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In this work Photosynthetically Active Radiation (PAR) in oceanic and mediterranean climates is modeled. Twenty-two different models have been developed and tested: eleven Multilinear Regression (MR) models and eleven Artificial Neuron Network (ANN) models, using combinations of variables such as Global Horizontal Irradiance (GHI), Global Extraterrestrial Irradiance (G0), Temperature (T) and Relative Humidity (RH). Data provided by Satellite Application Facility on Climate Monitoring (CM SAF) are used to develop and train the models, while the models have been validated using field data from four stations located in Spain, covering the different study climates. According to the results, zones with different climate conditions need different models, both for the case of MR and ANN. The results show the need of including the GHI in all models in order to obtain accurate estimates; in fact, the presence of more variables only improves slightly the results in mediterranean climate, while in oceanic climate no improvement is observed. On the other hand, comparing MR and ANN models, ANN models did not show better results than those of MR models in no one of the cases studied. Regarding the climate, both types of models are clearly better for the mediterranean case than for the oceanic one. In order to improve the performance of the model for oceanic climate a correction based on the site adaptation technique was carried out. The good results obtained by this technique fully justify its use. The best proposed models provide better performance than other models which are restricted to certain locations. Besides, the clustering technique based on the PAR variable, used in this work, allows obtaining useful models for a whole region. Finally, another advantage of this methodology is that there is no need of ground measurements for its development, except for the site adaptation technique.

Electrical energy production using renewable energies is one of the most important challenges in recent years. Among renewable energies, it is worth highlighting photovoltaic and thermoelectric systems due to their adaptation to the Canary Islands. One of the most important issues to ensure the stability for solar power systems, mostly in insular grids as Canary Islands, is the precise knowledge of solar radiation. In this paper, we focus in Gridded Satellite data suitability for modelling Global Horizontal Irradiation (GHI) in islands with complicated orography, as Canary Islands. Solar radiation data retrieved from CM SAF and McClear model were analysed and compared with 22 ground measurement stations in Canary Islands. Moreover, this analysis presents the results of including a site-adaptation methodology for improving satellite suitability. We used different procedures to perform this site adaptation depending on the solar radiation conditions (clear sky or cloudy sky hours), the location of the measurement station (we establish two clusters according to the climate conditions) and the season. This study could provide information about satellite models suitability in islands and a better knowledge of solar radiation behavior. Furthermore, accurate satellite radiation data for wide spatial and temporal coverage could improve solar radiation modelling and forecasting.

The European Union Green Deal aims at curbing planet-warming greenhouse gas emissions and introducing clean energy production. But to achieve energy efficiency, the opportunity cost of different energies must be assessed. In this article, we analyse two different systems for the treatment of wastewater that, at the same time, produce energy for its own operation. On the one hand, high-rate algal ponds system (HRAP) is considered; on the other hand, we study a conventional activated sludge system which uses photovoltaic power (AS+PV). This paper offers a viability analysis of both systems based on the capacity to satisfy their energetic consumption. In order to assess this viability, the probability of not achieving the energy consumption threshold at least one day is studied. The results point that the AS+PV system selfsufficiency is achieved using much lesser surfaces than those of HRAP system (for the former, less than 6.500 m2, for the latter 40.000 m2). However, the important AS capital cost makes still the HRAP system more economic, although storage provides a great advantage for using the AS+PV in locations where we have a lot of irradiance. This viability analysis, along with the opportunity cost analysis, will be used to assess these two projects devoted to the treatment of wastewater.

After the Great Recession of 2008, there was a strong commitment from several international institutions and forums to improve wellbeing economics, with a switch towards satisfaction and sustainability in people–planet–profit relations. The initiative of the European Union is the Green Deal, which is similar to the UN SGD agenda for Horizon 2030. It is the common political economy plan for the Multiannual Financial Framework, 2021–2027. This project intends, at the same time, to stop climate change and to promote the people’s wellness within healthy organizations and smart cities with access to cheap and clean energy. However, there is a risk for the success of this aim: the Jevons paradox. In this paper, we make a thorough revision of the literature on the Jevons Paradox, which implies that energy efficiency leads to higher levels of consumption of energy and to a bigger hazard of climate change and environmental degradation.

