Atmospheric extinction levels of solar radiation using aerosol optical thickness satellite data. Validation methodology with measurement system

Journal Pre-proof Atmospheric extinction levels of solar radiation using aerosol optical thickness satellite data. Validation methodology with measurement system Elena Carra, Aitor Marzo, Jesús Ballestrín, Jesús Polo, Javier Barbero, Joaquín Alonso-Montesinos, Rafael Monterreal, Edgar F.M. Abreu, Jesús Fernández-Reche PII:

S0960-1481(19)31603-9

DOI:

https://doi.org/10.1016/j.renene.2019.10.106

Reference:

RENE 12475

To appear in:

Renewable Energy

Received Date: 5 July 2019 Revised Date:

17 October 2019

Accepted Date: 18 October 2019

Please cite this article as: Carra E, Marzo A, Ballestrín Jesú, Polo Jesú, Barbero J, Alonso-Montesinos Joaquí, Monterreal R, Abreu EFM, Fernández-Reche Jesú, Atmospheric extinction levels of solar radiation using aerosol optical thickness satellite data. Validation methodology with measurement system, Renewable Energy (2019), doi: https://doi.org/10.1016/j.renene.2019.10.106. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2019 Published by Elsevier Ltd.

1

Atmospheric extinction extinction levels of solar radiation adiation using Aerosol Aerosol

2

Optical Thickness satellite data. data. Validation methodology with

3

measurement system

4 5

Elena Carra*1, Aitor Marzo2, Jesús Ballestrín1, Jesús Polo3, Javier Barbero4,

6

Joaquín AlonsoAlonso-Montesinos5, Rafael Monterreal1, Edgar F.M. Abreu6, Jesús

7

FernándezFernández-Reche1

8

*

9

1

Corresponding author email: [email protected] CIEMAT-Plataforma Solar de Almería. Concentrating Solar Systems Unit. Ctra.

10

de Senés km. 4,5. Tabernas (04200) (Almería), Spain

11

2

12

3

13

Complutense 40, 28040 Madrid, Spain

14

4

15

s/n, 04120 La Cañada, Almería, Spain

16

5

17

Spain

18

6

19

7000-671 Évora, Portugal

20

Highlights

21

- Extinction AOT method provides atmospheric extinction levels at places of

22 23 24 25 26 27

Universidad de Antofagasta, Centro de Desarrollo Energético Antofagasta, Chile Photovoltaic Solar Energy Unit (Renewable Energy Division, CIEMAT), Avda.

Departamento de Física Aplicada, Universidad de Almería, Ctra. Sacramento,

CIESOL, Joint Centre of the University of Almería-CIEMAT, 04120 Almería,

Instituto de Ciências da Terra, Universidade de Évora, R. Romão Ramalho 59,

interest - Extinction AOT method has been validated with real measurements with CIEMAT system - The use of AOT satellite data in the Extinction AOT method has been validated - Satellite data can be used to obtain accurate extinction levels from any part

28

of the world

29

Abbreviations

30

CSP (Concentrating Solar Power) 1

31

AOT (Aerosol Optical Thickness)

32

STE (Solar Thermal Electric)

33

DNI (Direct Normal Irradiance)

34

RTC (Radiative Transference Codes)

35

PSA (Plataforma Solar de Almería)

36

CIEMAT

37

Tecnológicas)

38

TMY (Typical Meteorological Year)

39

MERRA (Modern-Era Retrospective Analysis for Research and Applications)

40

GMAO (Global Modelling and Assimilation Office)

41

MODIS (Moderate Resolution Imaging Spectroradiometer)

42

DT (Dark Target)

43

DB (Deep Blue)

44

RMSE (Root Mean Square Error)

45

MBE (Mean Bias Error)

46

TAY (Typical Aerosol Year)

47

FS (Finkelstein-Schafer Statistic)

48

CDF (Cumulative Distribution Function)

49

SR (Slant Range)

50

AERONET (Aerosol Robotic Network)

51

Abstract

52

In order to make the concentrating solar power (CSP) more competitive, an

53

accurate prediction of the solar radiation incident on CSP tower plant receivers

54

is

55

important role in the optical loss of the solar field and therefore, in the

56

performance of the plant. In order to correctly operate these kind of plants

57

and to select new potential emplacements, it is necessary to know the on-site

58

levels of extinction. A methodology was developed in another published work,

59

called Extinction AOT method (EAM), with the purpose of finding out the levels

(Centro

necessary.

The

de

Investigaciones

extinction

in

the

2

Energéticas,

heliostat-receiver

Medioambientales

pathway

plays

y

an

60

of extinction present at a location using AERONET AOT as input data. However,

61

there is no AERONET AOT data available at any potential site of interest for

62

setting up a solar tower plant, so, alternative approaches were necessary. This

63

paper proposes to determine levels of extinction at any location using AOT

64

satellite data instead of AERONET data. These results have been compared

65

with results obtained applying the EAM with AERONET data and using real

66

extinction measurements obtained with the CIEMAT extinction measurement

67

system. The extinction obtained at PSA has been 5% in all cases considering

68

the error rates.

69

Keywords

70

Atmospheric extinction

71

Aerosol Optical Thickness (AOT)

72

AERONET

73

MERRA

74

MODIS

75

Attenuation losses

76

1. Introduction

77

At recent meetings regarding Climate Change (1), (2), experts have claimed

78

the need to make unprecedented global changes in order to limit the upgrade

79

of the Earth temperature to 1.5ºC, because passing this limit would have

80

catastrophic consequences.

81

For this reason, it is necessary to decrease the CO2 emissions (main cause of

82

the Climate Change) over the coming years with the aim of reaching zero

83

emissions in 2050 (1), (2). Thus, a powerful change in the energy sector during

84

the next years is necessary, i.e. future strategies should be based on

85

renewable energies such as the concentrating solar thermal power among

86

others, which entails more powerful Solar Thermal Electric (STE) plants in future

87

years. Within the different types of STE plants, there are solar tower plants. In

88

short, solar tower plants consist of a field of solar radiation collectors, called

89

heliostats, which are composed of sun-tracking systems and reflective facets

90

that redirect solar radiation to a receiver located at the top of a tower, where

91

the energy from the sun is converted into process heat. On one hand, greater

92

power involves more heliostats, and higher mean distances between the most

93

remote heliostats and the central receiver. On the other hand, a greater

94

distance means a greater effect of the lower atmosphere to cause effects of

95

absorption and scattering, in other words, extinction of solar radiation and

96

energy losses. Current knowledge of the extinction levels in the lower layers of 3

97

the atmosphere has become a crucial issue for the site selection, design and

98

operation of solar tower plants. Aerosols and water vapour are the atmospheric

99

constituents that produce more extinction in the boundary layer of the

100

atmosphere (3). Aerosols mainly produce scattering of solar radiation reaching

101

them, while water vapour and gases, absorb it mainly. The lack of reliable

102

measurement systems and databases on atmospheric extinction has increased

103

the industry’s interest in this topic, especially in places where the load of

104

aerosols, dust and moisture is large, such as many desert areas of the

105

Sunbelt.

106

The evaluation of power loss due to atmospheric extinction has usually been

107

treated with parametric models and introduced in the plant’s design codes. But

108

they have not been validated with real measurements. Parametric models

109

calculate solar extinction in extreme conditions of clear and very cloudy

110

environment (4), (5), (6). Most of them are not spectral models, when the solar

111

energy is of spectral nature, using parameters and standard atmospheres.

112

Some of them combine standard parameters with measurements from a

113

particular site (7), (8), but in any case they have not been contrasted with

114

reality due to the difficulties of measuring extinction in realistic conditions.

