Towards downscaling global AOD with machine learning

Conference item

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.

Authors: Millar, J; Harder, P; Freischem, L; Weiss, P; Stier, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: International Conference on Learning Representations
• Publication date: 2024-03-01
• Acceptance date: 2024-03-01
• Event title: 12th International Conference on Learning Representations (ICLR 2024): Tackling Climate Change with Machine Learning
• Event location: Vienna, Austria
• Event website: https://iclr.cc/Conferences/2024
• Event start date: 2024-05-07
• Event end date: 2024-05-11
• Language: English