A multi-model benchmarking of direct and global clear-sky solar irradiance predictions at arid sites using a reference physical radiative transfer model

•15 clear-sky radiation models are intercompared at 11 sites relevant to solar energy.•The RRTMG physical radiative transfer model is used for benchmarking.•Inter-model disagreement is related to atmospheric conditions and/or high elevation.•Compared to RRTMG, 9 models perform well for GHI, 5 models perform well for DNI.•Only REST2v5 and SMARTS are within 5% of benchmark for both GHI and DNI. This study provides a comparison of the predictions of fifteen clear-sky irradiance models against those from the RRTMG radiative transfer. RRTMG is selected as the benchmarking reference here because its code is open source and features a good compromise between accuracy, ease of use, and speed of execution, which should guarantee reproducible results by a majority of solar analysts. The model comparisons are undertaken at ten worldwide sites of importance for concentrating solar power (CSP) projects, and at a highly turbid desert site. The models’ inputs are directly or indirectly obtained from worldwide reanalyses having a spatial resolution of 1.125° × 1.125°. The simulations of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are done hourly over a whole year (2012). The present results confirm a previous validation study to the effect that significant inter-model disparities exist at arid sites. Large aerosol optical depth (AOD) and high site elevation appear to be the main drivers behind the disagreements. The comparison between four different Linke turbidity coefficient parameterizations shows that none of them provide good results for both GHI and DNI at all sites. Linke-based irradiance models are thus not recommended for accurate solar resource assessments. Considering all ten CSP sites combined, GHI predictions are found within ±5% of the benchmark for nine models. Only five models have the same success in the case of DNI. Finally, only two models, REST2v5 and SMARTS, are close to the benchmark for both GHI and DNI.