提高太阳辐射短时临近预报（<6 h）的准确率是确保电网调度的重要举措，也是极具挑战性的技术瓶颈之一。基于云-辐射关系，利用地面观测的太阳辐照度反演的云相对辐射强迫比值，构建了太阳辐射短时临近预报模型（R模型），并用美国南部大平原中心站16 a的辐照度观测数据，对R模型的预报性能进行了评估。结果表明：（1） 有云存在的个例中，R模型较传统的简单持续性模型（Simple模型）的预报性能有很大提升，相比于预报性能较高的智能持续性模型（Smart模型或RCRF模型）仍有2%~25%的改进。（2） 在16 a包含2.9×10⁵个8类云状个例的总体检验中，当预报时效超过1 h时，R模型的预报性能显著优于Simple模型和RCRF模型。相对于RCRF模型，R模型在6 h预报时效下，对总辐射和直接辐射的预报性能可分别提高25%和19%，预报时效分别延长了1.5 h和1 h。（3） R模型为太阳辐射短时临近预报提供了准确率更高的基准模型。同时，该模型可仅依靠地面短期的辐照度观测资料即可预报，为缺少同期气象要素观测的光伏电厂的辐射预报提供了新的途径或新的可能。

Improving the forecasting accuracy of solar irradiance, as an important safeguard ensuring the stability of grid operations, remains one of the challenging technical bottlenecks. This study constructs a short-time solar irradiance forecasting model (R model) based on the cloud relative radiative forcing ratio R derived by the ground-based solar irradiances from the relationship between clouds and radiation and then evaluates the forecasting accuracy of the R model using 16-year solar irradiance measurements at the Southern Great Plains Central Facility site in the United States. The results show that (1) the R model significantly outperforms the simple persistence model and exhibits a 2%-25% improvement relative to the advanced smart persistence model (also called cloud relative radiative forcing (RCRF) model herein) in the cloudy case. (2) For the overall validation with 2.9×10⁵ individual cloudy cases in 8 categories over 16 years, the forecast accuracy of the R model is significantly better than that of the simple model and the RCRF model at lead times longer than 1 h. Compared with the RCRF model, the forecast performance of the R model is improved by 25% and 19% in global horizontal irradiance and direct normal irradiance, respectively, and the lead time is extended by 1.5 and 1 h. (3) The R model with higher forecast performance sets a higher standard for the benchmark model of short-term solar irradiance forecasting. Meanwhile, the R model only requires short-term ground-based radiation observations for forecasting, facilitating and providing new possibilities for photovoltaic power plants without concurrent meteorological measurements in short-term solar irradiance forecasting.