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  • Other Sources  (2)
  • Jupiter  (1)
  • General Chemistry
  • Inorganic Chemistry
  • machine learning
  • English  (2)
  • 2020-2024  (2)
  • 2024  (2)
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  • English  (2)
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  • 2020-2024  (2)
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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
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    CITATION GEO-LEO
  • 2
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    Contributions of Jupiter's Deep‐Reaching Surface Winds to Magnetic Field Structure and Secular Variation (2024)
    Details
    Publication Date: 2024-05-23
    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"〉NASA's Juno mission delivered gravity data of exceptional quality. They indicate that the zonal winds, which rule the dynamics of Jupiter's cloud deck, must slow down significantly beyond a depth of about 3,000 km. Since the underlying inversion is highly non‐unique additional constraints on the flow properties at depth are required. These could potentially be provided by the magnetic field and its Secular Variation (SV) over time. However, the role of these zonal winds in Jupiter's magnetic field dynamics is little understood. Here we use numerical simulations to explore the impact of the zonal winds on the dynamo field produced at depth. We find that the main effect is an attenuation of the non‐axisymmetric field, which can be quantified by a modified magnetic Reynolds number Rm that combines flow amplitude and electrical conductivity profile. Values below Rm = 3 are required to retain a pronounced non‐axisymmetric feature like the Great Blue Spot (GBS), which seems characteristic for Jupiter's magnetic field. This allows for winds reaching as deep as 3,400 km. A SV pattern similar to the observation can only be found in some of our models. Its amplitude reflects the degree of cancellation between advection and diffusion rather than the zonal wind velocity at any depth. It is therefore not straightforward to make inferences on the deep structure of cloud‐level winds based on Jupiter's SV.〈/p〉
    Description: Plain Language Summary: The dynamics in Jupiter's cloud layer is dominated by eastward and westward directed wind jets that circumvent the planet and reach velocities of up to 150 m per second. For the first time, NASA's Juno mission could measure the tiny gravity changes caused by these winds. The data show that the winds reach down to a depth of about 3,000 km, roughly 4% of Jupiter's radius. However, the interpretation is difficult and several alternative wind profiles have been suggested. In this paper we use numerical simulations to explore how these winds would affect Jupiter's magnetic field, which has also been measured with high precision by Juno. The field shows a strong inward‐directed local patch just south of the equator, called the GBS. The impact of the winds on the magnetic field rapidly increases with depth because of the increase in the electrical conductivity. Our simulations show that winds reaching deeper than about 3,400 km would practically wipe out the GBS. This confirms that they have to remain shallower. Juno also observed an east‐ward drift of the GBS. While some of our simulations also show an east‐ward drift it is typically much too slow.〈/p〉
    Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉We study the magnetic field variations caused by Jupiter's deep‐reaching surface winds for various flow and electrical conductivity models〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Zonal winds reaching deeper than 3,400 km would yield a very axisymmetric surface field and are thus unrealistic〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉It seems questionable that Jupiter's secular variation carries any useful information on the zonal winds〈/p〉〈/list-item〉 〈/list〉 〈/p〉
    Description: Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266
    Description: https://doi.org/10.17617/3.CNVRWD
    Keywords: ddc:523 ; Jupiter ; magnetic field ; atmospheric dynamics ; zonal winds
    Language: English
    Type: doc-type:article
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    CITATION GEO-LEO
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