Atmospheric horizontal extinction determined with a single digital camera-based system in the scope of solar power tower plants

Measurement 149 (2020) 107025

Contents lists available at ScienceDirect

Measurement journal homepage: www.elsevier.com/locate/measurement

Atmospheric horizontal extinction determined with a single digital camera-based system in the scope of solar power tower plants F.J. Barbero a,⇑, J. Alonso-Montesinos a,b, J. Ballestrín c, M.E. Carra c, J. Fernández-Reche c a

Departamento de Química y Física, Universidad de Almería, Spain CIESOL, Joint Center of the University of Almería-CIEMAT, Almería, Spain c CIEMAT-Plataforma Solar de Almería, Solar Concentrating Systems Unit, Tabernas, Almería, Spain b

a r t i c l e

i n f o

Article history: Received 13 May 2019 Received in revised form 22 July 2019 Accepted 2 September 2019 Available online 5 September 2019 Keywords: Atmospheric transmittance Horizontal radiation attenuation Solar power tower plants Digital cameras

a b s t r a c t A methodology based on the use of a single digital camera has been developed to determine the horizontal attenuation of solar radiation between the field of heliostats and the receiver in a solar tower plant. For this purpose, only the measurement of ambient light and a dark object at known distance is needed. The scenario for this work has been the Plataforma Solar de Almería (PSA, SE Spain), where a large black surface is available. This surface has been designed to be used for a horizontal attenuation measurement system, developed and implemented at the PSA, which is based on the use of two identical digital cameras. Specifically, the horizontal atmospheric attenuation values provided from the single camera-based system and the two camera-based system were compared during several days of July 2018, in conditions of medium values of atmospheric turbidity. A good agreement between the attenuation values obtained with both methods, within their respective uncertainty margins, has been found. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction In solar power tower plants, the solar radiation reflected by the farthest heliostats must travel a great distance to reach the receiver, in which the solar radiation is transformed into useful energy, heat process. In this trip through the lower layer of the atmosphere, the solar radiation undergoes processes of absorption and scattering, in short, it is attenuated. In some central tower solar plants, there are concentric rows of heliostats at distances greater than one kilometer from the tower, and plants with heliostats at even greater distances are being designed. If the characteristic values of the horizontal atmospheric attenuation are significant at the site of the plant, the contribution to the energy production of the distant heliostats may be so low that, from the economic point of view, their integration into the plant would not be profitable. Estimates made with the MODTRAN radiation transfer code, based on a US1976 standard atmosphere and a concentration of rural-type aerosols that limit visibility to 5 km, show that the power losses by horizontal atmospheric attenuation at a distance of 1 km can reach 40% [1]. Accurately determining the horizontal atmospheric attenuation of solar radiation at a site is one of the problems that can condition

⇑ Corresponding author. E-mail address: [email protected] (F.J. Barbero). https://doi.org/10.1016/j.measurement.2019.107025 0263-2241/Ó 2019 Elsevier Ltd. All rights reserved.

the expansion of concentrated solar energy, which aims to expand the fields of heliostats at great distances. Several methodologies have been developed to estimate the power losses by the interposed atmosphere in these facilities, some of them based on measurement with different types of equipment designed to measure visibility, such as forward-scatter meters or transmissometers. Drawbacks with these equipments that measure visibility are that either measure monochromatic radiation or, even measuring in a wide spectral range, they only register the attenuation that takes place, by scattering and/or absorption, in a small sensitive volume or a short distance [2]. A methodology has been developed in [3] that use the visibility data obtained with a forward-scatter meter as input data to a radiative transfer code to estimate the broadband horizontal atmospheric attenuation. But this methodology has not been contrasted with real-time broadband visibility measurements. In the 1990s pioneering work demonstrated the use film cameras to determine the visibility or relative distances between objects in a scene, with a depth from scattering methodology [4]. Now, digital cameras are being used for twenty years as powerful tools with very different purposes; to determine atmospheric visibility, to estimate air pollutants concentration or to improve or restore the visual quality of haze degraded images [5,6]. In the case of the visibility determination, a methodology is described and applied in [5] that allow calculating the atmospheric visibility using images of a landscape taken with a single digital

