Using Artificial Intelligence for Aerosol Data Assimilation

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To study the aerosols in the atmosphere is an important aspect for getting a better understanding of climate change. Therefore, it is important to get accurate observations of aerosols in the atmosphere as well as accurate emission fluxes of aerosol species. Satellite instruments such as SPEXone are able to measure aerosol properties with a high accuracy. Unfortunately, the instrument has a low daily global coverage. To obtain full daily global coverage, methods such as data assimilation are used. However, these methods have a high computational cost. This report investigates the use of neural networks to obtain global daily coverage of aerosol properties and emission fluxes with a lower computational cost. Two networks are trained. One to get global coverages of the aerosol properties Aerosol Optical Depth at 550nm (AOD), Single Scattering Albedo at 550nm (SSA) and ̊Angstr ̈om Exponent between 550nm and 865nm(AE). The other network is trained for global emission fields of the species dimethylsulfide (DMS), sulfur dioxide (SO2), black carbon (BC), organic carbon (OC), sea salt (SS) and dust (DU). The results from these trained networks are compared to the results of a control experiment, which represents our prior knowledge on the aerosol fields and emissions, although not the truth. It is found that the network for aerosol properties has a significant decrease in errors compared to the control experiment. For both AOD and AE, the network has a large improvement, and for SSA the improvement is slightly smaller, likely due to a lower performance of the control experiment compared to AOD and AE. The network for the emissions also has a noticeable improvement over the control experiment for all species except DMS, where there is only a small improvement due to the already accurate DMS value for the control experiment. It is also found that the network for emissions overfits due to too little variation in training and testing data.