Photosynthetically active radiation (PAR) is a useful variable to estimate the growth of biomass or microalgae. However, it is not always feasible to access PAR measurements, in this work, two sets of nine hourly PAR models were developed. These models were estimated for mainland Spain from satellite data, using multilinear regressions and artificial neural networks. The variables utilized were combinations of global horizontal irradiance, clearness index, solar zenith angle cosine, relative humidity, and air temperature. The study territory was divided into regions with similar features regarding PAR through clustering of the PAR clearness index (kPAR). This methodology allowed PAR modeling for the two main climatic regions in mainland Spain (Oceanic and Mediterranean). MODIS 3 h data were employed to train the models, and PAR data registered in seven stations across Spain were used for validation. Usual validation indices assess the extent to which the models reproduce the observed data. However, none of those indices considers the exceedance probabilities, which allow the assessment of the viability of projects based on the data to be modeled. In this work, a new validation index based on these probabilities is presented. Hence, its use, along with the other indices, provides a double and thus more complete validation.

The main objectives of this work are to address the analysis of the spatial and temporal variability of the ratio between photosynthetically active radiation (PAR) and global horizontal irradiance (GHI), as well as to develop PAR models. The analysis was carried out using data from three stations located in mainland Spain covering three climates: oceanic, standard Mediterranean, and continental Mediterranean. The results of this analysis showed a clear dependence between the PAR/GHI ratio and the location, the oceanic climate showed higher values of PAR/GHI compared with Mediterranean climates. Further, the temporal variability of PAR/GHI was conditioned by the variability of clearness index, so it was also higher in oceanic than in Mediterranean climates. On the other hand, Climate Monitoring Satellite Facility (CM-SAF) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data were used to estimate PAR as a function of GHI over the whole territory. The validation with ground measurements showed better performance of the MODIS-estimates-derived model for the oceanic climate (root-mean-square error (RMSE) around 5%), while the model obtained from CM-SAF fitted better for Mediterranean climates (RMSEs around 2%).

A methodology is presented for determining optimal locations to install photosynthetically active radiation ( PAR ) measurement stations. Initially, a cluster analysis was performed from PAR satellite-derived estimations over mainland Spain. Once the optimal number of clusters was obtained, the total number of locations included in the monitoring network was distributed among different groups, according to the size and variability of each group. Finally, the specific locations for measurement stations placement was determined using an iterative technique: The largest region within each cluster was split into two new sub-regions, providing two new sites for substituting the initial location. Clustering analysis has previously been applied to determine locations to monitor solar radiation. However, this is the first implementation for PAR stations in mainland Spain. Another novelty developed in this work is the distribution employed for specific sites within each cluster. The outcome achieved using clustering analysis was compared to those obtained using three other methods: two methods without clustering analysis and the third where clustering is performed but not optimizing the number of clusters. In one technique without clusters, the largest region is split into two new sub-regions, similar to the clustering analysis with optimization. In the second without clustering analysis, since the data variability was not previously addressed, the region divided is those with the largest combined effect of variance and size. The results fully justify using a clustering process; however, clustering without optimization is the worst performing method.

The economic risk of solar plant projects due to the inter-annual solar resource variability is usually quantified by the exceedance probabilities. The estimation of these probabilities depends on the datasets used for the study; actually, the uncertainty associated to these datasets affects the viability study of the project. Due to this dependence, in this work two different types of datasets are used in the estimation of the exceedance probabilities and their uncertainties: synthetic datasets and satellite-derived datasets. Besides, the intra-annual variability of the irradiation variations (intermittency) can affect the production rate of the solar plant. Thus, the knowledge of this variability allows us to foresee the discontinuities in the production rate of the solar plants and to plan the needs for high or low storage capacity according to different temporal periods. In order to quantify the effect of the intra-annual variability in the viability study, a Viability Index based on Discontinuity in the production rate, VID, is proposed, which estimates viability from the exceedance probabilities of the variations. The inclusion of this analysis can provide us with important information about the viability assessment of the projects.

