Solar Irradiance Data Sets Research Articles

Comparing various solar irradiance categorization methods – A critique on robustness

Traditional ways of planning and operation of electricity networks have been challenged lately by the spread of variable renewable energy sources, especially solar photovoltaics, and the need for better forecasting has increased interest in various solutions. Categorization of solar irradiance data, as one of the earliest applied techniques, is a frequently discussed topic in the literature, but the efficiency of different methods may be significantly variable. The aim of this paper is to compare various categorization methods using a one-year-long solar irradiance dataset and reflect on their inefficiencies and the need for more timely solutions.Six methods have been selected and implemented, including deterministic and non-deterministic ones. The number of groups created by the methods varies between three and five, and they also use data with different temporal resolution. The aim of the comparison was to reveal the strengths and weaknesses of the implemented methods and to highlight possible contradictions among them, based on two tests: uniformity in identifying days with clear or cloudy conditions and contradictory identifications. The results have shown that the usability of certain methods is limited as they are very sensitive to input data, and categorization is often inconsistent, which limits the usability and dissuades users of such methods.

Solcast: Validation of a satellite-derived solar irradiance dataset

Solcast (https://solcast.com/) is a global solar forecasting and historical solar irradiance data company. Their datasets are important to the scientific community as substantial amounts of historical data is made freely available to researchers. Solcast’s hourly averages and sub-hourly instantaneous (10/15-min) global horizontal irradiance (GHI) estimates are validated at 48 BSRN ground stations that contain 1,014,792 valid hourly average measurements and 2,932,976 valid instantaneous measurements. The data are validated globally and across four climates under clear-sky, clouded-sky and all-sky periods, repeated for ‘all zenith angles’ (θ_z<85°) and high-sun zenith angles (θ_z<50°). A site-by-site validation is also performed for both hourly and instantaneous datasets. All validations and recommendations are impartial and unbiased.The hourly dataset validated similarly for both zenith angle scenarios, which is remarkable due to the added complexities at low-sun angles. In terms of rRMSE, the global performances of hourly GHI under clear-sky, clouded-sky and all-sky were 3.4%, 25.6% and 16.9%, respectively. With each ground station equally weighted, the overall bias was 0.33%, and standard deviation of biases at 1.7%. Solcast’s instantaneous GHI product validations were at times similar to the hourly validations though is naturally harder with increasing temporal resolution.The historical datasets from Solcast are found to be of good quality, thus, they are recommended for use by researchers. Identified areas for improvement herein will guide future developments of Solcast’s methodology.

Compilation and spatio-temporal analysis of publicly available total solar and UV irradiance data in the contiguous United States

Skin cancer is the most common type of cancer in the United States, the majority of which is caused by overexposure to ultraviolet (UV) irradiance, which is one component of sunlight. National Environmental Public Health Tracking Program at CDC has collaborated with partners to develop and disseminate county-level daily UV irradiance (2005–2015) and total solar irradiance (1991–2012) data for the contiguous United States. UV irradiance dataset was derived from the Ozone Monitoring Instrument (OMI), and solar irradiance was extracted from National Solar Radiation Data Base (NSRDB) and SolarAnywhere data. Firstly, we produced daily population-weighted UV and solar irradiance datasets at the county level. Then the spatial distributions and long-term trends of UV irradiance, solar irradiance and the ratio of UV irradiance to solar irradiance were analyzed. The national average values across all years are 4300 Wh/m2, 2700 J/m2 and 130 mW/m2 for global horizontal irradiance (GHI), erythemally weighted daily dose of UV irradiance (EDD) and erythemally weighted UV irradiance at local solar noon time (EDR), respectively. Solar, UV irradiances and the ratio of UV to solar irradiance all increased toward the South and in some areas with high altitude, suggesting that using solar irradiance as indicator of UV irradiance in studies covering large geographic regions may bias the true pattern of UV exposure. National annual average daily solar and UV irradiances increased significantly over the years by about 0.3% and 0.5% per year, respectively. Both datasets are available to the public through CDC's Tracking network. The UV irradiance dataset is currently the only publicly-available, spatially-resolved, and long-term UV irradiance dataset covering the contiguous United States. These datasets help us understand the spatial distributions and temporal trends of solar and UV irradiances, and allow for improved characterization of UV and sunlight exposure in future studies.

