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In this study, single and aggregated forecasters of half-hourly global horizontal irradiance are assessed. The models are the standard persistent model and four newly proposed static, dynamic, moving average, and amplified persistent models. These sub-forecasters are aggregated using equal, annual optimal, and monthly optimal weights. A particle swarm optimizer was used to find those weights. Measured data, obtained from two desert sites for the years 2015–2018, was used for fitting and training the different models, while the data of the year 2019 was used to test their prediction capabilities. For the single forecasters, the dynamic model is the most accurate, followed by the static and average models. When the aggregated model of annual optimal weights was tested, the three contributing forecasters were the dynamic, average, and amplified models. The dynamic forecaster held the largest weight due to its prediction superiority during overcast and partially cloudy days. When monthly optimal weights were used, all forecasters contributed, and the dynamic model held the largest weight during winter but not in the summer when the clear sky condition is dominant. The aggregated model was the most precise, with relative mean square errors lower than 15.0% and coefficients of determination higher than 98.8%.

High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions.

Knowledge of ultraviolet radiation (UVA) at the Earth’s surface is of great importance in different technological and scientific applications. In this study, UVA (315–400 nm) and global solar radiation (G, 400–1100 nm) data from Riyadh (Central Saudi Arabia) for the period between 2015 and April 2020 are used to characterize the UVA at different cloudiness levels. The sky was classified into several categories according to the clearness index. For all sky conditions, the mean values of UVA, G, and the ratio UVA/G were 11.58 ± 6.1 W/m2, 549.35 ± 264.55 W/m2, and 0.021 ± 0.003, respectively. While the mean UVA and G values under totally overcast skies were 5.51 ± 2.7 W/m2 and 267.90120.44 W/m2, respectively, the UVA was 21.63 ± 2.14 W/m2 and G was 933.19 ± 39.94 under clear skies. For partly cloudy skies, the amount of UVA and G increases as the sky gets clearer. The annual mean value of UVA decreased gradually from 2016 to 2018. In comparison with the mean value of UVA in 2016, the mean values of UVA were down about 7% and 18% in 2017 and 2018, respectively. This may be due to the heavy dust storms that occurred in those two years. The monthly and hourly variations in UVA and G are investigated and discussed. The distribution of the monthly values of UVA and G exhibits high symmetry. The UVA and G are at the maximum in summer and the minimum in winter. The amount of radiation appears similar in spring and summer and in autumn and winter. This may be explained by the seasonal symmetry of the summer and winter solstices. The hourly variations in the average UVA and G attain their minimum values in the early morning (06:00 local time) and reach their maximum values at around midday. Finally, several empirical models relating ultraviolet (UV), with solar global radiation (G), and the clearness index (Kt) under all sky conditions are established. The results reveal that the proposed empirical models accurately predict hourly values.

This study is based on the fifteen standard sky models proposed by the International Commission of Illumination (CIE) and explores the sky luminance distribution throughout the severe cold regions of China. The experiment analysis is based on 22,829 sets of effective luminance data from January 2019 to December 2019. The Tregenza method and the normalization ratio method are used to analyze changes in the sky environment throughout the year in Northeast China. The results indicate that the standard sky models proposed by the CIE can represent the basic characteristics of the sky over a whole year in Harbin, but the frequency of each sky type is clearly different. Type V.4 (CIE standard clear sky, low illuminance probability) has a frequency exceeding 22% over the year, and is the most common type of sky in Harbin, while type VI.6 (white blue turned sky with broad solar corona) has the lowest frequency type. The use of these two methods indicates that the frequency of clear sky in Harbin over the year exceeds 50%, and this mainly occurs in spring, autumn and winter. The probability of overcast and partly cloudy skies is over 20%, and in summer an overcast sky is more probable. When comparing against different solar elevations, it is found that for solar elevations below 50°, V.4 is the most common sky type with a probability above 15%, whilst for solar elevations above 50°, type I.1 is the most frequent.

The retrieval of aerosol parameters from passive satellite instruments in cloudy scenes is challenging, partly because clouds and cloud-related processes may significantly modify aerosol optical depth (AOD) and particle size, a problem that is further compounded by 3D radiative processes. Recent advances in retrieval algorithms such as the “color ratio” method, which utilizes the measurements at a shorter (470 nm) and a longer (860 nm) wavelength, have demonstrated the simultaneous derivation of AOD and cloud optical depth (COD) for scenes in which absorbing aerosols are found to overlay low-level cloud decks. This study shows simultaneous retrievals of above-cloud aerosol optical depth (ACAOD) and aerosol-corrected cloud optical depth (COD) from airborne measurements of cloud-reflected and sky radiances using the color ratio method. These airborne measurements were taken over marine stratocumulus clouds with NASA's Cloud Absorption Radiometer (CAR) during the SAFARI 2000 field campaign offshore of Namibia. The ACAOD is partitioned between the AOD below-aircraft (AOD_cloudtop) and above-aircraft AOD (AOD_sky). The results show good agreement between AOD_sky and sun-photometer measurements of the above-aircraft AOD. The results also show that the use of aircraft-based sun-photometer measurements to validate satellite retrievals of the ACAOD is complicated by the lack of information on AOD below aircraft. Specifically, the CAR-retrieved AOD_cloudtop captures this “missing” aerosol layer caught between the aircraft and cloud top, which is required to quantify above-cloud aerosol loading and effectively validate satellite retrievals. In addition, the study finds a strong anticorrelation between the AOD_cloudtop and COD for cases in which COD < 10 and a weaker anticorrelation for COD > 10, which may be associated with the uncertainties in the color ratio method at lower AODs and CODs. The influence of 3D radiative effects on the retrievals is examined, and the results show that at cloud troughs, 3D effects increase retrieved ACAOD by about 3 %–11 % and retrieved COD by about 25 %. The results show that the color ratio method has little sensitivity to 3D effects at overcast stratocumulus cloud decks. These results demonstrate a novel airborne measurement approach for assessing satellite retrievals of aerosols above clouds, thereby filling a major gap in global aerosol observations.

Sky conditions, and the corresponding luminance and radiance distribution patterns are essential to daylighting, energy and thermal environmental studies. The CIE Standard Skies define the overcast, partly cloudy, and clear sky conditions intuitively by rigorous luminance distributions, which are, however, usually determined by sophisticated and uncommon measurements (e.g. radiation in multiple vertical directions). This study proposes a simple approach to identifying the hourly sky conditions from the global horizontal irradiance (EHG) that is most widely accessible in weather stations with basic radiation measurement facilities. The sophisticated measurements are, therefore, avoided for simple applications. The partly cloudy sky, especially, is identified by the notable disparity of its EHG measurement from the theoretical cloudless condition. The proposed approach interprets the ISO/CIE Standard Sky conditions and their diffuse luminance and radiance distributions successfully. From the datasets at two sites with different climates, the approach correctly identifies 7% more sky conditions than a previous work when the direct beam and diffuse radiation measurements were not accessible. The model can thus be essential to solar energy studies for places with ground-based solar radiation measurements only.

