Clear-sky Model Research Articles

Simplified method for monitoring of cumulus clouds by using global horizontal irradiance

A simplified method for monitoring of cumulus clouds which is based on a variational analysis of a time series of measured global horizontal irradiance is proposed. A distinctive feature of the technique is that it does not use an amplitude analysis of the time series and, as a result, there is no need to use any clear-sky model.

Clear-sky models evaluation of two sites over Algeria for PV forecasting purpose

Clear-sky models are needed in various fields of solar energy, such as solar sensors calibration, quality control of solar data, CSP and PV energy forecasting, etc. The aim of this work is the assessment of the clear-sky models for global solar irradiance estimation. For this end, data of two sites in Algeria have been used. Models accuracy is achieved by comparison between their estimations and measurements (5-minute time steps for the Bouzareah site and 10-minute steps for the Ghardaia site). Atmospheric input data to the models enclose aerosol and water vapor measurements as well as Linke turbidity, which are determined experimentally from available solar and meteorological measurements. Results show that Inchein-Perez and ESRA models are the best clear-sky approaches which ensure excellent accuracies ($ nRMSE = 3.84\%$ and 6.26%) when compared to ground measurements.

Clear-sky direct normal irradiance estimation based on adjustable inputs and error correction

The accurate estimation of direct normal irradiance (DNI) under clear sky conditions plays an important role in the concentrated solar thermal plant. A hybrid model with adjustable inputs is proposed to calculate the clear-sky DNI, including a base clear-sky model and an error-correction model. The base clear-sky model is able to estimate the clear-sky DNI at any place with only the local date and location information, and the error-correction model serves as a supplementary to improve the calculating accuracy with available meteorological data. The error-correction model effectively integrates a linear part and a nonlinear part, and its inputs are adjustable according to the available meteorological observations. Several experiments have been conducted to evaluate the performance of the proposed model with data from three observation stations provided by the National Renewable Energy Laboratory open database. The results show that the hybrid model is able to provide great improvement over the base clear-sky model with 28%–70% on normalized root mean square error, and it also performs better than those using a linear or nonlinear error correction model. It is concluded that the performance of the hybrid model is comparable with other published methods in calculating the clear-sky DNI with concrete statistics.

Clear-sky ultraviolet radiation modelling using output from the Chemistry Climate Model Initiative

Abstract. We have derived values of the ultraviolet index (UVI) at solar noon using the Tropospheric Ultraviolet Model (TUV) driven by ozone, temperature and aerosol fields from climate simulations of the first phase of the Chemistry-Climate Model Initiative (CCMI-1). Since clouds remain one of the largest uncertainties in climate projections, we simulated only the clear-sky UVI. We compared the modelled UVI climatologies against present-day climatological values of UVI derived from both satellite data (the OMI-Aura OMUVBd product) and ground-based measurements (from the NDACC network). Depending on the region, relative differences between the UVI obtained from CCMI/TUV calculations and the ground-based measurements ranged between −5.9 % and 10.6 %. We then calculated the UVI evolution throughout the 21st century for the four Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0 and 8.5). Compared to 1960s values, we found an average increase in the UVI in 2100 (of 2 %–4 %) in the tropical belt (30∘ N–30∘ S). For the mid-latitudes, we observed a 1.8 % to 3.4 % increase in the Southern Hemisphere for RCPs 2.6, 4.5 and 6.0 and found a 2.3 % decrease in RCP 8.5. Higher increases in UVI are projected in the Northern Hemisphere except for RCP 8.5. At high latitudes, ozone recovery is well identified and induces a complete return of mean UVI levels to 1960 values for RCP 8.5 in the Southern Hemisphere. In the Northern Hemisphere, UVI levels in 2100 are higher by 0.5 % to 5.5 % for RCPs 2.6, 4.5 and 6.0 and they are lower by 7.9 % for RCP 8.5. We analysed the impacts of greenhouse gases (GHGs) and ozone-depleting substances (ODSs) on UVI from 1960 by comparing CCMI sensitivity simulations (1960–2100) with fixed GHGs or ODSs at their respective 1960 levels. As expected with ODS fixed at their 1960 levels, there is no large decrease in ozone levels and consequently no sudden increase in UVI levels. With fixed GHG, we observed a delayed return of ozone to 1960 values, with a corresponding pattern of change observed on UVI, and looking at the UVI difference between 2090s values and 1960s values, we found an 8 % increase in the tropical belt during the summer of each hemisphere. Finally we show that, while in the Southern Hemisphere the UVI is mainly driven by total ozone column, in the Northern Hemisphere both total ozone column and aerosol optical depth drive UVI levels, with aerosol optical depth having twice as much influence on the UVI as total ozone column does.