2

F.J. Barbero et al. / Measurement 149 (2020) 107025

camera. In the landscape, the authors identify two photometrically equivalent ‘‘dark objects” such as wooded areas at two very different distances away from the camera, and a common background. Applying contrast equations [7] to the R, G and B response bands of a JPG image provided by the digital camera, they calculate not only the visibility and the extinction coefficient in each band, but also the aerosol Ångström exponent. The results were contrasted with measurements made with a nephelometer, showing a good agreement between both, and also its usefulness for the determination of visibility in episodes of dust intrusions. This methodology referenced in [5] is intended to be very low cost; it is implemented through a conventional digital camera and free software, and does not use an artificially manufactured black objects. Currently, the only method that directly measures horizontal attenuation in a central tower plant has been developed at the Plataforma Solar de Almería (PSA, SE Spain), and it’s been working for two years. It makes use of two identical HamamatsuÒ model ORCA-flash4.0 v3 CMOS cameras. The cameras are housed inside refrigerated cabinets to protect them from the environment, keep them in a controlled temperature range to reduce thermal noise effects. Main characteristics of these cameras are: spectral range from 400 to 1000 nm, 16 bits resolution, and black and white. One of the cameras is situated near (82.9 m) and the other far (824.5 m) from a black and white Lambertian target; the optical system of each one of the cameras has been chosen so that it can see the target with identical spatial resolution [9–11]. Both cameras record the target image simultaneously and, also using a contrast formulation [8], calculates the horizontal attenuation at the distance that separates both cameras, 741.6 m. Results of the measured attenuation at the PSA during one year of operation of the two-camera system have been recently published [12]. In this article, the basis, methodology and results of the application of a single conventional camera-based system are shown. Attenuation values derived from this methodology have been compared with those obtained from the two-camera based PSA system showing a good agreement in the most of cases analyzed. In the different sections of this article, the system based on a single camera will be referenced as 1Cam, and the PSA two-camera system as 2Cam.

2. Materials and methods 2.1. Measurement system description and data The PSA (37.097005 N; 2.364750 W) is a Singular ScientificTechnical Installation, which belongs to the Spanish System of Science and Technology, and in which different solar receiver prototypes have been evaluated in the past two decades. The main tower installation with central receiver is the CESA-1, of 7 MWt [13]. A Canon EOS 5D MarkII digital camera, with resolution 5616
3744 pixels, and 8 bits depth per each channel (R, G and B) has been used to take the digital photographs on which the methodology described below will be applied. The camera was located on the terrace of the DISS building of PSA, close to the farthest camera of the two-camera PSA system, at 824 m from the target. The camera points in the north-south orientation, towards where there are some mountains of up to 1.5 km in height and approximately 10 km away in a straight line. Fig. 1 is an image of the reference landscape registered on July 24, 2018, around noon with the Canon EOS camera. On the left side of Fig. 1 it is seen the scope in which the measurements took place. The black and white target can be seen as a small square in the center of the lower side at the image. At right side of Fig. 1, the target enlarged can be also seen. At the foreground the refrigerated cabinet in which the nearest camera of

the PSA system is housed can be also seen. Target is a square of 2
2 m; half is painted with a white paint AmercoatÒ741 with 76% reflectivity, and the other half with black paint ZynolyteÒ with 96% weighted solar absorptance [10]. Such a high absorptance of the solar spectrum by the black side of the target assures that it can be considered, for all purposes, as a black surface. To implement the above described process and to obtain transmittance values between the camera and target, a procedure has been developed in MATLAB [14] software. The developed procedure performs the steps from selecting the suitable clear sky zone in G band of the JPG up to calculate mean transmittance and its standard deviation in the target selected zone. The most common air pollution (smoke, salt or fine aerosols in general) or dust (in different sizes) increases the concentration of particles in the lower layers of the atmosphere, in relation to a clean air situation. The scattering of the radiation in the atmosphere shows a spectral dependence with the size of the particles, but in case of desert dust (with coarse particles) with very little or no spectral dependence at all. It can also increase the absorption in the characteristic spectral bands of some pollutants. Scattering and absorption by the interposed atmosphere reduce the intensity received from any target, according to Beer’s law. Therefore, the presence of the contaminants increases the extinction of the radiation, although in a different way. Dust episodes are the most intense phenomena that attenuate the direct solar irradiance, and can be detected because the direct normal irradiance decreases clearly and the visual contrast impaired. The arrival of dust episodes can now be anticipated with a certain margin of time from several sources; the Spanish dust forecast center (https://dust.aemet.es/) provides estimates of the expected values of the aerosol optical depth (AOD) in map format. With this information, it is easy to consider if the future extinction in the solar plant will be caused by dust. On several days of July 2018 there was an intrusion of dust from the north of Africa. Given that medium to high levels of atmospheric turbidity were expected, the episode was considered a good opportunity to compare the response from both systems. The selected days were cloudless days, with direct normal irradiance records at PSA showing high stability. To mainly check the measurements repeatability, photographs have been taken in July 18, 24 and 27, 2018, in three series of photographs, one for each of the cited days. Each series covers an interval of approximately one hour from noon. Camera was fixed on a tripod to point exactly to the same panorama all the time in each day. Photographs were taken pointing from north to south, just as the PSA system does, and with the following parameters: f/10, exposure time 1/400 s, and ISO100. Images, automatically registered each 3 min, were recorded in JPG format, but only the G-band of each photograph was selected to be processed. To determine ambient light, the corresponding rectangular zone in the G-band was selected. It can be seen in Fig. 1 an example of the zone, which is about 10° above the target level.