In situ remote sensing reflectance (Rrs) measured using the above-water approach usually suffers from residual reflected skylight (Δ), and thus its applicability is limited. In this study, we proposed a simple method, which only required the in situ Rrs spectrum itself as input, to further remove the Δ effects in the in situ Rrs spectrum. The performance of the proposed method was evaluated using both simulation data and in situ Rrs spectra measured using a radiance sensor with a black tube (Δ-free in situ Rrs). The results showed that the proposed method outperformed other existing methods and can be applied to various types of waters. For example, from the two simulation experiments, it was seen that the proposed method can provide accurate Rrs spectra with a mean absolute percentage error (MAPE) = 6% even if they are measured under various sky conditions and wind speeds (using Hydrolight) and can estimate accurate Δ values with MAPE = 5% (using a simple bio-optical model). From a comparison analysis using Δ-free in situ Rrs data, the proposed method also showed the best performance with the smallest MAPE of 45% (MAPE ranged from 52% to 260% for other existing methods). In addition, the proposed method can improve the quality of Rrs spectra collected under various sky conditions (clear, scattered clouds and overcast), with an increased proportion of higher quality assurance scores. Two showcases indicate that the Δ corrections should be carried out for all above water approach-measured Rrs spectra before they are applied to develop water quality estimation algorithms or to evaluate the performances of atmospheric correction algorithms.

We estimated the Korea Shortwave Radiation Budget (KSRB) providing new insights into the Korea climate system. Monthly averaged clear-sky, overcast-sky, and all-sky flux measurements with cloud amounts from 2000 to 2015 were used to assess the impacts of cloud on the KSRB. A deterministic model for shortwave radiation transfer was utilized with climatological data to compute the monthly mean of KSRB at the surface, in the atmosphere and at the top-of-atmosphere (TOA). For 10 Korean stations ranging from 33.5°N to 37.7°N, the daily mean of incoming solar flux at TOA is 345.2 Wm−2. 45.5% of that is transmitted to the surface, 25.0% is absorbed in the atmosphere and 29.5% is reflected directly back to space by the cloud, atmosphere and ground surface under all-sky conditions. Under clear- and overcast-sky conditions, 63.2% and 24.9% are transmitted to the surface, 17.8% and 37.1% are absorbed in the atmosphere, whereas 19.0% and 38.0% are reflected to space, respectively, showing a remarkably invariant value with respect to locations. Clouds in all- (or overcast-) sky atmosphere diminish surface solar irradiances (SSI) from 218.1 Wm−2 to 156.9 Wm−2 (or 85.8 Wm−2) and enhance atmospheric absorptions (AA) from 61.5 Wm−2 to 86.3 Wm−2 (or 128.2 Wm−2). Clouds also enhance the reflected irradiances (RI) at the TOA from 65.6 Wm−2 to 102.0 Wm−2 (or 131.2 Wm−2) for all- (or overcast-) skies. As a result, the all- (or overcast-) sky shortwave (SW) cloud forcing (CF) is −61.2 Wm−2 (or −132.3 Wm−2) at the surface, AA is 24.8 Wm−2 (or 66.7 Wm−2) in the atmosphere and RI is 36.4 Wm−2 (or 65.6 Wm−2) at the TOA, respectively. Consequently, it is found that the values for SW-CF at the surface is 1.7 times and 2.0 times greater than that at the TOA in the KSRB under all- and overcast-sky conditions, respectively. We have also compared the KSRB with the global shortwave radiation budget (GSRB) and discussed on the shortwave radiation budget.

The Tibetan Plateau (TP) is one of the research hot spots in the climate change research due to its unique geographical location and high altitude. Downward longwave radiation (DLR), as a key component in the surface energy budget, has practical implications for radiation budget and climate change. A couple of attempts have been made to parametrize DLR over the TP based on hourly or daily measurements and crude clear-sky discrimination methods. This study uses 1 min shortwave and longwave radiation measurements at three stations over the TP to parametrize DLR during summer months. Three independent methods are used to discriminate clear sky from clouds based on 1 min radiation and lidar measurements. This guarantees the strict selection of clear-sky samples that is fundamental for the parametrization of clear-sky DLR. A total of 11 clear-sky and 4 cloudy DLR parametrizations are examined and locally calibrated. Compared to previous studies, DLR parametrizations here are shown to be characterized by smaller root-mean-square errors (RMSEs) and higher coefficients of determination (R2). Clear-sky DLR can be estimated from the best parametrization with a RMSE of 3.8 W m−2 and R2>0.98. Systematic overestimation of clear-sky DLR by the locally calibrated parametrization in one previous study is found to be approximately 25 W m−2 (10 %), which is very likely due to potential residual cloud contamination on previous clear-sky DLR parametrization. The cloud base height under overcast conditions is shown to play an important role in cloudy DLR parametrization, which is considered in the locally calibrated parametrization over the TP for the first time. Further studies on DLR parametrization during nighttime and in seasons except summer are required for our better understanding of the role of DLR in climate change.

Actual evapotranspiration (AET) can be studied and estimated using remote-sensing-based methods at multiple spatial and temporal scales. Reflectance and Land surface temperature are essential in these methods. However optical and thermal sensors fail to provide these data under overcast conditions and this creates gap in the AET product. Besides, there is a necessity of the AET method that requires less data and estimates AET with better accuracy. In this regard, AET was estimated for all-sky conditions using the vegetation coefficient (VI-Kv) method utilizing microwave, thermal, and optical data. Essential reference evapotranspiration (ET0) under cloudy conditions was estimated using LST-based Penman–Monteith temperature (PMT) and Hargreaves–Samani equations. Furthermore, LST predicted using the microwave polarization difference index (PLST) and LST of moderate resolution imaging spectroradiometer (MODIS) cloud product (MLST) were evaluated with in-situ air temperature (Ta) under cloudy sky conditions. Results revealed that the PLST correlated better with Ta than MLST with correlation coefficient ( r ) values of 0.71 and 0.81 for day and night times, respectively. Hence, PLST-based solar radiation (Rs) estimation yielded better accuracy with observed Rs with r and root mean square error values of 0.864 and 0.07 for Berambadi station under cloudy conditions, respectively. PMT-based ET0 values corresponded well with the observed ET0 under cloudy sky condition during this study. In addition, AET estimated using the VI-Kv method was compared with the simple two-source energy balance (TSEB) method under clear sky conditions. It was found that the improved VI-Kv method performed better than the TSEB method and could also fairly estimate AET even under cloudy sky conditions.

The Increased industrial subsector in Indonesia each year have some negative impact on health in Indonesia. Exhaust emissions of industrial activities is one of the sources of air pollution that occurs continuously. Exhaust gas quality monitoring needs to be done regularly to control the level of air pollution as required under KepMenLH 13/1995. During the determination of the opacity of the black smoke chimney industry performed manually using Ringlemann. Results opasitasbergantung on the observer on the ground so that the level of accuracy obtained is still less opacity akurat. Paper is about the development of imaging technology for monitoring parameters of black smoke in the exhaust stack opacity industri.Metode measurement used in this monitoring is a method Ringlemann. Segmentation algorithm used in this research is using thresholding. The result of the automatic segmentation using thresholding sample data has a high degree of accuracy, but still dependent on weather conditions. If you do smoke segmentation when the sky is clear, the level of accuracy of the segmentation is very high, but it is different when overcast or cloudy weather conditions. Results from this study is the obtainment function to be able to determine the level of black smoke opacity parameter. Searches made through a logarithmic function. Determination of the proper function is determined by looking at the highest value of Rଶ. Different sky conditions lead to different functions, so the algorithm functions to determine the parameters of black smoke opacity made in some circumstances under the conditions of the sky.

