Submitted as: development and technical paper
28 Mar 2024
Submitted as: development and technical paper |  | 28 Mar 2024
Status: this preprint is currently under review for the journal GMD.

NeuralMie (v1.0): An Aerosol Optics Emulator

Andrew Geiss and Po-Lun Ma

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).

Andrew Geiss and Po-Lun Ma

Status: open (until 23 May 2024)

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Andrew Geiss and Po-Lun Ma
Andrew Geiss and Po-Lun Ma


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Short summary
Particles in the Earth’s atmosphere strongly impact the planet’s energy budget and atmosphere simulations require accurately representing their interaction with light. This work introduces two approaches to representing light scattering by small particles. The first is a scattering simulation based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.