RETRACTED: Critical analysis and performance comparison of thirty-eight (38) clear-sky direct irradiance models under the climate of Chilean Atacama Desert

Abstract The present study evaluates the performance of thirty-eight (38) parametric solar radiation models under a large range of conditions. The models are used to predict the clear-sky DNI at a 1-min time resolution. High quality radiometric databases, recorded at PSDA station, located in the Atacama Desert, Chile, are used for this analysis. The models have been arranged into seven sets according to their crucial inputs to identify the impact of each parameter on models’ performance. The results show that turbidity-independent models (sets A, B and C) are not suitable for the modeling of solar direct irradiance. Besides, turbidity-dependent models (sets D, E, F, and G) perform so much better than turbidity-independent models. However, few models perform well under all conditions. Among 11 AOD-dependent models, only six models are considered “good”. The performance of the models of set F decreases with the increase of β (aerosols’ turbidity), while those belonging set G perform adequately even at high turbidity. Overall, the accuracy of most models is sensitive to solar elevation and atmospheric turbidity. Besides, some models are accurate only under limited conditions. As a result of this detailed investigation, six high-performance models can be recommended: ESRA, Ineichen, Yang, MLWT1, REST and MWLT2.

Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland

Rapid warming in northern ecosystems over the past four decades has resulted in earlier spring, increased precipitation, and altered timing of plant–animal interactions, such as herbivory. Advanced spring phenology can lead to longer growing seasons and increased carbon (C) uptake. Greater precipitation coincides with greater cloud cover possibly suppressing photosynthesis. Timing of herbivory relative to spring phenology influences plant biomass. None of these changes are mutually exclusive and their interactions could lead to unexpected consequences for Arctic ecosystem function. We examined the influence of advanced spring phenology, cloud cover, and timing of grazing on C exchange in the Yukon–Kuskokwim Delta of western Alaska for three years. We combined advancement of the growing season using passive-warming open-top chambers (OTC) with controlled timing of goose grazing (early, typical, and late season) and removal of grazing. We also monitored natural variation in incident sunlight to examine the C exchange consequences of these interacting forcings. We monitored net ecosystem exchange of C (NEE) hourly using an autochamber system. Data were used to construct daily light curves for each experimental plot and sunlight data coupled with a clear-sky model was used to quantify daily and seasonal NEE over a range of incident sunlight conditions. Cloudy days resulted in the largest suppression of NEE, reducing C uptake by approximately 2 g C m−2 d−1 regardless of the timing of the season or timing of grazing. Delaying grazing enhanced C uptake by approximately 3 g C m−2 d−1. Advancing spring phenology reduced C uptake by approximately 1.5 g C m−2 d−1, but only when plots were directly warmed by the OTCs; spring advancement did not have a long-term influence on NEE. Consequently, the two strongest drivers of NEE, cloud cover and grazing, can have opposing effects and thus future growing season NEE will depend on the magnitude of change in timing of grazing and incident sunlight.

Improving the McClear model estimating the downwelling solar radiation at ground level in cloud-free conditions – McClear‑v3

The fast McClear clear-sky model estimates the downwelling shortwave direct and diffuse irradiances received at ground level under cloud-free conditions. Several improvements are presented. They focus on the modeling of changes in irradiances with the solar zenithal angle and on a better exploitation of the aerosol properties offered by the Copernicus Atmosphere Monitoring Service (CAMS). Irradiances from this new version McClear-v3 were compared to 1 min measurements made in cloud-free conditions at 11 stations belonging to the Baseline Surface Radiation Network and being located in various climates. The correlation coefficient ranges between 0.982 and 0.999 for the global irradiance. The bias is positive (overestimation) and ranges between 1 W m−2 (0.1 % of the mean observed irradiance) and 20 W m−2 (3.2 %), with the exception of Barrow in Alaska (18 W m−2). The standard deviation ranges between 16 W m−2 (2.3 %) and 30 W m−2 (3.8 %). The correlation coefficient for the direct irradiance ranges between 0.902 and 0.995. As expected, since the direct in McClear does not comprise any circumsolar contribution, the bias is negative (underestimation) and ranges between 49 W m−2 (7.7 %) and 5 W m−2 (0.7 %), with two exceptions: Sede Boqer (79 W m−2) and Brasilia (13 W m−2). The standard deviation is comprised between 34 W m−2 (5.3 %) and 69 W m−2 (10.7 %). These results are similar to those obtained with McClear version 2. Compared to the latter, McClear-v3 removes several artifacts and its estimates are continuous in space and time.

