A comparison of data sources for creating a long-term time series of daily gridded solar radiation for Europe

Solar Energy ◽  
2014 ◽  
Vol 99 ◽  
pp. 152-171 ◽  
Author(s):  
Jędrzej S. Bojanowski ◽  
Anton Vrieling ◽  
Andrew K. Skidmore
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Data Sources

scholarly journals Compatibility of different measurement techniques. Long-term global solar radiation observations at Izaña Observatory

2016 ◽  
Author(s):  
Rosa Delia García ◽  
Emilio Cuevas ◽  
Omaira Elena García ◽  
Ramon Ramón ◽  
Pedro Miguel Romero-Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Sunshine Duration ◽  
Measurement Techniques ◽  
Temporal Stability ◽  
Global Solar Radiation ◽  
Surface Radiation ◽  
Multifilter Rotating Shadowband Radiometer ◽  
High Consistency

Abstract. A 1-year intercomparison of classical and modern radiation and sunshine duration instruments has been performed at Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain) starting on July 17, 2014. We compare global solar radiation (GSR) records measured with a CM-21 pyranometer Kipp & Zonen, taken in the framework of the Baseline Surface Radiation Network, with those measured with a Multifilter Rotating Shadowband Radiometer (MFRSR), and a bimetallic pyranometer (PYR), and GSR estimated from sunshine duration performed by a Campbell-Stokes sunshine recorder (CS) and a Kipp & Zonen sunshine duration sensor (CSD). Given the GSR BSRN records are subject of strict quality controls (based on principles of physical limits and comparison with the LibRadtran model), they have been used as reference in the intercomparison study. We obtain an overall root mean square error (RMSE) of ~0.9 MJm2 (4 %) for GSR PYR and GSR MFRSR, 1.9 MJm2 (7 %) and 1.2 MJm2 (5 %) for GSR CS and GSR CSD, respectively. Factors such as temperature, fraction of the clear sky, relative humidity and the solar zenith angle have shown to moderately affect the GSR observations. As application of the methodology developed in this work, we have re-evaluated the GSR time series between 1977 and 1991 obtained with two PYRs at IZO. By comparing with coincident GSR estimates from SD observations, we probe the high consistency of those measurements and their temporal stability. These results demonstrate that 1) the continuous-basis intercomparison of different GSR techniques offers important diagnostics for identifying inconsistencies between GSR data records, and 2) the GSR measurements performed with classical and more simple instruments are consistent with more modern techniques and, thus, valid to recover GSR time series and complete worldwide distributed GSR data. The intercomparison and quality assessment of these different techniques have allowed to obtain a complete and consistent long-term global solar radiation series (1977–2015) at Izaña.

scholarly journals Data Quality Assessment and the Long-Term Trend of Ground Solar Radiation in China

2008 ◽  
Vol 47 (4) ◽  
pp. 1006-1016 ◽  
Author(s):  
Guang-Yu Shi ◽  
Tadahiro Hayasaka ◽  
Atsumu Ohmura ◽  
Zhi-Hua Chen ◽  
Biao Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Data Quality ◽  
Quality Assessment ◽  
Global Radiation ◽  
Assessment Process ◽  
Radiation Data ◽  
Data Quality Assessment

Abstract Solar radiation is one of the most important factors affecting climate and the environment. Routine measurements of irradiance are valuable for climate change research because of long time series and areal coverage. In this study, a set of quality assessment (QA) algorithms is used to test the quality of daily solar global, direct, and diffuse radiation measurements taken at 122 observatories in China during 1957–2000. The QA algorithms include a physical threshold test (QA1), a global radiation sunshine duration test (QA2), and a standard deviation test applied to time series of annually averaged solar global radiation (QA3). The results show that the percentages of global, direct, and diffuse solar radiation data that fail to pass QA1 are 3.07%, 0.01%, and 2.52%, respectively; the percentages of global solar radiation data that fail to pass the QA2 and QA3 are 0.77% and 0.49%, respectively. The method implemented by the Global Energy Balance Archive is also applied to check the data quality of solar radiation in China. Of the 84 stations with a time series longer that 20 yr, suspect data at 35 of the sites were found. Based on data that passed the QA tests, trends in ground solar radiation and the effect of the data quality assessment on the trends are analyzed. There is a decrease in ground solar global and direct radiation in China over the years under study. Although the quality assessment process has significant effects on the data from individual stations and/or time periods, it does not affect the long-term trends in the data.

scholarly journals A Robust Data-Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bin Sun ◽  
Liyao Ma ◽  
Tao Shen ◽  
Renkang Geng ◽  
Yuan Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Operating Characteristic ◽  
Measured Data ◽  
Data Sources ◽  
Data Driven ◽  
Data Transport ◽  
Real World Data ◽  
Promising Solution ◽  
Labelling Method

Internet of Things (IoT) is emerging, and 5G enables much more data transport from mobile and wireless sources. The data to be transmitted is too much compared to link capacity. Labelling data and transmit only useful part of the collected data or their features is a promising solution for this challenge. Abnormal data are valuable due to the need to train models and to detect anomalies when being compared to already overflowing normal data. Labelling can be done in data sources or edges to balance the load and computing between sources, edges, and centres. However, unsupervised labelling method is still a challenge preventing to implement the above solutions. Two main problems in unsupervised labelling are long-term dynamic multiseasonality and heteroscedasticity. This paper proposes a data-driven method to handle modelling and heteroscedasticity problems. The method contains the following main steps. First, raw data are preprocessed and grouped. Second, main models are built for each group. Third, models are adapted back to the original measured data to get raw residuals. Fourth, raw residuals go through deheteroscedasticity and become normalized residuals. Finally, normalized residuals are used to conduct anomaly detection. The experimental results with real-world data show that our method successfully increases receiver-operating characteristic (AUC) by about 30%.

