0.02, 84.50%are between 0.02 and 0.15, and that 14.85% exceed 0.15. Theseresults are in agreement with those obtained by Djafer andIrbah (2013) who estimated the atmospheric turbidity nearGhardaïa city in Algerie (32.37°N, 3.77°E and 450 m above sealevel); Djafer and Irbah (2013) used the empirical formulagiven by Dogniaux (1974) to evaluate β; they obtained thatabout 9.45% of Ångström coefficient values are less than 0.02;75.4% are between 0.02 and 0.15 and 15.2% were greater than0.15. From the comparison we have obtained that the mostfrequent Ångström turbidity coefficient values range between0.02 and 0.15 and 15% exceeds 0.15 in both places. Fig. 2frequency distribution shows a positive tail, which indicatesskewed to higher turbidity values. Table 1 shows the basicstatistical of the turbidity-β data series and mean, median,mode and standard deviation are 0.11, 0.09, 0.09 and 0.09,respectively.

Fig. 3 shows the frequency distribution of daily precipi-table water-vapour data series. The analysis was performedwith cloudless day values; mean, median and mode are 1.33,1.28 and 0.94 cm respectively. From Fig. 3 it is seen that theprecipitable water-vapour data are distributed between 0.4and 2.8 cm, with maxima at 0.9 cm and the distribution isskewed on the right. 70% of the data fall between 0.4 and1.5 cm, and 30% of them between 1.5 and 2.8 cm.

3.2. Analysis of the hourly, daily and monthly Ångström turbiditycoefficient values

The monthly daily evolution of hourly turbidity β for fourrepresentative months of the 4 seasons, i.e. January, April, Julyand October is shown in Fig. 4. It can be seen in some months(e.g. January and October), that the atmospheric turbidity inthemorning hours increases, it remains nearly constant aroundnoon, and it increases or diminishes in the evening near sunset(Pedrós et al., 1999). Similar results to themonth of April wereobtained by Louche et al. (1987) in some months and theyconsidered that an average daily value could be representativeof the daily turbidity. The increase in the turbidity in theevening is typical in continental areas sometimes due to thedaily activity. In Fig. 4 it can be seen that the standard deviationincreases in summer and autumn when some desert dustevents and tasks on agricultural lands would bemore probableand the dispersion among β values would increase.

The daily and monthly variations of the Ångström turbiditycoefficient β are shown in Fig. 5. They display the same pattern,with maximum and minimum values in summer and wintermonths, respectively. Maximum β values are obtained in theperiod June-July to October. In Fig. 5, monthly turbidity βvalues show two maxima in April and June which can beexplained taking into account that on 7th April 2011maximumβwas due to the influence of a desert dust-storm event whichmajor variations in summer are caused by eastern and south-eastern winds under specific meteorological conditions. In thiscase, air masses bring particles of Saharan dust and sand withthem, leading to increased air turbidity (Djafer and Irbah,2013). Maximum β on the 26th, 27th, 28th June 2012 can beexplained by the backward trajectory (not shown) NOAAHYSPLIT model that these days produced air mass from thenorthern African area. Winter is characterized by rains thatcontribute to reducing the Ångström turbidity coefficient.

The turbidity results have been compared with those ob-tained by Salvador et al. (2008), who estimated the atmosphericturbidity in the SRSUVA, Spain (41°40′N, 4°50′W, 840 m abovesea level) during the period 2003–2006. They obtained that 80%of the turbidity values were lower than 0.1 and turbidity valuesgreater than 0.3 represented less than 1% of the data values. The

Table 1Statistical characteristics of the mean turbidity coefficient β in the period 2010–2012 for the Castilla and León region.

Mean Median Mode Standard deviation Variance Kurtosis Asymmetry Coefficient Number of data

0.11 0.09 0.09 0.09 0.01 24.77 4.32 7358

78 J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

monthly turbidity variation values in Valladolid were comparedwith the values obtained by Cañada et al. (1993) in Valencia andwith the values in Avignon, Ajaccio, and Dhahran, the lattershows the influence of the desert dust. The Valencia β is higherthan in Valladolid, whichmight be due to high humidity levels inValencia, particularly during the summer months. In addition,there are few rainy days and high temperatures in summer(similar to Valladolid), which can lead tomore turbidity becauseof vertical convection and particle entertainment. Turbidity mayalso increase due to south-easternwinds, which are frequent insummer, carrying dust from the Sahara. The monthly turbidityβ and precipitable water-vapour values are shown in Table 2where it can be observed that β monthly average high valuesare given from April to October and also for precipitablewater-vapour, the results indicate high β levels in summermonths than inwintermonths, in agreementwith Cañada et al.(1993).

