Radiation Time Series Research Articles

Testing for nonlinearity in solar radiation time series by a fast surrogate data test method

Recently, various time series models have been proposed to predict solar radiation, for instance the ARIMA (autoregressive integrated moving average) model and neural networks. Before building a model for the data, however, it is advisable to check whether the data suggest this type of modeling. More specifically, a nonlinearity test is suggested before further analysis with the linear or nonlinear tools are to be applied. In this paper, we test the presence of nonlinearity in the solar radiation time series by the method of surrogate data. The surrogate test method used in this paper is based on evaluation of the differences between the original time series and the linear model that best approximates it. Nonlinearity tests are carried out for four data sets including 5-min, hourly, daily and monthly global solar radiation time series from the UO (University of Oregon) Solar Radiation Monitoring Laboratory. The test statistics show that the 5-min, hourly, daily global solar radiation time series exhibit apparently nonlinearity while the monthly time series does not.

A Fast Model for the Reconstruction of Spectral Solar Irradiance in the Near- and Mid-Ultraviolet

We present a model for the reconstruction of spectral solar irradiance between 200 and 400 nm. This model is an extension of the total solar irradiance (TSI) model of Crouch et al. (Astrophys. J.677, 723, 2008) which is based on a data-driven Monte Carlo simulation of sunspot emergence, fragmentation, and erosion. The resulting time-evolving daily area distribution of magnetic structures of all sizes is used as input to a four-component irradiance model including contributions from the quiet Sun, sunspots, faculae, and network. In extending the model to spectral irradiance in the near- and mid-ultraviolet, the quiet Sun and sunspot emissivities are calculated from synthetic spectra at Teff=5750 K and 5250 K, respectively. Facular emissivities are calculated using a simple synthesis procedure proposed by Solanki and Unruh (Astron. Astrophys.329, 747, 1998). The resulting time series of ultraviolet flux is calibrated against the data from the SOLSTICE instrument on the Upper Atmospheric Research Satellite (UARS). Using a genetic algorithm, we invert quiet Sun corrections, profile of facular temperature variations with height, and network model parameters which yield the best fit to these data. The resulting best-fit time series reproduces quite well the solar-cycle timescale variations of UARS ultraviolet observations, as well as the short-timescale fluctuations about the 81 day running mean. We synthesize full spectra between 200 and 400 nm, and validate these against the spectra obtained by the ATLAS-1 and ATLAS-3 missions, finding good agreement, to better than 3 % at most wavelengths. We also compare the UV variability predicted by our reconstructions in the descending phase of sunspot cycle 23 to SORCE/SIM data as well as to other reconstructions. Finally, we use the model to reconstruct the time series of spectral irradiance starting in 1874, and investigate temporal correlations between pairs of wavelengths in the bands of interest for stratospheric chemistry and dynamics.

Fractal characteristics and its application in electromagnetic radiation signals during fracturing of coal or rock

The present study analyzed the electromagnetic radiation (EMR) time series of the destruction process of coal or rock sample under uniaxial loading and the monitoring process in working face by means of fractal geometry, and results of the correlation dimension change curve of EMR time series were obtained. In the meantime, the current study also sought the fractal characteristic to the EMR signals by contrast to the change curve of EMR signals and explored the precursory phenomenon of rock burst. This paper concluded the main findings as followed: the EMR time series of the destruction process of coal or rock sample under uniaxial loading and the monitoring process in working face corresponded to fractal; the correlation dimension of EMR time series reflected the process of coal or rock damage deformation, that is, the inner damage of coal or rock made a change from random to order. In the field application, the correlation dimension served as a new index of forecasting the coal or rock dynamic disaster.