Solar irradiance modelling using an offline coupling procedure for the Weather Research and Forecasting (WRF) model

The research proposed herein seeks to improve solar irradiance magnitude and variability results produced by the Weather Research and Forecasting (WRF) model using a novel offline coupling procedure (OCP). The OCP includes simulations in clear sky conditions, where the effects from varying atmospheric composition depend on the broadband clear sky model used, on cloud attenuation and on decomposition techniques to accurately separate the global irradiance into direct and diffuse components. Furthermore, shadowing and slope effects from orographic features and other obstacles are included with much greater detail. Benefits of the offline coupling procedure were quantified by comparison against local solar radiation measurements over a period of one year. A baseline test to explore the different configuration options was implemented. It considers two aerosol databases, three broadband clear sky models, two cloud attenuation corrections based on clear sky index (either global or by components) and four decomposition models. Given the amount of results from baseline tests, the relative root-mean-square error (rRMSE) was used as the criterion to identify the most suitable OCP configuration, which was subsequently used in the performance analysis. The current baseline test comprises seven sites selected from the Baseline Radiation Network (BSRN) in different terrain complexities and atmospheric conditions. Statistical indicators associated with the annual global horizontal irradiance reveal that OCP improved WRF results by 88.4% in terms of the relative mean bias error (rMBE) and by 5.0% in the value of the Nash-Sutcliffe efficiency (NSE). A standard OCP configuration to model global horizontal irradiance only is proposed. The rMBE of annual hourly global horizontal irradiance oscillated between −3.6% and 3.9%, whilst the NSE varied between 0.608 and 0.939. The analogous quantities for diffuse horizontal irradiance were -19.3%⩽rMBE⩽4.6% and 0.371⩽NSE⩽0.717, while for direct normal irradiance -6.5%⩽rMBE⩽23.2% and 0.101⩽NSE⩽0.656.

Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis

This study evaluates the performance of 75 clear-sky global irradiance models against 75 ground stations worldwide covering five major Köppen-Geiger climate classifications and overall global performance. After quality control, clear-sky detection and data-availability criteria, there are 4.36 million 1-min valid global horizontal irradiance (GHI) data points for evaluation. This study represents the most encompassing evaluation of its kind in terms of number of models assessed, number of ground stations used, most consistent selection of input variables in terms of temporal and spatial resolution, also using the most rigorous and fair performance assessment criteria and suitable ranking system. A statistically rigorous Principal Component Analysis (PCA) ranking procedure is proposed to replace the conventional ordering method based on single and simple statistics. In particular, it is demonstrated that all 13 error metrics contribute to the variance of GHI estimation and thus must all be accounted for in the ranking procedure. The best performing models in each climate zone of equatorial, arid, temperate, cold and polar are REST2v9.1, REST2v9.1, MAC2, REST2v5 and CLS, respectively. Globally, the top three performing models are MAC2, REST2v5, and REST2v9.1. Many models appear to suffer from over-fitting empirical relationships to training data, resulting in inconsistent worldwide performance. Furthermore, six different formulations of the Linke Turbidity factor (TL) are evaluated through its application to five clear-sky models that require it as an input. The best globally performing TL formulations are either by Ineichen or Gueymard when combined with four of the five Linke-dependent clear-sky models, whilst the Grenier formulation performs best with the Ineichen & Perez model. Significant performance variation is observed depending on the TL formulation selected, hence, careful selection is required for an optimal application. The model codes are available in R [1].

Performance analysis of hybrid photovoltaic thermal solar system in Iraq climate condition

In this paper, the hybrid photovoltaic/thermal solar domestic hot water (PVT-SDHW) has been numerically investigated to determine its thermal and electrical performance of a house consists of 5 persons in Iraq climate environment. Detailed mathematical formulation of the PVT-SDHW system have been developed. To validate the feasibility of the developed numerical code, the present results have been compared with the experimental and numerical results and the comparison shows a good agreement between them. The PVT-SDHW system has been tested using the solar radiation data of Mosul city/Iraq that was estimated from ASHRAE Clear Sky Model. The auxiliary heater power, electrical power generation, storage tank water temperature, thermal solar fraction and demand electrical fraction have been analysed. The packing factor (rc) has been considered in this study as a key parameter. The results show that the minimum auxiliary heater power required and the maximum thermal solar fraction occurred when rc=0. Most importantly, the results also indicated that the electricity production has been increased by increment in rc. For rc=1, it has been found that the PVT-SDHW provides hot water between 61.7% and 83% and electricity generation between 24.5% and 43.9% needed by a family of five persons for various months.