2.2. Determination of the atmospheric attenuation. In this section, we will describe the methodology for estimating the atmospheric attenuation using a conventional digital camera. It shares the bases as the dehazing methodology developed by He et al [15], called Dark Channel Prior, whose purpose is to restore, with the highest reliability, images whose visual quality has been degraded by the presence of haze. The so-called equation of the image ([7,16]) is taken as a starting point and it is shown that only the intensity of ambient light and a dark object, preferably black, are needed.

F.J. Barbero et al. / Measurement 149 (2020) 107025

3

Fig. 1. Left: Landscape from the DISS building towards the B/W target registered with the Canon EOS camera. Right: Zoom centered on the target.

Dehazing is a problem that has to do with restoring the image visual quality, but not with the ‘‘true” transmittance value determination in the line of sight from the dark objects; therefore, it is not critical to have in the landscape really black objects that, on the other hand, are very difficult to find in the natural environments. The advantage in our case is that a black object is nowadays available at the PSA site; a black target surface with a very low reflectance black painting (less than 5%), which has been specially designed for the application of the two-camera based PSA methodology. Taking advantage of this situation, concepts are adapted to determine the horizontal attenuation of solar radiation using a single camera with application to solar tower power plants. A scene is directly illuminated by the solar radiation and by the one reflected by other elements present in it. Each ‘‘object” in the scene reflects the light that fall on it according to its radiometric and photometric characteristics (from mirror to Lambertian reflectivity). When a scene is observed, the following phenomena can be found, as Fig. 2 shows: In the upper image, the radiance from an object is attenuated by the interposed atmosphere (both by absorption and by dispersion out of line of sight). The Lambert-Beer law parameterizes the relationship between the radiance emitted by the object and the one that reaches at a distance d from it (radiometric variables depend on the wavelength, although it is not explicitly written):

LðdÞ ¼ L0 tðdÞ ¼ L0 expðkdÞ

ð1Þ

where k is the total extinction coefficient of the interposed atmosphere, t(d) the transmittance of the atmosphere from the object at distance d, L0 is the object radiance; its response to the whole radiation, G (direct, diffuse and reflected by all the elements of the scene) that falls on it. In the case of a physical object L0 depends on its spectral and directional reflectivity, q. It is written therefore as L0 = q G. In the lower part of Fig. 1, it is seen the radiance coming from the interposed atmosphere; direct and diffuse radiation, and the one that reflect the different components of the scene, interact with the atmosphere creating an ambient light (airlight), which is added to the one that comes from the observed object. Ambient light is formed in a very complex way and contributes to create the sense of depth that is familiar to us, in what we would call radiometric formation of the scene. Thus, we consider the image of a scene before its passage through an optical system and to be registered (by eye or a by digital camera), as a two-dimensional matrix of elementary surfaces. Each one of these surfaces receives a radiant intensity from its counterpart in the scene. We write the equation of the radiant intensity in each of the surfaces as ([16]):

IðxÞ ¼ L0 ðxÞtðxÞ þ Að1  t ðxÞÞ

ð2Þ

where: x = position of the elementary surface in the two-dimensional matrix.

