Advances in aerosol optical depth evaluation from broadband direct normal irradiance measurements
Aerosols are part of the attenuation processes that impact solar radiation within the atmosphere. They influence the availability and spectrum of the solar resource for each location at the earth's surface. The present study presents advances in the development of a methodology intended to estimate the aerosol optical depth (AOD) at a given location from broadband direct normal irradiance (DNI) measurements and an appropriate radiative transfer model (RTM) operated backwards. For this purpose, databases provided by AERONET and BSRN at 16 stations throughout the world are jointly employed as inputs to the proposed methodology. The validation of two RTMs (SMARTS and SOLIS) is first undertaken to estimate DNI under clear-sky conditions at each station, assuming both AOD and additional atmospheric inputs are known from sunphotometric measurements. Results indicate that both models achieve good performance, characterized by a relative rRMSE of 3.2% for SMARTS and 3.8% for SOLIS. In the second, and most important stage, the AOD at 550 nm (AOD550) is derived using these models again, but in an iterative mode, now using the 1-minute DNI measurements as inputs. Periods of clear line of sight to the sun first need to be selected from the irradiance measurement record. This, along with other difficulties, make this operation prone to errors when only DNI measurements are available. In spite of this, the results show that AOD can be estimated with a 16-site average mean bias error of only between −0.024 and 0.015 AOD unit and an absolute RMSE between 0.025 and 0.050 AOD unit (compared to the AERONET ground truth), depending on model. Notable improvements are obtained if secondary atmospheric variables are extracted from the MERRA-2 reanalysis and are included as inputs for local computations. The present results suggest that the method is able to compare favorably with AOD estimates from MERRA-2 predictions or MODIS observations, for instance.