115

In the work of Ballestrín et al. (6), the authors emphasize the need to have

116

real measurements from each site, showing discrepancies in the results

117

between the different parametric models applied to a same location. There are

118

some

119

instrumentation used for meteorology, visibility and solar energy. In the work of

120

Sengupta et al. (9), authors propose the study of the impact of aerosols on

121

broadband atmosphere attenuation based on the comparison of direct normal

122

irradiance measurements (DNI), and DNI model estimations for an atmosphere

123

free of aerosols. In a similar way, Tahboub et al., (10), use four pyrheliometers

124

at different heights to obtain extinction. The disadvantage of this method is the

125

horizontal difference of the sensors; they are placed at different altitudes in a

126

mountain.

127

methodology developed by Sengupta (9) by using DNI measurements and the

128

model proposed by Sengupta, obtaining three transmittance models for the

129

attenuation losses between the heliostat and the receiver of a STE tower plant

130

(11). In parallel, Hanrieder et al. developed studies using transmissometers and

131

scatterometers for that end. The resulting methodology estimates atmospheric

132

spectral transmittances from transmissometer or scatterometer measurements.

133

To that end, the methodology needs to apply spectral corrections by using

134

radiative transference codes (RTC) and, in the case of scatterometers, also

135

absorption phenomena corrections (12).

works

In

where

2016,

authors

characterize

Hanrieder

et

al.

4

the

atmospheric

presented

an

extinction

improvement

of

with

the

136

A reliable system to measure direct atmospheric extinction has been developed

137

by CIEMAT at Plataforma Solar de Almería (PSA), (13), (14), hereinafter CIEMAT

138

system. This new system performs real-time measurements of atmospheric

139

extinction

140

uncertainty lower than 2%. The CIEMAT system is currently used as reference

141

for other systems and measuring methodologies. At present, there are some

142

works in process comparing other methodologies with the CIEMAT system.

143

Furthermore, with the development of the CIEMAT system, the atmospheric

144

extinction in the control panel of an operating room at a STE tower plant in

145

real time has been monitored for the first time, in conjunction with other

146

meteorological variables to consider. One year of atmosphere extinction

147

measurements was achieved in June of 2018 (15), showing an annual average

148

value of 5.8 ± 2.2% of atmospheric extinction at the PSA. The system is still

149

taking measurements today.

150

A previous simulation and analytical work (8) was necessary to develop the

151

CIEMAT system, in order to know the levels of extinction expected and select a

152

sensor able to detect them. For that reason, a methodology was carried out to

153

determine the atmospheric extinction levels at PSA using AERONET data, typical

154

meteorological year (TMY) (16) and Radiative Transfer Codes (RTC) (17). This

155

method, called Extinction AOT method from now onwards, was developed and

156

applied at PSA, but it can be extrapolated to any location where there is

157

experimental AOT data of quality.

158

The results of a year of measurements carried out with the CIEMAT system

159

(15) have been utilized to validate the results obtained with the Extinction AOT

160

method at PSA. The mean atmospheric extinction levels was 4%, up to the

161

distance at which CIEMAT system measures extinction (741.63 m) (14) in the

162

case of the Extinction AOT method, and 5.8 ± 2.2% employing the CIEMAT

163

system (15). Both values match, taking into account the error margins, thus,

164

the Extinction AOT method was validated (15).

165

Even though Extinction AOT method (8) can be applied at any place where an

166

AERONET station exists, there are placements of interest to CSP around the

167

Sunbelt, where there aren’t AERONET stations, or the existing ones only have

168

few years of measurements. As a consequence of the lack of AOT ground-

169

based measurements databases or atmospheric extinction information, in this

170

work a new approach to determine the solar extinction levels at any

171

emplacement of interest has been developed, based on the Extinction AOT

172

method (7), (8), but using satellite data, providing AOT data from around

173

practically all the Earth’s surface practically. To that end, the extinction at PSA

174

has been calculated with data from MODIS TERRA and AQUA satellites (18),

using

two

high

resolution

5

digital

cameras

with

an

absolute

175

and rom the forecasting model and analysis algorithm, MERRA2 (19). This new

176

approach has been compared and validated both with the CIEMAT system and

177

the Extinction AOT method, therefore the new methodology developed can be

178

applied anywhere.

179

2. AOT database

180

There are long-term databases of AOT at different wavelengths available, which

181

can be utilized without the need to carry out AOT measurements. The main

182

databases are AERONET, MERRA2 and MODIS. The AERONET database has

183

spatial and temporal limitations due to the fact that there is a net of

184

sunphotometers that measure AOT at different places. It depends on the

185

individual place maintenance, for this reason the data available varies from

186

one station to another. Conversely, MERRA2 is a reanalysis model that provides

187

data from around the world and for a long period of time. In the same way,

188

MODIS provides data from the satellites TERRA and AQUA which, in turn,

189

provide data from any location on the planet, since both satellites check the

190

entire surface of the planet every one or two days.

191

2.1.

AERONET

192

AERONET is a worldwide network of AOT measuring stations promoted by

193

NASA. The network of stations provides information on the physical, optical

194

and radiative properties of aerosols in numerous locations around the planet.

195

Each

196

properties of aerosols. There are about 800 stations around the world (20).

197

The sunphotometer measures voltage, which is proportional to the spectral

198

irradiance that the instrument located on the surface reaches. The spectral

199

irradiance estimated at the top of the atmosphere, in voltage terms, is

200

obtained from a sunphotometer located at the Observatory Mauna Loa in

201

Hawaii (20).

202

2.2.

station

consists

of

a

sunphotometer

that

determines

the

optical

MERRA2

203

The Modern-Era Retrospective Analysis for Research and Applications, version 2

204

(MERRA2), is an atmospheric reanalysis of the satellite Era produced by NASA’s

205

Global Modelling and Assimilation Office (GMAO) (21). MERRA2 is produced with

206

version 5.12.4 of the GEOS atmospheric data assimilation system (22), (23) and

207

the GSI analysis system (24) (25). The model uses an approximate resolution of

208

0.5° latitude by 0.625° longitude grid.

209

The reanalysis model processes meteorological observations of an historical

210

data record based on a forecast model, in order to produce data for a broad

211

range of variables directly and indirectly observed (21). MERRA2 includes 6

212

aerosol

213

meteorological observations are assimilated together within a global data

214

assimilation system.

215

The AOT at 550 nm is obtained by using an analysis splitting technique. Firstly

216

using covariance’s error, derived from innovation data, and secondly, projecting

217

horizontal increments vertically, using an ensemble method. So, AOT is derived

218

from different kinds of sources, such as reflectance from AVHRR [1979–2002,

219

ocean-only (26)], reflectance from MODIS on Terra [2000–present] and Aqua

220

[2002–present] (27), (28), AOT retrievals from MISR [2000–14, bright, desert

221

regions only (29), and direct AOT measurements from the ground-based

222

AERONET [1999–2014 (20)]. MODIS provides the largest part of the AOT

223

observations assimilated in MERRA-2, especially after 2002, when data from

224

Terra and Aqua satellites became available. Before the year 2000, only AVHRR

225

reflectance over ocean is used in MERRA2. AOT for both MODIS and AVHRR

226

are derived from cloud-cleared reflectance using a neural net procedure

227

trained on AERONET measurements (21). Thus, due to this structure, these

228

AOT retrievals are unbiased with respect to AERONET observations.

229

2.3.

data

assimilation,

providing

a

reanalysis

where

aerosol

and

MODIS: AQUA AND TERRA

230

Moderate Resolution Imaging Spectroradiometer (MODIS) is an instrument

231

placed on both Terra and Aqua satellites. Terra satellite orbits around the

232

Earth from north to south across the Equator in the morning. On the other

233

hand, Aqua satellite moves from south to north over the Equator in the

234

afternoon. Both of them provide images of the entire Earth’s surface every day

235

or 2, and obtain high radiometric sensitivity data in 36 spectral bands (from

236

0.4 to 14.4 µm) or in groups of wavelengths (30).