Correlation Between the Sunspot Number, the Total Solar Irradiance, and the Terrestrial Insolation

Our study deals with the correlations between the solar activity on the one hand and the solar irradiance above the Earth’s atmosphere and at ground level on the other. We analyzed the combined ACRIM I+II time series of the total solar irradiance (TSI), the Mauna Loa time series of terrestrial insolation data, and data of terrestrial cosmic ray fluxes. We find that the correlation between the TSI and the sunspot number is strongly non-linear. We interpret this as the net balance between brightening by faculae and darkening by sunspots where faculae dominate at low activity and sunspots dominate at high activity. Such a behavior is hitherto known from stellar analogs of the Sun in a statistical manner. We perform the same analysis for the Mauna Loa data of terrestrial insolation. Here we find that the linear relation between sunspot number and insolation shows more than 1% rise in insolation by sunspot number variations which is much stronger than for the TSI. Our conclusion is that the Earth atmosphere acts as an amplifier between space and ground, and that the amplification is probably controlled by solar activity. We suspect the cosmic rays intensity as the link between solar activity and atmospheric transparency. A Fourier analysis of the time series of insolation shows three dominant peaks: 10.5, 20.4, and 14.0 years. As a matter of fact, the cosmic rays data show the same pattern of significant peaks: 10.7, 22.4, and 14.9 years. This analogy supports our idea that the cosmic rays variation has influence on the transparency of the Earth atmosphere.

Application of Neuro-Fuzzy Techniques for Solar Radiation

Problem statement: The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with the use of additional meteorological parameters. After having simulated many different structures of neural networks and trained using measurements as training data, the best structures were selected in order to evaluate their performance in relation with the performance of a neuro-fuzzy system. As the alternative system, ANFIS neuro-fuzzy system was considered, because it combines fuzzy logic and neural network techniques that are used in order to gain more efficiency. ANFIS is trained with the same data. Results: The comparison and the evaluation of both of the systems were done according to their predictions, using several error metrics. Fuzzy model was trained using data of daily solar radiation recorded on a horizontal surface in National Research Institute of Astronomy and Geophysics, Helwan, Egypt (NARIG) at ten years (1991-2000). Conclusion: The predicting conclusion indicated that the TS fuzzy model gave a good accuracy of approximately 96% and a root mean square error lower than 6%.

The MED-CSD Project: Potential for Concentrating Solar Power Desalination Development in Mediterranean Countries

Within the MED-CSD project, feasibility studies of integrated hybrid concentrating solar power (CSP) and seawater desalination (DES) plants were carried out in selected locations in Morocco, Italy, Cyprus, Egypt and Gaza/West Bank. After a review on CSP and desalination technologies, ten typical sites within the five partner countries have been selected. For each location, a CSP-DES plant was modeled. The model bases on hourly time series of solar irradiance, ambient temperature, as well as wind speed and includes local seasonal and hourly load curves for power and water. Surplus thermal energy from the solar field is fed into the energy storage, so that operation is possible at night and during cloud transients; gaps between demand and solar power production are covered by cofiring with fossil fuel. Different plant components (solar field collectors and desalination technologies) have been compared. A techno-economic model is applied in order to analyze the economic feasibility and the required financial framework conditions of the projects. Furthermore, an analysis of the market potential of concentrating solar power for sea water desalination in the Mediterranean Region and socio-economic and environmental impact analyses were implemented.

TropFlux: air-sea fluxes for the global tropical oceans—description and evaluation

In this paper, we evaluate several timely, daily air-sea heat flux products (NCEP, NCEP2, ERA-Interim and OAFlux/ISCCP) against observations and present the newly developed TropFlux product. This new product uses bias-corrected ERA-interim and ISCCP data as input parameters to compute air-sea fluxes from the COARE v3.0 algorithm. Wind speed is corrected for mesoscale gustiness. Surface net shortwave radiation is based on corrected ISCCP data. We extend the shortwave radiation time series by using ''near real-time'' SWR estimated from outgoing longwave radiation. All products reproduce con- sistent intraseasonal surface net heat flux variations asso- ciated with the Madden-Julian Oscillation in the Indian Ocean, but display more disparate interannual heat flux variations associated with El Nino in the eastern Pacific. They also exhibit marked differences in mean values and seasonal cycle. Comparison with global tropical moored buoy array data, I-COADS and fully independent mooring data sets shows that the two NCEP products display lowest correlation to mooring turbulent fluxes and significant bia- ses. ERA-interim data captures well temporal variability, but with significant biases. OAFlux and TropFlux perform best. All products have issues in reproducing observed longwave radiation. Shortwave flux is much better captured by ISCCP data than by any of the re-analyses. Our ''near real-time'' shortwave radiation performs better than most re-analyses, but tends to underestimate variability over the cold tongues of the Atlantic and Pacific. Compared to independent mooring data, NCEP and NCEP2 net heat fluxes display *0.78 correlation and (65 W m -2 rms-difference, ERA-I performs better (*0.86 correlation and *48 W m -2 ) while OAFlux and TropFlux perform best (*0.9 correlation and *43 W m -2 ). TropFlux hence provides a useful option for