Echo state network-based spatio-temporal model for solar irradiance estimation

In this study, echo state network-based spatio-temporal (STESN) model is proposed to predict the solar irradiance of a given target station, depending on the historical data of its surrounding sites. Hourly solar irradiance datasets for the whole year 2017, obtained from California Irrigation Management Information System (CIMIS) at eight different stations are used. Comparison studies show that SPESN could achieve more accurate prediction, compared with the conventional persistence (PSS) benchmark model.

Estimating Ultraviolet Radiation From Global Horizontal Irradiance

Terrestrial ultraviolet radiation (UV) radiation is a primary factor contributing to the degradation of photovoltaic (PV) modules’ efficiency and reliability over time. Therefore, accurate knowledge of terrestrial UV incident on the surface of the PV materials is essential to understand the degradation of PV modules and provide reliable assessment of their service life. As PV is deployed in various climate zones, it is crucial that terrestrial UV information is available at various locations. However, the availability of terrestrial UV data—measured or modeled—is extremely limited. On the other hand, total solar irradiance (TS) datasets are relatively abundant. In this study, the National Renewable Energy Laboratory, its industry partners, and ASTM's International Subcommittee on Radiometry and Service Life Prediction are developing a simple method to estimate the clear-sky terrestrial UV irradiance (280–400 nm, 295–400 nm, 285–385 nm, or 295–385 nm) from total irradiance data (280–4000 nm). The goal is to provide reliable estimates of the UV received by samples as a function of location, orientation, tilt, and airmass, thus encompassing a variety of conditions. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) model is used to estimate the UV:TS ratio under various scenarios, and examines the influences of atmospheric constituents, such as aerosols, precipitable water vapor or ozone, and of the local surface characteristics (albedo), on the predicted UV.

Forecasting Hourly Solar Irradiance Using Hybrid Wavelet Transformation and Elman Model in Smart Grid

With the integration of photovoltaic (PV) power into an electrical network, the complexity of the grid management is increasing because of intermittent and fluctuation nature of solar energy. Solar irradiance forecasting is essential to facilitate planning and managing electricity generation and distribution in smart grid cyber-physical system (CPS). The performance of existing short-term forecasting methods is far from satisfactory due to a lack of reliable and fast time-frequency model for continuous-time solar irradiance data. To address this problem, this paper proposes a new method, Elman Neural Network (ENN) driven Wavelet Transform (WT-ENN), for hourly solar irradiance forecasting. Firstly, the solar irradiance series was decomposed into a set of constitutive series using wavelet transform. Secondly, the new wavelet coefficients were predicted by ENNs in every sub-series with the best network structure and parameters. Thirdly, Wavelet reconstruction will predict next hour solar irradiance through the aggregation of outputs of the ensemble of ENNs. Finally, the forecasting performance was evaluated using two large real-world solar irradiance datasets. Experiment results show that the new WT–ENN model outperforms a large number of alternative methods and an average forecast skill of 0.7590 over the persistence model. Thus, it is concluded that the proposed approach can significantly improve the forecasting accuracy and reliability.

Performance assessment of solar-powered high pressure proton exchange membrane electrolyzer: A case study for Erzincan

In this study, a performance assessment of a solar-powered high-pressure proton exchange membrane (PEM) electrolyzer for hydrogen production is conducted. The feasibility analysis of photovoltaic systems equipped with a high pressure PEM electrolyzer is presented for a university campus-scale community in Erzincan- Turkey. Variable solar irradiance data sets are utilized to assess the performance of the proposed system. A parametric study is conducted in order to evaluate the influence of some design parameters as well as operating conditions on the efficiency of the system. Efficiency of the overall system in the case of relevant inverter sizing is in the range of 11–12%. An ascent of the number of stacks leads to an increase in production rate which is almost linear by photovoltaic (PV) array size. The results shows that in order to have a higher efficiency, the inverter size should be higher than 0.75% of maximum excess power. The proposed system investigated in this study shows great promise of opening up opportunity to develop the high pressure PEM electrolyzer.

