Investigation of Climate Fluctuations Through Analysis of Meteorological Data and Forecasting in Port Harcourt, Nigeria
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https://doi.org/10.14419/212zmm85
Received date: June 18, 2025
Accepted date: August 10, 2025
Published date: August 16, 2025
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Climatic Change; Meteorological Data; Forecasting; Meteonorm 8 and Port Harcourt -
Abstract
The research on studying and predicting climatic phenomena in Port Harcourt, Nigeria, has evolved based on the vast knowledge and information collected. This has greatly improved the understanding and prediction of weather changes in the region. In addition to traditional climate change indicators like temperature, wind, humidity, clouds, and rainfall, this study also explores the variability of global radiation. The primary objective of this research was to assess climate variability using meteorological parameters and forecasting in Port Har-court, Rivers State, Nigeria. The study utilized data from the Meteonorm Version 8 database archives to analyze radiation, temperature, and precipitation trends. The Standard interpolation method (Shepard’s gravity interpolation model) was employed to obtain the data. Stochastic models and the Perez tilt radiation model were used to examine climate data from 1972–1991, 1965–1989, and 2020 and 2050. Results indicate a yearly Temperature average of 26.3°C between 1972 and 1991 and a projected value of 27.6°C in 2050. Furthermore, the analysis shows a decreasing trend in precipitation in Port Harcourt, with the maximum value expected to decrease to 323mm in 2050 from 400mm observed between 1972–1991. The projected global radiation levels are expected to reach 1722kWh/m2 by 2050, up from 1476kWh/m2 recorded between 1965 and 1989. It is strongly advised to consider data from various locations in Port Harcourt and Nigeria, as well as the local climatic characteristics, for future study. The study is useful for selecting suitable sites for green energy projects like solar power stations and wind farms.
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References
- Afrifa-Yamoah E.; I. Bashiru; I. Saeed and A. Karim. (2016). Sarima Modelling and Forecasting of Monthly Rainfall in the Brong Ahafo Region of Ghana. World Environment, 6(1): 1-9.
- Aweda, F. O. and Samson, T. K. (2020). Modelling the Earth’s Solar Irradiance across Selected Station in Sub-Saharan Region of Africa. Iranian (Iranica) Journal of Energy and Environment Journal, 11(3): 204-211. https://doi.org/10.5829/IJEE.2020.11.03.05.
- Bohra-Mishra, P., Oppenheimer M. and S.M. Hsiang, (2014). Nonlinear permanent migration response to climatic variations but minimal response to disasters, Proceedings of the National Academy of Sciences (PNAS) 2014, 111, 27, 9780-9785. https://doi.org/10.1073/pnas.1317166111.
- Cai, R., Feng, S., Oppenheimer, M. and M. Pytlikova, (2016). Climate variability and international migration: the importance of the agricultural linkage. J. Environ. Econ. Manag. 79, 135–151, https://doi.org/10.1016/j.jeem.2016.06.005.
- Eludoyin O.S, Oderinde F.A, Azubuike O.J. (2013). Heavy metals concentration under rubber plantation (Hevea brasiliensis) in Hydromophic Soil of South-south.
- Frimpong, K.; J. Oosthizen and E. J. Van Etten. (2014). Recent trends in temperature and relative humidity in Bawku East, Northern Ghana. Jour-nal of Geography and Geology, 6(2): 69 – 81. https://doi.org/10.5539/jgg.v6n2p69.
- Gansler, R.A., S.A. Klein and W.A. Beckman (1994): Assessment of the accuracy of generated meteorological data for use in solar energy simula-tion studies. Solar Energy, Vol. 53, No.3, pp. 279 - 287 https://doi.org/10.1016/0038-092X(94)90634-3.
- Geerts, B. (2003). Empirical estimation of the monthly-mean daily temperature range. Theoretical and Applied Climatology, 74: 145 – 165. https://doi.org/10.1007/s00704-002-0715-3.
- Gilgen H., M. Wild M., A. Ohmura (1998): Means and trends of shortwave incoming radiation at the surface estimated from Global Energy Bal-ance Archive data. Journal of Climate, 11, 2042-2061. https://doi.org/10.1175/1520-0442-11.8.2042.