A new physically based method to estimate hemispheric-directional reflectance factor (HDRF) from lightweight multispectral cameras that have a downwelling irradiance sensor is presented. It combines radiometry with photogrammetric computer vision to derive geometrically and radiometrically accurate data purely from the images, without requiring reflectance targets or any other additional information apart from the imagery. The sky sensor orientation is initially computed using photogrammetric computer vision and revised with a non-linear regression comprising radiometric and photogrammetry-derived information. It works for both clear sky and overcast conditions. A ground-based test acquisition of a Spectralon target observed from different viewing directions and with different sun positions using a typical multispectral sensor configuration for clear sky and overcast showed that both the overall value and the directionality of the reflectance factor as reported in the literature were well retrieved. An RMSE of 3% for clear sky and up to 5 for overcast sky was observed.

The overcast, partly cloudy and clear sky conditions can determine the diffuse sky radiance and luminance distributions over the sky dome, and thus are crucial for evaluating the solar energy and daylight on and through building envelopes. It is preferable to properly identify the sky condition and determining its diffuse radiance and luminance distribution patterns by the readily available data. In this work, we propose a new approach to identifying the sky conditions, especially the cloudy and clear skies mainly by the horizontal illuminance fluctuation frequency. Illuminance on ground level may fluctuate at high frequency under cloudy skies due to the broken cloud, while vary smoothly under clear skies with few cloud blockages. For input data, the proposed approach needs the horizontal global illuminance that can be readily accessible for many places instead of the “uncommon” measurements on vertical planes or in sky zenith. The fluctuation frequency factor we propose can reduce the misclassification rate for daily and half-day representative sky conditions by 5.7% and 11.5%, respectively, compared to the classifications using the clearness index only.

This paper proposes a corrective algorithm for improving the accuracy of global horizontal irradiation (GHI) forecasts obtained from the numerical weather prediction (NWP) model of the European Centre for Medium-range Weather Forecast (ECMWF). Firstly, GHI forecasts were compared with experimental values from two ground-based stations located at the south of Portugal (Evora and Sines), and the influence of Sun-Earth geometry and atmospheric variables on the differences between predictions and measurements was analysed in order to identify the most relevant parameters. These differences are shown to be correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative air humidity and total water column. Since the ECMWF model directly or indirectly provides all these variables, it is possible to estimate the bias of the predicted GHI as a function of forecast data taking as reference the measurements, which means that an algorithm based on correlations of such parameters can be used to correct forecasts in an operational time horizon. With that goal, an artificial neural network (ANN) based algorithm was developed in this work to improve GHI predictions, including also as input the global solar irradiation predicted by a reference clear sky model. The internal structure of the ANN was optimised, and a spatial and temporal downscaling procedure was also developed to obtain half-hour irradiation values. This algorithm was tested against the original ECMWF forecasts and a persistence model for four locations with different orography and climate as well as for various sky conditions (overcast, partly cloudy and clear sky), showing that it successfully improves the model predictions. Higher values of a Global Performance Index (GPI) based on seven statistical indicators and Forecast Score (FS) were found for the algorithm simulations, e.g. respectively 1.066 and 0.348 when considering all the locations and cloud cover conditions, while for the original ECMWF predictions the GPI is −1.874 and the FS is 0.282. This algorithm is useful if integrated into energy management tools of solar energy systems, namely low/medium temperature solar thermal and photovoltaic systems.

Weather prediction is a crucial element for power management in photovoltaic power plants (PVPP). In this paper, we propose a novel system for collecting essential data used for local short-term weather prediction. Image data consists of all-sky ground-based images obtained by an all-sky camera system with a fish-eye lens. Our proposed weather station collects meteorological data into database. The data include air temperature, humidity, wind speed, relative pressure, and spectrum of solar radiation. First, the whole setup for obtaining all-sky images is described, and setup for weather station is proposed. Then, our all-sky image database is characterized. Finally, to test sky images an experiment was performed to determine sky condition (clear sky, partly cloudy, mostly cloudy, overcast) with the use of a deep convolutional neural network (CNN). The accuracy of this method reached 97,80%.

We experimentally quantified the impact of cloud fraction and cloud type on the heating rates (HRs) of black and brown carbon (HRBC and HRBrC).In particular, in this work, we examine in more detail the average cloud effect (Ferrero et al., 2018) using high time-resolution measurements of aerosol absorption at multiple-wavelengths coupled with spectral measurements of the direct, diffuse and surface reflected radiation and lidar data in the Po Valley. The experimental set-up allowed a direct determination of HRBC and HRBrC in any sky condition. The highest values of total HR were found in the middle of the winter (1.43 ± 0.05 K day−1) while the lowest in spring (0.54 ± 0.02 K day−1) Overall the HRBrC accounted for 13.7 ± 0.2 % of the total HR, the BrC being characterized by an AAE of 3.49 ± 0.01. Simultaneously, sky conditions were classified (from clear-sky to cloudy) in terms of fraction of sky covered by clouds (oktas) and cloud types. Cloud types were grouped as a function of altitude into the following classes: 1) low level ( 7 km) cirrus, cirrocumulus-cirrostratus. Measurements carried out in different sky conditions at high-time resolution showed a constant decrease of HR with increasing cloudiness of the atmosphere enabling us to quantify for the first time the bias (in %) in the aerosol HR introduced by improperly assuming clear-sky conditions in radiative transfer calculations. In fact, during the campaign, clear sky conditions were only present 23 % of the time while the remaining time (77 %) was characterized by cloudy conditions. Our results show that, by incorrectly assuming clear-sky conditions, the HR of light absorbing aerosol can be largely overestimated (by 50 % in low cloudiness, oktas = 1–2), up to over 400 % (in complete overcast conditions, i.e., oktas = 7–8). The impact of different cloud types on the HR compared to a clear sky condition was also investigated. Cirrus were found to have a modest impact, decreasing the HRBC and HRBrC by −1– −5 %. Cumulus decreased the HRBC and HRBrC by −31 ± 12 and −26 ± 7 %, respectively, while cirrocumulus-cirrostratus by −60 ± 8 and −54 ± 4 %, which was comparable to the impact of altocumulus (−60 ± 6 and −46 ± 4 %). A high impact on HRBC and HRBrC was found for stratocumulus (−63 ± 6 and −58 ± 4 %, respectively) and altostratus (−78 ± 5 and −73 ± 4 %, respectively), although the highest impact was found to be associated to stratus that suppressed the HRBC and HRBrC by −85 ± 5 and −83 ± 3 %, respectively. Additionally, the cloud influence on the radiation spectrum that interacts with the absorbing aerosol was investigated. Black and brown carbon (BC and BrC) have different spectral responses (a different absorption Angstrom exponent, AAE) and our results show that the presence of clouds causes a greater decrease for the HRBC with respect to to HRBrC going clear sky to complete overcast conditions; the observed the difference is 12 ± 6 %. This means that, compared to BC, BrC is more efficient in heating the surrounding atmosphere in cloudy conditions than in clear sky. Overall, this study extends the results of a previous work (Ferrero et al., 2018), highlighting the need to take into account both the role of cloudiness and of different cloud types to better estimate the HR associated to both BC and BrC, and in turn decrease the uncertainties associated to the quantification of the impact of these species on radiation and climate.

