Application of Simple All-sky Imagers for the Estimation of Aerosol Optical Depth

Aerosol optical depth is a key atmospheric constituent for direct normal irradiance calculations at concentrating solar power plants. However, aerosol optical depth is typically not measured at the solar plants for financial reasons. With the recent introduction of all-sky imagers for the nowcasting of direct normal irradiance at the plants a new instrument is available which can be used for the determination of aerosol optical depth at different wavelengths. In this study, we are based on Red, Green and Blue intensities/radiances and calculations of the saturated area around the Sun, both derived from all-sky images taken with a low-cost surveillance camera at the Plataforma Solar de Almeria, Spain. The aerosol optical depth at 440, 500 and 675nm is calculated. The results are compared with collocated aerosol optical measurements and the mean/median difference and standard deviation are less than 0.01 and 0.03 respectively at all wavelengths

Cloud observations in Switzerland using hemispherical sky cameras

We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long-wave radiation observations, Meteosat, ceilometer, and visual observations were compared. Total cloud cover from the sky camera was in 65–85% of cases within ±1 okta with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 oktas but underestimates it by 0.8 ± 1.9 oktas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k-Nearest Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10%. The performance of the algorithm mainly depends on the atmospheric conditions, site-specific characteristics, the randomness of the selected images, and possible visual misclassifications: The mean success rate was 80–90% when the image only contained a single cloud class but dropped to 50–70% if the test images were completely randomly selected and multiple cloud classes occurred in the images

Digital image processing techniques for atmospheric constituents detection and evaluation

This PhD develops digital image processing techniques regarding atmospheric constituents detection and evaluation, having as input sky camera images representing the celestial dome.Ο στόχος της παρούσας διατριβής είναι η εφαρμογή τεχνικών ψηφιακής επεξεργασίας εικόνων με σκοπό την εξαγωγή ασφαλών λογικών συμπερασμάτων σχετικά με τον ουράνιο θόλο και την ατμόσφαιρα, έχοντας σαν αντικείμενο μελέτης φωτογραφίες που αναπαριστούν τον ουράνιο θόλο

Estimation of aerosol optical properties from all-sky imagers

Aerosols are one of the most important constituents in the atmosphere that affect the incoming solar radiation, either directly through absorbing and scattering processes or indirectly by changing the optical properties and lifetime of clouds. Under clear skies, aerosols become the dominant factor that affect the intensity of solar irradiance reaching the ground. It has been shown that the variability in direct normal irradiance (DNI) due to aerosols is more important than the one induced in global horizontal irradiance (GHI), while the uncertainty in its calculation is dominated by uncertainties in the aerosol optical properties. In recent years, all-sky imagers are used for the detection of cloud coverage, type and velocity in a bouquet of applications including solar irradiance resource and forecasting. However, information about the optical properties of aerosols could be derived with the same instrumentation. In this study, the aerosol optical properties are estimated with the synergetic use of all-sky images, complementary data from the Aerosol Robotic Network (AERONET) and calculations from a radiative transfer model. The area of interest is Plataforma Solar de Almería (PSA), Tabernas, Spain and data from a 5 month period are analyzed. The proposed methodology includes look-up-tables (LUTs) of diffuse sky radiance of Red (R), Green (G) and Blue (B) channels at several zenith and azimuth angles and for different atmospheric conditions (Angström � and �, single scattering albedo, precipitable water, solar zenith angle). Based on the LUTS, results from the CIMEL photometer at PSA were used to estimate the RGB radiances for the actual conditions at this site. The methodology is accompanied by a detailed evaluation of its robustness, the development and evaluation of the inversion algorithm (derive aerosol optical properties from RGB image values) and a sensitivity analysis about how the pre-mentioned atmospheric parameters affect the results

Aerosol Optical Properties and Type Retrieval via Machine Learning and an All-Sky Imager

