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  • 1
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    “Seeing” Beneath the Clouds—Machine‐Learning‐Based Reconstruction of North African Dust Plumes (2024)
    Details
    Publication Date: 2024-05-22
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉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. 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 satellite. 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.〈/p〉
    Description: Plain Language Summary: 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 Organization 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.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉We present the first fast reconstruction of cloud‐obscured Saharan dust plumes through novel machine learning applied to satellite images〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The reconstruction algorithm utilizes partial convolutions to restore cloud‐induced gaps in gray‐scaled Meteosat Second Generation‐Spinning Enhanced Visible and Infrared Imager Dust RGB images〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉World Meteorological Organization dust forecasts for North Africa mostly agree with the satellite‐based reconstruction of the dust plume extent〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: GEOMAR Helmholtz Centre for Ocean Research Kiel
    Description: University of Cologne
    Description: https://doi.org/10.5281/zenodo.6475858
    Description: https://github.com/tobihose/Masterarbeit
    Description: https://dust.aemet.es/
    Description: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview
    Description: https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:DUST
    Description: https://navigator.eumetsat.int/product/EO:EUM:DAT:MSG:CLM
    Description: https://doi.org/10.5067/KLICLTZ8EM9D
    Description: https://disc.gsfc.nasa.gov/datasets?project=MERRA-2
    Description: https://doi.org/10.5067/MODIS/MOD08_D3.061
    Description: https://doi.org/10.5067/MODIS/MYD08_D3.061
    Description: https://doi.org/10.5281/ZENODO.8278518
    Keywords: ddc:551.5 ; mineral dust ; North Africa ; MSG SEVIRI ; machine learning ; cloud removal ; satellite remote sensing
    Language: English
    Type: doc-type:article
    Location Call Number Limitation Availability
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    CITATION GEO-LEO
  • 2
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    “Seeing” beneath the clouds - machine-learning-based reconstruction of North African dust events (2023)
    Details
    Publication Date: 2023-11-29
    Description: 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. 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 gray-scaled and cloud-masked false-color daytime images for dust aerosols from 2021 and 2022, obtained from the SEVIRI instrument onboard the Meteosat Second Generation satellite. We find up to 15 \% of summertime observations in West Africa and 10 \% of summertime observations in Nubia by satellite images miss dust events due to cloud cover. The diurnal and seasonal patterns in the reconstructed dust occurrence frequency are consistent with known dust emission and transport processes. We use the new dust-plume data to validate the operational forecasts provided by the WMO Dust Regional Center in Barcelona from a novel perspective. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite-based reconstruction, but the latter computation is substantially faster. 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.
    Type: Article , NonPeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - without review
  • 3
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    Optical Characterization of Marine Aerosols Using a Morphologically Realistic Model With Varying Water Content: Implications for Lidar Applications and Passive Polarimetric Remote Sensing (2024)
    AGU (American Geological Society) | Wiley
    Details
    Publication Date: 2024-04-03
    Description: Retrieving the physical properties and water content of marine aerosols requires understanding the links between the particles' optical and microphysical properties. By using a morphologically realistic model with varying salt mass fractions fm, describing the transition from irregularly shaped, dry salt crystals to brine-coated geometries, optical properties relevant to polarimetric remote sensing are computed at wavelengths of 532 and 1,064 nm. The extinction cross section and its color ratio depend on particle size, but are insensitive to changes in fm; thus, measured extinction coefficients at two wavelengths contain information on both particle number and size. The lidar ratio's dependence on both size and wavelength has implications for inverting the lidar equation. The results suggest that active observations of the backscattering cross section's color ratio and the depolarization ratio, as well as, passive observations of the degree of linear polarization offer avenues to obtain the water content of marine aerosols. Key Points: - Modeled extinction coefficient of marine aerosol depends on particle radius and wavelength, but not on water content - The depolarization ratio and the color ratio of the backscattering cross section generally decrease with growing aerosol water content - The linear polarization peak near backscattering angles at NIR wavelength could be used in passive polarimetry to retrieve water content
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 4
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    Unknown
    “Seeing” Beneath the Clouds — Machine-Learning-Based Reconstruction of North African Dust Plumes (2024)
    AGU (American Geophysical Union) | Wiley
    Details
    Publication Date: 2024-05-28
    Description: 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. 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 satellite. 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. Key Points: - We present the first fast reconstruction of cloud-obscured Saharan dust plumes through novel machine learning applied to satellite images - The reconstruction algorithm utilizes partial convolutions to restore cloud-induced gaps in gray-scaled Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Dust RGB images - World Meteorological Organization dust forecasts for North Africa mostly agree with the satellite-based reconstruction of the dust plume extent
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    OceanRep: Article in a Scientific Journal - peer-reviewed
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