scholarly journals Time-Series Regression Model for Prediction of Mean Daily Global Solar Radiation in Al-Ain, UAE

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Hassan A. N. Hejase ◽  
Ali H. Assi
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Bias Error ◽  
Time Series Regression ◽  
Weather Model ◽  
Al Ain ◽  
Using Data ◽  
Long Term Prediction

The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily and monthly global solar radiation (GSR) for the city of Al-Ain, United Arab Emirates. Time-series analysis of solar radiation has shown to yield accurate average long-term prediction performance of solar radiation in Al-Ain. The model was built using data for 10 years (1995–2004) and was validated using data of three years (2005–2007), yielding deterministic coefficients (R2) of 92.6% and 99.98% for mean daily and monthly GSR data, respectively. The low corresponding values of mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) confirm the adequacy of the obtained model for long-term prediction of GSR data in Al-Ain, UAE.

scholarly journals Solar radiation trend across China in recent decades: a revisit with quality-controlled data

2010 ◽  
Vol 10 (8) ◽  
pp. 18389-18418 ◽  
Author(s):  
W.-J. Tang ◽  
K. Yang ◽  
J. Qin ◽  
C. C. K. Cheng ◽  
J. He
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Solar Radiation ◽  
The Other ◽  
Continuous Data ◽  
China Meteorological Administration ◽  
Surface Solar Radiation ◽  
Factors Affecting ◽  
Term Variation

Abstract. Solar radiation is one of the most important factors affecting climate and environment, and its long-term variation is of much concern in climate change studies. In the light of the limited number of radiation stations with reliable long-term time series observations, this paper presents a new evaluation of the long-term variation of surface solar radiation over China by combining quality-controlled observed data and two radiation models. One is the ANN-based (Artificial Neutral Network) model and the other is a physical one. The two models produced radiation trends comparable to the observed ones at a few stations possessing reliable and continuous data. Then, the trend estimation is extended by the ANN-based model to all 96 radiation stations and furthermore extended by the physical model to all 716 China Meteorological Administration (CMA) routine stations. The new estimate trend is different from previous ones in two aspects. First, the magnitude of solar radiation over China decreased by about −0.19 W m−2 yr−1 between 1961 and 2000, which is greatly less in magnitude than trends estimated in previous studies (ranging over −0.41 to −0.52 W m−2 yr−1). Second, the "From Dimming to Brightening" transition in China during the late 1980s and the early 1990s was addressed in previous studies, but this study indicates the solar radiation reached a stable level since the 1990s and the transition is not noticeable. These differences are attributed to inappropriate data and approaches in previous studies.

scholarly journals Compatibility of different measurement techniques of global solar radiation and application for long-term observations at Izaña Observatory

2017 ◽  
Vol 10 (3) ◽  
pp. 731-743 ◽  
Author(s):  
Rosa Delia García ◽  
Emilio Cuevas ◽  
Omaira Elena García ◽  
Ramón Ramos ◽  
Pedro Miguel Romero-Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Sunshine Duration ◽  
Measurement Techniques ◽  
Temporal Stability ◽  
Global Solar Radiation ◽  
Surface Radiation ◽  
Multifilter Rotating Shadowband Radiometer ◽  
High Consistency

Abstract. A 1-year inter-comparison of classical and modern radiation and sunshine duration (SD) instruments has been performed at Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain) starting on 17 July 2014. We compare daily global solar radiation (GSRH) records measured with a Kipp & Zonen CM-21 pyranometer, taken in the framework of the Baseline Surface Radiation Network, with those measured with a multifilter rotating shadowband radiometer (MFRSR), a bimetallic pyranometer (PYR) and GSRH estimated from sunshine duration performed by a Campbell–Stokes sunshine recorder (CS) and a Kipp & Zonen sunshine duration sensor (CSD). Given that the BSRN GSRH records passed strict quality controls (based on principles of physical limits and comparison with the LibRadtran model), they have been used as reference in the inter-comparison study. We obtain an overall root mean square error (RMSE) of  ∼  0.9 MJm−2 (4 %) for PYR and MFRSR GSRH, 1.9 (7 %) and 1.2 MJm−2 (5 %) for CS and CSD GSRH, respectively. Factors such as temperature, relative humidity (RH) and the solar zenith angle (SZA) have been shown to moderately affect the GSRH observations. As an application of the methodology developed in this work, we have re-evaluated the GSRH data time series obtained at IZO with two PYRs between 1977 and 1991. Their high consistency and temporal stability have been proved by comparing with GSRH estimates obtained from SD observations. These results demonstrate that (1) the continuous-basis inter-comparison of different GSRH techniques offers important diagnostics for identifying inconsistencies between GSRH data records, and (2) the GSRH measurements performed with classical and more simple instruments are consistent with more modern techniques and, thus, valid to recover GSRH data time series and complete worldwide distributed GSRH data. The inter-comparison and quality assessment of these different techniques have allowed us to obtain a complete and consistent long-term global solar radiation series (1977–2015) at Izaña.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

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