This study investigates the applicability of using the sky information from an all-sky imager (ASI) to retrieve aerosol optical properties and type. Sky information from the ASI, in terms of Red-Green-Blue (RGB) channels and sun saturation area, are imported into a supervised machine learning algorithm for estimating five different aerosol optical properties related to aerosol burden (aerosol optical depth, AOD at 440, 500 and 675 nm) and size (Ångström Exponent at 440–675 nm, and Fine Mode Fraction at 500 nm). The retrieved aerosol optical properties are compared against reference measurements from the AERONET station, showing adequate agreement (R: 0.89–0.95). The AOD errors increased for higher AOD values, whereas for AE and FMF, the biases increased for coarse particles. Regarding aerosol type classification, the retrieved properties can capture 77.5% of the total aerosol type cases, with excellent results for dust identification (>95% of the cases). The results of this work promote ASI as a valuable tool for aerosol optical properties and type retrieval

Short-term forecasting based on all-sky cameras

Abstract Solar resource and forecasting in very short spatial and timescales (0–100 m, 0–30 min) is a challenging task that cannot be accurately achieved by satellite products or numerical weather predictions due to technical and methodological restrictions. For this reason, sky images from ground-based cameras are widely used during the last decade to deal with the high spatial and temporal variability of clouds and provide the needed inputs for numerical models for the current and forecasted (in short-term) solar irradiance. In this chapter, the available types of all-sky cameras/imagers are shortly presented. The chapter is focused on the state-of-the-art methodologies used to derive parameters needed for the estimations of solar resource and forecasting by a calibrated all-sky camera: cloud coverage, type, height, and velocity as well as aerosol optical properties. The application of these methodologies at Plataforma Solar de Almeria, in the frame of project: “Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies” (DNICast, http://www.dnicast-project.net/) is discussed. Finally, the chapter finishes with some propositions for future work in the recent, but quickly, developed research area

Estimation of cloud coverage/ type and aerosol optical depth with all-sky imagers at Plataforma Solar de Almeria, Spain

International audienceAerosols and clouds affect strongly the incoming solar radiation. Under cloud-free skies, aerosols become the dominant factor that affect the direct normal and the global horizontal irradiance (DNI and GHI respectively). For cloudy skies, the spatial and temporal variability of clouds is a challenging issue on solar irradiance resource and forecasting. In recent years, all-sky imagers are used for the detection of aerosol optical properties, cloud coverage, type and velocity in numerous applications including meteorological observations and solar energy control systems. In this presentation, a detailed overview of the methodologies developed at the Plataforma Solar de Almeria (PSA), in the framework of the EU project " Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies " (DNICast, http://www.dnicast-project.net/) is presented. In the framework of DNICast, we develop and provide aerosol optical depth (AOD) as intermediate product of cloud camera images as a basis for an AOD/DNI nowcasting scheme. Our main purpose is to analyze sky images being created and produced by a low-cost Mobotix Q24M off-the-shelf surveillance camera in order to estimate the AOD at different wavelengths from RGB intensities (Red, Green, Blue channels) of the photo. We use the RGB intensities/radiances from the zenith point and the size of the saturated area around the sun as input for the AOD determination. These data are taken into account in a multi-linear approach to estimate the AOD values at 440, 500 and 675nm and compared with the measurements of a CIMEL sun photometer at the same wavelengths. According to results, the mean/median difference and the standard deviation are less than 0.01 and 0.03 for all wavelengths. For the estimation of cloud coverage and type, we used several spectral and textural metrics mainly based on the method proposed by Kazantzidis et al. (2012). Notably, the spectral metrics are now only applied on cloudy pixels instead of on the full image. The classification algorithm provided two possible implementations of cloud type classification: i) Global cloud classification: classification of dominant cloud type and ii) Grid-based cloud detection: a classification of the cloud type is provided per grid element in the image. Based on visual human observation, the accuracy of the global cloud classifier ranges between 76 % (for cirrus cloud) and 84 % (for cumulus)

Short-term forecasting of high resolution local DNI maps with multiple fish-eye cameras in stereoscopic mode

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