Sky classification is a complex problem, due in part to such abstract conceptual definitions as clear, intermediate, and overcast, as well as other intermediate ranges. The CIE (Commission Internationale de L’Éclairage) Standard classification offers a solution to this problem, although its application requires data on the luminance distribution of the whole sky that are less commonly available. A benchmarking and classification system of ten meteorological indices is introduced in this study to classify the sky types from overcast to clear. The indices can be calculated from measurements of global, diffuse, and direct irradiance that are widely available from meteorological ground stations. The classification system uses confusion matrices, a machine-learning tool that generates a visual display of the results of supervised-learning algorithms. The CIE Standard skies classification, applied to half hourly sky-scanner measurements in Burgos (Spain), over the period June 2016 - May 2017, is used in this study as a baseline reference for a comparative review of the results from the meteorological indices and their results. They are classified by four performance ratings: Accuracy, Jaccard, Cohen, and Matthews, which feature both classification similarity and the randomness of any agreement. All meteorological indices yielded a high average degree of accuracy - close to 80% - in a detailed review of their classification. Neverthless, the results suggested that Perez’s Clearness Index based on global, diffuse and direct radiation measurements offered the most precise classification of the skies, followed closely by the Klucher Clearness Index and the Perraudeau Nebulosity Index., Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (ORDEN EDU/667/2019) and the Spanish Ministry of Science, Innovation & Universities under the I + D + i state programme “Challenges Research Projects” (Ref. RTI2018-098900-B-I00)

The determination of optimal illumination conditions in buildings is of great interest both for reducing energy consumption and for exploiting solar resources with greater efficiency and sustainability. The most commonplace method of estimating daylight is the luminous efficacy approach, using the more widely measured solar irradiance. In this present study, a new model of diffuse luminous efficacy over a horizontal surface is proposed. A comparative study of twenty-two classic models is presented, to obtain diffuse illuminance, using both, the original mathematical models and the adapted models with local coefficients, in order to determine the most suitable models for Burgos, a city located in north-western Spain. With this purpose in mind, twelve models are selected for all sky conditions, five models for modelling clear sky, two for partly cloudy sky, and three for overcast sky. These twenty-two models are then compared with the new model both for all sky conditions and for particular sky conditions (clear, partly cloudy, and overcast). The behaviour of the new model showed greater accuracy than most of the classic models under analysis. Hence, the advantage of the diffuse luminous efficacy model that can be applied both to all sky and to particular sky conditions., Spanish Government (Ministerio de Economía y Competitividad) (ENE2014-54601-R). David González Peña would also like to thank the Junta de Castilla-León for economic support (PIRTU Program, ORDEN EDU/310/2015).

Thermal radiation is an important component of outdoor thermal environment since it accounts for a significant part of thermal load on human body. This study investigated the radiation fields in two outdoor spaces with different Sky View Factors (SVFs) in summer and autumn, and under sunny and overcast weather conditions. The result shows that the decrease of SVF significantly reduced the short-wave radiation due to the block of the sun. In contrast, the change in SVF had a much smaller impact on the level of long-wave radiation. The total level of radiation among seasons had a huge difference, as the difference in the Tmrt among seasons exceeded 30 °C. Weather condition had a smaller impact on the total level of radiation than season, as the Tmrt on a sunny day was only about 10 °C higher than that on an overcast day. The findings of this study provide a database for studying the radiation field and thermal comfort in outdoor spaces.

Standard sky-types of the established Commission International de l’Eclairage (CIE) sky-models are employed for generating sky-luminance distribution datasets in most building daylight simulation packages, which are used for predicting indoor illuminance distribution. However, sky-type can vary on diurnal and seasonal basis; and there is a need for identifying closest sky-type at different time-points for better indoor illuminance predictions. This paper aims at recommending closest sky-model for measured angular sky-luminance distribution sets through comparison with established CIE standard skies. A novel design of sky-luminance measurement device using calibrated Light Dependent Resistors is proposed to measure incident illuminance from 265 discrete sky-positions every 5 min. Statistical analysis is performed on 143,100 distinct data-points corresponding to 540 representative datasets measured across 5-month period at Mumbai, and the closest CIE sky-type for various hours is recommended based on frequency of occurrence. A range of 9 sky-types of the CIE sky-model was determined as closest model sky-set, with sky-types 2, 4, 5, 6, 7, 8, 10, 11 and 13 assessed as closest sky-type for different time-points across the measured datasets. CIE sky-type 8 is identified dominating over 50% of measured datasets — 51.6% in November, 53.6% in January, 52.6% in March and 71.5% in April. Measured sky distribution is observed to generally follow trend of overcast to intermediate to clear sky-types in morning hours, and opposite in evening hours. This approach of recommending closest sky-type as simulation input — as against using generic sky-types — can enhance the accuracy of building indoor daylight predictions.

We present an analysis of the zenithal night sky brightness (henceforth: NSB) measurements at 26 locations in Eastern Austria focussing on the years 2015-2016, both during clear and cloudy to overcast nights. All measurements have been performed with 'Sky Quality Meters' (SQMs). For some of the locations, simultaneous aerosol content measurements are available, such that we were able to find a correlation between light pollution and air pollution at those stations. For all locations, we examined the circalunar periodicity of the NSB, seasonal variations as well as long-term trends in the recorded light pollution. For several remote locations, a darkening of the night sky due to clouds by up to 1 magnitude is recorded - indicating a very low level of light pollution -, while for the majority of the examined locations, a brightening of the night sky by up to a factor of 15 occurs due to clouds. We present suitable ways to plot and analyze huge long-term NSB datasets, such as mean-NSB histograms, circalunar, annual ('hourglass') and cumulative ('jellyfish') plots. We show that five of the examined locations reach sufficiently low levels of light pollution - with NSB values down to 21.8 mag$_{SQM}$/arcsec$^2$ - as to allow the establishment of dark sky reserves. Based on the 'hourglass' plots, we find a strong circalunar periodicity of the NSB in small towns and villages (< 5.000 inhabitants), with amplitudes of of up to 5 magnitudes. Using the 'jellyfish' plots, on the other hand, we demonstrate that the examined city skies brighten by up to 3 magnitudes under cloudy conditions, which strongly dominate in those cumulative data representations. The long-term development of the night sky brightness was evaluated based on the 2012-17 data for one of our sites, possibly indicating a slight (~2%) decrease of the mean zenithal NSB at the Vienna University Observatory., Accepted by JQSRT, 2018

Clouds are the major modulator of the shortwave and longwave radiation components of the Earth's energy balance and, as such, help to regulate the planet's temperature. In the energy sector, clouds are a source of instability in the generation of energy using solar technologies. This study aims at comparing three approaches to get cloud cover information in the Southeastern region of Brazil during the period of approximately three months. The first method, assumed as reference, uses all-sky camera pictures for the cloud cover estimation. The other two methodologies use downward longwave radiation with surface meteorological data and geostationary satellite data. Both methods presented good agreement with the camera for clear sky and overcast conditions, with probabilities of detection of 92.8% and 80.7% for the longwave method and 93.3% and 87.6% for the satellite method, respectively. The major problem occurs with the broken-clouds sky scenario, with probabilities of detection above 38%, where each method has its own specificity, such as, longwave emissivity of the atmosphere, spatial resolution and view geometry. The long-wave method has the minor R correlation with the camera (87%) when compared with the satellite method (93%) and requires a daily normalization, which make it not usable for instantaneous measurements. Regarding the satellite method, the most important issue is the spatial resolution, which has the major impact on the broken-clouds sky scenarios. The cloud masking works properly for large clouds with, at least, the size comparable to the satellite image pixel. Furthermore, the method using the all-sky camera also needs to be improved, because it presented some deficiencies, like very bright areas around the sun, sometimes identified as clouds, leading to cloud cover overestimation.