HIRDLS observations of gravity wave momentum fluxes over the monsoon regions

[1] Measurements from the High Resolution Dynamics Limb Sounder (HIRDLS) on NASA's Aura satellite are used to quantify gravity wave momentum fluxes generated from monsoon regions across the globe over the 3 years 2005–2007. Momentum fluxes in the altitude range 16–60 km over eight individual monsoon regions in both hemispheres are examined and compared to convective data from National Oceanic and Atmospheric Administration's Outgoing Longwave Radiation time series and precipitation data from the Tropical Rainfall Monitoring Mission. Good correlations are observed over seven of these eight regions, the exception being the North American monsoon region. An increase in measured momentum flux of approximately 50% is observed over the 10°N–30°N latitude band during the boreal monsoon period, with around half of the annual momentum flux for this latitude range measured during this period; however, the limited correspondence in the North American region suggests that at least some of this increase may be due to other processes.

Trend analysis of surface cloud-free downwelling long-wave radiation from four Swiss sites

[1] We present a trend analysis of surface cloud-free downwelling long-wave radiation provided by pyrgeometer measurements of four stations of the Alpine surface radiation budget network in Switzerland. The stations cover an altitude range between 370 and 3580 meters above sea level. Cloud-free downwelling long-wave radiation, screen-level temperature, and relative humidity were selected from 10 min measurements, and monthly means were calculated. We performed two distinct trend analyses: the annual overall trend was determined applying least squares fitting, whereas nonparametric statistical methods were used to calculate the monthly trends. The cloud-free downwelling long-wave radiation time series shows a consistent and significant increase of 3.5 W m−2 per decade in the last 12 years at all four stations. The monthly trend analysis of the downwelling long-wave radiation revealed trend estimates exceeding the overall trend by a factor of 4 and partly with opposite signs. The monthly trends of the downwelling long-wave radiation are in agreement with the trends observed in screen-level temperature and specific humidity which have been determined using the same statistical methods. By applying a parameterization of cloud-free downwelling long-wave radiation, we quantitatively inferred the causes for the observed cloud-free trends. More than 50% of the downwelling long-wave radiation trends can be explained with the observed variations of temperature and humidity. There is some indication that the radiative effect of high-level clouds has changed and considerably contributed to the downwelling long-wave radiation trends that are not induced by screen-level temperature and humidity.

A simple approach to the synthetic generation of solar irradiance time series with high temporal resolution

Modeling the performance of some concentrating solar systems for thermal power plants may require high temporal resolution irradiance as input, in order to account for the impact of the cloud transient effects. This work proposes a simple method of generating synthetic irradiance of 10-min intervals from the hourly mean values. Boundary conditions are imposed to preserve the expected behavior under clear sky situations. The procedure consists basically on adding a random fluctuation, which characteristic amplitude depends on the sky conditions, to the hourly interpolated values. The assessment of the method with ground data have shown to main aspects to remark: daily and monthly means from the synthetic data are below 5% of root mean squared deviation compared to the original time series; despite the noticeable uncertainty in the 10-min synthetic irradiance values, the dynamic behavior of the fluctuations is comparable to the original data.

Statistical properties of the sunshine number illustrated with measurements from Timisoara (Romania)

The sunshine number is a Boolean random parameter stating whether the sun is covered or not by clouds. The first four statistical moments of the sunshine number are theoretically derived as a function of a single parameter, the cloud shade. Alternately, this single parameter may be the total cloud cover amount. These parameters are very often measured by the meteorological stations. Time series of global and diffuse solar irradiance recorded at 15-second time intervals between sunrise and sunset during all the days in 2009 in the Romanian town of Timisoara are used to validate the theory. In case of cloud shade representation, there is very good agreement between theory and measurements for all statistical indicators, for all summer and winter days analyzed here, which belong to various classes of cloud shade. In case of cloud cover amount representation, there is a good concordance between theory and measurements when the skewness is considered. In case of the variance and the kurtosis, the concordance is still reasonable. Three types of probability distributions for the sunshine number values have been defined. They were evaluated by using the existing sunshine number database and best fit analytical relationships have been obtained.