Fig. 2. Radiant intensity received from a scene. Left: Radiance attenuation by absorption and scattering phenomena through the interposed atmosphere. Right: Ambient light formation.

4

F.J. Barbero et al. / Measurement 149 (2020) 107025

I(x) = radiant intensity received on the elementary surface. L0(x) = emitted radiance from the surface counterpart in the scene. A = Ambient light, assumed homogeneous in depth and through the whole image. t(x) = transmittance along the path from the surface counterpart. The spectral radiant intensity passes through the camera optical system and is integrated through the CCD or CMOS spectral response. Then, the integrated radiant flux on the pixel is converted to a digital value, whose range depends on the camera resolution in bits/pixel. In most conventional cameras, there are three spectral bands (R, G and B) and 8 bits per pixel per band, like in our case. In this way, Data Numbers (DN) or grey levels for each band are between 0 and 255. Once an image of a landscape has been registered, the next step will be to detect in it the ‘‘dark objects” that meet the requirement of the proposed method. The term ‘‘dark object” does not necessarily refer to a real object. A ‘‘dark object” has very low radiance, and this fact may be due to not receiving any irradiance on it, or because it does not reflect radiation in the whole spectrum or on some wide spectral range; since, in the case of the Lambertian target of the PSA, it is a surface with a reflectivity less than 5% throughout the spectral range covered by the cameras, one can speak, for practical purposes, as of a black object. Under conditions of extremely clean atmosphere, the radiant intensity received from a dark object should be practically zero, either in a large range of the spectrum or in some significant spectral band. Examples of dark objects are intense shadows, black surfaces or saturated color surfaces in some spectral band. An open window could be considered as a good black object. For those pixels that correspond to dark (black) objects, L0 ffi 0, and (2) can be written as:

t ðxÞ ¼ 1  IðxÞ=A

ð3Þ

It is seen from Eq. (2) that ambient light, A, corresponds to pixels with transmittance 0. These pixels can be found at the sky in the image; they have the highest intensity values in the image that do not come from any object. Once the ambient light is determined on an image, the corresponding ‘‘transmittance” map will be obtained from (3). The quotes written in ‘‘transmittance” arise from the fact that the only true transmittances correspond to pixels of the sky or of the black objects. Transmittances range from 0 to 1, but the higher values in the image do not necessarily correspond to the black objects; can be found in objects that have a relevant intrinsic radiance in the spectral band in which they are being observed. Measuring the transmittance value on the dark object, which is at a known distance, the extinction coefficient of the interposed atmosphere can be also determined following Eq. (4):

1 k ¼  lnðt ðdÞÞ d

ð4Þ

Since we are dealing with not excessively large distances, the homogeneous atmosphere hypothesis can be done quite realistically. Therefore, the attenuation of radiation at any horizontal distance, z, can be calculated as shown in Eq. (5):

AttðzÞ ¼ 1  expðkzÞ

ð5Þ

The main objective of the proposed methodology is, therefore, to determine the value of the transmittance in those image pixels for dark objects at a known distance. Given the image, pixels from sky must be taken at a suitable selected area to calculate the ambient light. Then, histogram of values for the pixels included in this area will be calculated; the DN

