Using Machine Learning to Reconstruct Cloud-Obscured Dust Plumes – GWC Mag

Editors’ Highlights are summaries of recent papers by AGU’s journal editors.

Source: AGU Advances

Most dust and sand particles in the atmosphere originate from North Africa. Since ground-based observations of dust plumes in North Africa are sparse, investigations often rely on satellite observations. However, dust plumes are frequently obscured by clouds, making it difficult to study the full extent.

Kanngießer and Fiedler [2024] use machine learning methods to restore information about the extent of dust plumes beneath clouds in 2021 and 2022 at 9, 12, and 15 UTC. The reconstructed dust patterns demonstrate a new way to validate the dust forecast ensemble provided by the WMO Dust Regional Center in Barcelona, Spain. This proposed method is computationally inexpensive and provides new opportunities for assessing the quality of dust transport simulations. The method can also be transferred to reconstruct other aerosol and trace gas plumes.

Citation: Kanngießer, F., & Fiedler, S. (2024). “Seeing” beneath the clouds—Machine-learning-based reconstruction of North African dust plumes. AGU Advances, 5, e2023AV001042. https://doi.org/10.1029/2023AV001042

—Don Wuebbles, Editor, AGU Advances

Text © 2024. The authors. CC BY-NC-ND 3.0
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