Analysis of Multi-Scale and Multi-Fractal Characteristic of Flame Optical Radiation Signal

In this paper, we take flame optical radiation signal in low-frequency on a gas combustion test-bed as research objects, multi-fractal spectrum can analyze the micro structures and characteristic of flame optical radiation time series is corroborated through computing. We research the trend of the sharps and key parameters of the multi-fractal spectrum under different scales. Result shows that as one of the most important parameters of multi-fractal spectrum, the width of the multi-fractal spectrum has the significant correlation with variance, which reflects the status of the combustion stability, and the accuracy of the combustion stability judgement is increased further through multi-scale and multi-fractal analysis. As a developmental assisted analysis method, the multi-scale and multi-fractal analysis method can effectively reveal the dynamic process of the flame radiation, which provides to the operation staff with multi-angle supplementary method in judging the combustion stability.

Analysis and synthesis of the variability of irradiance and PV power time series with the wavelet transform

The irradiance fluctuations and the subsequent variability of the power output of a PV system are analysed with some mathematical tools based on the wavelet transform. It can be shown that the irradiance and power time series are nonstationary process whose behaviour resembles that of a long memory process. Besides, the long memory spectral exponent α is a useful indicator of the fluctuation level of a irradiance time series. On the other side, a time series of global irradiance on the horizontal plane can be simulated by means of the wavestrapping technique on the clearness index and the fluctuation behaviour of this simulated time series correctly resembles the original series. Moreover, a time series of global irradiance on the inclined plane can be simulated with the wavestrapping procedure applied over a signal previously detrended by a partial reconstruction with a wavelet multiresolution analysis, and, once again, the fluctuation behaviour of this simulated time series is correct. This procedure is a suitable tool for the simulation of irradiance incident over a group of distant PV plants. Finally, a wavelet variance analysis and the long memory spectral exponent show that a PV plant behaves as a low-pass filter.

Seasonality of spectral albedo and transmittance as observed in the Arctic Transpolar Drift in 2007

The first continuous and high temporal resolution record of spectral albedo and transmittance of snow and sea ice in the Arctic Ocean over an entire summer season is presented. Measurements were performed at a manned station on multiyear sea ice in the Transpolar Drift during the drift of the schooner Tara from April to September 2007. Concurrent autonomous measurements of ice mass balance and weekly observations of snow and sea‐ice properties complement the data set. The seasonality of physical and biological processes of snow and sea ice is characterized, including quantification of melt onset (10 June), melt season duration, and freeze onset (15 August). Over one year, approximately two thirds of the transmitted energy reached the ocean during the 66‐day‐long melt season. During the second half of July, transmitted irradiance decreased by 90% and absorption in and directly under the ice increased, significantly affecting the vertical partitioning of irradiance. The spectral radiation time series suggests that high biomass abundance in or below the sea ice caused this decrease. Comparing the spectral data set with broadband albedo data measured at the same location shows that 90% of the temporal variability of broadband albedo can be explained by variability in spectral albedo integrated over the limited wavelength range. The combination of spectral radiation and ice mass balance measurements allows a comprehensive description, and quantification, of snow and sea‐ice processes, even with minimal additional in situ observations, suggesting such data sets can be collected autonomously to provide insight into the physical and biological processes on sea ice.

Forecasting of preprocessed daily solar radiation time series using neural networks

In this paper, we present an application of Artificial Neural Networks (ANNs) in the renewable energy domain. We particularly look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. We have used a MLP and an ad hoc time series pre-processing to develop a methodology for the daily prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE ∼ 21% and RMSE ∼ 3.59 MJ/m 2. The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors. Moreover we found that the data pre-processing approach proposed can reduce significantly forecasting errors of about 6% compared to conventional prediction methods such as Markov chains or Bayesian inference. The simulator proposed has been obtained using 19 years of available data from the meteorological station of Ajaccio (Corsica Island, France, 41°55′N, 8°44′E, 4 m above mean sea level). The predicted whole methodology has been validated on a 1.175 kWc mono-Si PV power grid. Six prediction methods (ANN, clear sky model, combination…) allow to predict the best daily DC PV power production at horizon d + 1. The cumulated DC PV energy on a 6-months period shows a great agreement between simulated and measured data ( R 2 > 0.99 and nRMSE < 2%).