The European Union Green Deal aims at curbing greenhouse gas emissions and introducing clean energy production. But to achieve energy efficiency, the opportunity cost of different energies must be assessed. In this article, two different energy self-sufficient systems for wastewater treatment are compared. On the one hand, high-rate algal ponds system (HRAP) is considered; on the other hand, a conventional activated sludge system (AS) which uses photovoltaic power (PV) is studied. The paper offers a viability analysis of both systems based on the capacity to satisfy their energetic consumption. This viability analysis, along with the opportunity cost study, will be used in the article to compare these two projects devoted to the treatment of wastewater. In order to assess viability, the probability of not achieving the energy consumption threshold at least one day is studied. The results point that the AS+PV system self-sufficiency is achieved with much lesser land requirements than the HRAP system (for the former, less than 6500 m2, for the latter 40,000 m2). However, the important AS capital cost makes still the HRAP system more economic, although storage provides a great advantage for using the AS+PV in locations where a lot of irradiance is available.

The temporal variability of global irradiance estimated from daily satellite data and numerical models has been compared for different spans of time. According to the time scale considered, a different behaviour can be expected for each climate. Indeed, for all climates and at small scale, the persistence decreases as this scale increases, but the mediterranean climate, and its continental variety, shows higher persistence than oceanic climate. The probabilities of maintaining the values of irradiance after a certain period of time have been used as a first approximation to analyse the quality of each source, according to the climate. In addition, probability distributions corresponding to variations of clearness indices measured at several stations located in different climate zones have been compared with those obtained from satellite and modelling estimations. For this work, daily radiation data from the reanalysis carried out by the European Centre for Medium-Range Weather Forecasts and from the Satellite Application Facilities on climate monitoring have been used for mainland Spain. According to the results, the temporal series estimation of irradiance is more accurate when using satellite data, independent of the climate considered. In fact, the coefficients of determination corresponding to the locations studied are always above 0.92 in the case of satellite data, while this coefficient decreases to 0.69 for some cases of the numerical model. This conclusion is more evident in oceanic climates, where the most important errors can be observed. Indeed, in this case, the RRMSE derived from the CM-SAF estimations is 20.93%, while in the numerical model, it is 48.33%. Analysis of the probabilities corresponding to variations in the clearness indices also shows a better behaviour of the satellite-derived estimates for oceanic climate. For the standard mediterranean climate, the satellite also provides better results, though the numerical model improves significantly. In fact, for continental climate, both sources offer similar outcomes.

In this work, solar radiation estimations obtained from a satellite and a numerical weather prediction model in mainland Spain have been compared. Similar comparisons have been formerly carried out, but in this case, the methodology used is different: the temporal variability of both sources of estimation has been compared with the annual evolution of the radiation associated to the different study climate zones. The methodology is based on obtaining behavior patterns, using a Principal Component Analysis, following the annual evolution of solar radiation estimations. Indeed, the adjustment degree to these patterns in each point (assessed from maps of correlation) may be associated with the annual radiation variation (assessed from the interquartile range), which is associated, in turn, to different climate zones. In addition, the goodness of each estimation source has been assessed comparing it with data obtained from the radiation measurements in ground by pyranometers. For the study, radiation data from Satellite Application Facilities and data corresponding to the reanalysis carried out by the European Centre for Medium-Range Weather Forecasts have been used.

Senegal has a great solar potential, so it could be used to shift from a diesel-based power generation to cheaper renewable energy resources. To exploit this inexhaustible natural resource, the global horizontal irradiation remains one of the key parameters for any solar energy project at a given location. This work establishes a multiple linear regression approach to estimate the solar radiation in the Senegalese territories using the information of the global network of weather geostationary satellites (Meteosat and GOES), satellites database and the ground measurement data available in the website of the World Radiation Data Center (WRDC) as inputs to the model. Jointly a set of multivariate regression models, a statistical analysis between Meteonorm data and outputs of different linear combinations are presented in this work, which also gives the opportunity to appreciate the precision and consistency of each solar radiation model on different locations in the study area.

