Skip to main content
U.S. flag

An official website of the United States government

Publication Date
5 May 2023

Emulating aerosol optics with randomly generated neural networks



Atmospheric aerosols have a substantial impact on climate and remain one of the largest sources of uncertainty in climate prediction. Accurate representation of their direct radiative effects is a crucial component of modern climate models. However, direct computation of the radiative properties of aerosol populations is far too computationally expensive to perform in a climate model, so optical properties are typically approximated using a parameterization. This work develops artificial neural networks (ANNs) capable of replacing the current aerosol optics parameterization used in the Energy Exascale Earth System Model (E3SM). A large training dataset is generated by using Mie code to directly compute the optical properties of a range of atmospheric aerosol populations given a large variety of particle sizes, wavelengths, and refractive indices. Optimal neural architectures for shortwave and longwave bands are identified by evaluating ANNs with randomly generated wirings. Randomly generated deep ANNs are able to outperform conventional multilayer-perceptron-style architectures with comparable parameter counts. Finally, the ANN-based parameterization produces significantly more accurate bulk aerosol optical properties than the current parameterization when compared with direct Mie calculations using mean absolute error. The success of this approach makes possible the future inclusion of much more sophisticated representations of aerosol optics in climate models that cannot be captured by extension of the existing parameterization scheme and also demonstrates the potential of random-wiring-based neural architecture search in future applications in the Earth sciences.

Geiss, Andrew, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin. 2023. “Emulating Aerosol Optics With Randomly Generated Neural Networks”. Geoscientific Model Development 16 (9). Copernicus GmbH: 2355-2370. doi:10.5194/gmd-16-2355-2023.
Funding Program Area(s)
Additional Resources:
NERSC (National Energy Research Scientific Computing Center)