Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1809-2025
https://doi.org/10.5194/gmd-18-1809-2025
Development and technical paper
 | 
17 Mar 2025
Development and technical paper |  | 17 Mar 2025

NeuralMie (v1.0): an aerosol optics emulator

Andrew Geiss and Po-Lun Ma

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Cited articles

Adachi, K., Chung, S. H., and Buseck, P. R.: Shapes of soot aerosol particles and implications for their effects on climate, J. Geophys. Res.-Atmos., 115, D15206, https://doi.org/10.1029/2009JD012868, 2010. a, b, c
Aden, A. L. and Kerker, M.: Scattering of electromagnetic waves from two concentric spheres, J. Appl. Phys., 22, 1242–1246, 1951. a
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, 1989. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding global aerosol radiative forcing of climate change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a
Belochitski, A. and Krasnopolsky, V.: Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model, Geosci. Model Dev., 14, 7425–7437, https://doi.org/10.5194/gmd-14-7425-2021, 2021. a
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Short summary
Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator 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.
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