A simple method for correcting satellite-derived direct normal irradiance, with important deviations to the experimental data, is presented in this work and illustrated for the particular case of Rajasthan (India). Northwest India is expected to have a high level of solar radiation and it is an interesting area for solar concentrating power systems. However uncertainty in direct normal irradiance estimations from satellite may affect negatively to the bankability of solar plants. Direct normal irradiance have been estimated for a site in Rajasthan from satellite information for the period 2003 to 2011, and ground measurements during 2011 have been used to analyze the uncertainties and to develop a simple correction method. The original satellite estimations showed important deviations from the experimental values and high bias. A systematic underestimation of direct irradiance has been observed during the dryer seasons that could be attributed to an overestimation of the aerosol optical depth input to the model. These observations have allowed the design of a correction methodology. Unbiased new estimations of direct normal irradiance have been generated with this methodology with important reduction in the deviations and with an agreement in the distribution functions compared to the distribution function of the ground data.

A study on the spatial variability of long term solar radiation in Vietnam and the results of clustering solar radiation into different regions are presented here. Vietnam has a dense and long network of sunshine duration measurements, and on the contrary the country has a scarce availability of solar irradiance measuring sites. The variability of long term solar radiation can be analyzed by determining daily global irradiation from sunshine duration. A model inspired in Angstrom equation has been developed using the canonical correlation analysis with good performance. The output of the model has been used for characterizing the dispersion of long term solar radiation and its spatial distribution has been also studied by clustering techniques. The comparison with the Koppen climatic information remarks that Vietnam could be divided into 3–4 well defined zones of different solar radiation variability.

The monthly mean variation of the solar global reaching the Earth's surface has been characterized at a global level by a regression model. This model considers the monthly variation itself (to different horizons and even the maximum annual variation) as the study variable, and it is applied without using data corresponding to measured meteorological variable. Two explicative variables have been used, the variation of the extraterrestrial irradiation and the variation of the clear sky global horizontal irradiation. The work has been carried out from datasets including average global daily solar irradiation for each month of the year measured on the ground. The model quality has been proven to be very dependent of the temporal variation considered, in such a way that higher variations, that is to say, higher distances between months, lead to an improvement in the model outcomes.

This chapter reviews the state of the art of forecasting and nowcasting of direct normal irradiance (DNI) for concentrating solar thermal (CST) systems. After a review of the main methodologies involved in solar radiation forecasting, specific considerations for CST plants have been taken into account. Before highlighting the main challenges in DNI forecasting, the baseline of solar radiation is presented showing a review of benchmarking exercises. Most of this comparison exercises are based on global horizontal irradiance. Some review articles as well as selected works dealing specifically with DNI and solar thermal power plants have also been discussed. Typical forecasting classifications are based on the time period forecasted or the treatment technique. Numerical weather prediction models (NWPMs) are the most suitable models for predicting solar radiation from 4 h up to several days. Statistical forecasting is usually related to the use of local measurements and learning processes in order to derive future behavior. Depending on the time-frequency, this behavior can provide data for years, months, days, hours, minutes, or seconds; then statistical models can cover all time resolutions and forecasting horizon. Nowcasting is a common denomination for the forecasting period up to 6 h. During this period, in addition to NWPM and statistical models, satellite- and sky cameras–derived forecasting are involved. This chapter focuses on forecasting from the point of view of a CST power plant, but at the end of the chapter, some considerations related to single vs. aggregated or regional forecast are also presented. Main challenges are concentrated in the nowcasting period, that is, up to 6 h, as it needs harmonization of signals to provide reliable forecasts of high space–temporal resolution at the plant level: this means, to predict maps with the resolution of several meters and some seconds. Improvements in aerosol knowledge and circumsolar radiation forecasting are also needed for the improvement of all methodologies.

This paper deals the study of variability and intermittency of solar irradiation using an analogy with the turbulence and thus making use of some methodologies used in the study of intermittency of the turbulence. An analysis of the shape of the PDFs corresponding to the increments in the clearness and transmittance indexes, for direct and global radiations, is presented. In addition a study of the relations between the scaling exponents of the structure functions of the clearness and transmittance indexes and the orders of these structure functions has been carried out. According to the study, the range of relative variability is due to changes in the atmospheric components that play a role in the attenuation of solar irradiation. This range of variability is higher in the case of the global irradiation than in the case of the direct. Moreover, the multifractality is showed more intense in sites where, due to local effects, sharper variations in the radiation can be expected, as the case of deserts.