3.3. Measured and estimated AOD550

Satellite and ground-based aerosol optical characteristicsare compared to understand processes related to the surfaceand the atmosphere around the world. Spatial and temporalcharacteristics of aerosols and their properties are evaluatedcomparing remote and ground-based sensors. Christopher andJones (2010) compared AODs from Aeronet and Modis at CaboVerde (16°N, 24°W) and at La Paguera (18°N, 67°W), usingaircraft in situ measurements and the results found somediscrepancies. Johnson et al. (2009) studied satellite, Aeronetand Microtops data and they found that satellite AODs arewithin 1% of the Aeronet and Microtops, at Niamey, Nigerregion. Esteve et al. (2012) compared Aeronet and in-situaircraft measurements; they found that possible sources ofAOD discrepancies would be due to an adjustment to ambientRH of the scattering coefficient.

Fig. 3. As in Fig. 2, but of daily precipitable water vapour.

Dailyβ andAOD550 values for cloudless dayswere estimatedat solar noon from DNI measurements and the daily Ångströmexponent α from MODIS. Daily AOD550 data at solar noonwere also retrieved fromMODIS. The two series of AOD, namelyAOD estimated and AOD retrieved from MODIS satellite, werecompared. The MODIS data were found to be lower than theevaluated ones. The two series AOD550 MODIS and AOD550

measured present an average value of 0.12 and 0.18 respec-tively being the average standard deviation of 0.05. Fig. 6 showsthe linear regression analysis applied to AOD550 estimated andAOD550 satellite values; the two series show a correlationcoefficient of 0.78. It can be observed in Fig. 6 that the standarddeviation of AOD550 estimated values is low for small AOD550

and increases for high AOD550 values.In order to compare the results with another estimations

we know that AOD550 values were also evaluated by Salvadoret al. (2008) at the SRSUVA station, Valladolid, Spain, fromglobal and diffuse solar irradiance and using Louche et al.(1987) and Gueymard and Vignola (1998) proposed methodsand the yearly average values of AOD550 obtained were 0.25and 0.15 respectively. Also, optical properties of typical aerosolmixtures are given by Hess et al. (1998) who show that thevalue of AOD550 according to an average continental atmo-sphere (between clean and polluted) is 0.15. We can say that,the AOD550 average value obtained in thiswork (0.18) is higherthan MODIS average (0.12) and Hess et al. (1998) recom-mended value (0.15); and MODIS average (0.12) is lower thanHess et al. (1998) recommended value. We also can observedthat the AOD550 average value (0.15) obtained by Salvador etal. (2008) using Gueymard and Vignola (1998) method isaccording to the recommended values (0.15) given by Hess etal. (1998). As a result we can conclude that more measure-ments and studies are necessary in order to increase theknowledge about the most exact AOD550 value in the region.

3.4. Turbidity and precipitable water

On the other hand, turbidity and precipitable water-vapour show a more significant correlation during spring,summer, and autumn, as may be deduced from Table 2. Theminimum and maximum of precipitable water vapour inthe atmosphere of the region are between 0.36 ± 0.07 and1.9 ± 0.5 cm occurring in February and August respectivelyand it can be seen in Table 2. Similar results were obtained by(Khoshsima et al., 2013). The relationship between turbidityand precipitable water-vapour was estimated using the linearregression method and the following results were obtained formonthly average values: β = (0.05 ± 0.02) ∗ ω + (0.04 ±0.02), where ω is the precipitable water-vapour in (cm) andthe correlation coefficient was 0.60. The obtained relationshipmodel is similar to the report by Cañada et al. (1993) whoobtained a correlation of 0.69 for Valencia, Spain and theestimation by Katz et al. (1982) who obtained a correlation of0.40 for Montfavet, France.

