IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v107y2013icp384-393.html
   My bibliography  Save this article

Estimating the daily global solar radiation spatial distribution from diurnal temperature ranges over the Tibetan Plateau in China

Author

Listed:
  • Pan, Tao
  • Wu, Shaohong
  • Dai, Erfu
  • Liu, Yujie

Abstract

Daily global solar radiation is fundamental to most ecological and biophysical processes because it plays a key role in the local and global energy budget. However, gridded information about the spatial distribution of solar radiation is limited. This study aims to parameterise the Bristow–Campbell model for the daily global solar radiation estimation in the Tibetan Plateau and propose a method to rasterise the daily global solar radiation. Observed daily solar radiation and diurnal temperature data from eleven stations over the Tibetan Plateau during 1971–2010 were used to calibrate and validate the Bristow–Campbell radiation model. The extra-terrestrial radiation and clear sky atmospheric transmittance were calculated on a Geographic Information System (GIS) platform. Results show that the Bristow–Campbell model performs well after adjusting the parameters, the average Pearson’s correlation coefficients (r), Nash–Sutcliffe equation (NSE), ratio of the root mean square error to the standard deviation of measured data (RSR), and root mean-square error (RMSE) of 11 stations are 0.85, 2.81MJm−2day−1, 0.3 and 0.77 respectively. Gridded maximum and minimum average temperature data were obtained using Parameter-elevation Regressions on Independent Slopes Model (PRISM) and validated by the Chinese Ecosystem Research Network (CERN) stations’ data. The spatial daily global solar radiation distribution pattern was estimated and analysed by combining the solar radiation model (Bristow–Campbell model) and meteorological interpolation model (PRISM). Based on the overall results, it can be concluded that a calibrated Bristow–Campbell performs well for the Tibetan Plateau and can provide reasonably accurate global solar radiation estimates. The Bristow–Campbell radiation model coupled with the PRISM is effective in rasterising global solar radiation.