Development of an ANN based corrective algorithm of the operational ECMWF global horizontal irradiation forecasts

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 (Évora 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.

A Hybrid Technique for Day-Ahead PV Generation Forecasting Using Clear-Sky Models or Ensemble of Artificial Neural Networks According to a Decision Tree Approach

PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.

On the Use of Geophysical Parameters for the Top-of-Atmosphere Shortwave Clear-Sky Radiance-to-Flux Conversion in EarthCARE

AbstractWe have investigated whether differences across Clouds and the Earth’s Radiant Energy System (CERES) top-of-atmosphere (TOA) clear-sky angular distribution models, estimated separately over regional (1° × 1° longitude–latitude) and temporal (monthly) bins above land, can be explained by geophysical parameters from Max Planck Institute Aerosol Climatology, version 1 (MAC-v1), ECMWF twentieth-century reanalysis (ERA-20C), and a MODIS bidirectional reflectance distribution function (BRDF)/albedo/nadir BRDF-adjusted reflectance (NBAR) Climate Modeling Grid (CMG) gap-filled products (MCD43GF) climatology. Our research aimed to dissolve binning and to isolate inherent properties or indicators of such properties, which govern the TOA radiance-to-flux conversion in the absence of clouds. We collocated over seven million clear-sky footprints from CERES Single Scanner Footprint (SSF), edition 4, data with above geophysical auxiliary data. Looking at data per surface type and per scattering direction—as perceived by the broadband radiometer (BBR) on board Earth Clouds, Aerosol and Radiation Explorer (EarthCARE)—we identified optimal subsets of geophysical parameters using two different methods: random forest regression followed by a permutation test and multiple linear regression combined with the genetic algorithm. Using optimal subsets, we then trained artificial neural networks (ANNs). Flux error standard deviations on unseen test data were on average 2.7–4.0 W m−2, well below the 10 W m−2 flux accuracy threshold defined for the mission, with the exception of footprints containing fresh snow. Dynamic surface types (i.e., fresh snow and sea ice) required simpler ANN input sets to guarantee mission-worthy flux estimates, especially over footprints consisting of several surface types.

Machine learning regressors for solar radiation estimation from satellite data

In this paper we evaluate the performance of several Machine Learning regression techniques in a problem of global solar radiation estimation from geostationary satellite data. Different types of neural networks, Support Vector Regression and Gaussian Processes have been selected as regression techniques to be evaluated, due to their good performance in similar problems in the past. The study area is located in the surroundings of the radiometric station of Toledo, Spain. In order to train the regression techniques considered, one complete year of hourly global solar radiation data is used as the target of the experiments, and different input variables are considered: a cloud index, a clear-sky solar radiation model and several reflectivity values from Meteosat visible images. To assess the results obtained by the Machine Learning algorithms, we have selected as a reference three different physical-based methods, a model based on the Heliosat-2 method (Heliosat-2), the Copernicus Atmosphere Monitoring Service (CAMS) and the SolarGIS model (Soevaluate the performance of Machine Learning regressors when the physical models are included as input variables, in a class of post-processing of these physical approaches. The results obtained show the capacity of Machine Learning regressors to obtain reliable global solar radiation estimation by using satellite measurements.