average value of the histogram will be the value for the ambient light, and the standard deviation will be the corresponding uncertainty. With the ambient light value, a transmittance map is derived applying Eq. (3) to all the pixels in the image. In the same way as before, an area inside the dark object is selected on the transmittance map, and the histogram of the pixels included in this area will be calculated. Average value of the pixels included in the histogram is the atmospheric transmittance from the dark object to the camera and the standard deviation its associated uncertainty. The described procedure to determine the transmittance could be done in each one of the three response bands of a JPG image and, therefore, be able to calculate the extinction coefficient in each of them. In general, the extinction coefficient is very different for each spectral band, greater in the B band than in the R band, mainly due to Rayleigh scattering and the atmospheric aerosol characteristics. In the case of the proposed methodology, in which images are registered from north to south, the received radiation at the camera is mainly forward scattered, and then, the sky in the B band is the brightest one, and the sky in it somewhat may appear also very bright in areas near the horizon. This occasional proximity of sky to saturation may produce an uncertainty that can cause this B band to be discarded in many situations, especially if it is to make a system for automatic measurement and processing. The G band, which it assumed to be centered on the wavelength of 550 nm, follows the photopic response of the human eye, and has a very good response from digital cameras, according with human visibility. In our case, since the objective is to compare the results of our methodology with that obtained from the PSA system, we will work directly with the G response band; the reason is that the spectral response of the PSA system is also centered around a wavelength of 550 nm. An alternative to better simulate the PSA system spectral response would be to work with the combined B/W image (B/ W = 0.30 R + 0.59 G + 0.11B), which provide a weighted average extinction coefficient over the whole spectrum covered by the camera; in this case, the influence of the B band should not be as relevant. At the Results section this aspect will be discussed. 3. Results In this section, the horizontal attenuation values determined by using the described methodology are presented and compared with the corresponding values obtained from the two-camera PSA system. 3.1. Ambient light determination One of the uncertainty sources, at least from a theoretical point of view, is the determination of ambient light. To evaluate the dependence of ambient light value with the sky selected area, sky profiles from the horizon line to the zenith have are shown. Profiles for three photographs taken at 14:30 h (local time), one for each of the selected days in the G photometric band, are shown in Fig. 3. It can be seen how the choice of the area in which to determine ambient light was not critical, because the relative error that occurs considering two areas 300 pixels apart is less than 2% in the worst case. In Fig. 1, can be seen the selected sky zone to calculate ambient light (identified by a small square over the mountains). The sample covers more than 105 pixels, and the histogram of DN values is found to be very sharp, and the standard deviation is less than 1.5 DN. But at the black target only about 350 pixels have been selected as representative for to calculate transmittances.

F.J. Barbero et al. / Measurement 149 (2020) 107025

Fig. 3. G-band ambient light sky DN profiles starting from horizon towards the zenith. Blue: July, 18; Red: July, 24; Black: July, 27. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Histogram for these pixels was built and corresponding average and standard deviation values have been used for to calculate transmittances and their uncertainties. In Fig. 4, a transmittance map for the image in Fig. 1 can be seen. Ambient light in the sky appears almost black, with values nearly 0 and the black target is seen very bright. In this case a transmittance value between camera and target of 0.910 ± 0.012 was measured. The corresponding extinction coefficient at the G band is (1.15 ± 0.16)*104 m1, and the visibility (the meteorological optical range, MOR), 26 ± 4 km. To be able to compare attenuation values derived from the 1Cam system, obtained for a distance of 824 m, with those registered by the 2Cam system, 1Cam attenuation values were standardized at a distance of 742 m, which is the distance between the two cameras in the 2Cam system. Distance between cameras and target has been topographically measured with an absolute error of less than 0.1 m; therefore, these variables do not contribute to the calculated uncertainty when the attenuation at this

5

new distance is calculated. Results for the calculated attenuation from both systems are shown in Fig. 5. The absolute uncertainty bars associated with each measurement has not been represented in Fig. 5 for clarity, but in the case of 1Cam it is about ±1.3% and in the 2Cam system it is about ±2.0%. Therefore, simultaneous 1Cam and 2Cam attenuation values fall well into the respective uncertainty ranges. It is observed in Fig. 5 that the 1Cam system seems to be less stable than the 2Cam. This fact can be explained by analyzing the behavior of ambient light and target DN values in photographs; although the measured attenuation with the 2Cam system has remained fairly stable, DN target value differences of up to 15% have been found between two consecutive photographs; differences of 2 DN are usually found on values of 20 DN at the target. But also the location of the camera in an open environment makes it subject to variations in ambient temperature and small vibrations; the consequence is that you would not be observing and measuring exactly the same area of the black target. This fact can be a consequence of the response of the camera when it converts the received intensity to the corresponding DN value, which is done with a resolution of 8 bits per band in commercial cameras. In the general case, these 15% difference can lead to absolute attenuation differences up to 1.5%. The case for July 27th deserves a further analysis. It can be seen that the same tendencies are reproduced in both systems, but systematic differences are found between the time series of both systems. These differences can be explained from two facts: the horizontal attenuation measured by the 2Cam system was characteristic of a medium visibility situation, and also that the direct normal irradiance (DNI) measured in-situ was the high; in fact, the highest of these days (910 Wm2), showing that it would be the final phase of the intrusion episode, as it can be verified in Fig. 6, from the Spanish dust forecasting center [18]. It has not been possible to directly verify the low concentration of aerosols at the PSA, since the AERONET network node ‘‘Tabernas_PSA-DLR” was in maintenance during those days. Indirect evidence comes from the ‘‘Granada” node (about 150 km in a straight line, in the same latitude and area of influence of dust

Fig. 4. G band transmittance maps for landscape and for the black and white target. Transmittance values range from 0 (black) to 1 (brighter white).