Daylight has a significant role for energy conservation in buildings. Luminous efficacy models have been applied by many researchers to estimate the illuminance level from available irradiance data. In the present study, the characteristics of global (Kg) and diffuse (Kd) luminous efficacies are reported based on measured data for the humid sub-tropical region of New Delhi, India. Four existing efficacy models have been compared based on the measured irradiance and illuminance data. Statistical analysis indicates that the existing models performed poorly for the selected location. The performance of the models was significantly improved when the optimized coefficients were adopted using the measured data. Global and diffuse efficacy under clear, intermediate, overcast and all sky conditions, using existing and developed efficacy models were computed and compared. A good agreement has been exhibited for variation of efficacies with the measured values for different sky conditions from cloud to clear skies. Variation of global and diffuse efficacy for sky (clear, intermediate and overcast) w.r.t. solar altitude angle and sky clearness index has been presented. Finally, mean monthly global (Lg) and diffuse (Ld) illuminance were also estimated using the original and optimized version of efficacy models to provide impression of deviation of the models.

This paper assesses accuracy of solar radiation from different generation reanalysis datasets against ground measurements for different climatic conditions. The range of rMBE for NCEP-DOE, CFSR, MERRA, MERRA-2, ERA-Interim and ERA-5 is 4–34, 12–24, 14–29, 8–19, 5–14, 6–13% respectively, using sub-daily data. The performance of newer versions of the datasets is better than previous versions. The performance of datasets varies in different months depending upon seasonal and monthly meteorological conditions. The datasets perform better under clear-sky conditions and estimations are poor for overcast conditions. The evaluation of three datasets was performed under different sky conditions. The rMBE using hourly data under clear-sky, intermediate-sky and cloudy-sky range −3–2, 4–17 and 134–170% respectively. The performance of MERRA-2 is better than ERA-5 under clear-sky and intermediate-sky whereas ERA-5 performs better under cloudy-sky conditions for region under study. A method based on clearness index, solar zenith angle and periodicity factor is developed to correct hourly GHI estimated by datasets. Twenty models were used to correct data considering monthly variation of errors. The rMBE and rRMSE were reduced to 100% and upto 50% for months having lower clearness index and higher errors in the original data.

This paper reports a study on the variation in the solar reflectance of fiber-cement roof tiles used in buildings at the Federal University of Santa Catarina (UFSC) applying the technical standard ASTM E1918 . Laboratory tests were employed using the ASTM E903 procedures for comparison. The solar reflectance was evaluated for newly painted roofs and also for old roofs with different ages to compare their degradation over the years. In addition, to provide possible changes in ASTM E1918 limits, tests were performed to determine the influence of clouds, extended hours of measurement and the distance between the sensor (pyranometer) and the surface on the solar reflectance values. The results obtained for newly painted roofs revealed a considerable improvement in solar reflectance after the first paint layer. The mean solar reflectance value for the white roofs (painted and measurement taken in 2016) was 0.70. For the same roofs (samples), the mean solar reflectance value determined in the laboratory was 0.76. Results obtained in 2016 in the field for roofs painted in 2014 were in the range of 0.49–0.64 and for roofs painted in 2013 they were 0.59–0.64. The variation in the values obtained for the solar reflectance indicates that factors such as the skill of the painter, condition of the substrate, nature of the paint and the weather during the painting process can influence the final coating quality. The study also revealed that, in January (summer in the southern hemisphere), the results obtained in field tests on overcast or cloudy days differ from those obtained when the sky is clear by ± 0.02. Small changes ( ± 15 cm) in the sensor height did not appear to affect the solar reflectance values obtained. On applying extended times in the field tests (around 5 h), the differences in the measurements did not exceed 0.02.

Solar irradiance and daylight illuminance are essential for solar energy and daylighting designs. Recently, the International Commission of Illuminance (CIE) adopted 15 Standard Skies to represent the possible sky-diffuse luminance and radiance distributions. Such distributions would improve the solar irradiance and daylight illuminance estimations for the building envelope and photovoltaic panels in any directions. The important issue is whether the sky conditions could be correctly identified by the available variables. Previously, many climatic parameters including sky luminance distributions, vertical diffuse irradiance and illuminance were proposed for identifying sky conditions. However, such data may not always be available from the routine measurements of a weather station. This paper proposes an approach to interpreting the sky conditions using the variables that are readily accessible from meteorological stations for many years. The approach appropriately identified 83.2% of the 3 typical overcast, partly cloudy and clear skies, and further 62.7% of the 15 individual CIE Standard Skies for Hong Kong. The %RMSE of the vertical solar irradiance and daylight illuminance estimated by the approach was found less than 23%. The results show that the proposed approach would be reliable for sky classification.

This study examines the accuracy of annual daylight simulation method (ADSM) in predicting illuminance for spaces with shading device conditions. ADSM algorithms were developed separately for the sun and sky to predict their effect on indoor daylight illuminance. Sun-matching and daylight coefficient methods were developed for the sun, while sky-matching and daylight coefficient methods with one and four sky patches were developed for the sky. The daylight illuminance computed from ADSM under various daylight conditions was compared with those calculated from Radiance and field measurements. Results imply strong linear correlations existed between the predicted daylight illuminance levels by the ADSM and Radiance under diverse sky conditions based on weather data. The predicted illuminance from ADSM was lower than field measurements for all sky conditions. ADSM computations mostly agree with field measurements. For clear and partly cloudy sky conditions, the daylight coefficient approach for sky of ADSM generated a stronger correlation to measured data, but the sky-matching algorithm showed a stronger correlation to field data. The daylight coefficient approach for sky, combined with ADSM computation algorithms for sun, effectively reduced the difference between the predicted and measured illuminance under clear or partly cloudy sky conditions. Under overcast conditions, there was no significant reduction in difference between simulated and measured illuminance.

This study examines daylight distribution and illumination levels with daylight factors (DFs) inside an air-supported (pneumatic) structure (50.0 m long, 25.0 m wide and 11.0 m high). The DFs values were determined experimentally for 45 different points on the playground floor. Two sets ( set1 and set2 ) of time dependent illuminance measurements were performed at overcast but variable (5400-2400 lx) sky conditions similar to the CIE overcast sky. Results revealed that the DFs values were in the range from 1.1% to 1.7% and were not evenly distributed across the interior, with the greatest values at the centre and the smallest at the edges of the playground floor. Composition of the membrane was studied with SEM micrographs and by measuring the Raman and Infrared spectra. Hemispherical (total) transmittance (TT(λ)) and reflectance (TR(λ)) spectra (300–2500 nm) of the membrane used were measured and averaged over the visible spectral region giving TT vis and TR vis and haze, the latter confirming that fabrics strongly scattered the incoming solar radiation. The surface of the fabrics was characterized by contact angle measurements which indicates that the fabrics were weakly hydrophobic and strongly soiled. Daylight factor values obtained from the DAYSIM computer simulations matched well the experimental ones allowing further studies of daylighting inside the translucent dome. It was found that various colour patterns substantially affected the DFs values. DAYSIM calculations also demonstrated the possible use of aerogel insulation blanket as composite fabrics for air-supported structures.