Sequences of two consecutive days of solar irradiation (global horizontal and direct normal) have been studied here by different approaches. The frequency vs. rank relationships have been analyzed as an attempt to explore whether the Zifp's law is fulfilled, yielding to a partial fulfillment and observing that a good logarithmic fit can be applied to the data in the whole range. In addition, the pdfs of increments in two consecutive daily irradiation values are also studied, showing a relationship between persistence and the coefficients of the logarithmic fit. Finally, it has been shown that a Markov process can represent properly sequences of two consecutive daily irradiation values, for both global horizontal and direct normal components. Thus, synthetic series can be generated by Markov chains for characterizing daily global and direct irradiation.

Many models for derived solar radiation from geostationary satellites images are based upon the determination of the cloud index as a measure of the transmittance of the atmosphere. The estimation of the cloud index relies in the proper normalization of the planetary albedo within the dynamic range, which is the range between the upper bound reflectivity (associated to clouds) and the lower bound reflectivity (referred as ground albedo). The angular influence of the albedo can be noticeable in geostationary satellites due to its large viewing angle. This paper presents a study of the dependence of the ground albedo with the scattering angle for different geostationary satellites (GOES, Meteosat Prime, Meteosat IODC and Meteosat Second Generation). The ground albedo is determined by different satellites in quite different geographical points and a methodology that fits the local angular dependence is evaluated. The robustness of ground albedo estimations is proved by comparing solar radiation estimated in Thesalonica during the same period using two different positions of Meteosat images with ground data. The combination of the information observed by each satellite has improved the global irradiance retrievals in Thesalonica. Finally, an illustrative comparison of ground albedo with well-known meteorological products (MODIS and CM-SAF) is done in a qualitative way.

The shape of the curves relating the scaling exponents of the structure functions to the order of these functions is shown to distinguish the Dow Jones index from other stock market indices. We conclude from the shape differences that the information-loss rate for the Dow Jones index is reduced at smaller time scales, while it grows for other indices. This anomaly is due to the construction of the index, in particular to its dependence on a single market parameter: price. Prices are subject to turbulence bursts, which act against full development of turbulence.

We show the relationship between the intermittency of turbulence and the type of stratification for different atmospheric situations during the SABLES98 field campaign. With this objective, we first demonstrate the scaling behaviour of the velocity structure functions corresponding to these situations; next, we analyze the curvature of the scaling exponents of the velocity structure functions versus the order of these functions (ζ p vs. p), where ζ p are the exponents of the power relation for the velocity structure function with respect to the scale. It can be proved that this curve must be concave, under the assumption that the incompressible approximation does not break down at high Reynolds numbers. The physical significance of this kind of curvature is that the energy dissipation rate increases as the scale of the turbulent eddies diminishes (intermittency in the usual sense). However, the constraints imposed by stability, preventing full development of the turbulence, allow the function ζ p versus p to show any type of curvature. In this case, waves of high frequency trapped by the stability, or bursts of turbulence caused by the breaking up of internal waves, may produce a redistribution of energy throughout the scaling range. Due to this redistribution, the variation with the scale of the energy dissipation rate may be smaller (decreasing the intermittency) and, even in more stable situations, this rate may diminish (instead of increasing) as the scale diminishes (convex form of the curve ζ p vs. p).

This article shows turbulent behavior in a series of financial indexes assuming that they follow a cascade process of the same type as do turbulent fluids. With such a model, the energy flux between the eddies that emerge in the fluid is analogous to the financial information flux over the course of time. The results obtained confirm the variability of variation of the indexes for the considered time scale (the turbulent intermittency typical for fluids), and they also confirm that when we descend along the cascade, that is to say, when we consider smaller time intervals, the rate at which the hypothetical eddies of information dissipate becomes greater than the rate at which the information is transmitted. This fact can explain the cyclical nature of crises: ultimately, financial events have a memory of the past. Besides, the NASDAQ singular behavior regarding the number of jumps, the degree of intermittency of the turbulence and the life time of the hypothetical eddies has been analysed.

