Evaluation of atmosphere clearness and cloudiness parameters in the southern regions of Ukraine using statistical analysis

Keywords: clearness index, cloudiness index, sky clarity, solar energy, solar energy resource

Abstract

Introduction. This paper deals with the specific aspects of insolation of the terrestrial surface in the southern regions of Ukraine, namely the clearness index and diffuse fraction of the atmosphere. The study was based on satellite data of the average daily insolation and total cloudiness in the nodes of the two-degree grid for the domain with coordinates 48°-45° N and 29°-39° E for the period of 1981-2020.

The purpose of article. The purpose was to develop statistic models of horizontal surface insolation for various locations of study domain. Main focus was put on special characteristics in conditions of fixed cloudiness. Satellite data for the summer season had been used to evaluate the maximum solar energy potential of Ukraine.

Methods. Application statistical analysis and means of cartographic data layout were used in the paper.

Results. It was found that with the highest (more than 50%) frequency the total cloud cover can be characterized by the atmosphere clarity  corresponding to a clear sky condition. The significance of irradiation of the terrestrial surface with diffuse solar radiation has been observed, with the share of such radiation in the global irradiation (diffuse ratio) being closely inversely related to the clearness index (correlation about -0.97). In turn, both diffuse ratio and clearness index are statistically dependent on the sky clarity, that allowed deriving analytical functions - diffuse ratio and clearness index - of the sky clarity, which appeared to be S- and Z-shaped curves, respectively. Dispersion of the clearness index ( ) and the diffuse fraction ( ) values and the strength of their statistical relationship significantly depend on the sky clarity. The empirical distribution of the two-dimensional random variable ( ; ) well meets the Gaussian distribution, and the obtained dispersion ellipses allowed calculating the confidence intervals of the two-dimensional random variable (clearness index: diffuse fraction) for a given confidence level. The spatial distribution of the clearness index and diffuse fraction of the atmosphere in the southern regions of Ukraine revealed a significant dependence of these indices on the latitude and the type of underlying surface. At the end of the summer a seasonal effect has been observed in the spatial distribution of the clearness index and diffuse fraction, which can be explained by the specific seasonal features of atmospheric circulation, caused by the spreading out of the eastern ridge of the Azores anticyclone and the general situation with blocking developments in the Atlantic-European sector of the Northern Hemisphere.

The scientific novelty. Correlation and regression models of special insolation characteristics in conditions of various cloudiness that are represented in this paper are new to Ukraine. Analysis of two-dimensional random value spread (clearness index: cloudiness index) allowed to assess probabilities of integral solar radiation flows. The obtained analytical membership functions for monthly average values of clearness and cloudiness indices depending on the level of sky clarity proved to be applicable for determining respective indices for daily time scale.

Practical significance. The obtained results are important for comprehensive assessment of the solar / photovoltaic resources of southern regions of Ukraine. Specifically, analytical dependences have practical values for the purpose of forecasting direct and diffuse solar radiation in various time scales based on publically available global records of solar radiation.

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Author Biographies

Vasyl Zatula, Taras Shevchenko National University of Kyiv

PhD (Geography), Associate Professor

Yaroslav Kyhtenko, Taras Shevchenko National University of Kyiv

Master's student

Rostyslav Oliinyk, Taras Shevchenko National University of Kyiv

PhD (Geophysics), Associate Professor

Sergiy Snizhko, Taras Shevchenko National University of Kyiv

DSc (Geography), Professor

References

Bakirci, K. (2015). Models for the estimation of diffuse solar radiation for typical cities in Turkey. Energy, 82, 827-838. https://doi.org/10.1016/j.energy.2015.01.093

Rybchenko, L.S., Savchuk, S.V. (2017). Monitorynh helioenerhetychnykh resursiv Ukrainy [Monitoring the solar energy resources of Ukraine]. Ukrainskyi hidrometeorolohichnyi zhurnal – Ukrainian hydrometeorological journal, 19, 65-71. [in Ukrainian]

Rybchenko, L.S., Savchuk, S.V. (2015). Potentsial helioenerhetychnykh klimatychnykh resursiv soniachnoi radiatsii v Ukraini [Potential of the climatic solar radiation energy resources in Ukraine]. Ukrainskyi heohrafichnyi zhur-nal - Ukrainian Geographical Journal, 4. 16-23. https://doi.org/10.15407/ugz2015.04.016 [in Ukrainian]