Climate-specific and global validation of MODIS Aqua and Terra aerosol optical depth at 452 AERONET stations

Aerosol optical depth (AOD) is a highly influential variable in solar resource assessment and clear-sky radiation modelling. Hence, the accuracy of solar energy estimates ultimately depends on the accuracy of the measured or assumed AOD. Gridded satellite information is often used for solar modelling due to its geographical coverage, and so a global validation of commonly utilised AOD products is imperative. Here, all Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) daily observations of AOD (at 470, 550 and 660 nm, noted AOD470, AOD550 and AOD660, respectively) from the Aqua and Terra satellites (of 1° × 1° spatial resolution) from 2000 to 02/2018 are compared and validated against all of NASA’s ground sensing Aerosol Robotic NETwork (AERONET) V3 Level 2 AOD daily averages from sites that reported at least one year of observations during 2000–2018 (452 sites representing at least 653,000 observations per variable). Furthermore, sub-categorisation by Köppen-Geiger climate regions enables a novel climate-specific validation to ascertain any distinct climatic influence. The results demonstrate significant climatological influences that impact the derived AOD product at all three wavelengths. It is found that blending the two Aqua and Terra products results in a higher accuracy of daily estimates of all AOD products. Each AOD product is validated similarly and separately. AOD550, which is most commonly used in solar resource assessment, is found worst in the equatorial climate (absolute root mean square error (RMSE) of 0.194) and best in the temperate climate (RMSE of 0.126). Globally, the combined Aqua + Terra AOD550 experiences an absolute RMSE of 0.106 and a mean absolute error of 0.109. The most common MODIS AOD retrievals are between 0.01 and 0.25, suggesting that the MODIS daily AOD products may introduce a significant source of uncertainty in modelled irradiance estimates, and that other sources of input data should be used instead whenever their applications demand high accuracy.

Comparison of vineyard evapotranspiration estimates from surface renewal using measured and modelled energy balance components in the GRAPEX project

Surface renewal (SR) is a biometeorological technique that uses high frequency air temperature measurements above a crop surface to estimate sensible heat flux (H). The H derived from SR is then combined with net radiation (Rn) and ground heat flux (G) measurements to estimate latent heat flux (LE) as the residual of an energy balance equation. Recent advances in SR theory enabled its use beyond research settings, and led to the development of an inexpensive, stand-alone SR system for use in commercial agricultural settings. However, these commercial applications require replacing expensive net radiometers with clear sky models designed to estimate Rn for the energy balance approach, while also assuming G is zero on a daily basis. The accuracy of substituting Rn measurements with modelled values is unknown, and the assumption of an inconsequential G requires additional testing. Here, we compare the accuracy of the SR derived estimates of H and LE when Rn is either measured directly or modelled, and we compare results to two eddy covariance (EC) LE observations, namely LE measured via EC with an infrared gas analyzer (ECIRGA) and LE solved as a residual in the surface energy balance (ECresid). These measurements were collected at the Grape Remote sensing Atmospheric Profile & Evapotranspiration eXperiment (GRAPEX) conducted over a vineyard within the Lodi, CA wine growing region. LE from SR using tower Rn data measured directly onsite was significantly correlated with LE from ECresid and from ECIRGA with a least squares regression slope ~ 1. LE derived with the modelled incoming solar radiation (SWi) and DisALEXI Rn approaches were also significantly correlated with LE from ECresid, but both modelling approaches overestimated LE at higher fluxes. Patterns were similar, but with more scatter for correlations between LE from ECIRGA and LE from SR using either modelled or remotely sensed Rn. Incorporating direct measurements of G had minimal impact on the agreement of several SR approaches and LE from both EC approaches, however, when differences did occur direct measures of G reduced scatter and bias especially for the empirical SR approach. Our results suggest that LE derived from the new SR method requires fairly accurate Rn modelling approaches to obtain reliable and unbiased estimates of daily LE in comparison to measured LE using EC techniques.

The effect of screen level and upper air temperatures and atmospheric moisture on the downward atmospheric radiation in the South Australian region

Determination of the long wave (LW) atmospheric radiation (λ = 4–100 μm) is of great importance for several applications. In this study, the effect of screen level and upper air temperatures and water vapour contents on the LW radiation for clear skies has been investigated using 4 years of measurements conducted in Adelaide (34.9° S;130.6° E), South Australia. A two-variable model, which depends on the screen level temperature and vapour pressure, has been developed to calculate the LW radiation. The model can predict the hourly measured data with a correlation coefficient of 0.88, giving mean bias error (MBE) of 0.21 W/m2, root mean square error (RMSE) of 17.3 W/m2, and a mean percentage error (MPE) of − 0.11% with respect to hourly observations. The performance of the multilinear model was tested against a 1-year independent data set. In this case, the MBE, RMSE, MPE, and correlation coefficient were 0.94 W/m2, − 2.2 W/m2, 18.1 W/m2, − 0.5%, and 0.85, respectively. Using radiosonde data, the total atmospheric water content has been calculated and its effect on the LW radiation has been investigated. A simple model that uses the screen level temperature and the precipitable water vapour (PWV) has been proposed. The correlation coefficient, MBE, and RMSE for this model were 0.93, 0.08 W/m2, and 12.12 W/m2, respectively; an MPE of 0.09% was reported by the model. The predictions of ten well-known clear sky models have been evaluated against the measured LW radiation data. These models were, then, adjusted and their performances have been assessed. The impact of the atmospheric mean weighted temperature on the LW radiation was studied. We have found that this temperature has less of an effect on the LW atmospheric radiation than screen level temperature. Finally, the clear sky model which uses the screen level parameters has been adjusted to account for the effect of clouds on the LW radiation. The model shows an acceptable predictability for the LW radiation under all sky conditions. The MBE, RMSE, MPE, and correlation coefficient for this model were 0.51 W/m2, 22.5 W/m2, 0.2% and 0.80, respectively.

