Determination of the Availability and Variation of Surface Downwelling Shortwave Radiation in Saudi Arabia Using EUMETSAT Satellite Imagery
Mohammad Ibna Anwar *
Department of Civil and Environmental Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
Khatib Zada Farhan
Department of Civil and Environmental Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.
Aiyesha Anwar
Department of Information and Communication Technology, Dhaka Residential Model College, Dhaka, Bangladesh.
Saba
Department of Architecture, Govt. Polytechnic College Jammu, India.
*Author to whom correspondence should be addressed.
Abstract
Solar energy is expected to be a viable alternative sustainable energy source in the near future due to diminishing fossil fuel resources and escalating changes in the climate. Surface Downwelling Shortwave (SDS) radiation is an important component for the characterization of energy deposition of the earth’s surface. The monthly means of SDS radiation for Saudi Arabia were extracted from EUMETSAT (SARAH-2.1 climate data record) between 1983 and 2022. The country experienced the lowest level (188 ± 40 W m-2) of SDS radiation in the winter season with a range of 67-271 W m-2. The summer season in Saudi Arabia has the highest level of radiation with a mean value of 304± 27 W m-2, which is 1.62 times the mean of the winter season. The region experienced the highest (10.6 W m-2/10y) and lowest (-10.80 W m-2/10y) levels of a statistically significant upward and downward trend in SDS radiation during the spring and summer seasons respectively in the first decade of the 21st century (2001-2010). During the spring season, there was an upward trend in the availability of SDS radiation across the region, with a magnitude of 3.8 W m-2/10y with a 99% confidence level. In contrast, there was a downward trend in SDS radiation in the summer with a magnitude of 1.0 W m-2/10y with a 90% confidence level followed by the autumn season when the trend was lowest (-0.45 W m-2/10y) and statistically non-significant.
Keywords: Surface downwelling shortwave radiation, EUMETSAT, spatiotemporal variation, trend analysis
How to Cite
Downloads
References
Li J, Wang MH, Ho YS. Trends in research on global climate change: A Science citation index Expanded-based analysis. Glob. Planet. Change. 2011;77(1–2):13–20.
DOI: 10.1016/j.gloplacha.2011.02.005
National education development project. Energy from the sun student guide intermediate; 2017-2018.
Available: www.NEED.org
Samuel Chukwujindu N. A comprehensive review of empirical models for estimating global solar radiation in Africa. Renew. Sustain. Energy Rev. 2017;78:955–995.
DOI: 10.1016/j.rser.2017.04.101
Nwokolo S, Ogbulezie J. Performance evaluation of existing sunshine-based computing models for estimating global solar radiation at Lagos, Nigeria. Int. J. Adv. Astron. 2017;5(2):106.
DOI: 10.14419/ijaa.v5i2.8308
United Nations. Energy statistics pocketbook; 2019.
Available: https://unstats.un.org/unsd/energy/pocket/2018/2018pb-web.pdf
Carpentieri A, Folini D, Wild M, Vuilleumier L, Meyer A. Satellite-derived solar radiation for intra-hour and intra-day applications: biases and uncertainties by season and altitude. Sol. Energy. 2022;255(9):274–284.
DOI: 10.1016/j.solener.2023.03.027
Huang G et al. Estimating surface solar irradiance from satellites: Past, present, and future perspectives. Remote Sens. Environ. 2019;233(7):111371
DOI: 10.1016/j.rse.2019.111371
Watanabe T, Oishi Y, Nakajima TY. Characterization of surface solar-irradiance variability using cloud properties based on satellite observations. Sol. Energy. 2016;140:83–92.
DOI: 10.1016/j.solener.2016.10.049
Amran YHA, Amran YHM, Alyousef R, Alabduljabbar H. Renewable and sustainable energy production in Saudi Arabia according to Saudi Vision 2030; Current status and future prospects. J. Clean. Prod. 2020;247:119602.
DOI: 10.1016/j.jclepro.2019.119602
Al-Sefri AK, Al-Shaalan AM. Availability, Performance and reliability evaluation for PV distributed generation. World J. Eng. Technol. 2019;07(03):429–454.
DOI: 10.4236/wjet.2019.73032
Daffallah KO. Modelling and sizing of a 12 V DC photovoltaic refrigerator. Int. J. Ambient Energy. 2019;0(0):1–5.
DOI: 10.1080/01430750.2019.1672580
Almasoud AH, Gandayh HM. Future of solar energy in Saudi Arabia. J. King Saud Univ. - Eng. Sci. 2015;27(2):153–157.
