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The aerosol optical depth (AOD) is an indicator of particles suspended in the atmosphere, which measures the attenuation of transmitted spectral radiant energy through aerosols. It has been proven to be an essential factor in the estimation of air pollutants concentration and surface solar radiation. However, due to the sparse distribution of ground-based measurements and the high missing rate of satellite retrieval products, the reanalysis data with high spatial–temporal continuity and large spatial range is an excellent supplementary data source. However, compared with satellite monitoring and ground-based measurements, the reliability of the reanalysis data has been questioned. Therefore, in this paper, we evaluate the accuracy of the Monitoring Atmospheric Composition and Climate (MACC) AOD values at the wavelength of 550 nm (AOD550) using the ground-based observations data from 19 AERONET stations in the Chinese mainland for the period 2003–2007. In addition, a data fusion correction model based on the random forest regression is constructed and analyzed. The correction results demonstrate that the correction model, which captures the non-linear relationship, has a satisfactory correction effect overall, with R from 0.771 to 0.978, MAE from 0.225 to 0.047, and RMSE from 0.400 to 0.120. Furthermore, with the aid of model-agnostic machine learning explanation algorithms, we weigh the relative importance of the independent variables and visualize the complicated relationship between the model errors and the important input features. In general, the availability of MACC AOD was improved, and the correction model also provided a reference for the correction of other reanalysis data. [ABSTRACT FROM AUTHOR]

• A new aerosol direct transmittance scheme is developed and can be used by simple DNI models to consider effect of aerosols. • A fast DNI scheme (SUNFLUX) is improved with the new aerosol scheme for DNI prediction. • The improved DNI scheme is validated against observations and compared with other five schemes. • Sensitivity tests are conducted to examine the sensitivity of DNI model to variability of input data. A new aerosol direct transmittance parameterization is developed based on detailed radiative transfer model calculations for improving direct normal irradiance (DNI) for the solar energy forecast in the fast radiation scheme SUNFLUX. The modified SUNFLUX scheme is evaluated against observational datasets collected from the 14 Baseline Surface Radiation Network (BSRN) stations. The aerosol data from AERONET stations collocated with the BSRN network are used in the evaluations. The evaluation results have indicated that the accuracy of the calculated DNI using the new version of SUNFLUX has been improved significantly compared to the old version. The relative mean bias difference (MBD) and relative root mean square difference (RMSD) are −3.03% and 4.95%, respectively. The performance of the modified DNI model is also compared with those from the other five models. The results predicted by the current model are comparable with those from the best reference models. More importantly, the evaluation results show that the new aerosol scheme developed in this study can also be applied to other models to improve their performance. [ABSTRACT FROM AUTHOR]

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In this study, we use the level-1.5 and level-2.0 aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET) Version 3 dataset at 12 sites in China to evaluate the MODIS Collection 6.1 (C6.1) AOD products retrieved using three distinct algorithms: Dark Target (DT), Deep Blue (DB), and DTB merged from DT and DB from Terra and Aqua. The performance of the three algorithms is evaluated. Based on these evaluations, a simple and efficient AOD retrieval algorithm at a high spatial resolution of 1.0 km for China is proposed. AODs at this spatial resolution over China are then retrieved using the methods of DT and DB, respectively. The evaluation results showed that: (1) there is little difference in the AOD products derived from Terra and Aqua. The differences in the determination coefficient (R2) between the two platforms at most sites are less than 0.05. (2) There are relatively large differences in AODs between the AERONET level-1.5 dataset and MODIS dataset while these differences are mostly filtered out by the quality assurance process of the AERONET level-2.0 dataset. The statistical tests indicate that the MODIS DTB AOD product is generally better than those from the other two algorithms. Using the new algorithm developed in this study, the AODs at a high spatial resolution of 1 km in the whole of China are determined and the results are compared with the MODIS DTB product. The results show that the AODs determined using the new method agree reasonably well with those of the MODIS DTB dataset though the new results have slightly negative biases in the wintertime. However, these negative biases may not be a negative sign due to the fact that the MODIS AODs are subject to a positive bias relative to the AERONET AODs. • There is little difference in the AOD products derived from Terra and Aqua. • The MODIS DTB AOD product is generally better than DT&DB. • a simple and efficient AOD retrieval algorithm is proposed for the whole of China. • The AODs determined using the new method agree well with MODIS DTB dataset. [ABSTRACT FROM AUTHOR]