Simulating the Earth system with interactive aerosols at the kilometer scale
Physics-informed machine learning-based cloud microphysics parameterization for earth system models
Abstract:
In this study, we develop a physics-informed machine learning (ML)-based cloud microphysics parameterization for the ICON model. By training the ML parameterization on high-resolution simulation data, we aim to improve Earth System Models (ESMs) in comparison to traditional parameterization schemes. We investigate the usage of a multilayer perceptron (MLP) with feature engineering and physics-constraints, and use explainability techniques to understand the relationship between input features and model output. Our novel approach yields promising results, with the physics-informed ML-based cloud microphysics parameterization achieving an R2 score up to 0.777 for an individual feature. Additionally, we demonstrate a notable improvement in the overall performance in comparison to a baseline MLP, increasing its average R2 score from 0.290 to 0.613 across all variables. This approach to improve the representation of cloud microphysics in ESMs promises to enhance climate projections, contributing to a better understanding of climate change.
Towards Downscaling Global AOD with Machine Learning
Towards downscaling global AOD with machine learning
Abstract:
Poor air quality represents a significant threat to human health, especially in urban areas. To improve forecasts of air pollutant mass concentrations, there is a need for high-resolution Aerosol Optical Depth (AOD) forecasts as proxy. However, current General Circulation Model (GCM) forecasts of AOD suffer from limited spatial resolution, making it difficult to accurately represent the substantial variability exhibited by AOD at the local scale. To address this, a deep residual convolutional neural network (ResNet) is evaluated for the GCM to local scale downscaling of low-resolution global AOD retrievals, outperforming a non-trainable interpolation baseline. We explore the bias correction potential of our ResNet using global reanalysis data, evaluating it against in-situ AOD observations. The improved resolution from our ResNet can assist in the study of local AOD variations.