0

0.1

0.2

Tur

bidi

ty, β

January (2 years)

0

0.1

0.2

April (2 years)

4 6 8 10 12 14 16 18

UTC Time (hours)4 6 8 10 12 14 16 18 20

UTC Time (hours)

0

0.1

0.2

July (3 years)

0

0.1

0.2

October (3 years)

Tur

bidi

ty, β

Tur

bidi

ty, β

Tur

bidi

ty, β

Fig. 4. Monthly daily evolutions of hourly turbidity β for the period 2010–2012 in the region of Castilla y León, Spain.

79J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

3.5. Air mass back trajectories, case study

The turbidity dependence on air masses was studied usingairmass back trajectories ending at the SRSUVA station at threealtitudes: 500, 1500 and 4000 mAGL. Use of theHYSPLITmodel(http://ready.arl.noaa.gov/HYSPLIT.php) (Draxler and Rolph,2012) wasmade. Re-analysis data from the National Center forEnvironmental Predictions (NCEP) were used as input to themodel. The trajectories ended at SRSUVA at 12:00 UTC, themost representative hour regarding DNI measurements in thisstudy.

Adamopoulos et al. (2007) indicated that selecting a4000-m altitude for identifying Sahara dust particles is ap-propriate for the Mediterranean area. Trajectories ending at1500 m are associated with cases of dust transported in thelower atmospheric layers, or when the mixed layer is low as inwinter. Conversely, when air masses originate from otherregions (e.g. continental Europe), they are generally observedwithin the lowest 2–3 km.

In the present study, trajectories at 4000-m provided infor-mation on the transport of desert dust that generally travelsover the mixing layer. In addition, trajectories at 500 m and1500 m are indicative of circulation in the lower troposphere,mainly anthropogenic aerosols or vertical dust transport. Eachtrajectory would be associated with the corresponding aerosolcharacteristics (Pace et al., 2006). Air mass trajectories are usedto identify aerosol types, although in this study they are used to

show certain specific events. Air mass-origin can be: 1) fromthe Sahara desert; 2) from the Atlantic Ocean and 3) fromcontinental Europe.

One important issue is the knowledge of aerosol types in theatmosphere and authors like Kaskaoutis et al. (2007) studiedthe curvature effect of Ångström exponent for distinguishingand characterizing aerosol types in differentworld places, usingAERONET station data.

In our study, the highest aerosol load was recorded at themeasuring station on 7th April 2011. The Ångström exponentobtained from MODIS measurements was 0.63. This value washigher than the normal values at the station and corresponds tocoarse particles, characteristics of desert dust. In order to give aglobal idea of the amount of aerosols on that day, we provide a“True Colour” image corresponding to the information re-trievals from MODIS. Fig. 7 shows a diagonal layer of dust overthe Iberian Peninsula, running from SE to NW. This imageconfirms a qualitative idea that the aerosol layer might bedesert dust and due to similar images are often obtained, theorigin of the air masses appearing on the Iberian Peninsula thisday needs to be studied.

Fig. 8 shows the 120-hour back-trajectories obtainedbetween 500 m and 5000 mAGL, ending at measurementstation at 12:00 UTC. The trajectories come from North Africa,and the presence of desert dust is plausible. Also itwas observedthat the sunphotometermeasurements in outdoor conditions atSRSUVA showed an increase of AOD500 the same day.

Months

0

0.1

0.2

0.3

Dai

ly T

urbi

dity

, β

J F M A M J J A S O N D

2011 2011

2012 2012

All period

0

0.1

0.2

0.3

Dai

ly T

urbi

dity

, β

0

0.1

0.2

0.3

Mon

thly

Tur

bidi

ty, β

0

0.1

0.2

0.3

0.4

Mon

thly

Tur

bidi

ty, β

0

0.1

0.2

0.3

0.4

Mon

thly

Tur

bidi

ty, β

MonthsJ F M A M J J A S O N D

MonthsJ F M A M J J A S O N D

MonthsJ F M A M J J A S O N D

MonthsJ F M A M J J A S O N D

Fig. 5. Daily and monthly variation of the Ångström turbidity β coefficient, for the period 2010–2012 in the region of Castilla y León, Spain.