- Intergovernmental Panel on Climate Change. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
- Khatib, T. and Elmenreich, W. (2015). A model for hourly solar radiation data generation from daily solar radiation data using a generalized regres-sion artificial neural network. International Journal of Photoenergy, 13(1): 1-13. https://doi.org/10.1155/2015/968024
- Khedhiri, S. (2015). Forecasting temperature record in PEI, Canada. Letters in Spatial and Resource Sciences, 9: 43-55, https://doi.org/10.1007/s12076-014-0135-x.
- Lefèvre, M., M. Albuisson and L. Wald (2002): Joint Report on Interpolation Scheme "Meteosat" and Database "Climatology I (Meteosat). SoDa Deliverable D3-8 and D5-1-4. Internal document.
- Lobell, D.B., G. I. Hammer; G. Mclean; C. Messina; M. J. Roberts and W. Schlenker. (2013). The critical role of extreme heat for maize production in the United States. Nature Climate Change, 3: 1-13. https://doi.org/10.1038/nclimate1832.
- Mmom P.C, Fred-Nwagwu F.W., (2013). Analysis of Landuse and Landcover Change around the City of Port Harcourt, Nigeria. Global Adv Res J Geogr Reg Plann 2: 076-86.
- Muhammet B. (2012). The analyse of precipitation and temperature in Afyonkarahisar (Turkey) in respect of Box-Jenkins technique. J. Academic Social Sci. Studies, 5(8): 196-212
- Murat, M; I. Malinowska; M. Gos and J. Krzyszczak. (2018). Forecasting daily meteorological time series using ARIMA and regression models. Int. Agrophys., 32: 253-264. https://doi.org/10.1515/intag-2017-0007.
- Poudyal, K. N.; B. K. Bhattarai; B. Sapkota and B. Kjeldstad. (2012). Estimation of global solar radiation using clearness index and cloud transmit-tance factor at trans-Himalayan region in Nepal. Energy and power Engineering, 4(6): 415 – 421. https://doi.org/10.4236/epe.2012.46055.
- Rebetez, M. (2001). Changes in daily and nightly day – to – day temperature variability during the twentieth century for two stations in Switzer-land. Theoretical and Applied Climatology, 69(1 - 2): 13-21. https://doi.org/10.1007/s007040170032.
- Ridley, B., J. Boland, and P. Lauret (2010), Modelling of diffuse solar fraction with multiple predictors. Renewable Energy. 35(2): p. 478-483. https://doi.org/10.1016/j.renene.2009.07.018.
- Semenov M.A. and Shewry P.R. (2011). Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Scientific Reports, 1: 66. https://doi.org/10.1038/srep00066.
- Semenza, J.C. and K. Ebi, (2019). Climate change impact on migration, travel, travel destinations and the tourism industry. Journal of travel medi-cine vol. 26,5 (2019): taz026. https://doi.org/10.1093/jtm/taz026
- Ukhurebor, K. E.; T. B. Batubo; I. C. Abiodun and E. Enoyeze. (2017). The influence of air temperature on the dew point temperature in Benin City, Nigeria. Journal of Applied Sciences and Environmental Management, 21(4): 657-660. https://doi.org/10.4314/jasem.v21i4.5.
- Wald, L. and M. Lefèvre (2001): Interpolation schemes - Profile Method (a process-based distance for interpolation schemes). SoDa Deliverable D5-1-1. Internal document
- World Meteorological Organisation (WMO) (1998): 1961 – 90 Climatological Normals (Clino). Version 1.0 – November 1998.
- Yousif, T. A. and Tahir, M. H. (2013). The relationship between relative humidity and dew point temperature in Khartoum State, Sudan. Journal of Applied and Industrial Sciences, 1(5): 20 – 23.
- Zelenka, A., G. Czeplak.,V. D'Agostino, J. Weine., E. Maxwell., R. Perez, M. Noia, C. Ratto and R. Festa (1992): Techniques for supplementing solar radiation network data, Volume 1-3. IEA Report No.IEA-SHCP-9D-1.
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How to Cite
Menegbo , E. M. ., & Kurotamuno, J. P. . . (2025). Investigation of Climate Fluctuations Through Analysis of Meteorological Data and Forecasting in Port Harcourt, Nigeria. International Journal of Advanced Geosciences, 13(2), 1-12. https://doi.org/10.14419/212zmm85