Applying GIS Technology for optimum selection of Photovoltaic Panels “Spatially at Defined Urban Area in Alexandria, Egypt”

This paper introduces an improved method to specify the potential areas at buildings' top surface for installation of photovoltaic (PV) power units in a defined urban area (UA). Additionally, optimum selection between various (PV) modules is addressed. The proposed approach is based on spatial data analysis and implementation of probabilistic approach (PA) in order compute the power capacity factor (CF) of the PV modules. According this estimation the module with highest average capacity factor is selected for installation at the defined UA. A dedicated case study is proposed and implemented through three main stages. In the first stage, the spatial data of studied buildings are analyzed based on the digitized SIR-DS using Google Earth imagery and ArcGIS software as a Geo-Model. Thus, the planner defines the potential areas for installing PV modules which linked with the buildings' database. In the second stage, various PV modules which produced by different manufacturers, are compared together based on the concept of the highest average CF estimated. In proceedings, firstly, a mathematical modeling of solar irradiance data-set (SIR-DS) is presented using statistical probability distribution function (PDF). These data are approved by Egyptian Meteorological Authority, and collected over a long-term period (7 years). Then, the most fitted PDF in matching with the measured data is then utilized to determine the average output power of each PV module. After that, the CF is estimated for all modules analyzed, such that the module with the highest average CF over the year is identified. Finally, the last stage integrates the results obtained from the prior stages. Accordingly, this paper introduces effective solution for the optimum selection between different PV modules at a given UA, in addition to specifying the potential areas for PV system installation which subjected to the studied buildings' database.

Solar irradiance: from multiple observations to a single composite

AbstractWe review recent developments in combining solar irradiance datasets from different instruments to obtain one single composite, which is the key to understanding how irradiance varies on decadal timescales and beyond.

Decoupling Solar Variability and Instrument Trends Using the Multiple Same-Irradiance-Level (MuSIL) Analysis Technique

The solar spectral irradiance (SSI) dataset is a key record for studying and understanding the energetics and radiation balance in Earth’s environment. Understanding the long-term variations of the SSI over timescales of the 11-year solar activity cycle and longer is critical for many Sun–Earth research topics. Satellite measurements of the SSI have been made since the 1970s, most of them in the ultraviolet, but recently also in the visible and near-infrared. A limiting factor for the accuracy of previous solar variability results is the uncertainties for the instrument degradation corrections, which need fairly large corrections relative to the amount of solar cycle variability at some wavelengths. The primary objective of this investigation has been to separate out solar cycle variability and any residual uncorrected instrumental trends in the SSI measurements from the Solar Radiation and Climate Experiment (SORCE) mission and the Thermosphere, Mesosphere, Ionosphere, Energetic, and Dynamics (TIMED) mission. A new technique called the Multiple Same-Irradiance-Level (MuSIL) analysis has been developed, which examines an SSI time series at different levels of solar activity to provide long-term trends in an SSI record, and the most common result is a downward trend that most likely stems from uncorrected instrument degradation. This technique has been applied to each wavelength in the SSI records from SORCE (2003 – present) and TIMED (2002 – present) to provide new solar cycle variability results between 27 nm and 1600 nm with a resolution of about 1 nm at most wavelengths. This technique, which was validated with the highly accurate total solar irradiance (TSI) record, has an estimated relative uncertainty of about 5% of the measured solar cycle variability. The MuSIL results are further validated with the comparison of the new solar cycle variability results from different solar cycles.