80 J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

Synoptic meteorological conditions over the Iberian Penin-sula were evaluated in order to understand the Sahara dustintrusion on 7th April 2011. Fig. 9 shows the isobaric lines at sealevel and the geopotential height of the 700 hPa surface onApril 7th 2011, at 12:00 UTC, over the Iberian Peninsula andlarge part of North Africa. These data were obtained by theNOAA–ESRL Physical Sciences Division (http://www.esrl.noaa.gov/psd/). In addition Toledano (2005) shows that there arefour synoptic conditions that can produce Sahara dust intru-sions over the Iberian Peninsula. In this case, there is an Atlanticdepression facing Portugal, a frequent event between Januaryand June. As a low-pressure system off Portugal produces airadvection in a northerly direction, so much dust from Africaconsequently arrives over the Peninsula from the East. Fig. 9also shows high pressures over Great Britain (at sea level) andSpain (at a higher level). High-pressure systems push dustfrom SE to NW as can be seen in Figs. 8 and 9. As we havementioned before, dust intrusions over Iberian Peninsula have

been observed through their air mass trajectories (not shown)on 6 different summer days during the studied period.

4. Conclusions

In this paper, atmospheric turbidity was estimated fromDNI measurements at SRSUVA station located 35 km awayfrom Valladolid, Spain. The method used was described byPedrós et al. (1999), Cañada et al. (1993) and Louche et al.(1987).

Maximum attenuation of solar radiation by the atmosphererecorded at ground level was observed in summer and aminimum in winter. Monthly mean turbidity β values rangedbetween 0.02 and 0.16. The statistical characteristics of theturbidity coefficient β obtained were: mean 0.11, median0.09, mode 0.09 and standard deviation 0.09. The AOD550 atsolar noon values estimated from DNI measurements and

0 0.2 0.4 0.6 0.8MODIS AOD550

0

0.4

0.8

1.2

Est

imat

ed A

OD

550

Y = (0.91 ± 0.06) * X + ( 0.08± 0.02)r = 0.78N = 86

Fig. 6. Comparison of AOD550 estimated from DNI measurements andretrieved from MODIS, both at solar noon, for the period 2010–2012 in theregion of Castilla y León, Spain.

Table2

Mon

thly

averag

eÅng

ström

turbidityco

effic

ient

βan

dprecipitab

lewater

vapo

ur,w

(cm)in

thepe

riod

2010

–20

12fortheCa

stillaan

dLe

ónregion

.

Janu

ary

Februa

ryMarch

April

May

June

July

Aug

ust

Septem

ber

Octob

erNov

embe

rDecem

ber

β0.04

±0.01

0.05

±0.01

0.08

±0.03

0.16

±0.10

0.10

±0.04

0.16

±0.09

0.10

±0.06

0.11

±0.04

0.12

±0.06

0.11

±0.05

0.04

±0.01

0.05

±0.01

w0.46

±0.08

0.36

±0.07

0.58

±0.07

0.73

±0.12

1.18

±0.23

1.63

±0.16

1.7±

0.4

1.9±

0.5

1.4±

0.3

0.9±

0.2

0.74

±0.01

0.65

±0.04

81J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

retrieved from MODIS was compared and a high correlationwas obtained.

Atmospheric influence on turbidity was observed by exam-ining the precipitable water vapour and by studying air-massback trajectories for ascertaining the origin and sources ofaerosols.

Different events causing the highest aerosol load(AOD550 ~ 0.9) were characterized from the back-trajectorymodel and by synoptic conditions. The most important eventwas observed on 7th April 2011, which was a dust intrusionfrom Africa, which crossed Spain from SE to NW. From ameteorological standpoint, air masses with dust were forcednorthward by a low-pressure system in west Portugal, whilethe diagonal direction of the aerosol plume (from south tonorthwest) was due to high pressures over the northern Spainand southern Great Britain at different heights, which led todust transport being deposited at the measuring station. Theresults reported in the present work may be used to study andinvestigate the effects of solar radiation attenuation on theperformance of solar energy systems. It is known that aerosol

Fig. 7. Terra-MODIS image, 7 April 2011, composed by two images, left takenat 10:20 UTC and right at 12:00 UTC. Blue circle is the location of themeasurement station.

Fig. 8. Three-day trajectories ending at the SRSUVA station at 12:00 UTC, 07 April 2011 for different heights: 500 (red), 1000 (blue), and 2000 (green), 3000 (red),4000 (blue), 5000 (green) m a.s.l. Source: NOAA Air Resources Laboratory (ARL) and READY website (http://ready.arl.noaa.gov).