Data from the SABLES98 experimental campaign (Cuxart et al., 2000) have been used in order to study the relationship of the probability distribution of velocity increments (PDFs) to the scale and the degree of stability. This connection is demonstrated by means of the velocity structure functions and the PDFs of the velocity increments. Using the hypothesis of local similarity, so that the third order structure function scaling exponent is one, the inertial range in the Kolmogorov sense has been identified for different conditions, obtaining the velocity structure function scaling exponents for several orders. The degree of intermittency in the energy cascade is measured through these exponents and compared with the forcing intermittency revealed through the evolution of flatness with scale. The role of non-homogeneity in the turbulence structure is further analysed using Extended Self Similarity (ESS). A criterion to identify the inertial range and to show the scale independence of the relative exponents is described. Finally, using least-squares fits, the values of some parameters have been obtained which are able to characterize intermittency according to different models.

This paper presents a sensitivity analysis of satellite-based methods for deriving solar irradiance components for analyzing the impact of the external inputs that are normally associated to the satellite model. Different sensitivity calculations have been performed using as reference site the PSA (Solar Platform of Almeria) station placed at the south-east of Spain. Thus, the sensitivity to the aerosol information input has been addressed by comparing the estimations using aerosol input from AERONET data with those using aerosol dataset such as MODIS or MISR (based on satellite) and MACC (based on reanalysis). Sensitivity to the clear sky model choice has been also studied by using three different models, from the simpler ESRA model (in terms of input parameters) to the most sophisticated REST2. Finally, three global to direct conversion models (Louche, DirInt and DirIndex) have been included to explore the sensitivity of the direct normal irradiance estimations. The sensitivity analysis has shown the interrelations between the different cases according to the uncertainty of the input information used. The results have been analyzed for clear and non-clear sky conditions separately and for the DNI irradiance range of 400-900Wm-2 as a case of special interest for the concentrating solar power applications. The work presented here has as novelty the analysis of the propagation of uncertainty of individual models and atmospheric datasets in the framework of a satellite-based model for solar irradiance computation and their relative weights to the final performance of the model. An underestimation of AOD by 50% causes an error in the global horizontal irradiance calculated by a clear sky model of 3-5% depending on the model used, and slightly less for an overestimation of AOD. For DNI the error ranges are 12-15% and 9-12% for 50% underestimation and overestimation of AOD respectively. © 2014 Elsevier Ltd.

A comparison of the DirIndex model for computing direct normal solar irradiance from global horizontal using different clear sky transmittance models is presented for four ground stations that belong to Baseline Surface Radiation Network (BSRN) and Aerosol RObotic NETwork (AERONET) networks. The results of DirInt model, which does not include any clear sky transmittance contribution, are also shown. The input for the different clear sky models selected (European Solar Radiation Atlas (ESRA), SOLar Irradiance Scheme (SOLIS) simplified, and Reference Evaluation of Solar Transmittance, 2 bands (REST2)) was generated from the original aerosol optical depth and water vapour measurements provided by AERONET. The results show different trends in the performance of the DirIndex model combined with the clear sky methods. An attempt to correct the trends to the proper one is finally proposed here and the improvement achieved is shown.

This article analyzes the possibility of applying a GOY theoretical model to atmospheric boundary layer data. Bearing this in mind, relative scaling exponents of velocity structure functions are used to compare the model with the data under study. In the model, these exponents are set based on two parameters (q and δ), which are appropriate to define the model that better features a certain atmospheric state. From these scaling exponents, the gap between 2-D and 3-D turbulence is observed in the model, depending on the fact that δ is higher or lower than unity, respectively. Atmospheric data corresponding to very different states of stratification stability have been analyzed. For convective or near-neutral situations (usually associated to 3-D turbulence), it is possible to find parameters q and δ to define a model that fits the measured data. More stable situations can be featured by GOY models with higher values of δ. However, it is clear that it is impossible to represent nocturnal situations of strong stable stratification (with a more similar behaviour to 2-D) with this type of model.