Reply to comment by P. Foukal on “A homogeneous database of sunspot areas covering more than 130 years”

Fil: Balmaceda, Laura Antonia. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico San Juan. Instituto de Ciencias Astronomicas de la Tierra y del Espacio; Argentina

Trends in solar radiation in NCEP/NCAR database and measurements in northeastern Brazil

The database from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis project available for the period from 1948 to 2009 was used for obtaining long-term solar radiation for northeastern Brazil. Measurements of global solar radiation ( R s ) from data collection platform (DCP) for four climatic zones of northeastern Brazil were compared to the re-analysis data. Applying cluster analysis to R s from database, homogeneous sub-regions in northeastern Brazil were determined. Long times series of R s and sunshine duration measurements data for two sites, Petrolina (09°09′S, 40°22′W) and Juazeiro (09°24′S, 40°26′W), exceeding 30 years, were analyzed. In order to exclude the decadal variations which are linked to the Pacific Decadal Oscillation, high-frequency cycles in the solar radiation and sunshine duration time series were eliminated by using a 14-year moving average, and the Mann–Kendall test was employed to assess the long-term variability of re-analysis and measured solar radiation. This study provides an overview of the decrease in solar radiation in a large area, which can be attributed to the global dimming effect. The global solar radiation obtained from the NCEP/NCAR re-analysis data overestimate that obtained from DCP measurements by 1.6% to 18.6%. Results show that there is a notable symmetry between R s from the re-analysis data and sunshine duration measurements.

New approach to measure the stability of the solar radiative regime

This paper focuses on different ways of characterizing the solar radiative regime of a day and the stability of this regime. The days may be stratified in classes of cloud shade, observed total cloud cover amount, daily averaged clearness index, and fractal dimension of the solar global irradiance signal. A new Boolean parameter related to solar irradiance fluctuation is defined, namely the sunshine stability number. The time averaged value of the sunshine stability number is used for the characterization of the radiative regime stability during a given time interval. Ranking the days from the view-point of the stability of their radiative regime is performed by using the daily average value of the sunshine stability number and appropriately defined values of disorder and complexity, respectively. Measurements performed in the Romanian town of Timisoara (latitude 45°46′ N, longitude 21°25′ E and 85 m altitude above mean sea level) are used here. They refer to time series of global and diffuse solar irradiance recorded at 15-s time interval between sunrise and sunset during all the days in 2009.

Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning

Due to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production. This work presents a comparisons of statistical models based on time series applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models.

Influence of climate shifts on decadal variations of surface solar radiation in Alaska

From past studies it has been known that the Pacific Decadal Oscillation (PDO) shifted toward a positive mode in 1976 and a new climate regime occurred that produced a warming of the mean annual and seasonal temperatures and associated increases in cloud cover and precipitation in the North Pacific including Alaska. In this study, this climate shift is examined with regard to the variations in surface solar radiation before and after 1976 during the period 1961–2005. The results show greatest changes occurring in the southeast region in winter with a significant rise of 1.67% yr−1 during 1961–1975 before the shift and a nonsignificant decline of −1.07% yr−1 during 1977–1991 after the shift. In addition to the variations in solar radiation that occurred after 1976, the PDO and the solar radiation time series show common changes around 1989 and 1998. Thus, these two variables are compared to assess the strength of their relationship. The results show a good to moderate correlation with a maximum found in winter in Big Delta (interior region). The Pacific North American (PNA) circulation pattern is also strongly correlated with the solar radiation, with a maximum in winter in Anchorage (south‐central region). In addition, a close relationship is found between cloud cover and solar radiation, particularly in the south‐central and southwest regions. These results suggest that circulation patterns and associated cloud cover changes may play a large role, especially during wintertime, in the variations in the surface solar radiation in these regions of Alaska.