In this study, a method is used to develop a model for estimating luminance distributions of Thai’s sky. From the sky characterization, five out of the fifteen CIE standard skies are selected for the distribution estimation. This selection represents the minimum sky types that best fit to the distribution records of Bangkok (Thailand) and cover three sky conditions of clear, partly cloudy, and overcast. The developed model uses the five standard skies each to estimate prevailing luminance distributions within a particular range of insolation condition defined by lower and upper limit values of Perez’s clearness (e) and sky brightness (Δ). In this scheme, a stochastic search technique of evolutionary computation was applied to optimize limit values for an accurate sky classification. Equations of optimal limit values were also formulated as part of the method. From the comparative evaluation with other sky models (i.e. Perez’s model, ASRC-CIE model, Harrison’s model, CIE-Si model, and Igawa’s all-sky model), the proposed model offers an alternative option for generating luminance distribution of Thai’s sky.

The statistics of clear-sky index can be used to determine solar irradiance when the theoretical clear sky irradiance and the cloud cover are known. In this paper, observations of hourly clear-sky index for the years of 2010–2013 at 63 locations in the UK are analysed for over 1 million data hours. The aggregated distribution of clear-sky index is bimodal, with strong contributions from mostly-cloudy and mostly-clear hours, as well as a lower number of intermediate hours. The clear-sky index exhibits a distribution of values for each cloud cover bin, measured in eighths of the sky covered (oktas), and also depends on solar elevation angle. Cloud cover is measured either by a human observer or automatically with a cloud ceilometer. Irradiation (time-integrated irradiance) values corresponding to human observations of “cloudless” skies (0 oktas) tend to agree better with theoretical clear-sky values, which are calculated with a radiative transfer model, than irradiation values corresponding to automated observations of 0 oktas. It is apparent that the cloud ceilometers incorrectly categorise more non-cloudless hours as cloudless than human observers do. This leads to notable differences in the distributions of clear-sky index for each okta class, and between human and automated observations. Two probability density functions—the Burr (type III) for mostly-clear situations, and generalised gamma for mostly-cloudy situations—are suggested as analytical fits for each cloud coverage, observation type, and solar elevation angle bin. For human observations of overcast skies (8 oktas) where solar elevation angle exceeds 10°, there is no significant difference between the observed clear-sky indices and the generalised gamma distribution fits.

The work describes the creation of a new global climatology of short-wave radiation incoming to the ocean surface; the climatology is based on a new parameterization of short-wave fluxes. Advantages of the new parameterization are allowance for nonlinearity of the dependence of the atmospheric transmission factor on the height of the Sun under a clear sky and allowance for different morphological types of clouds under the overcast. It is shown that taking into account these factors leads to substantial differences in short-wave radiation fluxes in comparison to existing parameterizations.

Light pollution is closely correlated with the meteorological factors, specifically cloudiness that is one of the major amplifiers of night sky radiances in urban regions. Although the decisive effects of cloud deck on artificial nighttime skyglow have been recognized experimentally, the radiative transfer modelling in a heterogeneous nocturnal environment illuminated from many light sources is a non-trivial problem that is difficult to solve both theoretically and numerically. A satisfactorily accurate evaluation of ground-reaching diffuse light is, however, an important issue as some optical properties (e.g. horizontal irradiance) are usually difficult to obtain with common instruments. Overcast sky represents a special class of situations in which clouds can act as amplifiers of the light pollution of the city. In this paper we proceeded with a simple two-stream approach to solve the scalar radiative transfer equation (RTE) under overcast conditions. The technique we are using allows for a rapid prediction of ground irradiances in densely populated regions assuming various emission functions. We have shown that the classical RTE concept can be adopted in determining the diffuse irradiance, while the model abilities are illustrated in a set of numerical experiments for low and high turbidity states.

Occupant centric performance approaches in daylighting studies promote design decisions that support human visual comfort, productivity, and visual preferences, along with more conventional energy efficiency criteria. Simulating per-pixel luminance values and luminance distribution patterns for the entire scene allows us to analyze the occupant centric metrics and performance criteria. However, there are a number of different sky models, complex fenestration models, and simulation techniques that produce either conventional point in time images or annual luminance maps. This paper discusses the similarities and differences between different techniques; and a comparison analyses provides insight about their impact on occupant centric lighting measures. The comparisons for sky modeling include the conventional CIE skies (Clear, Intermediate, and Overcast), measurement based CIE models, Perez all-weather skies, and high dynamic range image based skies. The comparison of simulation techniques include point in time simulations, image based lighting simulations, and annual luminance simulations (three-phase and five-phase methods). Results demonstrate that measurement based sky models match real world conditions with reasonable proximity, and generic CIE skies consistently underestimate the indoor lighting conditions. Annual simulation methods provide a large database of temporal luminance variations, where individual instances are comparable to point in time simulations. Long term luminance simulations provide opportunities to evaluate the percentage of the year that a given luminance based criteria is met or violated.

The aim of this work is to investigate whether snow albedo seasonality and trend under all sky conditions at Johnsons Glacier (Livingston Island, Antarctica) can be tracked using the Moderate Resolution Imaging Spectroradiometer (MODIS) snow albedo daily product MOD10A1. The time span is from December 2006 to February 2015. As the MOD10A1 snow albedo product has never been used in Antarctica before, we also assess the performance for the MOD10A1 cloud mask. The motivation for this work is the need for a description of snow albedo under all sky conditions (including overcast days) using satellite data with mid-spatial resolution. In-situ albedo was filtered with a 5-day windowed moving average, while the MOD10A1 data were filtered using a maximum filter. Both in-situ and MOD10A1 data follow an exponential decay during the melting season, with a maximum decay of 0.049/0.094 day&minus, 1 (in-situ/MOD10A1) for the 2006&ndash, 2007 season and a minimum of 0.016/0.016 day&minus, 1 for the 2009&ndash, 2010 season. The duration of the decay varies from 85 days (2007&ndash, 2008) to 167 days (2013&ndash, 2014). Regarding the albedo trend, both data sets exhibit a slight increase of albedo, which may be explained by an increase of snowfall along with a decrease of snowmelt in the study area. Annual albedo increases of 0.2% and 0.7% are obtained for in-situ and MOD10A1 data, respectively, which amount to respective increases of 2% and 6% in the period 2006&ndash, 2015. We conclude that MOD10A1 can be used to characterize snow albedo seasonality and trend on Livingston Island when filtered with a maximum filter.

