Correlation Analysis of Solar Radiation and Cloud Parameters: A Case Study at Jambi Climatological Station Using Ground Observation and Reanalysis Data

Abstract

Accurate quantification of solar radiation is essential for renewable energy planning and climate research. Automatic Solar Radiation System (ASRS) offers direct measurements of Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI), and Direct Normal Irradiance (DNI). Spatial reanalysis Data products such as Solcast provide valuable data for regions lacking ground-based observations. Surface solar radiation is significantly influenced by atmospheric cloud properties, particularly cloud cover, as measured from ground observations, and cloud opacity derived from reanalysis data. This study analyses statistical relationship between solar radiation observation parameters obtained from ASRS, Solcast, cloud cover and cloud opacity. Data used were hourly observations in 2023 at the Jambi Climatological Station. Pearson correlation, Spearman correlation, and partial correlation analyses were used to determine linear and nonlinear dependencies between variables. The results showed that GHI, DNI, and DHI parameters obtained from ASRS and Solcast exhibited highest correlation of 0.72 using Pearson and 0.70 using Spearman. At the same time, cloud cover observed in ground observation and cloud opacity of Solcast also showed a significant relationship with Pearson of 0.67 and Spearman of 0.69. Partial correlation analysis, controlling for cloud effects, revealed a strong intrinsic relationship between GHI and DHI from ASRS at 0.76 and GHI and DHI in Solcast reanalysis data at 0.72, indicating an important relationship between global radiation components and diffuse radiation. These results highlight fundamental interconnection between global radiation components and diffuse radiation. These findings highlight critical role of cloud conditions in modulating surface solar radiation and confirm value of reanalysis products for solar resource assessment and operational forecasting, particularly in cloudy regions such as Jambi, Indonesia.