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Atmospheric aerosols have a major influence on the earths climate and publichealth. Hence, studying their properties and recovering them from lightscattering measurements is of great importance. State of the art retrievalmethods such as pre-computed look-up tables and iterative, physics-basedalgorithms can suffer from either accuracy or speed limitations. Theselimitations are becoming increasingly restrictive as instrumentation technologyadvances and measurement complexity increases. Machine learning algorithmsoffer new opportunities to overcome these problems, by being quick and precise.In this work we present a method, using invertible neural networks to retrieveaerosol properties from in situ light scattering measurements. In addition, thealgorithm is capable of simulating the forward direction, from aerosolproperties to measurement data. The applicability and performance of thealgorithm are demonstrated with simulated measurement data, mimicking in situlaboratory and field measurements. With a retrieval time in the millisecondrange and a weighted mean absolute percentage error of less than 1.5%, thealgorithm turned out to be fast and accurate. By introducing Gaussian noise tothe data, we further demonstrate that the method is robust with respect tomeasurement errors. In addition, realistic case studies are performed todemonstrate that the algorithm performs well even with missing measurementdata.

Retrieval of aerosol properties from in situ, multi-angle light scattering measurements using invertible neural networks