Sizing of Photovoltaic-Wind-Battery Hybrid System for a Mediterranean Island Community Based on Estimated and Measured Meteorological Data

Deploying renewable energy systems (RES) to supply electricity faces many challenges related to cost and the variability of the renewable resources. One possible solution to these challenges is to hybridize RES with conventional power systems and include energy storage units. In this study, the feasibility analysis of a grid-connected photovoltaic (PV)-wind-battery hybrid system is presented as a microgrid for a university campus-scale community on a Mediterranean island. Models for PV and wind turbine systems are presented to estimate energy production, and net present cost (NPC) and levelized cost of electricity (LCOE) are used as economic metrics. A parametric study is performed with hourly time-steps to determine the sizes of energy generation and storage units to minimize the NPC for a small community as the case study. Two alternate configurations with and without storage are proposed. In both cases, the resulting LCOE is 0.15 USD/kWh while the current electricity tariff for the analyzed location was 0.175 USD/kWh in 2015. This lower unit cost of electricity leads to a lower NPC considering a 25-year lifetime. Different estimated and measured solar irradiance and wind speed data sets are used to evaluate the performance of the designed microgrid. Sensitivity analysis on different available weather data sets shows that the uncertainty in wind resource estimations is much higher than the uncertainty in solar resource estimations. Moreover, the results show that solar and wind resources could be utilized synergistically for the studied location.

When univariate model-free time series prediction is better than multivariate

The delay coordinate method is known to be a practically useful technique for reconstructing the states of an observed system. While this method is theoretically supported by Takens' embedding theorem concerning observations of a scalar time series, we can extend the method to include a multivariate time series. It is often assumed that a better prediction can be obtained using a multivariate time series than by using a scalar time series. However, multivariate time series contains various types of information, and it may be difficult to extract information that is useful for predicting the states. Thus, univariate prediction may sometimes be superior to multivariate prediction. Here, we compare univariate model-free time series predictions with multivariate ones, and demonstrate that univariate model-free prediction is better than multivariate one when the prediction steps are small, while multivariate prediction performs better when the prediction steps become larger. We show the validity of the former finding by using artificial datasets generated from the Lorenz 96 models and a real solar irradiance dataset. The results indicate that it is possible to determine which method is the best choice by considering how far into the future we want to predict.

High solar cycle spectral variations inconsistent with stratospheric ozone observations

Some of the natural variability in climate is understood to come from changes in the Sun. A key route whereby the Sun may influence surface climate is initiated in the tropical stratosphere by the absorption of solar ultraviolet (UV) radiation by ozone, leading to a modification of the temperature and wind structures and consequently to the surface through changes in wave propagation and circulation. While changes in total, spectrally-integrated, solar irradiance lead to small variations in global mean surface temperature, the `top-down' UV effect preferentially influences on regional scales at mid-to-high latitudes with, in particular, a solar signal noted in the North Atlantic Oscillation (NAO). The amplitude of the UV variability is fundamental in determining the magnitude of the climate response but understanding of the UV variations has been challenged recently by measurements from the SOlar Radiation and Climate Experiment (SORCE) satellite, which show UV solar cycle changes up to 10 times larger than previously thought. Indeed, climate models using these larger UV variations show a much greater response, similar to NAO observations. Here we present estimates of the ozone solar cycle response using a chemistry-climate model (CCM) in which the effects of transport are constrained by observations. Thus the photolytic response to different spectral solar irradiance (SSI) datasets can be isolated. Comparison of the results with the solar signal in ozone extracted from observational datasets yields significantly discriminable responses. According to our evaluation the SORCE UV dataset is not consistent with the observed ozone response whereas the smaller variations suggested by earlier satellite datasets, and by UV data from empirical solar models, are in closer agreement with the measured stratospheric variations. Determining the most appropriate SSI variability to apply in models...

Making of a solar spectral irradiance dataset I: observations, uncertainties, and methods

Context. Changes in the spectral solar irradiance (SSI) are a key driver of the variability of the Earth’s environment, strongly affecting the upper atmosphere, but also impacting climate. However, its measurements have been sparse and of different quality. The “First European Comprehensive Solar Irradiance Data Exploitation project” (SOLID) aims at merging the complete set of European irradiance data, complemented by archive data that include data from non-European missions.