We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN (on a combination of synthetic and real images) to take as input an LDR panorama, and regress the parameters of the Lalonde-Matthews outdoor illumination model. This model is trained such that it a) reconstructs the appearance of the sky, and b) renders the appearance of objects lit by this illumination. We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network. We demonstrate, via extensive experiments, that both our panorama and single image networks outperform the state of the art, and unlike prior work, are able to handle weather conditions ranging from fully sunny to overcast skies., 8 pages, CVPR 19. Project page: http://lvsn.github.io/allweather

Artificial skyglow, the fraction of artificial light at night that is emitted upwards from Earth and subsequently scattered back within the atmosphere, depends on atmospheric conditions but also on the ground albedo. One effect that has not gained much attention so far is the amplification of skyglow by snow, particularly in combination with clouds. Snow, however, has a very high albedo and can become important when the direct upward emission is reduced when using shielded luminaires. In this work, first results of skyglow amplification by fresh snow and clouds measured with all-sky photometry in a suburban area are presented. Amplification factors for the zenith luminance of 188 for snow and clouds in combination and 33 for snow alone were found at this site. The maximum zenith luminance of nearly 250 mcd/m&sup2, measured with snow and clouds is a factor of 1000 higher than the commonly used clear sky reference of 0.25 mcd/m&sup2, Compared with our darkest zenith luminance of 0.07 mcd/m&sup2, measured for overcast conditions in a very remote area, this leads to an overall amplification factor of ca. 3500. Horizontal illuminance measurements show values of up to 0.79 lx, exceeding maximum possible full-moon illuminance levels by more than a factor of two. Additional measurements near the Arctic Circle for clear and overcast conditions are presented and strategies for further studies are discussed. We propose the term &ldquo, snowglow&rdquo, to describe the amplification of skyglow by snow with and without clouds.

A novel daily and 15-min period solar resource classification algorithm, based only on the Fourier transform of global horizontal irradiance (GHI) and calculable parameters, is presented. These methods can identify GHI series that are not attenuated by clouds, whether the clouds may or may not be visible. Additional sky condition classes are also adopted in which the representative conditions of the remaining days or periods are classified. Although the proposed day classification is too conservative to be widely applicable, the 15-min period classification is generally in good agreement with the Perez sky clearness index while providing greater sensitivity to turbid conditions and algorithm flexibility. It is shown, however, that the adoption of the clearness index in the proposed 15-min period method less successfully distinguished between clear and constant overcast conditions under winter continental conditions due to relatively large solar zenith angles. It is also shown that the proposed 15-min period and Reno and Hansen statistical methods are generally in good agreement on the detection of clear conditions under temperate conditions. The classification results of the proposed method are further shown in comparison to high-resolution sky images to elucidate the character of each class and the classification algorithm flexibility.

Daylight in nature is characterized by daily permanent changes of sunlight and skylight. Real measurements of daylight availability are showing that different daylight exterior illuminance for building interiors can be expected each minute. This brings some complications in the window design and the choice of criteria for daylight evaluations. There are several ways how to find basic conditions and typical relations combining sunlight beam with diffuse skylight from the whole sky vault. The older assumption considered that interiors have to be sufficiently illuminated under the worst overcast conditions. The newer approach is based on the utilization of daylight in specific localities and the determination of changes in sunlight and skylight occurrence probability. Therefore, both daylight sources are researched in detail specifying sky luminance distributions and sun influences to find conditions for their simulation in laboratory facilities. The most sophisticated equipment to study daylighting in exterior and interior architectural spaces are artificial skies with the artificial sun. These have to be precisely calibrated with a verified zenith luminance and horizontal illuminance levels by theoretical calculations and checked by experimental measurements. Reference daylight conditions defined in the ISO/CIE 15469:2004 standard have to be respected with trials to determine natural sun and sky as sources of daylight in the real environment and modelling these in the artificial sky in a certain intensity scale. This paper presents the method and results of modelling daylight applying electrical light sources in artificial sky which is installed in the Institute of Construction and Architecture, Slovak Academy of Sciences (ICA SAS) and discusses possibilities of their simulation in laboratory conditions.

In order to model daylight availability and distribution in architectural spaces, simulation tools require reliable representations of boundary conditions – typically in terms of sky luminance distribution models. However, the impact of sky model errors on simulation-based indoor illuminance predictions is not well documented. There are different tools and methods to simulate indoor illuminance conditions and related daylight indicators. In the present study, we selected Radiance lighting simulation program. In order to generate sky scene description, Radiance contains two routines, namely, Gendaylit and Gensky. These routines require, as input, information on both direct and diffuse components of solar radiation. To explore the implications of the sky model selection on the fidelity of simulation results, we used Radiance to compute the indoor illuminance in an existing test space on the rooftop of a university building. Thereby, the aforementioned two sky models were considered. A third option (SC) was a sky model generated based on measured values obtained from a sky scanner. Simultaneously, the actual illuminance levels in this room were monitored under different outdoor conditions (clear, intermediate, overcast). The comparison of the measurement results with multiple model prediction results facilitates an empirically based evaluation of the reliability of indoor illuminance predictions in the face of different assumptions pertaining to the prevailing boundary conditions.

The perforated shadow band enables the decomposition of global horizontal solar irradiance (GHI) so as to obtain diffuse horizontal and direct normal irradiance components (DHI and DNI). As a single-pyranometer system, it offers a low-cost alternative to dual-sensor radiometric schemes, but a comprehensive performance analysis has only been conducted under clear sky conditions. This study addresses perforated band (PB) behavior in the presence of cloud when the resulting radiometric trace exhibits stochasticity and a sensor exposure model is required to separate resulting fragmented GHI and DHI components prior to reconstitution as continuous time-series. Various interpolation techniques are tested for replacing patches of missing data caused by the band’s operation with the aim of minimizing root mean square difference (RMSD) relative to reference data. Given the effect of cloud on performance, uncertainties are reported as a function of daily clearness index, rather than as constants. For GHI, the RMSD uncertainties range between 6% for high clearness indices and around 36% for values between 0.3 and 0.4. Bias is typically negative, and the PB system underestimates GHI by between 1% (clearer skies) and 7% (partly cloudy skies). It is found that deploying interpolation schemes adaptively in response to the prevailing clearness index effectively reconstitutes fragmented GHI curves when cloud is present, while decomposition models assist in replacing missing DHI data under heavily cloudy and overcast conditions. Root mean square differences of around 20% can be expected for DHI measurements with mean bias varying between −5% for overcast conditions and +8% for clearer skies. The DNI results are obtained by calculation from DHI and GHI values, with RMSD peaking at about 200 W/m 2 in the mid-clearness index range, reducing to 30 W/m 2 under mainly overcast conditions and to 90 W/m 2 for high clearness index values. The results of the study also offer insight to the more general problem of replacing missing data in radiometric time series.

During the first daylight photometry studies in the 18th Century three fundamental source types of daylight were characterised as direct sunlight, clear sky luminance distribution and overcast sky luminance uniformity. For minimum illumination and simplicity the last became the basic state to determine computational methods and practical graphical tools to evaluate daylight distribution especially in poorly naturally lit interiors. The simultaneously present sunlight and skylight under cloudless or cloudy conditions attracted the research of sky luminance distributions caused by the scattering of sunbeams within the atmosphere. The arbitrary real sky luminance patterns influenced by air molecule and aerosol turbidity as well as cloudiness were studied and measured to determine the relatively complex their changes. The historical search after the interrelated definition and mathematical expression of gradation and scattering indicatrix functions is explained in the gradually evolving progress in this paper. Recently in sustainable solar energy utilisation and daylighting studies via building apertures and photovoltaic panels take into account also radiance and luminance gains from the sky vault under any weather conditions. The luminance distribution on homogeneous sky types is today based on mathematically defined ISO/CIE standards of relative gradation and indicatrix functions in the range between overcast and cloudless skies. The review of the development and the different methodologies of categorising typical sky luminance patterns is discussed and summarised in this paper.