237

MODIS monitors the ambient AOT over oceans and continents. The aerosol

238

size distribution is acquired over the oceans, and the aerosol type is acquired

239

over continents. Daily data is produced at the spatial resolution of a 10x10

240

pixel scale (10 km at nadir). The MODIS aerosol algorithm is comprised from

241

two independent algorithms, one for acquiring aerosols over land, and the

242

other one for aerosols over ocean. The Deep Blue Algorithm to get AOT over

243

bright land areas is included, as well as the Dark Target Algorithm, applied

244

over ocean and dark land (vegetation) (27). The aerosol products available

245

over land include AOT at three different wavelengths. The aerosol retrieval

246

uses seven channels of MODIS to acquire aerosol characteristics. It also uses

247

additional wavelengths from other parts of the spectrum to identify clouds and

248

sediments (27). The Ångström exponent over land is defined by 0.47 and 0.66

249

µm wavelengths.

7

250

2.4.

251

2.4.1.

Comparison of the Satellite and AERONET AOT in the Literature AOT MODIS-AERONET comparison

252

The AOT retrieved by MODIS has been validated by comparing two years of

253

AOT from 132 AERONET stations (27). It was done by using 2-3 months of

254

data from MODIS and comparing it with the same data from AERONET

255

measurements, obtaining a standard deviation of AOT from MODIS of ±0.05

256

and ±0.15 over ocean and over land respectively.

257

Other studies have investigated the variability of AOT ranging from seasonal to

258

inter-annual time scales using satellite retrievals from MISR and

259

together, with ground-based measurement from AERONET, over the Arabian

260

Peninsula. The MODIS AOT values exhibit 0.81 and 0.85 correlations with

261

AERONET observations during the wet and dry seasons, respectively (31).

262

In another works, the AOT from MODIS has been compared with 20 AERONET

263

stations (32), and the results show a correlation coefficient for Dark Target

264

(DT) retrievals of 0.946. Deep Blue (DB) retrievals have a correlation coefficient

265

of 0.931, with a higher expected error. In this study, the authors compare

266

results from DT and DB at different scenarios. DB is better than DT in urban

267

areas, as in the case of low and high elevation vegetation areas.

268

Also, using the literature, it can be affirmed that DB AOT products show the

269

best performance in most regions, especially in Europe and North America (18).

270

In bright surfaces like deserts and arid/semi-arid areas, DB products match

271

more closely with the AERONET data than the DT over medium or densely

272

vegetated areas (18).

273

In addition, satellite data has been used to evaluate solar radiation, and after

274

applying a correction, slightly skewed estimations were obtained regarding

275

radiometric observations on the ground (33). The same authors have compared

276

Level-3 Modis AOT products from TERRA satellite, against observed daily

277

observed AOT values at 550nm from more than 5000 AERONET stations

278

around the globe. The mean error is of 0.03 and RMSE 0.14 (34). Others

279

authors, (35), show the unfeasibility to obtain quality AOT under cloudy

280

conditions, as well as regional biases due to some other reasons. For that

281

reason, they use a methodology for bias reduction proven in North American,

282

where the initial mean error is reduced from 0.067 to 0.001 and the RMSE

283

from 0.130 to 0.057, with respect to ground measurements.

8

MODIS

284

2.4.2.

AOT MODIS-MERRA2-AERONET comparison

285

Some works of the literature have compared MERRA2 and MODIS AOT, by

286

using AERONET data (36), (37). In the work of Sun et al. (36) 4258

287

instantaneous AOT values at 550 nm have been used, which were compared

288

between the MERRA2 data and AERONET values in four seasons at 12

289

AERONET sites across China. The correlation coefficients in the four seasons

290

were 0.88 in spring, 0.92 in summer, 0.91 in autumn and 0.87 in winter. In

291

addition, the MERRA2 AOT was compared with the MODIS-Aqua AOT over

292

China from 2003 to 2017, using 4501 daily AOT values from MERRA2 and

293

MODIS from 16 sites across China, obtaining a good agreement too. In another

294

work, (37), authors obtain the global distribution of AOT and evaluate the

295

results using AERONET.

296

Other studies have used AOT data from 400 AERONET stations to develop an

297

evaluation dataset with the purpose of evaluating the reanalysis AOT datasets

298

from MERRA2 and satellite data from MODIS Terra and Aqua (38). The results

299

of this study show that MERRA2 has a comparable accuracy as well as the

300

MODIS results (RMSE=0.119, MBE=-0.008), (RMSE=0.110, MBE=0.011) (38).

301

3. Atmospheric extinction methodologies

302

3.1.

CIEMAT measurement system with digital digital cameras

303

The

atmospheric

304

parametric models that use standard atmospheres, which do not accurately

305

represent the specific atmospheric conditions of each location (4), (5), (6).

306

Instrumentation for meteorology has also been used to measure extinction, but

307

the errors associated with the measurements are higher than the levels of

308

extinction that are intended to be measured in many cases (9), (10), and in

309

others, the instrumentation is monochromatic, when extinction is a spectral

310

variable (12).

311

Due to this background, some simulations were carried out with a radiative

312

transfer code in order to obtain an estimation of the levels of atmospheric

313

extinction that can be found at the PSA (6), using a typical aerosols year, i.e.

314

an artificial year of AOT values representing the long-term, (8). This method is

315

called

316

simulations, a methodology for measuring extinction was developed. This

317

methodology employs digital cameras with CMOS sensors, with a spectral

318

range from 400 to 1000 nm, and with an absolute measurement uncertainty

319

less than 2%. This measurement system, called CIEMAT measurement system,

320

is currently working on a daily basis at PSA (14).

Extinction

extinction

AOT

of

method.

solar

Based

9

radiation

on

the

has

levels

been

obtained

obtained

from

with

the

321

The CIEMAT measurement system consists of two digital cameras pointing

322

towards a lambertian target at difference distances, all of them aligned along

323

the north-south line of the PSA. The digital cameras take simultaneous images

324

of the target at different distances using two identical optical schemes (digital

325

camera sensor, lens and filter). The filters in both cameras are used to prevent

326

saturation of the camera sensor. The filter only lets pass the 6% of the

327

irradiance. The distance between cameras is 741.63 ± 0.01 meters, which is

328

enough to detect extinction, according to previous simulations, (6), (8). The

329

distances between the cameras and the centre of the target are 82.88 ±

330

0.01m and 824.51 ± 0.01m respectively. This way, the target area projected per

331

pixel is similar in both cameras, 10 mm in diameter approximately, so the

332

matrix of values generated for the image in both cameras has the same size.

333

The intensity levels of the digital images are proportional to the solar radiance

334

coming from the target, and the difference of intensity between the images is

335

due to the extinction of the solar radiation in the path between both cameras,

336

since the airlight is eliminated with the black part of the target.

337

The target is Lambertian due to the fact that the position of the cameras and

338

the relative angles to the target can never be known for sure at such large

339

distances. This way, with a perfect diffuser surface, an image of the target

340

taken from any viewing angle will accurately represent the same brightness or

341

luminance (39).

342

3.2.

Extinction AOT method (with (with AERONET data)

343

Aerosols and water vapour are the atmospheric constituents responsible for

344

most of the extinction in the boundary layer of the Atmosphere (3). The

345

aerosols can be characterized with the aerosol optical thickness (AOT), which

346

is a dimensionless parameter that represents the intensity of the attenuation

347

due to aerosols, being closely related to the density of airborne aerosols in

348

column and the particle size. This parameter can be used to calculate the

349

extinction with parametric methods. A data source of AOT at different

350

wavelength is AERONET (40).

351

A new approach to estimate the extinction in STE tower plants was elaborated

352

(Extinction AOT method), (8), based on the existing methodologies to estimate

353

the extinction in the same kind of plants (6), (7), and applied to the PSA

354

environment. The model uses atmospheric realistic conditions present in a real

355

location, using for this purpose AOT data from the PSA_Tabernas_AERONET

356

station (41). With the AOT data, a typical aerosols year (TAY) is elaborated

357

using the NREL methodology for typical meteorological years (TMY) (42). With

358

the TAY obtained, the Ångström parameters at the medium, maximum and 10

359

minimum levels of AOT that you can find at PSA are estimated.

