Validation of direct normal irradiance predictions under arid conditions: A review of radiative models and their turbidity-dependent performance

In this study, a detailed review of the performance of 24 radiative models from the literature is presented. These models are used to predict the clear-sky surface direct normal irradiance (DNI) at a 1-min time resolution. Coincident radiometric and sunphotometric databases of the highest possible quality, and recorded at seven stations located in arid environments, are used for this analysis. At most sites, an extremely large range of aerosol loading conditions and high variability in their characteristics are noticed. At one site (Solar Village), DNI was measured routinely with an active cavity radiometer with very low uncertainty compared to field pyrheliometers, which makes its dataset exceptional. The reviewed models are categorized into 5 classes, depending on the number of aerosol-related inputs they require. One of the models (RRTMG) is considerably more sophisticated (and thus less computationally efficient) than the other models which are all of the parametric type currently in use in solar applications, and specifically devised for cloudless conditions. RRTMG is more versatile and is selected here for benchmarking purposes. The results show good consistency between the different stations, with generally higher prediction uncertainties at sites experiencing larger mean aerosol optical depth (AOD). Disaggregation of the performance results as a function of two aerosol optical characteristics (AOD at 1 mu m, beta, and Angstrom exponent, alpha) shows that the simplest parametric models' performance decreases when subjected to turbidity conditions outside of what is normal or typical under temperate climates. Only a few parametric models perform well under all conditions and at all stations: REST2, CPCR2, MMAC, and METSTAT, in decreasing order of performance. The Ineichen and Hoyt models perform adequately at low AODs, but diverge beyond a specific limit. REST2 is the only parametric model that performs similarly to the RRTMG benchmark under all AOD regimes observed here and even sometimes better. The inspection of the models' performance when considering the simultaneous effects of both beta and alpha reveals a clear pattern in the models' error, which is influenced by the frequency distribution of a values. This suggests most models may have difficulty in correctly capturing the effect of alpha, and/or that observational and instrumental issues at high AOD values may also enhance the apparent model prediction errors. A study of the specific sensitivity of DNI on AOD confirmed previous findings. It is concluded that assuming a perfect model, DNI can be modeled within 5% accuracy only if beta is known to within approximate to 0.02. (C) 2015 Elsevier Ltd. All rights reserved.