In this paper, a procedure for generating a family of parametrical broadband clear-sky models is described. The key element is the conversion of one or more model input variables into tunable mathematical parameters. The approach is tested on the PS model [Paulescu, M. and Schlett, Z., Theor. Appl. Climatol. 75, 203 (2003)], with the free parameter being the Ångström exponent (α). This allows us to fine-tune for conditions dominated by desert dust, urban-industrial, and mixed aerosols. We find that for an arbitrary set of data, the optimal value of the free parameter is not the same as its actual measured value (inserting the measured value in the model would result in a lower performance). We attribute this fact to the inaccurate nature of the base model. The optimal α value varies with the considered solar irradiance component, aerosol type and loading, and the error measure(s) used for assessing model accuracy. A set of recommended models for each aerosol type and loading class is given. The tabled values for the aerosol transmittance coefficients are also listed. A preliminary validation shows that the newly developed models are very reliable. The optimal version generally falls within a few percent of the results of REST2v5, a benchmark model in clear-sky solar irradiance estimation. While some established models outperform REST2v5 for certain aerosol types and for only one solar irradiance component, the new models prove competitive under most scenarios. Beyond showing the performance of the developed model family, these results hint at great potential of our approach.

中文翻译：

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适用于不同气溶胶类型的晴空太阳辐照度模型的一个参数系列

在本文中，描述了生成一系列参数宽带晴空模型的过程。关键要素是将一个或多个模型输入变量转换为可调数学参数。该方法已在PS模型上进行了测试[Paulescu，M.和Schlett，Z.，Theor。应用 Climatol。75，203（2003）]，与自由参数是埃指数（α）。这使我们可以微调以沙漠尘埃，城市工业和混合气溶胶为主的条件。我们发现，对于任意一组数据，自由参数的最佳值与其实际测量值不同（将测量值插入模型中会导致性能降低）。我们将此事实归因于基本模型的不准确本质。最佳α该值随所考虑的太阳辐照度分量，气溶胶类型和负载以及用于评估模型准确性的误差度量而变化。给出了针对每种气溶胶类型和负荷类别的一组推荐模型。还列出了气溶胶透射系数的表格值。初步验证表明，新开发的模型非常可靠。最佳版本通常不超过REST2v5（晴空太阳辐照度估算的基准模型）结果的百分之几。虽然某些模型在某些气溶胶类型和仅一个太阳辐照度组件方面的性能优于REST2v5，但新模型在大多数情况下都具有竞争力。这些结果不仅显示了已开发的模型系列的性能，而且还暗示了我们方法的巨大潜力。