Earth Science Malaysia (ESMY)

This research presents an analysis of the estimated solar radiation using maximum and minimum daily temperatures (Tdmax and Tdmin) by applying three models, with calibration using the recently developed CSR model and extensive digital datasets from satellite observations compiled in the Atlas of Solar Radiation for Saudi Arabia, edited by KACST in collaboration with the Center for Renewable Energy Resources of Colorado; the model’s performance was analyzed using four statistical metrics (RMSE, ME, R², MAE), aiming to assess variability and identify the best model for estimating daily solar radiation based on temperature data from 1985 to 2018, relying on a daily dataset of extreme temperatures collected over 34 years (1985–2018) from meteorological stations in Abha (41112), Makkah (41030), Tabouk (40375), Yanbu (40439), Qaysumah (40373), Dammam (40417), Al Jouf (40361), Qasim (40405), and Najran (41118), all supervised by the National Center of Meteorology (NCM); the methodology involves analyzing the statistical distribution of the selected dataset, including maximum daily temperature (Tx), minimum daily temperature (Tm), and daily average temperature (T’), over the period 1985–2018 using the Kolmogorov-Smirnov test, while the statistical significance of temperature trend variations was examined using the semi-averages method and the T-student test, with results visually represented on thematic graphs, revealing spatial variations in daily temperatures and their trends across the selected meteorological stations, showing notable differences in the spatial distribution of daily temperatures, and in the context of trend analysis, the T-student test indicated clear differences between the semi-averages for the two periods 1985–2001 (X’1) and 2002–2018 (X’2), where the temperature differences (X’2–X’1) for minimum and maximum values were generally smaller than (2 SE) across different months in the selected stations, with the mean daily temperature exhibiting an increasing but not statistically significant trend in the studied stations; this study effectively represents the spatial distribution of daily temperature variations using statistical tests to determine the significance of trends from 1985 to 2018, demonstrating that the integrated application of these methods provides more accurate results in identifying climate change indicators across regions of Saudi Arabia.