6

F.J. Barbero et al. / Measurement 149 (2020) 107025

Fig. 6. AOD map from BDFC for July 27th, at 12 h, showing the synoptic situation for the final phase of the episode. Red circle marks the area where PSA and Granada are located. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

a superficial layer of aerosol and water vapor of low thickness and a clean atmosphere above it. With this configuration, the value of the ambient light, which is measured in an area above the mountain, would be underestimated; the line of sight from the camera to that area would cross through a smaller concentration of pollutants than in the line of sight to the target. As it can be seen in Fig. 3, the ambient light measured on July 27 was the lowest of those days. The consequence is that the attenuation would be overestimated in this case. Statistical analysis parameters for each attenuation time series and system in Fig. 5 are shown in Table 1. It should be mentioned that the uncertainties that appear in the table refer only to the standard deviation of the time series in relation with the time series average. Behavior of 1Cam attenuation time series can be characterized, taking as true reference the 2Cam system values, by using the following normalized statistical parameters:

nMBE ¼

  1 X Att1C  Att 2C ; N Att2C

nRMSE ¼

Fig. 5. From top to bottom: 1Cam and 2Cam horizontal atmospheric attenuation (%) time series (Blue, 1Cam; Red, 2Cam) in July 18, 24 and 27, 2018. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

intrusions) which showed very low values of the aerosol optical depth (AOD at 550 nm = 0.05) in that day. DNI simulation with the SMARTS2 code [19], for the PSA emplacement in that day and hour, with low atmosphere water vapor content (the relative humidity measured in situ in July 27th was less than 30%), and a dust type aerosol with AOD at 550 nm of 0.05, provide a value of 940 Wm2, close to the measured one; therefore, the atmosphere at PSA would not contain appreciable concentrations of aerosols and water vapor. The combination of a low attenuation of DNI with medium values for horizontal visibility leads, schematically, to the existence of

X

1 ðAtt 1C  Att2C Þ2 N Att22C

!!1=2 ð6Þ

In these expressions, each attenuation value determined with the 1Cam system is related to the corresponding attenuation that the 2Cam system has supplied at the same moment. They are, therefore, parameters that allow a direct comparison for consistency between both systems. Resulting values from applying the previous expressions to both sets of attenuation values, with N = 56 data, are: nMBE = 8.3%; nRMSE = 12.7%. Therefore, in these conditions of cloudless sky and visibilities in the range 25–40 km, horizontal atmospheric attenuation value determined with a single camera would be

Table 1 Attenuation (%) mean values and standard deviations for 1Cam and 2Cam time series for the selected days. N is the number of photographs in each day. Day

N

1Cam

2Cam

July 18 July 24 July 27

20 18 18

6.4 ± 0.5 7.9 ± 0.4 9.1 ± 0.6

6.2 ± 0.2 7.7 ± 0.2 7.6 ± 0.3

F.J. Barbero et al. / Measurement 149 (2020) 107025

within a relative error margin below 10% of the value provided by the method using two cameras. It has been studied the possible dependence of the obtained results with the fact that the spectral responses of the cameras of both systems are different. For this, the attenuation values with the single camera method were also calculated using the images converted to the B/W format. Results show that no significant differences were found by using the converted B/W image instead of the G band of the same image.