DOI: 10.1016/j.jksues.2014.03.007
Buffat R, Grassi S. Validation of CM SAF SARAH solar radiation datasets for Switzerland. Proc. 2015 IEEE Int. Renew. Sustain. Energy Conf. IRSEC 2015. 2016;16–21.
DOI: 10.1109/IRSEC.2015.7455044
Kendall MG. A New Measure Of Rank Correlation. Biometrika. 1938;30(1–2):81–93.
DOI: 10.1093/biomet/30.1-2.81
Henry M. Nonparametric tests against trend author ( s ): Henry B . Mann published by : The econometric society stable references Linked references are available on JSTOR for this article : You may need to log in to JSTOR. Econometrica 1945;13(3):245–259.
Available:https://www.jstor.org/stable/1907187
Alifujiang Y, Abuduwaili J, Maihemuti B, Emin B, Groll M. Innovative trend analysis of precipitation in the Lake Issyk-Kul Basin, Kyrgyzstan. Atmosphere (Basel). 2020;11(4):1–16.
DOI: 10.3390/atmos11040332
Sen PK. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 1968;63(324):1379–1389.
DOI: 10.2307/2285891
Xing L. Huang L. Chi G, Yang L, Li C, Hou X. A dynamic study of a karst spring based on wavelet analysis and the Mann-Kendall trend test. Water (Switzerland). 2018; 10(6).
DOI: 10.3390/w10060698
Wang W, Yi Z, Chen D. Mann-Kendall mutation analysis of temporal variation of apparent stress in Qinba Mountains and Its adjacent areas. IOP Conf. Ser. Earth Environ. Sci. 2021;660(1).
DOI: 10.1088/1755-1315/660/1/012112
Tong S et al. Spatial and temporal variability in extreme temperature and precipitation events in Inner Mongolia (China) during 1960-2017. Sci. Total Environ. 2019;649:75–89.
DOI: 10.1016/j.scitotenv.2018.08.262
Ay M, Kisi O. Investigation of trend analysis of monthly total precipitation by an innovative method. Theor. Appl. Climatol. 2014;120.
DOI: 10.1007/s00704-014-1198-8
Qu W, Jin Z, Zhang Q, Gao Y, Zhang P, Chen P. Estimation of evapotranspiration in the yellow river basin from 2002 to 2020 Based on GRACE and GRACE Follow-On Observations. Remote Sens. 2022;14(3).
DOI: 10.3390/rs14030730
HHE. Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng. 1951;116(1):770–799.
DOI: 10.1061/TACEAT.0006518
Subir Mansukhani. The hurst exponent: Predictability of time series. The institute for operations research and the management sciences (INFORMS). 2012;1–10.
DOI: https://doi.org/10.1287/LYTX.2012.04.05
Gómez-Águila A, Trinidad-Segovia JE, Sánchez-Granero MA. Improvement in hurst exponent estimation and its application to financial markets. Financ. Innov. 2022;8(1).
DOI: 10.1186/s40854-022-00394-x
Lo AW. Long-term memory in stock market prices. Econometrica. 1991;59(5):1279–1313.
DOI: 10.2307/2938368
Pérez-Sienes L, Grande M, Losada JC, Borondo J. The hurst exponent as an indicator to anticipate agricultural commodity prices. Entropy. 2023;25(4):1–11.
DOI: 10.3390/e25040579
Monahan AH, Fyfe JC, Ambaum MHP, Stephenson DB,. North GR. Empirical orthogonal functions: The medium is the message. J. Clim. 2009;22(24):6501–6514.
DOI: 10.1175/2009JCLI3062.1
Ma Y, Liu H, Xu G, Lu Z. Empirical orthogonal function analysis and modeling of global tropospheric delay spherical harmonic coefficients. Remote Sens. 2021;13(21):0–18.
DOI: 10.3390/rs13214385
Tang C, Zhu Y, Wei Y, Zhao F, Wu X, Tian X. Spatiotemporal characteristics and influencing factors of sunshine duration in china from 1970 to 2019. Atmosphere (Basel). 2022;13: 12.
DOI: 10.3390/atmos13122015
Pfeifroth U, Trentmann J, Kothe S, Hollmann R, Werscheck M. Validation report: Meteosat solar surface radiation and effective cloud albedo climate data record SARAH-2. 1 climate data records. EUMETSAT Satell. Appl. Facil. Clim. Monit. vol. SAF/CM/DWD, 2019;(2.3):1–96.
DOI: 10.5676/EUM