360

the transmittances at different slant ranges (SR) are calculated using the

361

Radiative Transfer Code (RTC) LibRadtran (17). Finally, a polynomial equation

362

for the attenuation based on the SR for the levels of extinction expected at

363

PSA is obtained. This polynomial equation can be introduced in simulations

364

tools of CSP to calculate the operation and optimization of this kind of plants

365

(43), (44). All these steps are developed in the methodology of this work.

366

3.3.

After that,

Satellite model

367

With the purpose of applying the Extinction AOT method to determine

368

extinction at any place where there isn’t an AERONET station, the extinction

369

levels at PSA with the Extinction AOT method have been calculated using AOT

370

data from satellite instead of AERONET AOT data. The results were compared

371

in order to validate the new methodology and to know if it is possible to

372

determine extinction levels at any location with satellite data.

373

4. Methodology

374

4.1.

Data retrieved

375

The AOT data of MERRA2 and MODIS have been retrieved using the tool

376

Giovanni data service (45). Giovanni is a web promoted by NASA which

377

provides a simple way to visualize, download and access Earth science remote

378

sensing

379

(https://giovanni.gsfc.nasa.gov/giovanni/).

380

The Extinction AOT method (8) with AERONET data was applied at PSA and the

381

TAY required in the method was developed using 5 years of data (2011-2015)

382

(46), (47). For this reason the same years of AOT MERRA2 and MODIS data

383

from Giovanni have been chosen in order to develop the TAY. This range of

384

years have been consider in order to obtain the highest similarity in the data

385

comparison, but data from over 15 years can be used in another works.

386

data,

4.1.1.

particularly

from

satellites

MERRA2 Data

387

The downloaded data has been the AOT at 550 nm and the Ångström

388

parameter (470-870 nm), with an hourly time average, spatial resolution

389

0.5x0.6250 and a bounding box correspondent to the PSA location (-2.45,

390

36.95,

391 392 393

MERRA2tavg1_2d_aer_Nx: 2d,1-Hourly, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics V5.12.4 and MERRA-2 tavg1_2d_aer_Nx: 2d,1-Hourly,Timeaveraged,Single-Level,Assimilation,Aerosol Diagnostics V5.12.4. -2.26,

37.12).

The

complete

name

11

of

the

products

are:

394

4.1.2.

MODIS Data

395

Four types of data from MODIS, AOT at 550 nm from Terra and Aqua, with

396

the Deep Blue Algorithm and Land only data, with a spatial resolution of 10,

397

bounding box correspondent to the PSA location (-2.45, 36.95, -2.26, 37.12)

398

and time average daily have been downloaded, being the name of the

399

products

400 402

mean_MOD08_D3_6_1_Deep_Blue_Aerosol_Optical_Depth_550_Land_Mean and mean_MYD08_D3_6_1_Deep_Blue_Aerosol_Optical_Depth_550_Land_Mean, for Terra and Aqua respectively. The other data is the Deep Blue Ångström

403

exponent (412-470 nm) for land, from Terra and Aqua, being the name of the

404

products mean_MOD08_D3_6_1_Deep_Blue_Angstrom_Exponent_Land_Mean and

405

MYD08_D3_6_1_Deep_Blue_Angstrom_Exponent_Land_Mean.

406

algorithm applied to bright areas, for that, the data obtained from where only

407

this algorithm is applied and only over land has been selected, since some

408

studies have proven that it gives better results for AOT (27). This way, in

409

Figure 1, the AOT at 550 nm obtained from MODIS combined Dark Target and

410 411

Deep Blue AOT over land and ocean, for the same bounding box (-2.45, 36.95, -2.26, 37.12), has been compared with the only Deep Blue and land data at

412

the same conditions, and both of them with AERONET. It can be seen that

413

data with only Deep Blue algorithm over land has values close to the data

414

obtained from AERONET at the same emplacement. The data with Dark Target

415

and Deep Blue algorithm applied over land and ocean are close to AERONET

416

too, but slightly overestimated, as the fit lines of both data show. The Pearson

417

coefficient R for DB and only land is 0.7380, while for the DB and DT

418

combined and for land and ocean is 0.6561. Due to this fact, DB algorithm

419

only for land data present a better fit than the DT and DB algorithm for land

420

and ocean data. This coincides with the information obtained in the scientific

421

literature where it is shown that for land the AOT data from DB algorithm only

422

land are better than the others.

401

12

Deep

Blue

is

an

423 424

Figure 1. Comparison of TERRA DB only and TERRA DB+DT with AERONET for AOT of 500 nm.

425

In order to compare the differences between DB and DB+DT data with the

426

reference data of the AERONET AOT ground measures, as shown in Figure 1,

427

the reference data (AERONET) has been depicted in the x-axis and the satellite

428

data (DB and DB+DT) in the y-axis. At this point, if the data were identical to

429

the AERONET data, the plot in Figure 1 would be a perfect linear fit. However,

430

it can be seen that the data from both sources (DB and DB+DT) present

431

differences

432

differences lower when using the DB data than the DB+DT data, as the

433

Pearson coefficient explains. For this reason, this statistical coefficient has been

434

used to quantify the differences between data from any source and the

435

reference ones (AERONET). To check the statistical significance of the Pearson

436

coefficient obtained from DB and DB+DT data, a hypothesis analysis has been

437

performed to prove that the variables are related (null hypothesis, ρ=0). In

438

order to do this, the t-student is calculated (α=0.05 and N-2 degrees of

439

freedom, being N the population size), finding that t is higher than tα,N-1. For

440

this reason, the null hypothesis is rejected, so the correlation obtained does

441

not come from a population with ρ=0. A bi-variately test of normality, the

442

Kolmogorov

443

continuous distribution), has also been applied to the data. It has been found

444

that x and y (AERONET and TERRA DB/DB+DT) originate from different

445

continuous distribution, are independent observations and bi-variately normally.

with

respect

to

the

Smirnov test (null

AERONET

reference

hypothesis=x and y

13

data,

are

being

from

those

the same

446

These analyses have been done with the statistical software SPSS. The null

447

hypothesis for the t-student and the Kolmogorov Smirnov test has been

448

rejected in all the comparisons made in this work.

449

MERRA2 and MODIS only provide AOT at one wavelength (550 nm). To do the

450

TAY as in the Extinction AOT method, it is necessary AOT at eight different

451

wavelengths (1640nm, 1020nm, 870nm, 675nm, 500nm, 440nm, 380nm, 340nm).

452

For that reason, it is used the Ångström parameter (470-870 nm) from MERRA2

453

and

454

wavelengths obtained by the Equation 1, acquired from the Ångström law.

(412-470

nm)

from

MODIS,

being

the

AOT

at

= 455

4.2.

the

eight

different

Equation 1

Typical Aerosol Year calculation

456

The Extinction AOT method is used with the same purpose, but employing

457

satellite data instead of AERONET data. In the Extinction AOT method (8), the

458

authors use AOT data and make a typical aerosols year (TAY), prepared by

459

using the NREL methodology to create a typical meteorological year (TMY) (42).

460

The TAY, in this case is generated by processing aerosol data from the PSA

461

location provided by a satellite.

462

The AOT data from a satellite must have more than 5 years of record,

463

because TMY is produced with at least 15 or 30 years of data, although some

464

authors assure that only 5 years of data is sufficiently representative of the

465

climate of a place (46), (47).

466

typical meteorological years (TMY), but in the Satellite and Extinction AOT

467

method it is used to create a TAY, i.e. a TMY only made up of data from AOT

468

measured at different wavelengths, in this case AOT_1640nm, AOT_1020nm,

469

AOT_870nm,

470

AOT_340nm. The statistical analysis to configure a standard year consists of

471

the concatenation of 12 different months selected from the sample of years of

472

agreement with the values of the Finkelstein-Schafer (FS) statistic (Equation 2),

473

which is used to determine the value of meteorological variables from a broad

474

period of time (48). ,

AOT_675nm,

=

1



!"