3.2. Conditions that may influence the application of the method. 3.2.1. Influence of clouds on ambient light determination. The developed method would not be applicable when there are too many clouds at the horizon near the dark object; the determination of the ambient light is compromised by the difficulty in finding free zones in which we can affirm that the received radiation does not come from any reflection, which is the basic condition for to determine ambient light. But it is also clear that, when there are clouds in the scene, the ambient light may cease to be homogeneous. When the cloud is in front of the camera, and gives shade in the path of ambient light, it is a daily and verifiable fact that visibility improves when there is a shadow interposed in the line of sight [17]. Ambient light (which is forward scattered from the object in the case of the PSA system) decreases because the interposed atmosphere receives less radiation than can be scattered and then less scattered radiance from the target is received. Consequently, the calculated transmittance with (3) could be overestimated depending on the size of the cloud, and then the attenuation could be lower than in conditions without clouds. In the case when the cloud is behind or on top of the camera, sunlight reflection in the cloud (especially if it’s a dense cloud) increases the radiation on the target, but in case of a black target there is any effect. The ambient light also may increase, but as it occurs in backscatter its effect is very small. Therefore, this situation should not be relevant for the attenuation determination.

3.2.2. Influence of the height above the horizon on the ambient light determination. Received intensity from the sky may depend on the pixel position above the horizon; this fact responds to the structure of the atmosphere, more concentrated at the boundary layer. When selecting pixels located well above the horizon, the radiation received in them has crossed a smaller thickness of the boundary layer. But in the case of pixels too close to the horizon, ambient light could be affected by multiple scattering, and some spectral bands may become almost saturated, usually the B band. Therefore, the ambient light value could be high that in the real case. These considerations could be relevant when the dark object has a very high horizon behind it (as it will be our case), but if the dark object it is in front of a low horizon (such as a solar plant in a plain), the location of the sky area for determining the ambient light would not be relevant.

3.2.3. Case of low attenuation. In situations of low atmospheric attenuation, and due to the short distance between the camera and the target (824 m in our case), the radiant intensity that is received from the black zone (mainly ambient light) is very low, and the mean value of DN calculated in it will be low and comparable in value to its standard deviation; therefore, the calculated attenuation would be very low and with a high absolute uncertainty.

7

4. Conclusions and final remarks In the present work a methodology to calculate the horizontal atmospheric attenuation has been proposed, based on images taken with a conventional digital camera of a landscape in which there is a broad cloudless area in the sky and a dark object. The measurement of the radiant intensity from the sky in the image allows us to calculate the transmittance of the interposed atmosphere between the dark object and the camera. The methodology has been proposed taking advantage of the fact that at the PSA a black surface, with a reflectivity of less than 5%, has been specially built to implement a methodology that directly measures the horizontal atmospheric attenuation using two identical digital cameras. Without a ‘‘black object”, the methodology proposed in this work would be too dependent on the reflectivity of the dark object selected, given the short distance between the object and the camera. Results of the application of this methodology have been checked against the results of the two-camera system at the PSA. The comparison was made using photographs taken during some days in July 2018. Under the special conditions in which the comparison was made, of days without clouds and with visibility values in the range 25–40 km, it is found that the proposed system provides attenuation values within the 10% of those provided by the two-camera system. It is noteworthy that these results have been obtained despite the handicap that the ambient light has been measured 10° above the target level, due to the disposition of it in front of the mountains in the background. The differences found between the values measured by the two systems on July 27 are a consequence of the special arrangement between the camera and the target, with the mountains behind. In the case of a real solar plant, in which the objective and ambient light measurement area would be at the same level, it is not likely that this type of situation could occur. It has been found that the location of the area in which to fix the value of ambient light is not very critical, and has little influence on the uncertainty in the determination of transmittance. However, the calculated transmittance is very dependent on the radiant intensity from the black object, especially in low turbidity circumstances. Due to this last aspect, it has been observed that, in circumstances of high atmospheric stability, as detected by the two-camera system, the proposed methodology may show some margin of variability, although within its associated uncertainty. Part of the problem can be attributed to the fact of working with a photometric resolution of 8 bits/pixel. The proposed methodology is limited, as has been mentioned, to clear sky situations or where there are no clouds on the horizon near the dark object. This is not the case with the two-camera PSA system, which can measure attenuation in virtually all sky conditions. In this sense, the proposed methodology cannot be a competition to a system that can provide a continuous measurement and obtaining long attenuation series. But, on the other hand, it shares with the two camera methodology, and against other methods proposed to estimate atmospheric attenuation in solar plant environments, the fact that it works both in a wide spectral range and in real distances of the application. It is difficult to find black objects in all the spectral range covered by the camera, although it would be enough to be dark in the range of the G-band. But, in any case, in the field of a central tower facility, arrange artificial black surfaces it is totally affordable from the economic point of view and its profitability. As an alternative, some structural elements of the central, even at more than 1 km distances (tower, painted zones in buildings or others), could be used as such objects. Consequently, this methodology can