Bortolini, M., Gamberi, M., Graziani, A., Manzini, R., Mora, C. (2013). Multi-location model for the estimation of the horizontal daily diffuse fraction of solar radiation in Europe. Energy Conversion and Management, 67, 208–216. https://doi.org/10.1016/j.enconman.2012.11.008

Kuo, C.W., Chang, W.C., Chang, K.C. (2014). Modeling the hourly solar diffuse fraction in Taiwan. Renewable Energy, 66, 56-61. https://doi.org/10.1016/j.renene.2013.11.072

Berrizbeitia, S.E., Gago, E.J., Muneer, T. (2020). Empirical Models for the Estimation of Solar Sky-Diffuse Radia-tion. A Review and Experimental Analysis. Energies 2020, 13(3), 701. https://doi.org/10.3390/en13030701

Bailek, N., Bouchouicha, K., Al-Mostafa, Z., El-Shimy, M., Aoun, N., Slimani, A., Al-Shehri, S. (2018) A new empiri-cal model for forecasting the diffuse solar radiation over Sahara in the Algerian Big South. Renewable Energy, 117, 530-537. https://doi.org/10.1016/j.renene.2017.10.081

Despotovic, M., Nedic, V., Despotovic, D., Cvetanovic, S. (2016). Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation. Renewable and Sustainable Energy Reviews, 56(C), 246-260. https://doi.org/10.1016/j.rser.2015.11.058

Paulescu, E., Blaga, R. (2016). Regression models for hourly diffuse solar radiation. Solar Energy, 125, 111-124. http://dx.doi.org/10.1016/j.solener.2015.11.044

Wang, L., Kisi, O., Zounemat-Kermani, M., Salazar, G.A., Zhu, Z., Gong, W. (2016). Solar radiation prediction using different techniques: Model evaluation and comparison. Renewable and Sustainable Energy Reviews, 61, 384–397. DOI: https://doi.org/10.1016/j.rser.2016.04.024

Zhang, J., Zhao, L., Deng, S., Xu, W., Zhang, Y. (2017). A critical review of the models used to estimate solar radia-tion. Renewable and Sustainable Energy Reviews, 2017, 70, 314–329. https://doi.org/10.1016/j.rser.2016.11.124

Boland, J., Huang, J., Ridley, B. (2013). Decomposing global solar radiation into its direct and diffuse compo-nents. Renewable and Sustainable Energy Reviews, 28, 749–756. https://doi.org/10.1016/j.rser.2013.08.023

Lipіnskyi, V.M., Dyachuk, V.A., Babichenko, V.M. (Eds). (2003). Klimat Ukrainy [Climate of Ukraine]. Kyiv: Ra-yevskyi Publ., 343. [in Ukrainian]

Pashinsky, V.A., Butko, A.A., Cherkasova, A.A. (2015). Ocenka padajushhej solnechnoj radiacii na gorizontal'nuju poverhnost' territorii v uslovijah Respubliki Belarus' [Assessment of incident solar radiation on the horizontal surface of the territory in the conditions of the Republic of Belarus]. Jekologicheskij vestnik – Ecological Bulletin, 2(32), 77-82. [in Russian]

Muneer, T., Younes, S., Munawwar, S. (2007). Discourses on solar radiation modeling. Renewable and Sustainable Energy Reviews, 11(4), 551–602. https://doi.org/10.1016/j.rser.2005.05.006

Safi, S., Zeroual, A., Hassani, M. (2002). Prediction of global daily solar radiation using higher order statistics. Renewable Energy, 27(4), 647–666. https://doi.org/10.1016/S0960-1481(01)00153-7

Younes, S., Muneer, T. (2006). Improvements in solar radiation models based on cloud data. Building Services En-gineering Research and Technology, 27(1), 41–54. https://doi.org/10.1191/0143624406bt143oa

