# Physics-Informed Learning of Aerosol Microphysics

arXiv: https://arxiv.org/abs/2207.11786 · HTML: https://mlsanity.com/paper/2207.11786/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 24 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [DC3: A learning method for optimization with hard constraints](https://mlsanity.com/paper/2104.12225/) (2021)
- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models](https://mlsanity.com/paper/2111.14671/) (2021)
- [Towards Physically-consistent, Data-driven Models of Convection](https://mlsanity.com/paper/2002.08525/) (2020)
- [Emulating Aerosol Microphysics with Machine Learning](https://mlsanity.com/paper/2109.10593/) (2021)

## Built on by (most cited first, 9 of 9)

- [Hard-Constrained Deep Learning for Climate Downscaling](https://mlsanity.com/paper/2208.05424/) (2022)
- [Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling](https://mlsanity.com/paper/2305.14452/) (2023)
- [Generative Diffusion-based Downscaling for Climate](https://mlsanity.com/paper/2404.17752/) (2024)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [Multi-variable Hard Physical Constraints for Climate Model Downscaling](https://mlsanity.com/paper/2308.01868/) (2023)
- [Enforcing Equity in Neural Climate Emulators](https://mlsanity.com/paper/2406.19636/) (2024)
- [A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction](https://mlsanity.com/paper/2408.16109/) (2024)
- [Downscaling Neural Network for Coastal Simulations](https://mlsanity.com/paper/2408.16553/) (2024)
- [Assessing Emulator Design and Training for Modal Aerosol Microphysics Parameterizations in E3SMv2](https://mlsanity.com/paper/2604.21233/) (2026)

## Abstract (arXiv)

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. In order to represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. We are able to learn the variables' tendencies achieving an average R2 score of 77.1% . We further explore methods to inform and constrain the neural network with physical knowledge to reduce mass violation and enforce mass positivity. On a GPU we achieve a speed-up of up to over 64x compared to the original model.