首页|Researchers from University of Cologne Report Findings in Machine Learning ('seeing' Beneath the Clouds-machine-learning-based Re- construction of North African Dust Plumes)
Researchers from University of Cologne Report Findings in Machine Learning ('seeing' Beneath the Clouds-machine-learning-based Re- construction of North African Dust Plumes)
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NETL
NSTL
Amer Geophysical Union
Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Cologne, Germany, by NewsRx correspondents, research stated, "Mineral dust is one of the most abundant atmospheric aerosol species and has various far-reaching effects on the climate system and adverse impacts on air quality. Satellite observations can provide spatio-temporal information on dust emission and transport pathways." Funders for this research include Helmholtz Association, University of Cologne, Deutsches Klimarechen- zentrum (DKRZ) - Scientific Steering Committee (WLA). Our news editors obtained a quote from the research from the University of Cologne, "However, satellite observations of dust plumes are frequently obscured by clouds. We use a method based on established, machine-learning-based image in-painting techniques to restore the spatial extent of dust plumes for the first time. We train an artificial neural net (ANN) on modern reanalysis data paired with satellite-derived cloud masks. The trained ANN is applied to cloud-masked, gray-scaled images, which were derived from false color images indicating elevated dust plumes in bright magenta. The images were obtained from the Spinning Enhanced Visible and Infrared Imager instrument onboard the Meteosat Second Generation satel- lite. We find up to 15% of summertime observations in West Africa and 10% of summertime observations in Nubia by satellite images miss dust plumes due to cloud cover. We use the new dust-plume data to demonstrate a novel approach for validating spatial patterns of the operational forecasts provided by the World Meteorological Organization Dust Regional Center in Barcelona. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite-based reconstruction, but once trained, the reconstruction is computationally inexpensive. Our proposed reconstruction provides a new opportunity for validating dust aerosol transport in numerical weather models and Earth system models. It can be adapted to other aerosol species and trace gases. 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. Dust plumes are frequently obscured by clouds, making it difficult to study the full extent. We 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. We use the reconstructed dust patterns to demonstrate a new way to validate the dust forecast ensemble provided by the World Meteorological Orga- nization Dust Regional Center in Barcelona, Spain. Our proposed method is computationally inexpensive and provides new opportunities for assessing the quality of dust transport simulations. The method can be transferred to reconstruct other aerosol and trace gas plumes."
CologneGermanyEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Cologne
NETL
NSTL
Amer Geophysical Union
