Deep learning for Aerosol Forecasting

• Main Authors: Hoyne, Caleb, Mukkavilli, S. Karthik, Meger, David
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2019
• Subjects: Machine Learning cs.LG; Computer Vision and Pattern Recognition cs.CV; Atmospheric and Oceanic Physics physics.ao-ph; Data Analysis, Statistics and Probability physics.data-an; Machine Learning stat.ML; FOS Computer and information sciences; FOS Physical sciences; Canada; Merra; Aerosol Robotic Network
• Online Access: https://dx.doi.org/10.48550/arxiv.1910.06789; https://arxiv.org/abs/1910.06789

Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally. This study combines a convolutional neural network (CNN) with MERRA-2, tested against all AERONET sites. The new hybrid CNN-based model provides better estimates validated versus AERONET ground truth, than only using MERRA-2 reanalysis. : Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada