NeuralMie (v1.0): An Aerosol Optics Emulator

Abstract

Abstract. The direct interactions of atmospheric aerosols with radiation significantly impact the Earth's climate and weather and are important to represent accurately in simulations of the atmosphere. This work introduces two new contributions to enable more accurate representation of aerosol optics in atmosphere models: 1) "TAMie," a new Python-based Mie scattering code that can represent both homogeneous and coated particles and achieves comparable speed and accuracy to established Fortran Mie codes. 2) "NeuralMie," a neural network Mie code emulator trained on data from TAMie, that can directly compute the bulk optical properties of a diverse range of aerosol populations and is appropriate for use in atmosphere simulations where aerosol optical properties are parameterized. NeuralMie is highly flexible and can be used for a large range of particle types and wavelengths. It can represent core-shell scattering, and by directly estimating bulk optical properties, is more efficient than existing Mie code and Mie code emulators while incurring negligible error (0.08 % mean absolute percentage error).