Assessment of Hourly Solar Direct Normal Irradiance using Eight Broadband Clear Sky Models: Study of Four Moroccan Arid Sites

This paper presents a statistical comparative study of eight important parametric broadband clear sky models, against Meteosat-derived data, to estimate solar hourly direct normal irradiance (DNI) for four Moroccan arid zone sites. To achieve this aim, four sites well spread across this climatic zone were selected, and four references days were chosen. The statistical parameters such as relative mean bias error (rMBE), the relative root mean square error (rRMSE), and the determination coefficient are calculated. According to these results, the Bird model produces acceptable estimates with 62.5% of good results of rMBE ranging between 0 and 6%; 25% of average results of rRMSE ranging between 11% and 14%, and 62.5% of good results of ranging between 0.90 and 0.96. The comparison of both means of DNI, calculated and measured for a given site and day of reference, using the paired t-test, states that for a level of significance of 1%, the Bird model and the REST2 model give in the quarter of the studied cases conforming means of hourly DNI. For a level of significance of 0.1%, the Bird model is more proficient and gives conforming means in almost half of the cases studied. Despite all these performances displayed by the Bird Model, it remains average and does not reach the required level in some sites at certain times. The elaboration of a new simpler DNI estimation model giving higher performances for this zone of high solar potential is of great interest.

Solar map of India under clear sky conditions

ABSTRACTThis paper focusses on generation of solar irradiation map under clear sky conditions using r.sun model. Direct Irradiation, diffuse irradiation and global irradiation maps are plotted using programme which was developed in C language. This programme calculates different components of solar irradiation using clear sky model (r.sun). Further Surfer software was used to plot different irradiation maps. All three values (Direct Irradiation, Global Irradiation and Diffuse Irradiation) were compared by IMD values for performing statistical analysis i.e. Mean Bias Error (MBE), Root Mean Square Error (RMSE) and standard deviation. MBE was found within ±10%, RMSE lies within <20% and standard deviation was found to have very low value which indicated good fitting between model results and calculated values. Therefore the r.sun model is good model and can be used for computing solar irradiation for India.

A Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT): Part I. The clear-sky model

The solar energy industry often uses individual steps to empirically compute plane-of-array (POA) irradiance from horizontal irradiance and decompose it to narrow-wavelength bands. Conventional radiative transfer models designed for meteorological applications requires significant computing efforts in practice; however, they provide a physics-based solution of radiance and therefore are capable of computing spectral POA irradiances in a single step. In this study, we integrate the advantages of the current models and develop an innovative radiative transfer model, the Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT), to efficiently compute irradiances on inclined photovoltaics (PV) panels for 2002 narrow-wavelength bands from 0.28 to 4.0 µm. This study is reported in two parts. Part I presents the methodology and performance evaluation of the new model under clear-sky conditions. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS), which was designed to compute clear-sky irradiances, is employed to rapidly provide the optical properties of a given clear-sky atmosphere. The clear-sky radiances in the narrow-wavelength bands are computed by considering three paths of photon transmission and solving the radiative transfer equation with the single-scattering approximation. The Bi-directional Transmittance Distribution Function (BTDF) of aerosols is given by their single-scattering phase function with a correction using a two-stream approximation. The validation analysis confirms that FARMS-NIT has improved accuracy compared to TMYSPEC as evaluated by both surface observations and a state-of-the-art radiative transfer model. This model substantially improves computational efficiency compared to other radiative transfer models though it uses slightly more computing time than TMYSPEC. Part II of this study addresses the model in cloud-sky conditions and will be published as a companion paper.