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