The estimation of solar heat gain via vertical fenestration is important in energy-efficient building design and operation especially for subtropical Hong Kong. Being one of the most populated cities in the world, buildings in Hong Kong tend to be densely constructed. In this regard, the shading effect of surrounding buildings could significantly limit solar radiation from the sun and sky to the window while certain amount of solar radiation would be reflected from the opposite facades. This paper presents a calculation approach to compute solar radiation on obstructed fenestration for both overcast and non-overcast skies. Performance of the proposed method is evaluated by comparing with simulated results of a sophisticated lighting program and field measurements. The findings are essential for the assessment of both active and passive building energy saving techniques including shading and building integrated photovoltaic systems in metropolitan environment.

To estimate global solar radiation from easy available weather forecast data (sky condition), Markov model is used for this estimation. The five-year (1996-2000) global radiation data that are taken at an hour intervals from Nakhon Pathom station, Thailand (latitude 13.81ºN and longitude 100.04ºE) are used to construct the Markov transition matrices. The global radiation sequences in 2000 will be generated by based on the characteristic probability of moving global radiation values which were observed from the obtained data during 1996-1999. The autocorrelation function is used for checking the order of probability of moving obtained data. In this study, the five first and five second-order Markov transition matrices (MTMs), which are selected from the autocorrelation functions, are constructed, each MTMs will be used for generating global radiation values in each day with different sky conditions (clear, partly cloudy, mostly cloudy, cloudy and overcast). From the results of comparison between the statistical characteristics of observed and two synthetic generated data, global radiation data behavior slightly improved by the second order Markov model.

As part of the High Definition Clouds and Precipitation for advancing Climate Prediction Observational Prototype Experiment (HOPE), a high-density network of 99 silicon photodiode pyranometers was set up around Jülich (10 km × 12 km area) from April to July 2013 to capture the small-scale variability of cloud-induced radiation fields at the surface. In this paper, we provide the details of this unique setup of the pyranometer network, data processing, quality control, and uncertainty assessment under variable conditions. Some exemplary days with clear, broken cloudy, and overcast skies were explored to assess the spatiotemporal observations from the network along with other collocated radiation and sky imager measurements available during the HOPE period.

Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1–2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

Cloud cover information is used alongside weather forecasts in various fields of research; however, ground observation of cloud cover is conducted by human observers, a method that is subjective and has low temporal and spatial resolutions. To address these problems, we have developed an improved algorithm to calculate cloud cover using sky image data obtained with Skyviewer equipment. The algorithm uses a variable threshold for the Red Blue Ratio (RBR), determined from the frequency distribution of the Green Blue Ratio (GBR), to calculate cloud cover more accurately than existing algorithms. To verify the accuracy of the algorithm, we conducted daily, monthly, seasonal, and yearly statistical analyses of human observations of cloud cover, obtained every hour from 0800 to 1700 Local Standard Time (LST) for the entirety of 2012 at the Gangwon Regional Meteorological Administration (GRMA), Korea. A case study compared daily images taken at 1200 LST in each season with pixel images of cloud cover calculated by our improved algorithm. The selected cases yielded a high correlation coefficient of 0.93 with the GRMA data. A monthly case study showed low root mean square errors (RMSEs) and high correlation coefficients (Rs) for December (RMSE = 1.64 tenths and R = 0.92) and August (RMSE = 1.43 tenths and R = 0.91). In addition, seasonal cases yielded a high correlation of 0.9 and 87% consistency within ± 2 tenths for winter and a correlation of 0.83 and 82% consistency for summer, when cases of cloud-free or overcast conditions are frequent. Annual analyses showed that the bias of GRMA and Skyviewer cloud cover data for 2012 was -0.36 tenth, and the RMSE was 2.12 tenths, with the GRMA data showing more cloud cover. Considering that the GRMA and Skyviewer data were gathered at different spatial locations, GRMA and Skyviewer data were well correlated (R = 0.87) and showed a consistency of 80% in their cloud cover data (consistent within ± 2 tenths).

Nowadays, many researchers – especially engineers and architects – apply daylighting simulation tools during the building design process to investigate daylighting performance for a range of purposes. However, International Commission on Illumination (CIE, from the French name of the organisation, Commission Internationale de l’Eclairage) sky models are considered for many such simulation tools. The characteristics of tropical sky are different from those of CIE skies, hence the usage and validity of these tools could be unreliable for research involving tropical regions. The aim of this study is to validate the Radiance program in the Integrated Environmental Solution – Virtual Environment (IES-VE) software by measuring daylight in furnished and unfurnished rooms under Malaysian tropical sky. Daylighting measurement and simulation were carried out over eight days in unfurnished and furnished rooms under both intermediate and overcast skies. The case room was drawn by ModelIT and the related furni...

Daylighting is recognized as an important and useful strategy in the design of energy efficient buildings. Daylight is still the best source of light for good colour rendering and visual comfort. In this study, a new model of global luminous efficacy over a horizontal surface is proposed. A comparative study of eighteen classic models is presented, to obtain global horizontal illuminance, using both, the original formulation and new formulae with local adaptations, in order to determine the most suitable models for the conditions in Burgos (Spain). With this aim in mind, the selected models consisted of six models developed for all sky conditions, five models for clear sky conditions, three for partly cloudy sky and four for modelling overcast sky conditions. These eighteen models were also compared with the proposed model using experimental global illuminance measurements for different sky conditions. It was shown that the proposed model behaved in a better way than most of the classic models selected from the literature; both for all sky conditions and for particular sky conditions (clear, partly cloudy and overcast). The proposed model was therefore generally applicable, with no need to employ a different model for each particular sky condition., Spanish Government (Ministerio de Economía y Competitividad) (ENE2014-54601-R)

In the absence of ground reflected radiation measured data, an average albedo value of 0.2, which describes the reflective properties of the bare ground is generally used. The variation of albedo based on different foreground surfaces and under different atmospheric conditions is an area which is under research. This paper presents an experimental investigation of albedos of different foreground materials that can be used for photovoltaic (PV) applications. Overcast skies are predominant in northerly locations and will have a high frequency of low solar altitudes, and thus ground reflection is an important contributor towards total solar energy gain. The foreground surfaces include common materials: Grass, sand, and cement slabs, and some non-conventional materials: White pebbles, white boards, white tiles, and aluminium foil. The impact of factors, such as ageing, solar elevation, rain, and cloud cover (sky conditions) is analysed to determine the changes in albedos of these materials. Each material was observed to have individual performance characteristics under these factors. It was found that the non-conventional materials were least prone to weather-related changes and have higher albedo values as compared to the conventional materials, and also have good potential to replace the conventional materials for any given PV application.

Cloud detection is an essential process to extract cloud information for solar irradiance forecast. This process is generally applied to sky images to identify cloud and sky pixels. The glare in circumsolar region is a main obstacle for cloud detection. Pixels are incorrectly identified when using basic thresholding techniques. Thus, we propose an algorithm with brightness reduction of circumsolar region to deal with this issue. Non-uniform illumination of sky images is corrected by using homomorphic filtering, then sky conditions of images are categorized. Cloudy and overcast images use normalized blue/red ratio (NBRR) thresholding for identifying cloud pixels, whereas clear sky and partly cloudy images use thresholding technique. We take 100 images to evaluate the performance of the proposed algorithm. The results show that our algorithm outperforms basic NBRR method with accuracy improvement of 6% and 5.3% for clear sky and partly cloudy images, respectively.