Khan Muhammad Umair, Jama Mohamed Abdi
Department of Electrical, Computer and Biomedical Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
Heliyon. 2024 Aug 6;10(16):e35256. doi: 10.1016/j.heliyon.2024.e35256. eCollection 2024 Aug 30.
Properly understanding solar irradiance can help accurately quantify the solar energy resource and guide sustainable development projects, particularly where measured solar data are scarce or suffer from detrimental data quality issues. This study aims to assess and improve solar global horizontal irradiance (GHI) data from a diverse range of global reanalysis datasets by utilizing measured data from two ground weather stations located in Somaliland. A comprehensive evaluation framework is employed, combining various statistical and regression error metrics, whereas bias correction methods are implemented. The comparative study covers several analytical facets such as the hourly, daily and monthly data analysis before and after correction along with analyzing the seasonal variations, clear-sky and all-sky conditions. The analysis revealed pattern of an overall underestimation of GHI with varying degrees of accuracy in the estimated GHI datasets before and after correction. The annual ranges for rMBE, rMAE, rRMSE and R extend from 3-31%, 12-33%, 19-53% and 0.797-0.979, respectively, across all datasets for six-hourly data in the two observed stations. Following bias correction, the ranges for rMBE, rMAE, rRMSE reduce to is 0%, 8-31%, 11-34% and R increase to 0.897-0.984 respectively. While certain datasets such as MERRA-2 and SARAH-2 demonstrate close alignment with ground data before correction, other especially ERA5-Land exhibit exceptional improvement after the bias correction.
正确理解太阳辐照度有助于准确量化太阳能资源,并指导可持续发展项目,特别是在实测太阳数据稀缺或存在有害数据质量问题的地区。本研究旨在利用位于索马里兰的两个地面气象站的实测数据,评估和改进来自各种全球再分析数据集的全球水平总辐照度(GHI)数据。采用了一个综合评估框架,结合了各种统计和回归误差指标,并实施了偏差校正方法。比较研究涵盖了几个分析方面,如校正前后的每小时、每日和每月数据分析,以及分析季节变化、晴空和全天条件。分析揭示了在估计的GHI数据集中,校正前后GHI总体上存在不同程度的低估模式。在两个观测站,所有数据集的六小时数据的rMBE、rMAE、rRMSE和R的年范围分别为3 - 31%、12 - 33%、19 - 53%和0.797 - 0.979。经过偏差校正后,rMBE、rMAE、rRMSE的范围分别降至0%、8 - 31%、11 - 34%,R增加到0.897 - 0.984。虽然某些数据集,如MERRA - 2和SARAH - 2在校正前与地面数据显示出密切的一致性,但其他数据集,特别是ERA5 - Land在偏差校正后表现出显著的改善。