8

F.J. Barbero et al. / Measurement 149 (2020) 107025

be used to have an easy and almost real-time estimate of the atmospheric attenuation in the environment of a central tower solar plant. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors would like to thank the PRESOL Project (references ENE201459454-C3-1, 2 and 3) and the PVCastSOIL Project (ENE2017-83790-C3-1, 2 and 3), which were funded by the Ministerio de Economía y Competitividad and co-financed by the European Regional Development Fund. References [1] J. Ballestrín, A. Marzo, Solar radiation attenuation in solar tower plants ISSN: 0038-092X Solar Energy 86 (2012) 388, https://doi.org/10.1016/ j.solener.2011.10.010. [2] Guide to Meteorological Instruments and Methods of Observation. World Meteorological Organization, 2014. 291-309. [3] N. Hanrieder et al., Atmospheric extinction in solar tower plants: absorption and broadband correction for MOR measurements, Atmos. Meas. Tech. 2015 (8) (2015) 3467–3480. [4] F. Cotzman, E. Krotov, Depth from scattering, Proc. 1997 IEEE Conf. on Comput. Vision Pattern Recognit. 31 (1997) 801–806.

[5] F. Carretas, F. Wagner, F.M. Janeiro, Atmospheric visibility and Ångström exponent measurements through digital photography, Measurement 64 (2015) 147–156. [6] K. Gadnayak, P. Panda, N. Panda, A survey on image dehazing methods, Int. J. Eng. Res. Technol. (IJERT) 2 (10) (2013), ISSN: 2278-0181. [7] W.E.K. Middleton, Vision through the Atmosphere, University of Toronto Press, Toronto, 1963. [8] H. Horvath, On the applicability of the Koschmieder visibility formula, Atmos. Environ. 5 (1971) 177–184. [9] J. Ballestrín, R. Monterreal, M. E. Carra, et al., Measurement of solar extinction in tower plants with digital cameras, in: American Institute of Physics Conf. Proc. 1734, 2016, pp. 130002-1–130002-8, http://dx.doi.org/10.1063/ 1.4949212. [10] J. Ballestrín, R. Monterreal, M.E. Carra, J. Fernandez-Reche, J. Polo, R. Enrique, J. Rodríguez, M. Casanova, F.J. Barbero, J. Alonso-Montesinos, G. Lopez, J.L. Bosch, F.J. Batlles, A. Marzo, Solar extinction measurement system based on digital cameras. Application to solar tower plants, Renewable Energy 125 (2018) 648– 654. [11] J. Ballestrín, M.E. Carra, R. Enrique, R. Monterreal, J. Fernández-Reche, J. Polo, M. Casanova, F.J. Barbero, A. Marzo, Diagnosis of a Lambertian target in solar context, Measurement 119 (2018) 265–269. [12] J. Ballestrín, E. Carra, R. Monterreal, R. Enrique, J. Polo, J. Fernandez-Reche, F.J. Barbero, A. Marzo, J. Alonso-Montesinos, G. Lopez, F.J. Batlles, One year of solar extinction measurements at Plataforma Solar de Almería. Application to solar tower plants, Renewable Energy 136 (2019) 1002–1011. [13] Plataforma Solar de Almería PSA. Annual Technical Report. CIEMAT. 2014-17. http://www.psa.es/. [14] MATLAB 2014a, The MathWorks, Natick, 2014. [15] K. He, J. Sun, J.X. Tang, Single image haze removal using dark channel prior, IEEE Trans. Pattern Anal. Mach. Intell. 33 (12) (2011), 2341–1353. [16] S. Narasimhan, S. Nayar, Vision and the Atmosphere, Int. J. Comput. Vision 48 (3) (2002) 233–254. [17] H. Horvath, Atmospheric visibility, Atmos. Environ. 15 (10) (1981) 1785–1796. [18] Barcelona Dust Forecasting Center. http://dust.aemet.es. [19] C.A. Gueymard, Simple Model for the Atmospheric Radiative Transfer of Sunshine (SMARTS2), Algorithms and performance assessment. Rep. FSEC-PF270-95, Florida Solar Energy Center (Cocoa, FL, USA, 1995).