Suggested Citation

  • Pan, Tao & Wu, Shaohong & Dai, Erfu & Liu, Yujie, 2013. "Estimating the daily global solar radiation spatial distribution from diurnal temperature ranges over the Tibetan Plateau in China," Applied Energy, Elsevier, vol. 107(C), pages 384-393.
  • Handle: RePEc:eee:appene:v:107:y:2013:i:c:p:384-393
    DOI: 10.1016/j.apenergy.2013.02.053
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913001712
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2013.02.053?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Qin, Jun & Chen, Zhuoqi & Yang, Kun & Liang, Shunlin & Tang, Wenjun, 2011. "Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products," Applied Energy, Elsevier, vol. 88(7), pages 2480-2489, July.
    2. Chineke, Theo Chidiezie & Okoro, Ugochukwu Kingsley, 2010. "Application of Sayigh “Universal Formula” for global solar radiation estimation in the Niger Delta region of Nigeria," Renewable Energy, Elsevier, vol. 35(3), pages 734-739.
    3. Liu, Xiaoying & Xu, Yinlong & Zhong, Xiuli & Zhang, Wenying & Porter, John Roy & Liu, Wenli, 2012. "Assessing models for parameters of the Ångström–Prescott formula in China," Applied Energy, Elsevier, vol. 96(C), pages 327-338.
    4. Furlan, Claudia & de Oliveira, Amauri Pereira & Soares, Jacyra & Codato, Georgia & Escobedo, João Francisco, 2012. "The role of clouds in improving the regression model for hourly values of diffuse solar radiation," Applied Energy, Elsevier, vol. 92(C), pages 240-254.
    5. Janjai, S. & Sricharoen, K. & Pattarapanitchai, S., 2011. "Semi-empirical models for the estimation of clear sky solar global and direct normal irradiances in the tropics," Applied Energy, Elsevier, vol. 88(12), pages 4749-4755.
    6. Bahel, V. & Bakhsh, H. & Srinivasan, R., 1987. "A correlation for estimation of global solar radiation," Energy, Elsevier, vol. 12(2), pages 131-135.
    7. Coskun, C. & Oktay, Z. & Dincer, I., 2011. "Estimation of monthly solar radiation distribution for solar energy system analysis," Energy, Elsevier, vol. 36(2), pages 1319-1323.
    8. Zarzo, Manuel & Martí, Pau, 2011. "Modeling the variability of solar radiation data among weather stations by means of principal components analysis," Applied Energy, Elsevier, vol. 88(8), pages 2775-2784, August.
    9. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    10. Juan Moreno-Cruz & Katharine Ricke & David Keith, 2012. "A simple model to account for regional inequalities in the effectiveness of solar radiation management," Climatic Change, Springer, vol. 110(3), pages 649-668, February.
    11. Janjai, S. & Pankaew, P. & Laksanaboonsong, J., 2009. "A model for calculating hourly global solar radiation from satellite data in the tropics," Applied Energy, Elsevier, vol. 86(9), pages 1450-1457, September.
    12. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong & Zhao, Liang, 2011. "Global solar radiation estimation with sunshine duration in Tibet, China," Renewable Energy, Elsevier, vol. 36(11), pages 3141-3145.
    13. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong, 2010. "Estimating daily global solar radiation by day of year in China," Applied Energy, Elsevier, vol. 87(10), pages 3011-3017, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu, Rui, 2020. "An improved estimation of net primary productivity of grassland in the Qinghai-Tibet region using light use efficiency with vegetation photosynthesis model," Ecological Modelling, Elsevier, vol. 431(C).
    2. Kang, Byung O & Tam, Kwa-Sur, 2015. "New and improved methods to estimate day-ahead quantity and quality of solar irradiance," Applied Energy, Elsevier, vol. 137(C), pages 240-249.
    3. Wang, Lunche & Kisi, Ozgur & Zounemat-Kermani, Mohammad & Salazar, Germán Ariel & Zhu, Zhongmin & Gong, Wei, 2016. "Solar radiation prediction using different techniques: model evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 384-397.
    4. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    5. Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    6. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    7. Zang, Haixiang & Jiang, Xin & Cheng, LiLin & Zhang, Fengchun & Wei, Zhinong & Sun, Guoqiang, 2022. "Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations," Renewable Energy, Elsevier, vol. 195(C), pages 795-808.
    8. Koo, Choongwan & Li, Wenzhuo & Cha, Seung Hyun & Zhang, Shaojie, 2019. "A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques," Renewable Energy, Elsevier, vol. 133(C), pages 575-592.
    9. Halabi, Laith M. & Mekhilef, Saad & Hossain, Monowar, 2018. "Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation," Applied Energy, Elsevier, vol. 213(C), pages 247-261.
    10. Halawa, Edward & GhaffarianHoseini, AmirHosein & Hin Wa Li, Danny, 2014. "Empirical correlations as a means for estimating monthly average daily global radiation: A critical overview," Renewable Energy, Elsevier, vol. 72(C), pages 149-153.
    11. Jiandong Liu & Tao Pan & Deliang Chen & Xiuji Zhou & Qiang Yu & Gerald N. Flerchinger & De Li Liu & Xintong Zou & Hans W. Linderholm & Jun Du & Dingrong Wu & Yanbo Shen, 2017. "An Improved Ångström-Type Model for Estimating Solar Radiation over the Tibetan Plateau," Energies, MDPI, vol. 10(7), pages 1-28, July.