82 J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

turbidity is an atmospheric parameter that attenuates thesolar radiation through the atmosphere and its knowledge isimportant for evaluation DNI under cloudless sky condi-tions. In addition solar resource data and radiation modelling

Fig. 9. Sea level pressure contours and 700-hPa geopotential heights on 7th Apri

will improve their accuracy if the aerosol turbidity is available.Therefore, the results of this studywill permit the evaluation ofatmospheric turbidity from global diffuse solar irradiation overregions with interest for solar industry.

l 2011 at 12:00 UTC over the Iberian Peninsula and much of North Africa.

83J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

Acknowledgements

The authors gratefully acknowledge the support of theSpanish Research and Economy Ministry through ProjectsCGL2010-25385 and CGL2010-12410-E. The authors alsothank the principal investigator, Dr V. E. Cachorro and herteam for making the use of AERONET data informationpossible and for their efforts in establishing and maintainingthe AERONET station of Autilla, Spain. The authors gratefullythank the use of HYSPLIT model (Hybrid Single ParticleLagrangian Integrated Trajectory Model) http://ready.arl.noaa.gov/HYSPLIT.php and the use of data products of NCEP/NCARReanalysis data service http://www.esrl.noaa. gov/psd/data/gridded/data.ncep reanalysis html. We would like to thankthe National Aeronautics and Space Administration and theEuropean Space Agency for MODIS, OMI and GOME-2 dataproducts.

References

Adamopoulos, A.D., Kambezidis, H.D., Kaskaoutis, D.G., Giavis, G., 2007. Astudy of aerosol particle sizes in the atmosphere of Athens, Greece,retrieved from solar spectral measurements. Atmos. Res. 86, 194–206.http://dx.doi.org/10.1016/j.atmosres.2007.04.003.

Antón, M., Loyola, D., López, M., Vilaplana, J.M., Bañón, M., Zimmer, W.,Serrano, A., 2009. Comparison of GOME-2/MetOp total ozone data withBrewer spectroradiometer data over the Iberian Peninsula. Ann.Geophys. 27, 1377–1386.

Antón, M., López, M., Vilaplana, J.M., Kroon, M., McPeters, R., Bañón, M., 2009.Validation of OMI_TOMS and OMI-DOAS total ozone column using fiveBrewer spectroradiometers at Iberian peninsula. J. Geophys. Res. 114,D14307. http://dx.doi.org/10.1029/2009JD012003.

Bilbao, J., Miguel, A., 2010. Estimation of UV-B irradiation from total globalsolar meteorological data in central Spain. J. Geophys. Res. 116, 1–10.http://dx.doi.org/10.1029/2009JD12505.

Bilbao, J., DeMiguel, A., Ayuso, A., Franco, J.A., 2003. Iso-radiationmaps for tiltedsurfaces in the Castile and Leon region, Spain. Energy Convers. Manag. 44,1575–1588. http://dx.doi.org/10.1016/S0196-8904(02)00161-9.

Bilbao, J., Román, R., de Miguel, A., Mateos, D., 2011. Long-term solarerythemal UV irradiance data reconstruction in Spain using a semiem-pirical method. J. Geophys. Res. 116, D22211. http://dx.doi.org/10.1029/2011JD015836.

Bird, R.E., Hulstrom, R.L., 1981. A simplified clear sky model for direct anddiffuse insolation on horizontal surfaces. SERI/TR-642-761Solar EnergyResearch Institute, Colorado.

Cañada, J., Pinazo, J.M., Bosca, J.V., 1993. Determination of Ångström turbiditycoefficient at Valencia. Renew. Energy 3, 621–626. http://dx.doi.org/10.1016/0038-092X(94)00117-V.

Christopher, S.A., Jones, S.A., 2010. Satellite and surface-based remote sensingof Saharan dust aerosols. Remote Sens. Environ. 114, 102–1007. http://dx.doi.org/10.1016/j.rse.2009.12.007.

D'Almeida, G.A., Koepke, P., Shettle, E.P., 1991. Atmospheric Aerosols:Global Climatology and Radiative Characteristics. A. Deepak Publishing(561 pp.).