Evaluation of broadband surface solar irradiance derived from the Ozone Monitoring Instrument

Surface solar irradiance (SSI) data are important for planning and estimating the production of solar power plants. Long-term high quality surface solar radiation data are needed for monitoring climate change. This paper presents a new surface solar irradiance dataset, the broadband (0.2–4μm) surface solar irradiance product derived from the Ozone Monitoring Instrument (OMI). The OMI SSI algorithm is based on the Heliosat method and uses the OMI O2–O2 cloud product as main input. The OMI SSI data are validated against the globally distributed Baseline Surface Radiation Network (BSRN) measurements at 19 stations for the year 2008. Furthermore, the monthly mean OMI SSI data are compared to independent surface solar irradiance products from International Satellite Cloud Climatology Project Flux Data (ISCCP-FD) and Clouds and the Earth's Radiant Energy System (CERES) data for the year 2005. The mean difference between OMI SSI and BSRN global (direct+diffuse) irradiances is −1.2Wm−2 (−0.2%), the root mean square error is 100.1Wm−2 (18.1%), and the mean absolute error is 67.8Wm−2 (12.2%). The differences between OMI SSI and BSRN global irradiances are smaller over continental and coastal sites and larger over deserts and islands. OMI SSI has a good agreement with the CERES shortwave (SW) model B surface downward flux (SDF) product. The correlation coefficient and index of agreement between monthly mean 1-degree gridded OMI SSI and CERES SW SDF are >0.99. OMI SSI is lower than CERES SW SDF which is partly due to the solar zenith angle. On average, OMI SSI is 13.5Wm−2 (2.5%) lower than the ISCCP-FD SW surface downward flux and the correlation coefficient and index of agreement are >0.98 for every month.

Solar Energy and Global Climate Change

Previous studies have shown that the solar energy input to the earth system underwent significant decadal variations at individual surface energy budget stations, with a global dimming from 1950s to 1980s, but a global brightening from 1980s to 2000s, and a mixed tendency at different locations thereafter. Here we use a new global gridded solar irradiance dataset to show that the previous results from individual stations represent well the regional means but not the global mean or hemisphere means. The global mean has a decadal variation that is quite different from the individual station results reported in previous studies, which comes from the fact that the southern hemisphere mean has an opposite trend with the northern hemisphere mean. No long-term global dimming trend is found associated with global warming

Homogeneity Analysis of the CM SAF Surface Solar Irradiance Dataset Derived from Geostationary Satellite Observations

A satellite-based climate record of monthly mean surface solar irradiance (SIS) is investigated with regard to possible inhomogeneities in time. The data record is provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) for the period of 1983 to 2005, covering a disk area between ±70° in latitude and longitude. The Standard Normal Homogeneity Test (SNHT) and two other homogeneity tests are applied with and without the use of reference SIS data (from the Baseline Surface Radiation Network (BSRN) and from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA -Interim reanalysis. The focus is on the detection of break-like inhomogeneities, which may occur due to satellite or SIS retrieval algorithm changes. In comparison with the few suitable BSRN SIS observation series with limited extension in time (no data before 1992), the CM SAF SIS time series do not show significant inhomogeneities, even though slight discrepancies in the surface measurements appear. The investigation of the full CM SAF SIS domain reveal inhomogeneities related to most of the documented satellite and retrieval changes, but only for relatively small domain fractions (especially in mountainous desert-like areas in Africa). In these regions the retrieval algorithm is not capable of adjusting for the changes of the satellite instruments. For other areas, e.g., Europe, no such breaks in the time series are found. We conclude that the CM SAF SIS data record has to be further assessed and regionally homogenized before climate trend investigations can be conducted.

Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements

The recent advent of very high spectral resolution measurements by the Fourier Transform Spectrometer (FTS) on board the Greenhouse gases Observing SATellite (GOSAT) platform has made possible the retrieval of sun-induced terrestrial chlorophyll fluorescence (Fs) on a global scale. The basis for this retrieval is the modeling of the in-filling of solar Fraunhofer lines by fluorescence. This contribution to the field of space-based carbon cycle science presents an alternative method for the retrieval of Fs from the Fraunhofer lines resolved by GOSAT-FTS measurements. The method is based on a linear forward model derived by a singular vector decomposition technique, which enables a fast and robust inversion of top-of-atmosphere radiance spectra. Retrievals are performed in two spectral micro-windows (∼2–3nm width) containing several strong Fraunhofer lines. The statistical nature of this approach allows to avoid potential retrieval errors associated with the modeling of the instrument line shape or with a given extraterrestrial solar irradiance data set. The method has been tested on 22 consecutive months of global GOSAT-FTS measurements. The fundamental basis of this Fs retrieval approach and the results from the analysis of the global Fs data set produced with it are described in this work. Among other findings, the data analysis has shown (i) a very good comparison of Fs intensity levels and spatial patterns with the state-of-the-art physically-based Fs retrieval approach described in Frankenberg et al. (2011a), (ii) the overall good agreement between Fs annual and seasonal patterns and other space-based vegetation parameters, (iii) the need for a biome-dependent scaling from Fs to gross primary production, and (iv) the apparent existence of strong directional effects in the Fs emission from forest canopies. These results reinforce the confidence in the feasibility of Fs retrievals with GOSAT-FTS and open several points for future research in this emerging field.

Sensitivity of the Earth’s middle atmosphere to short-term solar variability and its dependence on the choice of solar irradiance data set

We simulate the time evolution of the neutral and charged species in the terrestrial middle atmosphere using a 1-D radiative-convective model with interactive neutral and ion chemistry driven by four different sets of daily spectral solar irradiance (SSI) available in the literature for the year 2000. Obtained daily time series of ozone, hydroxyl and electron densities are used to calculate their sensitivity to the short-term SSI variability at 205 nm. All applied SSI data sets possess 27-day solar rotation cycle; however, its amplitude and phase as well as the correlation between considered SSI time series differ among data sets leading to the different behavior of the atmospheric response. Contrary, the ozone and hydroxyl sensitivities to the SSI changes during solar rotation cycle are almost identical for all applied SSI data sets in the stratosphere. In the mesosphere, the difference in correlation between SSI in Herzberg continuum and Lyman-α line in considered SSI data sets leads to substantial scatter of the sensitivity estimates based on 205 nm. Our results show that for the sensitivity analysis in the stratosphere based on the SSI at 205 nm any considered SSI data sets can be applied. For the mesosphere, where the sensitivity strongly varies among applied SSI data sets more robust results can be obtained using the sensitivity calculations based on the SSI in Lyman-α line.

Estimation of total cloud cover from solar radiation observations at Lake Rotorua, New Zealand

The DYRESM-CAEDYM model is a valuable tool for simulating water temperature for biochemical studies in aquatic ecosystem. The model requires inputs of surface short-wave radiation and long-wave radiation or total cloud cover fraction ( TC). Long-wave radiation is often not measured directly so a method to determine TC from commonly measured short-wave solar irradiance ( E 0) and theoretical short-wave solar irradiance under a clear sky ( E c) has broad application. A more than 17-year (15 November 1991 to 20 February 2009) hourly solar irradiance data set was used to estimate the peak solar irradiance for each ordinal date over one year, which was assumed to be representative of solar irradiance in the absence of cloud. Comparison between these daily observed values and the modelled clear-sky solar radiation over one year was in close agreement (Pearson correlation coefficient, r = 0.995 and root mean squared error, RMSE = 12.54 W m −2). The downloaded hourly cloudiness measurements from 15 November 1991 to 20 February 2009 was used to calculate the daily values for this period and then the calculated daily values over the 17 years were used to calculate the average values for each ordinal date over one year. A regression equation between (1 − E 0/ E c) and TC produced a correlation coefficient value of 0.99 ( p > 0.01, n = 71). The validation of this cloud cover estimation model was conducted with observed short-wave solar radiation and TC at two sites. Values of TC derived from the model at the Lake Rotorua site gave a reasonable prediction of the observed values (RMSE = 0.10, r = 0.86, p > 0.01, n = 61). The model was also tested at Queenstown (South Island of New Zealand) and it provided satisfactory results compared to the measurements (RMSE = 0.16, r = 0.67, p > 0.01, n = 61). Therefore the model’s good performance and broad applicability will contribute to the DYRESM-CAEDYM accuracy of water temperature simulation when long-wave radiation is not available.