Atmospheric Science Data Center. Retrieved from: https://eosweb.larc.nasa.gov

Forecasts | ECMWF. Retrieved from: www.ecmwf.int

Solar Irradiance Data. Retrieved from: https://solcast.com/solar-radiation-data

Rodrigues, V.S., Nunes, M.V.A., Silva, V.S., Rodrigues, G.S., Ramkeerat, P.F.R., Batista, C.M.N., Moraes, W.A. (2016). Clarity Index in the city of Manaus in Global Atmospheric Radiation Measurement function by Meteorological Observation Station in the Amazon ranking. Journal of Engineering and Technology for Industrial Applications, 02(08), 136-144. https://www.itegam-jetia.org. ISSN ONLINE: 2447-0228. https://dx.doi.org/10.5935/2447-0228.20160050

Bawazir, R.O., Chakchak, J., Çetin, N.S., Ulgen, K. (2016). Investigating the Optimum Tilt Angle for Solar Receiver in Izmir. ISEM2016, 3rd International Symposium on Environment and Morality, 4-6 November 2016, Alanya – Turkey. Alanya: Sakarya University, 809-817. Retrieved from: http://i-sem.info/PastConferences/ISEM2016/ISEM2016/papers/1-ISEM2016ID250.pdf

Al-Enezia, F.Q., Sykulskia, J.K., Ahmed, N.A. (2011) Visibility and Potential of Solar Energy on Horizontal Surface at Kuwait Area. Energy Procedia, 12, 862–872. https://dx.doi.org/10.1016/j.egypro.2011.10.114

Perez-Burgos, A., Diez-Mediavilla, M., Alonso-Tristan, C., Dieste-Velasco, M.I. (2018). Retrieval of monthly average hourly values of direct and diffuse solar irradiance from measurements of global radiation in Spain. Journal of Renewable and Sustainable Energy, 10(2), 023707. https://doi.org/10.1063/1.5016926

Berger, A., Yin, Q. (2012) Astronomical theory and orbital forcing. In The Sage Handbook of Environmental Change, J.A. Matthews (Managing editor). Vol. 1, Section III Causes, Mechanisms and Dynamics of Environmental Change. 403-423.

Insolation in The Azimuth Project. Retrieved from: www.azimuthproject.org

Declination Angle. Retrieved from: www.pveducation.org

The Sun As A Source Of Energy. Part 4: Irradiation Calculations. Sections: Solar Photovoltaics. Retrieved from: https://www.itacanet.org/the-sun-as-a-source-of-energy/part-4-irradiation-calculations/

Salhi, H., Belkhiri, L., Tiri. A. (2020). Evaluation of diffuse fraction and diffusion coefficient using statistical anal-ysis. Applied Water Science, 10:133. https://doi.org/10.1007/s13201-020-01216-0

Yang, D. (2016). Solar radiation on inclined surfaces: Corrections and benchmarks. Solar Energy, 136, 288-302. https://doi.org/10.1016/j.solener.2016.06.062

Scarpa, F., Marchitto, A., Tagliafico, L.A. (2017). Splitting the solar radiation in direct and diffuse components; insights and constrains on the clearness-diffuse fraction representation. International journal of heat and tech-nology, 35(2), 325-329. https://doi.org/10.18280/ijht.350213 ISSN: 0392-8764

Yang, L., Cao, Q., Yu, Y., Liu, Y. (2020). Comparison of daily diffuse radiation models in regions of China without solar radiation measurement. Energy, 191:116571. https://doi.org/10.1016/j.energy.2019.116571

Bazalieieva, Y.O., Balabukh, V.O. (2016). Povtoriuvanist, tryvalist ta intensyvnist blokuvalnykh protsesiv, shcho zumovliuiut anomalni pohodni umovy v Ukraini [Frequency, duration and intensity of the blocking processes, which causes abnormal weather conditions in Ukraine]. Naukovi pratsi Ukrainskoho naukovo-doslidnoho hidro-meteorolohichnoho instytutu – Proceedings of Ukrainian Research Hydrometeorological Institute, 268, 44-51. Retrieved from: http://nbuv.gov.ua/UJRN/Npundgi_2016_268_7. [in Ukrainian]

Published
2021-12-01
Cited
How to Cite
Zatula, V., Kyhtenko, Y., Oliinyk, R., & Snizhko, S. (2021). Evaluation of atmosphere clearness and cloudiness parameters in the southern regions of Ukraine using statistical analysis. Visnyk of V. N. Karazin Kharkiv National University, Series "Geology. Geography. Ecology", (55), 159-173. https://doi.org/10.26565/2410-7360-2021-55-12