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  2. Code of Dietel et al.: "Combined impacts of temperature, sea ice coverage, and mixing ratios of sea spray and dust on cloud phase over the Arctic and Southern Oceans", submitted to Geophysical Research Letters
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    Dataset: Code of Dietel et al.: "Combined impacts of temperature, sea ice coverage, and mixing ratios of sea spray and dust on cloud phase over the Arctic and Southern Oceans", submitted to Geophysical Research Letters

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    • Technical Metadata
    Alternate identifier:
    -
    Related identifier:
    -
    Creator/Author:
    Dietel, Barbara [Institut für Meteorologie und Klimaforschung Troposphärenforschung]

    Andersen, Hendrik https://orcid.org/0000-0003-2983-8838 [Atmosphärische Spurenstoffe und Fernerkundung, Institut für Photogrammetrie und Fernerkundung]

    Cermak, Jan https://orcid.org/0000-0002-4240-595X [Atmosphärische Spurenstoffe und Fernerkundung, Institut für Photogrammetrie und Fernerkundung]

    Stier, Philip [Stier, Philip]

    Hoose, Corinna https://orcid.org/0000-0003-2827-5789 [Institut für Meteorologie und Klimaforschung Troposphärenforschung]
    Contributors:
    -
    Title:
    Code of Dietel et al.: "Combined impacts of temperature, sea ice coverage, and mixing ratios of sea spray and dust on cloud phase over the Arctic and Southern Oceans", submitted to Geophysical Research Letters
    Additional titles:
    -
    Description:
    (Technical Remarks) # Code of Dietel et al.: "Combined impacts of temperature, sea ice coverage, and mixing ratios of sea spray and dust on cloud phase over the Arctic and Southern Oceans", submitted to Geophysical Research Letters ## Scripts to train a machine learning model (Histogram based gradient boosting regressi... # Code of Dietel et al.: "Combined impacts of temperature, sea ice coverage, and mixing ratios of sea spray and dust on cloud phase over the Arctic and Southern Oceans", submitted to Geophysical Research Letters ## Scripts to train a machine learning model (Histogram based gradient boosting regression with scikitlearn) and calculate SHapley Additive exPlanation (SHAP) values The machine learning model can predict the liquid fraction in different cloud types based on four parameters, namely the cloud top temperature, the sea ice concentration, the dust mixing ratio and the sea salt mixing ratio. More information on the used dataset can be found here: [Dietel et al. 2023](https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2281/) ### Bash-scripts The bash scripts are used to run the python scripts for different cloud types and regions on a cluster. bash-scripts starting with ***GBR_[...]*** (Gradient Boosting Regression) run the python-script ***hist_gbr_subset_final2_with_comments.py*** for different regions (Arctic Ocean (AO), Southern Ocean (SO)) and different cloud types (low-level, mid-level,mid-to-low-level). bash-scripts starting with ***shap_values_***[...] run the python-script ***shap_values-subset-final2_with_comments.py*** to calculate SHAP values based on the trained machine learning models for a 500 000 sample subset of the validation dataset. ### Python scripts ***hist_gbr_subset_final2_with_comments.py*** Python script to train the a Histogram-based Gradient Boosting Regression model using the scikitlearn python package. More detailed information can be found as comments in the scripts. ***shap_values-subset-final2_with_comments.py*** Calculates SHAP values for a 500 000 sample subset of the validation dataset to make the machine learning model explainable. More detailed information can be found as comments in the scripts.

    Code of Dietel et al.: "Combined impacts of temperature, sea ice coverage, and mixing ratios of sea spray and dust on cloud phase over the Arctic and Southern Oceans", submitted to Geophysical Research Letters

    Scripts to train a machine learning model (Histogram based gradient boosting regression with scikitlearn) and calculate SHapley Additive exPlanation (SHAP) values

    The machine learning model can predict the liquid fraction in different cloud types based on four parameters, namely the cloud top temperature, the sea ice concentration, the dust mixing ratio and the sea salt mixing ratio. More information on the used dataset can be found here: Dietel et al. 2023

    Bash-scripts

    The bash scripts are used to run the python scripts for different cloud types and regions on a cluster. bash-scripts starting with GBR_[...] (Gradient Boosting Regression) run the python-script hist_gbr_subset_final2_with_comments.py for different regions (Arctic Ocean (AO), Southern Ocean (SO)) and different cloud types (low-level, mid-level,mid-to-low-level). bash-scripts starting with ***shap_values_***[...] run the python-script shap_values-subset-final2_with_comments.py to calculate SHAP values based on the trained machine learning models for a 500 000 sample subset of the validation dataset.

    Python scripts

    hist_gbr_subset_final2_with_comments.py Python script to train the a Histogram-based Gradient Boosting Regression model using the scikitlearn python package. More detailed information can be found as comments in the scripts. shap_values-subset-final2_with_comments.py Calculates SHAP values for a 500 000 sample subset of the validation dataset to make the machine learning model explainable. More detailed information can be found as comments in the scripts.

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    Keywords:
    python-code
    machine learning
    Histogram based gradient boosting regression
    cloud phase
    Related information:
    -
    Language:
    -
    Publishers:
    Karlsruhe Institute of Technology
    Production year:
    2024
    Subject areas:
    Allgemeines, Hochschulwesen, Wissenschaft und Forschung
    Resource type:
    Software
    Data source:
    -
    Software used:
    -
    Data processing:
    -
    Publication year:
    2024
    Rights holders:
    Dietel, Barbara

    Andersen, Hendrik https://orcid.org/0000-0003-2983-8838

    Cermak, Jan https://orcid.org/0000-0002-4240-595X

    Stier, Philip

    Hoose, Corinna https://orcid.org/0000-0003-2827-5789
    Funding:
    -
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    Name Storage Metadata Upload Action
    Status:
    Published
    Uploaded by:
    kitopen
    Created on:
    2024-04-30
    Archiving date:
    2024-05-06
    Archive size:
    34.3 kB
    Archive creator:
    kitopen
    Archive checksum:
    de8fbed6b539538f923e85a0edcbc495 (MD5)
    Embargo period:
    -
    DOI: 10.35097/VEbaqHtbXdEzreqO
    Publication date: 2024-05-06
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    Rights statement for the dataset
    This work is licensed under
    CC BY-SA 4.0
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    Cite Dataset
    Dietel, Barbara; Andersen, Hendrik; Cermak, Jan; et al. (2024): Code of Dietel et al.: "Combined impacts of temperature, sea ice coverage, and mixing ratios of sea spray and dust on cloud phase over the Arctic and Southern Oceans", submitted to Geophysical Research Letters. Karlsruhe Institute of Technology. DOI: 10.35097/VEbaqHtbXdEzreqO
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