The NREL methodology (16) is used to produce

AOT_500nm,

−

AOT_440nm,

AOT_380nm

and

Equation 2

,

475

CDFM is the long-term cumulative distribution function (5 years in the study),

476

CDFu,M is the cumulative short-term distribution function (in year u) of the daily 14

477

variable xk for month M. The TAY generated is composed of one year of daily

478

data of AOT at eight wavelengths (AOT_1640nm, AOT_1020nm, AOT_870nm,

479

AOT_675nm, AOT_500nm, AOT_440nm, AOT_380nm, AOT_340nm), and it is a

480

year of daily values of the different variables than compose the TAY, being

481

each month of the year real months from different years which present the

482

lower values of the Finkelstein-Schafer statistic.

483

From the generated TAY, the mean AOT values are calculated for each

484

wavelength, as well as the maximum and minimum levels of them at the PSA,

485

with the same procedure that the mean and extremes are calculated in

486

climatology. The average is calculated just as the monthly average, divided by

487

the number of months. The maximum is calculated just as the sum of the

488

maximum of each month that makes up the TAY divided by the number of

489

months, and the minimum will be determined in the same way as the

490

maximum.

491

(AOTmean, AOTmax, AOTmin) and the Ångström parameters characteristics

492

(αÅmean, βÅmean, αÅmax, βÅmax, αÅmin, βÅmin) at the PSA site applying the Ångström Law

493

(Equation 3).

So,

the

typical

= βÅ 494

4.3.

levels

of

aerosols

are

obtained at

the

PSA

Equation 3

Å

Extinction levels calculations

495

After the TAY is elaborated and the Ångström parameters characteristics

496

obtained, LibRadtran is used as a radiative transfer code to calculate the

497

extinction levels for different slant ranges with the atmospheric scenario of the

498

PSA. The average and extreme levels of AOT for the PSA are entered in the

499

RTC in form of Ångström parameters (αÅmean, βÅmean, αÅmax, βÅmax, αÅmin, βÅmin), in

500

addition to other parameters that define the atmosphere of the PSA (Spectral

501

range 250-2500 nm, extraterrestrial spectrum Kurucz with a resolution of 0.1

502

nm (49), Pressure 1013.25 hPa, Temperature 15.15

503

atmosphere, altitude 500 m). With this input LibRadtran generates outputs of

504

spectral transmittances for the different slant ranges for the average and

505

extremes values at PSA.

506

The spectral transmittance, τλ, obtained with LibRadtran is used in Equation 4

507

to obtain the transmittance for different slant ranges (SR) (6).

% & =

,-

',-

,-

',-

% ( )* + ( )* +

0

C, US-standard rural

Equation 4



15

508

The spectral reflectance, ρλ, of the Equation 4 has been obtained from a

509

mirror sample of an heliostat of the CESA 1 field at PSA, evaluated with a

510

Perkin-Elmer spectrophotometer lambda 9 UV-Vis-NIR, with a spectral range of

511

250-2500 nm, and weighted with the standard solar irradiance ASTM G173, air

512

mass 1.5 (6). The direct spectral irradiance used in the same equation, Gbλ, has

513

been calculated for the atmospheric conditions of the PSA, using the SMARTS

514

software (50) applying the TAY generated results.

515

The characteristic transmittance levels obtained at PSA become extinction

516

terms with Equation 5. . / % = 100 1 − %

Equation 5

517

Subsequently, a model is calculated for the levels of extinction depending on

518

the slant range of the form of Equation 6, which is the way some codes of

519

design and operation allow to introduce the attenuation (43), (44), (51). Equation 6

. / % = +" &2 + +, &, + +2 & + +4 520

With Equation 6 the extinction at the PSA at any distance can be obtained,

521

including the distance at which the cameras of the CIEMAT system measure

522

extinction are.

523

5. Results

524

5.1.

525 526

Retrieved data and comparison of the AOT data from MERRA2 and MODIS with AERONET

5.1.1.

MERRA2 and AERONET comparison

527

Data from AERONET at different wavelengths and data from MERRA2 at the

528

same wavelengths have been compared, both obtained at the PSA and between

529

01-01-2011 and 31-12-2015. Data from AERONET has been retrieved from the

530

AERONET station PSA_Tabernas_AERONET. In order to retrieve the AOT MERRA2

531

data, Giovanni data service tool has been employed. AOT data has been

532

retrieved at one wavelength, while the other wavelengths have been obtained

533

with Equation 1.

534

The comparison is showed in the Figure 2, where the regression lines and the

535

Pearson coefficient of the comparisons can be seen. The mean coefficient

536

value in all cases is 0.9235. Moreover, some statistics of the comparison are

537

represented, showing good levels of RMSE, NRMSE, and NMBD, being NRMSE

538

between 3 and 4.5% in all the cases, which is normalized with the maximum of

539

the reference population. The population size used in the comparison has been 16

540

N=1302. For that, we can conclude that there is a good correspondence

541

between

542

measurements on land from AERONET, which is considered a reliable AOT

543

database (52).

the

AOT

of

MERRA2

and

544

17

the

data

of

the

sunphotometer

545 546 547 548

Figure 2. Comparison of AOT at different wavelengths from MERRA2 and AERONET sources. Black line is the trend line of the comparison data, being the reference AERONET. Red line is the equality line; it is the trend that data should follow if they are unbiased.

5.1.2.

MODIS TERRA and AQUA with AERONET comparison

549

In the same way, data from MODIS TERRA and AQUA with AERONET has been

550

compared, obtained from the PSA and during the same period (01/01/2011-

551

31/12/2015), using the same AERONET data from the aforementioned station.

552

MODIS TERRA and AQUA data have been retrieved only at one wavelength as

553

in the MERRA2 case, so the other wavelengths have been obtained using

554

Equation 1. The size population for TERRA is N=1098 and N=1021 for AQUA.

555

Results from the data obtained from TERRA are shown in Figure 3, and the

556

ones obtained from AQUA in Figure 4. Results show a low correlation between

557

the MODIS data from both satellites and the land measurements from AOT

558

retrieved from AERONET, with a mean Pearson coefficient in all cases from

559

TERRA around 0.7019 and 0.6673 in the AQUA case, lower than in MERRA2.

560

The NRMSE in both cases is between 8 and 15%, depending on the

561

wavelength,

562

Accordingly, data from MODIS has a lower correspondence with respect to

563

AERONET than the data from MERRA2, which has less deviation from the

564

reference ones (AERONET).

normalized

with

the

maximum

18

of

the

reference

population.

19

565

Figure 3. Comparison of the AOT at different wavelengths from TERRA and AERONET sources

20

566

Figure 4. Comparison of the AOT at different wavelengths from AQUA and AERONET sources

567

Besides, the AOT at 550 nm data obtained from satellite TERRA and AQUA

568

have been compared. Results can be observed in Figure 5, and it shows that

569

data from the same product, for the same time period and locations, generate

570

different results, with a RMSE=0.1, NRMSE=10.53% and a Pearson coefficient

571

R=0.5970.

21

572 573 574

Figure 5. Comparison of the AOT at 550nm from TERRA and AQUA sources

5.2.

Typical Aerosol Year and typical Ångström parameters

575

The TAY for MERRA2, AERONET and MODIS, are generated using a Matlab

576

code developed in the work of Abreu et al. (53), and they are showed in

577

Figure 6. In all cases, AOT exhibits a peak in the warm seasons (summer and

578

spring), behaviour that coincides with the scientific literature where it is

579

contrasted

580

emplacement of mainly desert dust, industrial wastes and marine aerosols (54).