De Miguel, A., Bilbao, J., 2005. Test reference year generation frommeteorological and simulated solar radiation data. Sol. Energy 78,695–703. http://dx.doi.org/10.1016/j.solener.2004.09.015.

De Miguel, A., Román, R., Bilbao, J., Mateos, D., 2011. Evolution of erythemaland total shortwave solar radiation in Valladolid, Spain: effects ofatmospheric factors. J. Atmos. Sol. Terr. Phy. 73, 578–586. http://dx.doi.org/10.1016/j.jastp.2010.11.021.

Di Sarra, A., Cacciani, M., Chamard, P., Cornwall, C., DeLuisi, J.J., Di Iorio, T.,Disterhoft, P., Fiocco, G., Fuà, D., Monteleone, F., 2002. Effects of desertdust on ozone on the ultraviolet irradiance at the Mediterranean islandof Lampedusa during PAURII. J. Geophys. Res. 107 (D18), 8135. http://dx.doi.org/10.1029/2000JD000139.

Djafer, D., Irbah, A., 2013. Estimation of atmospheric turbidity overGhardaïa city. Atmos. Res. 128, 76–84. http://dx.doi.org/10.1016/j.atmosres.2013.03.009.

Dogniaux, R., 1974. Representation analytique des composantes du rayonnementsolaire. Institut Royal de Métèorologie de Belgique, Série A, No.83.

Draxler, R.R., Rolph, G.D., 2012. HYSPLIT (HYbrid Single-Particle LagrangianIntegrated Trajectory) Model. access via NOAA ARL READY Website

(http://ready.arl.noaa.gov/HYSPLIT.php) NOAA Air Resources Laboratory,Silver Spring, MD.

Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B.N., Mishchenko, M., Yang, P.,Eck, T., Volten, H., Munoz, O., Veihelmann, B., Van Der Zande, W.J., Leon, J.,Sorokin, M., Slutsker, I., 2006. Application of spheroid models to accountfor aerosol particle nonsphericity in remote sensing of desert dusts.J. Geophys. Res. Atmos. 111, D11208. http://dx.doi.org/10.1029/2005JD006619.

Esteve, A.R., Ogren, J.A., Sheridan, P.J., Andrews, E., Holben, B.N., Utrillas, M.P.,2012. Sources of discrepancy between aerosol optical depth obtainedfrom AERONET and in-situ aircraft profiles. Atmos. Chem. Phys. 12,2987–3003. http://dx.doi.org/10.5194/acp-12-2987-2012.

Gao, B.C., Kaufman, Y.J., 2003. Water vapour retrievals using ModerateResolution Imaging Spectroradiometer (MODIS) near-infrared chan-nels. J. Geophys. Res. 108 (D13), 4389. http://dx.doi.org/10.1029/2002JD003023.

Gueymard, C., 2005. Importance of atmospheric turbidity and associateduncertainties in solar radiation and luminous efficacy modelling. Energy30, 1603–1621. http://dx.doi.org/10.1016/j.energy.2004.04.040.

Gueymard, C.A., 2012. Temporal variability in direct and globalirradiance at various time scales as affected by aerosols. Sol. Energy86, 3544–3553.

Gueymard, C.A., Vignola, F., 1998. Determination of atmospheric turbidity fromdiffuse beam broad band irradiance ratio. Sol. Energy 63, 135–146.

Hess, M., Koepke, P., Schult, I., 1998. Optical properties of aerosols andclouds: the software package OPAC. Bull. Am. Meteorol. Soc. 79 (5),831–844.

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

Johnson, B.T., Christopher, S., Haywood, J.M., Osborne, S.R., McFarlane, S.,Hsu, C., Salustro, C., Kahn, R., 2009. Measurements of aerosol propertiesfor aircraft, satellite and ground-based remote sensing: a case-studyfrom the Dust and Biomass-burning Experiment (DABEX). Q. J. R.Meteorol. Soc. 135, 922–934. http://dx.doi.org/10.1002/qj.420.

Kambezidis, H.D., Founda, D.H., Papanikolaou, N.S., 1993. Linke and Unsworth–Monteith turbidity parameters in Athens. QJRMS 119, 367–374.

Kambezidis, H.D., Katevatis, E.M., Petrakis, M., Asimakapoulos, D.N., 1998.Estimation of the Linke and Unsworth–Monteith turbidity factors inthe visible spectrum: application for Athens, Greece. Sol. Energy 62,39–50.