    12. Yongxun Zhang & Qingwen Min & Guigen Zhao & Wenjun Jiao & Weiwei Liu & Dhruba Bijaya G.C., 2016. "Can Clean Energy Policy Improve the Quality of Alpine Grassland Ecosystem? A Scenario Analysis to Influence the Energy Changes in the Three-River Headwater Region, China," Sustainability, MDPI, vol. 8(3), pages 1-14, March.
    13. Bellido-Jiménez, Juan Antonio & Estévez Gualda, Javier & García-Marín, Amanda Penélope, 2021. "Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions," Applied Energy, Elsevier, vol. 298(C).
    14. Jiang, Chengcheng & Zhu, Qunzhi, 2023. "Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer," Applied Energy, Elsevier, vol. 348(C).
    15. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).
    16. Jiang, Hou & Lu, Ning & Qin, Jun & Yao, Ling, 2021. "Hierarchical identification of solar radiation zones in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    17. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    18. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    19. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    20. Ghimire, Sujan & Nguyen-Huy, Thong & AL-Musaylh, Mohanad S. & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2023. "A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction," Energy, Elsevier, vol. 275(C).
    21. Gutiérrez-Trashorras, Antonio J. & Villicaña-Ortiz, Eunice & Álvarez-Álvarez, Eduardo & González-Caballín, Juan M. & Xiberta-Bernat, Jorge & Suarez-López, María J., 2018. "Attenuation processes of solar radiation. Application to the quantification of direct and diffuse solar irradiances on horizontal surfaces in Mexico by means of an overall atmospheric transmittance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 93-106.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiandong Liu & Tao Pan & Deliang Chen & Xiuji Zhou & Qiang Yu & Gerald N. Flerchinger & De Li Liu & Xintong Zou & Hans W. Linderholm & Jun Du & Dingrong Wu & Yanbo Shen, 2017. "An Improved Ångström-Type Model for Estimating Solar Radiation over the Tibetan Plateau," Energies, MDPI, vol. 10(7), pages 1-28, July.
    2. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    3. Purohit, Ishan & Purohit, Pallav, 2015. "Inter-comparability of solar radiation databases in Indian context," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 735-747.
    4. Wang, Lunche & Gong, Wei & Li, Chen & Lin, Aiwen & Hu, Bo & Ma, Yingying, 2013. "Measurement and estimation of photosynthetically active radiation from 1961 to 2011 in Central China," Applied Energy, Elsevier, vol. 111(C), pages 1010-1017.
    5. Li, Shuai & Ma, Hongjie & Li, Weiyi, 2017. "Typical solar radiation year construction using k-means clustering and discrete-time Markov chain," Applied Energy, Elsevier, vol. 205(C), pages 720-731.
    6. Furlan, Claudia & de Oliveira, Amauri Pereira & Soares, Jacyra & Codato, Georgia & Escobedo, João Francisco, 2012. "The role of clouds in improving the regression model for hourly values of diffuse solar radiation," Applied Energy, Elsevier, vol. 92(C), pages 240-254.
    7. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    8. Akarslan, Emre & Hocaoglu, Fatih Onur & Edizkan, Rifat, 2018. "Novel short term solar irradiance forecasting models," Renewable Energy, Elsevier, vol. 123(C), pages 58-66.
    9. Bayrakçı, Hilmi Cenk & Demircan, Cihan & Keçebaş, Ali, 2018. "The development of empirical models for estimating global solar radiation on horizontal surface: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2771-2782.
    10. Božnar, Marija Zlata & Grašič, Boštjan & Oliveira, Amauri Pereira de & Soares, Jacyra & Mlakar, Primož, 2017. "Spatially transferable regional model for half-hourly values of diffuse solar radiation for general sky conditions based on perceptron artificial neural networks," Renewable Energy, Elsevier, vol. 103(C), pages 794-810.
    11. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    12. Chen, Ji-Long & He, Lei & Yang, Hong & Ma, Maohua & Chen, Qiao & Wu, Sheng-Jun & Xiao, Zuo-lin, 2019. "Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 91-111.
    13. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
    14. Prieto, Jesús-Ignacio & García, David, 2022. "Global solar radiation models: A critical review from the point of view of homogeneity and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    15. Baser, Furkan & Demirhan, Haydar, 2017. "A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation," Energy, Elsevier, vol. 123(C), pages 229-240.
    16. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    17. Yao, Wanxiang & Zhang, Kang & Cao, Weixue & Li, Xianli & Wang, Yan & Wang, Xiao, 2022. "Research on the correlation between solar radiation and sky luminance based on the principle of photothermal integration," Renewable Energy, Elsevier, vol. 194(C), pages 1326-1342.
    18. Kocifaj, Miroslav & Kómar, Ladislav, 2016. "Modeling diffuse irradiance under arbitrary and homogeneous skies: Comparison and validation," Applied Energy, Elsevier, vol. 166(C), pages 117-127.
    19. Bikhtiyar Ameen & Heiko Balzter & Claire Jarvis & James Wheeler, 2019. "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks," Energies, MDPI, vol. 12(1), pages 1-28, January.
    20. El-Sebaii, A.A. & Al-Hazmi, F.S. & Al-Ghamdi, A.A. & Yaghmour, S.J., 2010. "Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia," Applied Energy, Elsevier, vol. 87(2), pages 568-576, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:107:y:2013:i:c:p:384-393. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.