581

Also, in places close to the sea, aerosols exhibit a seasonal variability,

582

increasing in warm months, especially in summer. This peak appears because

583

levels of precipitation are lower during these seasons (summer and spring) and

584

this increases the photochemical production of secondary organic aerosols

585

(55). Furthermore, during these seasons aerosols appear due to the dust of

586

the cyclonic activity in North Africa (56). This dust has high seasonality,

587

showing maximum levels in spring at emplacements near to the sea (52), (55).

that

the

Mediterranean

Basin

22

presents

aerosols

due

to

the

588 589

Figure 6. TAY for the different AOT sources: AERONET, MERRA2 and MODIS (TERRA-AQUA)

590

The Ångström parameters characteristics at PSA site (αÅmean, βÅmean, αÅmax, βÅmax,

591

αÅmin, βÅmin) are obtained with the TAY. The Ångström parameters characteristics

592

for AERONET, MERRA2 and MODIS can be observed in table 1.

593

Table 1. Typical Ångström parameters at the PSA from different sources of AOT

AERONET MERRA2 MODIS-TERRA MODIS-AQUA

αÅmax/βÅmax 0.5050/0.2924 0.3930/0.2575 0.498/0.3330 0.6310/0.2875

αÅmean/βÅmean 0.7620/0.0775 0.7200/0.0797 1.0660/0.0848 1.1550/0.0707

αÅmin/βÅmin 1.1420/0.0194 1.1630/0.0250 1.4510/0.0169 1.4930/0.0172

594 595

5.3.

Atmospheric extinction levels at PSA

596

The spectral transmittances at different slant ranges, obtained with the

597

Ångström parameters and other characteristic variables at PSA from MERRA2,

598

AERONET and MODIS, are used to obtain the attenuation equations.

599

The attenuation equations as a function of the slant range for levels at the

600

PSA, using the different sources of AOT, have the form of Equation 6, of which

601

coefficients are expressed in table 2. Also, they are graphed in Figure 7, where

602

it can be observed that there are lower differences between the extinction 23

603

levels calculated using the different sources, AERONET, MODIS and MERRA2,

604

being MERRA2 the closest in land measurements (AERONET).

605 606

Table 2. Typical Attenuation equation coefficients (Equation 6) for the PSA from the different AOT sources.

Mean Levels

Max. Levels

Min. Levels

d1 d2 d3 d4 d1 d2 d3 d4 d1 d2 d3 d4

AERONET 0.0010 -0.0124 0.0621 0.0004 0.0039 -0.0431 0.1767 0.0006 0.0006 -0.0064 0.0340 0.0003

MERRA2 0.0012 -0.0137 0.0682 0.0040 0.3397 -3.8720 16.8250 0.1250 0.0665 -0.6845 3.5880 0.0280

MODISMODIS-TERRA 0.0014 -0.0165 0.0805 0.0005 0.5591 -6.0851 23.6730 0.0600 0.0618 -0.6493 3.4590 0.0292

MODISMODIS-AQUA 0.0012 -0.0146 0.0719 0.0005 0.4826 -5.3492 21.3650 0.0655 0.0625 -0.6542 3.4758 0.0291

607

608 609 610

Figure 7. Attenuation levels as a function of Slant Range using MERRA2, AERONET and MODIS sources

611

In Figure 7 it is shown that the mean extinction levels at the PSA are closer to

612

the minimum levels than to the maximum levels, indicating that the extinction 24

613

at the PSA has lower levels. This means that the sky at the PSA is, in general,

614

a clear one throughout the whole year, although some episodes of high

615

extinction happen due to the presence of Saharan dust in the Peninsula, as

616

shown in Figure 6. The maximum levels of extinction are registered in the

617

warmer seasons.

618

6. Comparison and validation of the extinction obtained with

619

AERONET, AERONET, MERRA2 and MODIS with the CIEMAT measurement

620

system

621

The

extinction

622

Extinction AOT method, was developed to obtain the extinction levels at PSA

623

and to be able to choose an appropriate device capable of measuring them.

624

The measurement system developed has obtained measurements during two

625

years at the PSA, with reliable results (13), (14), (15). Therefore, in order to

626

validate the Extinction AOT method with AERONET data (8) the ground

627

measurements of the CIEMAT system have been used, calculating the extinction

628

at 741.63 m, distance which the CIEMAT system measures extinction (distance

629

between cameras) (13), (14), (15). The results are shown in Figure 8, where the

630

extinction

631

measured with the CIEMAT system are both shown. Taking into account the

632

error bars of the measurement system, it can be verified that the extinction

633

values are coincident; therefore, the Extinction AOT method to determine

634

average extinction levels at a site is validated, since the levels determined by

635

this methodology match the measurements made by the two cameras among

636

the CIEMAT system.

levels

determination

calculated

method

with

the

637 25

recently

Extinction

published

AOT

(8),

method,

called

and

the

those

638

Figure 8. Validation Extinction AOT method with CIEMAT system measurements

639

The calculated levels of extinction for a distance of 741.63 ± 0.01 m with the

640

Extinction AOT method would be 4.0% of mean level and a maximum

641

extinction of 11.0%. The average value of a year of extinction measured with

642

the experimental device (CIEMAT system) at the same distance is 5.8 ± 2.2%

643

(15), which validates the Extinction AOT method (8), as the extinction values

644

are coincident within the margins of error.

645

are, 13.2%, and 11.0% using the Extinction AOT method which refers to the

646

most usual maximum that can be found. Therefore, taking into account the

647

margin of statistical error (2.2%), the maximum values calculated at PSA with

648

the Extinction AOT method and those measured with the experimental device,

649

there is a coincidence within the margins of error. The minimum values

650

calculated with the Extinction AOT method model give a 2.2% extinction value,

651

and the obtained with the CIEMAT system is a 1.5%, taking into account the

652

margin of error. Thus, the data obtained is again validated using simulations

653

carried out with radiative transfer codes (Extinction AOT method).

654

The extinctions at a 741.63 m distance calculated with the model of MERRA2,

655

TERRA and AQUA are expressed in table 3, where the similarities in the levels

656

obtained from different methods are shown.

657

Table 3. Extinction levels at the PSA from different sources

Mean Maximum Minimum

CIEMAT SYSTEM 5.80% 13.20% 1.50%

The maximums measured at PSA

AERONET

MERRA2

TERRA

AQUA

4.00% 11.00% 2.20%

4.75% 10.61% 2.34%

5.17% 14.50% 2.26%

4.63% 13.17% 2.27%

658

In addition, Figure 8 shows the mean, maximum and minimum extinction values

659

obtained at the PSA with the models where the differences between the

660

Extinction AOT method and the CIEMAT measurement system are seen with

661

their error margins. It is verified in Figure 8 that the extinction levels of

662

MERRA2, TERRA and AQUA are in line with the measurements of the CIEMAT

663

system taking into account the margins of error. Therefore the models that use

664

data from these satellites would be validated, and can be used to estimate the

665

extinction levels at any location, with the same confidence that the levels that

666

are obtained with the Extinction AOT method with AERONET data. MERRA2 is

667

the one that most resembles the results of AERONET, since TERRA and AQUA

668

slightly overestimate extinction levels, but they have also been validated to be

669

used for the purposes described above.

26

670

7. Conclusions and Outlook

671

The Extinction AOT method was developed to obtain the levels of solar

672

extinction that can be found on a site and to develop an adequate

673

measurement system that is able to detect these levels, as well as to analyse

674

new sites. With the Extinction AOT method and other previous studies, the real-

675

time extinction measurement system was developed at PSA (CIEMAT System). In

676

this work, using the data obtained from one year of measurements by the

677

CIEMAT system (which measures extinction with two digital cameras of wide

678

spectral range) the Extinction AOT method has been validated since the same

679

extinction results have been obtained at the PSA than those measured by the

680

CIEMAT system. Therefore, the Extinction AOT method would be valid to

681

determine extinction at any location where there is an AERONET station with

682

sufficient AOT data.

