2021/09/22 by Paula Harder, Duncan Watson‐Parris, Harder, Paula +12 · 1 citation
Computer Science · Earth and Planetary Sciences · Environmental Science · #Atmospheric aerosols and clouds #Atmospheric chemistry and aerosols #FOS: Computer and information sciences #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations #cs.LG
paper · pdf · doi:10.48550/arxiv.2109.10593
openalex publication_date 2021/09/22 · arxiv created 2021/09/29 · arxiv updated 2021/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. In order to achieve higher accuracy, 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 model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate 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. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average R2 score of 89%. On a GPU we achieve a speed-up of 120 compared to the original model.