Regional validation of the solar irradiance tool SolaRes in clear-sky conditions, with a focus on the aerosol module

• Published in: Atmospheric Measurement Techniques
• Main Authors: Elias, Thierry, Ferlay, Nicolas, Chesnoiu, Gabriel, Chiapello, Isabelle, Moulana, Mustapha
• Format: Text
• Language: English
• Published: 2024
• Subjects: Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/amt-17-4041-2024; https://amt.copernicus.org/articles/17/4041/2024/

The Solar Resource estimate (SolaRes) tool based on the Speed-up Monte Carlo Advanced Radiative Transfer code using GPU (SMART-G) has the ambition to fulfil both research and industrial applications by providing accurate, precise, and high-time-resolution simulations of the solar resource. We investigate the capacity of SolaRes to reproduce the radiation field, relying on 2 years of ground-based measurements by pyrheliometers and pyranometers acquired in northern France (Lille and Palaiseau). Our main objective is to provide, as a first step in clear-sky conditions, a thorough regional validation of SolaRes, allowing us to investigate aerosol impacts on solar resource. We perform comparisons between SolaRes-simulated and clear-sky-measured global horizontal irradiance (GHI), direct normal irradiance (DNI), diffuse horizontal irradiance (DifHI), and global and diffuse irradiance on a tilted plane (GTI, DifTI), and we even consider the circumsolar contributions. Using spectral aerosol optical thickness (AOT) data sets as input, which are delivered by the AErosol RObotic NETwork (AERONET) and the Copernicus Atmosphere Monitoring Service (CAMS), we examine the influence of aerosol input data sets in SolaRes on the comparison scores. Two aerosol models are mixed to compute aerosol optical properties. We also perform a sensitivity study on the aerosol parametrisation and investigate the influence of applying more or less strict cloud-screening methods to derive ground-based proof data sets of clear-sky moments. SolaRes is validated with the (relative) root mean square difference (RMSD) in GHI as low as 1 % and a negligible mean bias difference (MBD). The impact of the cloud-screening method in GHI is 0.5 % of RMSD and 0.3 % of MBD. SolaRes also estimates the circumsolar contribution, which improves MBD in DNI and DifHI by 1 % and 4 %, respectively, and RMSD by ∼ 0.5 %. MBD in DNI is around − 1 % and RMSD around 2 %, and MBD in DifHI is 2 % and RMSD around 9 %. RMSD and MBD in both DNI and DifHI are larger than in GHI ...