Kambezidis, H.D., Fotiadi, A.K., Katsoulis, B.D., 2000. Variability of the Linkeand Unsworth–Monteith turbidity parameters in Athens, Greece.Meteorol. Atmos. Phys. 75, 259–269.

Kaskaoutis, D.G., Kambezidis, H.D., Hatzianastassiou, N., Kosmopoulos,P.G., Badarinath, K.V.S., 2007. Aerosol climatology: dependence of theÅngström exponent on wavelength over four AERONET sites. Atmos.Chem. Phys. Discuss. 7, 7347–7397. http://dx.doi.org/10.5194/acpd-7-7347-2007.

Kasten, F., 1966. A new table and approximate formula for relative optical airmass. Arch. Meteor. Geophys. Bioklimatol. Ser. 14, 206–223.

Katz, M., Baille, A., Mermier, M., 1982. Atmospheric turbidity in a semi-ruralsite-II. Sol. Energy 28, 329–334.

Kaufman, Y.J., Tanré, D., Remer, L.A., Vermote, E.F., Chu, A., Holben, B.N.,1997. Operational remote sensing of tropospheric aerosol over landfrom EOS moderate resolution imaging spectroradiometer. J. Geophys.Res. 102, 17,051–17,076.

Khoshsima, M., Bidokhti, A., Ahmadi-Givi, F., 2013. Variations ofAerosol Optical Depth and Angstrom Parameters at a Sub-UrbanLocation in Iran during 2009–2010. JESS-13-00057. Indian Acad-emy of Sciences.

Lacis, A.A., Hansen, J.E., 1974. A parameterization for the absorption of solarradiation in the Earth's atmosphere. J. Atmos. Sci. 31, 118–133. http://dx.doi.org/10.1175/1520-0469.

Levy, R.C., Mattoo, S., Munchak, L.A., Remer, L.A., Sayer, A.M., Hsu, N.C., 2013.The Collection 6 MODIS aerosol products over land and ocean. Atmos.Meas. Tech. Discuss. 6, 159–259.

López, G., Batlles, J., 2004. Estimate of the atmospheric turbidity from threebroad-band solar radiation algorithms. A comparative study. Ann.Geophys. 22, 2657–2668. http://dx.doi.org/10.5194/angeo-22-2657-2004.

Louche, A., Maurel, M., Simommot, G., Peri, G., Iqbal, M., 1987. Determinationof Angström's turbidity coefficient from direct total solar irradiancemeasurements. Sol. Energy 38, 89–96.

Mayer, B., Kylling, A., 2005. Technical note: the libRadtran software packagefor radiative transfer calculations — description and examples of use.Atmos. Chem. Phys. 5, 1855–1877.

Oña, G., Yousif, C., Bilbao, J., 2013. Comparison of solar radiation inMarsaxlokk, Malta and Valladolid, Spain. Renew. Energy 49, 203–206.http://dx.doi.org/10.1016/j.renene.2012.01.031.

84 J. Bilbao et al. / Atmospheric Research 143 (2014) 73–84

Pace, G., di Sarra, A., Meloni, D., Piacentino, S., Chamard, P., 2006. Aerosoloptical properties at Lampedusa (Central Mediterranean). 1. Influ-ence of transport and identification of different aerosol types.Atmos. Chem. Phys. 6, 697–713. http://dx.doi.org/10.5194/acp-6-697-2006.

Pedrós, R., Utrillas, M.P., Martinez-Lozano, J.A., Tena, F., 1999. Values of broadband turbidity coefficients in a Mediterranean coastal site. Sol. Energy 66,11–20.

Power, H.C., 2001. Estimating atmospheric turbidity from climate data.Atmos. Environ. 35, 125–134.

Salvador, P., Bilbao, J., Miguel, A., 2008. Ultraviolet-B solar irradiance, theinfluence of Ångström turbidity. J. Atmos. Sol. Terr. Phy. 70, 1643–1650.http://dx.doi.org/10.1016/j.jastp.2008.07.013.

Toledano, C., 2005. Climatología de los aerosoles mediante la caracterizaciónde propiedades ópticas y masas de aire en la estación “El Arenosillo” dela red Aeronet. (PhD Thesis) University of Valladolid.