683

Throughout the Sun Belt there are interesting sites for the CSP sector where

684

AOT data is not available from any AERONET station. Therefore, the procedure

685

to obtain extinction, developed in the Extinction AOT method, has been applied

686

in this work, but using AOT data from MERRA2 and from the MODIS AQUA and

687

TERRA satellites, which are available from any location. The extinction results

688

obtained at PSA applying this methodology with the satellite data have been

689

validated when compared with the results of the Extinction AOT method with

690

AERONET data and the CIEMAT system. Although there are differences in the

691

AOT

692

calculated with them are trivial. The extinction obtained at PSA with the CIEMAT

693

method is 5.8 ± 2.2% at 741.63 ± 0.01 m. With the AOT Extinction method

694

using AERONET data it is 4% at the same distance. Using the AOT Extinction

695

method with MERRA2, TERRA and AQUA at the same distance the extinction is

696

4.75%, 5.17%

697

with the CIEMAT system ones within the margin of error. These levels of

698

extinction indicate that the PSA is a clean environment with lower levels of

699

particles pollution, taking into account the normal seasonally changes and the

700

occasional dust episodes from the Sahara desert.

701

The AOT data from several sources have been also compared at PSA location,

702

being these AERONET, MERRA2 and MODIS TERRA/AQUA. Some differences

703

have been found between them, being the MERRA2 AOT data the one that less

704

dispersion presents respect to the measured with a sunphotometer on land

705

(AERONET). On the other hand, the extinction values obtained with AOT from

706

these different sources show unimportant differences between them.

707

Therefore, the methodology proposed to determine the extinction levels at any

708

location using satellite data has been validated at the PSA with the CIEMAT

from

the

different

sources,

the

differences

between

the

extinction

and 4.63% respectively. Thus, the results are coincident

27

709

system. Even though the Extinction AOT method has been applied at PSA, The

710

results of the validation indicate that the AOT method could be applied at any

711

location around the world of interest for CSP or with meteorological and

712

climate purposes. The procedure developed in this work allows obtaining the

713

expected

714

measurement instrumentation. The method will be in a short time implemented

715

in the desert of Atacama (Chile) in near future works. After that, the method

716

could be useful for the installation of future STE tower plants, and for the

717

good operation of the existing ones.

718

levels

of

extinction

with

accuracy,

without

the

need

of

any

8. Acknowledgements

719

This work is included in the activities of PRESOL project (Forecast of Solar

720

Radiation at the Receiver of a Solar Power Plant) funded by the Spanish

721

government in the framework of the PRESOL project (Ref. ENE2014-59454-C3-1,

722

2, 3-R) with ERDF funds.

723

The authors acknowledge the generous financial support provided by the

724

Innova

725

CONICYT/FONDAP/ 15110019 “Solar Energy Research Center” SERC-Chile. Also,

726

the work was supported by MINEDUC-UA project, code ANT 1855.

727

The authors wish to thank to the principal investigators and staff from

728

AERONET program, and particularly PhD. Stefan Wilbert (DLR), directly involved

729

in the station of Tabernas-PSA, for delivering so useful data to the scientific

730

community. The authors would also like to thank Marta Ruiz McEwan and Dra.

731

Alba Beas Catena for the language consulting.

732

Edgar F.M. Abreu acknowledges the support of the FCT (The Portuguese

733

Science

734

SFRH/BD/136433/2018, the funding provided by the European Union via the

735

European

736

(Operational Program Competitiveness and Internationalization) through the ICT

737

project (UID/GEO/04683/2013) with the reference POCI-01-0145-FEDER-007690

738

and the project DNI-Alentejo with reference ALT20-03-0145-FEDER-000011.

739

Chile

and

-

CORFO,

Technology

Regional

PROJECT

CODE:

Foundation)

Development

Fund,

17BPE3-83761,

through included

the in

grant the

as

with

well

as

reference

COMPETE

2020

9. References 1. IPCC. Intergovernmental panel on climate change. [Online].; 2018. Available from: https://www.ipcc.ch/activities/activities.shtml. 2. Willis R, Aid C, Green A, Greenpeace , RSPB , WWF. Paris 2015: getting a global agreement on climate change. London:; 2014. 28

3. Gueymard C. Importance of Atmospheric Turbidity and Associated Uncertainties in Solar Radiation and Luminous Efficacy Modelling. Energy 2005. 2005; 30: p. 1603– 1621. 4. Vittitoe CN, Biggs G. Terrestrial propagation loss. Denver:; 1978. 5. Pitman CL, Vant-Hull LL. Atmospheric transmittance model for a solar beam propagating between an heliostat and a receiver. ASES progress in Solar Energy. 1982;: p. 1247-1251. 6. Ballestrín J, Marzo A. Solar radiation attenuation in solar tower plants. Solar Energy. 2011;: p. 388-392. 7. Polo

J,

Ballestrín

J,

Carra

E.

Sensitivity

study

for

modelling

atmospheric

attenuation of solar radiation with radiative transfer models and the impact in solar tower plant production. Solar Energy. 2016; 134: p. 219-227. 8. Carra E, Ballestrín J, Polo J, Barbero J, Fernández-Reche J. Atmospheric extinction levels of solar radiation at Plataforma Solar de Almería. Application to solar thermal electric plants. Energy. 2018; 145: p. 400-407. 9. Sengupta M, Wagner M. Atmospheric attenuation in central receiver systems from DNI measurements. In SolarPACES; 2012; Marrakech. 10. Tahboub Z, Oumbe Z, Hassar Z, Obaidli A. Modelling of irradiance attenuation from a heliostat to the receiver of a solar central tower. Energy Procedia. 2014; 49: p. 2405-2413. 11. Hanrieder N, Sengupta M, Xie Y, Wilbert S, Pitz-Paal R. Modelling beam attenuation in solar tower plants using common DNI measurements. Sol Energy. 2016; 129: p. 244-255. 12. Hanrieder N, Wilbert S, Pitz-Paal R, Emde C, Gasteiger J, Mayer B, et al. Atmospheric extinction in solar tower plants: absorption and broadband correction for MOR measurements. Atmospheric Measurement Technology. 2015; 8: p. 34673480. 13. Ballestrín J, Monterreal R, Carra ME, Fernandez-Reche J, Barbero J, Marzo A. Measurement of solar extinction in tower plants with digital cameras. AIP Conference Proceedings. 2016; 1734: p. 130002. 14. Ballestrín J, Monterreal R, Carra ME, Fernández-Reche J, Polo J, Enrique R, et al. Solar extinction measurement system based on digital cameras. Application to 29

solar tower plants. Renewable Energy. 2018; 125: p. 648-654. 15. Ballestrín J, Carra E, Monterreal R, Enrique R, Polo J, Fernández-Reche J, et al. One year of solar extinction measurements at Plataforma Solar de Almería. Application to solar tower plant. Renewable Energy. ;(136): p. 1002-1011. 16. Hall I, Prairie R, Anderson H, Boes E. Generation of Typical Meteorological Years for 26 SOLMET Stations. Alburquerque:; 1978. 17. Mayer B, Kylling A. LibRadTran user’s guide. Edition for LibRadTran version 2.0. ; 2015. 18. Wei J, Li Z, Peng Y, Sun L. MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison. 2019. 19. Randles CA, da Silva AM, Buchard V, Colarco PR, Darmenov A, Govindaraju R, et al. The MERRA-2 aerosol reanalysis, 1980 onward. Part I: System description and data assimilation evaluation. 2017; 30(17). 20. Holben BN, Eck EF, Slutsker I, Tanre D, Buis JP, Setzer A, et al. AERONET - A federated instrument network and data archive for aerosol characterization. Rem. Sens. Environ. 1998; 66: p. 1-16. 21. Gelaro R, W. , McCarty W, Suárez MJ, Molod A, Takacs L, et al. The Modern-ERra Retrospective Analysis for Research and Applications. Journal of Climate. 2017; 2: p. 5419-5454. 22. Rienecker E, Ryan J, Blum M, Dietz C, Coletti L, Marin R, et al. Mapping phytoplankton

in

situ

using

a

laser-scattering

sensor.

Limnology

and

Oceanography: Methods. 2008; 6(3): p. 153-161. 23. Molod A, Takacs L, Suarez M, Bacmeister J. Development of the GEOS-5 atmospheric

general

circulation

model:

Evolution

from

MERRA

to

MERRA2.

Geoscientific Model Development. 2015; 8(5): p. 1339-1356. 24. Wu WS, Purser RJ, Parrish DF. Three-dimensional variational analysis with spatially inhomogeneous covariances. Monthly Weather Review. ; 130(12): p. 2905-2916. 25. Kleist DT, Parrish DF, Derber JC, Treadon R, Errico RM, Yang R. Improving incremental balance in the GSI 3DVAR analysis system. Monthly Weather Review. 2009; 137(3): p. 1046-1060. 26. Heidinger AK, Anne VR, Dean C. Using MODIS to estimate cloud contamination of 30

the AVHRR data record. Journal of Atmospheric and Oceanic Technology. 2002; 19(5): p. 586-601. 27. Remer LA, Kaufman YJ, Tanré D, Mattoo S, Chu DA, Martins JV, et al. The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric Sciences. ; 62(4): p. 947-973. 28. Levy RC, Remer LA, Dubovik O. Global aerosol optical properties and application to moderate resolution imaging spectroradiometer aerosol retrieval over land. Journal of Geophysical Research Atmospheres. 2007; 112(13). 29. Kahn R, Li W, Martonchik JV, Bruegge CJ, Diner DJ, Gaitley BJ, et al. MISR calibration and implications for low-light-level aerosol retrieval over dark water. Journal of the Atmospheric Sciences. 2005; 62(4): p. 1032-1052. 30. NASA. MODIS, Moderate Resolution Imaging Spectroradiometer. [Online].; 2019. Available from: https://modis.gsfc.nasa.gov/about/. 31. Kumar KR, Attada R, Dasari HP, Vellore RK, Langodan S, Abualnaja YO, et al. Aerosol Optical Depth variability over the Arabian Peninsula as inferred from satellite measurements. Atmospheric Environment. 208;(187): p. 346-357. 32. Zhuang W, Vladan R, Bo H, Zoran O. Aerosol Optical Depth Prediction from Satellite Observations. In ; 2008: Proceedings of the 2008 SIAM International Conference on Data Mining.. 33. Ruiz-Arias JA, Quesada-Ruiz S, Fernández EF, C.A. G. Optimal combination of gridded and ground-observed solar radiation data for regional solar resource assessment. Solar Energy. 2015; 24: p. 112-411. 34. Ruíz-Arias JA, Dudhia J, Gueymard CA, Pozo-Vázquez D. Assessment of the Level-3 MODIS daily aerosol optical depth in the context of surface solar radiation and numerical weather modelling. Atmospheric Chemistry and Physics. 2013; 13: p. 675-692. 35. Ruíz-Arias JA, Dudhia J, Lara-Fanego V, Pozo-Vázquez D. A geostatistical approach for producing daily Level-3 MODIS aerosol optical depth analyses. Atmospheric Environment. 2013; 79: p. 395–405. 36. Sun E, Xu X, Che H, Tang Z, Gui K, An L, et al. Variation in MERRA-2 aerosol optical depth and absorption aerosol optical depth over China from 1980 to 2017. Journal of Atmospheric and Solar-Terrestrial Physics. 2019;(186): p. 8-19.

31

37. L. S, V. S. Global distribution of aerosol optical depth in 2015 using CALIPSO level 3 data. Journal of Atmospheric and Solar-Terrestrial Physics. 2018;(173): p. 150159. 38. Shi H, Xiao Z, Zhan X, Ma H, Tian X. Evaluation of MODIS and two reanalysis aerosol

optical

depth

products

over

AERONET sites.

Atmospheric Research.

2019;(220): p. 75-80. 39. Ballestrín J, Carra ME, Enrique R, Monterreal R, Fernández-Reche J, J. P, et al. Diagnosis of a Lambertian target in solar context. Measurement. 2018; 119: p. 265-269. 40. NASA. Aerosol Robotic Network. AERONET Stations. AOT Data Sites. [Online].; 2018 [cited

2017

05

09.

Available

from:

https://aeronet.gsfc.nasa.gov/cgi-

bin/type_piece_of_map_opera_v2_new. 41. NASA.

Estación

AERONET

Tabernas_PSA-DLR.

[Online].;

2018.

Available

from:

https://aeronet.gsfc.nasa.gov/cgi-bin/data_display_aod_v3. 42. Wilcox S, Marion W. User’s manual for TMY3 Data Sets. 2008.. 43. Blair N, Dobos AP, Freeman J, Neises T, Wagner M, Ferguson T, et al. System Advisor Model, SAM, 2014.1.14: General Description. Technical Report. Golden, USA:; 2014. 44. Dobos A, Neises T, Wagner M. Advances in CSP Simulation Technology in the System Advisor Model. In Energy Procedia; 2013. p. 2482–2489. 45. NASA. Giovanni User Guide. [Online].; 2017 [cited 2019 04 24. Available from: https://giovanni.gsfc.nasa.gov/giovanni/doc/UsersManualworkingdocument.docx.html. 46. Vignola F, McDaniels K. Value of long-term solar radiation data. In Proceedings of the Solar ’93 Conference, American Solar Energy Society; 1993; Boulder, CO: American Solar Energy Society (ASES). 47. Festa R, Ratto CF. Proposal of a numerical procedure to select reference years. Solar Energy. 1993; 50(1): p. 9-17. 48. Finkelstein JM, Schafer RE. Improved goodness-of-fit tests. Biometrika. 1971; 58: p. 641–645. 49. Kurucz R. Synthetic infrared spectra. In Proceedings of the 154th Symposium of the International Astronomical Union (IAU); 1992; Tucson, Arizona. 32

50. Gueymard C. SMARTS2, A simple model of the atmospheric Radiative Transfer of sunshine: algorithms and performance assessment. Clearlake Road, Cocoa, PL:; 1995. 51. Blanco MJ, Amieva JM, Mancilla A. The Tonatiuh software development project: AN open source approach to the simulation of solar concentrating systems. American Society of Mechanical Engineers, Computers and Information in Engineering Division, CED; 2005. 52. Gueymard CA. Temporal variability in direct and global irradiance at various time scales as affected by aerosols. Solar Energy. 2012; 86(12): p. 3544-3553. 53. Abreu EFM, Ferrão Canhoto PM, Rui Melício da Conceição M. Avaliação do Recurso Solar e Modelação de um Sistema de Energia Fotovoltaica Ligado à Rede Elétrica da Ilha da Madeira. 2016.. 54. Benkhalifa J, Léon JF, Chaabane M. Aerosol optical properties of Western Mediterranean basin from multi-year AERONET data. Journal of Atmospheric and Solar-Terrestrial Physics. 2017; 164: p. 222-228. 55. Georgoulias AK, Alexandri G, Kourtidis KA, Lelieveld J, Zanis P, Pöschl U, et al. Spatiotemporal variability and contribution of different aerosol types to the aerosol optical depth over the Eastern Mediterranean. Atmospheric Chemistry and Physics. 2016; 21: p. 13853-13884. 56. Barbero J, Alonso-Montesinos J, Batlles FJ, Polo J, López G, Bosch JL, et al. Evolution of the aerosol extinction coefficient at 100 m above ground during an episode of Saharan dust intrusion as derived from data registered by a ceilometer in Almería (SE Spain). AIP Conference Proceedings. 2018;(2033). 741 742

33

Conflict of Interest and Authorship Conformation Form Please check the following as appropriate:

All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript: Author’s name María Elena Carra Aitor Marzo Jesús Ballestrín Jesús Polo Javier Barbero Joaquín Alonso Rafael Monterreal Edgar F.M. Abreu Jesús Fernández-Reche

Affiliation CIEMAT-PSA Universidad de Antofagasta CIEMAT-PSA CIEMAT Universidad de Almería Universidad de Almería CIEMAT-PSA Universidade de Évora CIEMAT-PSA