ANN model for prediction a spatio-temporal distribution of supercooled liquid in mixed-phase clouds using Doppler cloud radar spectra.

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Deep LearningVoodoo
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VOODOO

Revealing supercooled liquid beyond lidar attenuation
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Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Roadmap
  5. Contributing
  6. License
  7. Contact
  8. Acknowledgments

About The Project VOODOO

Machine learning approach using a convolutional neural network (CNN) classifier to relate Doppler spectra morphologies to the presence of (supercooled) liquid cloud droplets in mixed-phase clouds. Preprint will be available soon!

The release version provides the pre-trained machine learning model. Predictions are made by providing a list of Doppler radar time-spectrograms with dimensions:

  • number of spectral bins = 256
  • number of time steps = 6 (equivalent to 30 sec of observations)

The model was trained on RPG-FMCW94 data collected during DACAPO-PESO, therefore we recommend using this device for analysis. Supervision and validiation is provided by the CloudnetPy target classification and detection status.

Two examples are provided:

  • RPG-FMCW94 Doppler cloud radar Voodoo_predictor_RPG-FMCW94.ipynb test data is provided in the examples_data folder. The script requires a (hourly) LV0 binary file from RPG-FMCW94 and the corresponding Cloundet categorization file (for quicklooks and temporal resolution).
  • for KAZR Doppler cloud radar: Voodoo_predictor_KAZR.ipynb
  • help me test and add more devices :)

The CNN will ultimately be a feature within the Cloudnet processing suite.

Some examples of enhancend Cloudnet mixed-phase detection

previews.png

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Getting Started

The examples given use hourly radar spectra files in there specific file formats, i.e. LV0 binaries form RPG-FMCW94 and NetCDF files from KAZR. Th Cloudnet categorization file provides the temporal resolution where the high resolution radar profiels are mappend onto the 30 sec Cloudnet grid. Additionately, radar reflectivity and attenuated backscatter coefficient are plotted.

Installation

Below is an example of how run the example script, which prepares the data, makes predictions and plots quicklooks. This method relies on external dependencies such as torch, xarray and others (see setup.py).

  1. Clone the repo

    git clone https://github.com/remsens-lim/Voodoo.git

  2. Install the package

    python setup.py install

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Examples

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

  1. Open jupyter notebook 
    jupyter notebook
  2. Open one of the example files Voodoo_predictor_KAZR.ipynbor Voodoo_predictor_RPG-FMCW94.ipynb to review the processing chain.

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Roadmap

  • Released version 1
  • Add Tests
  • ???

See the open issues for a full list of proposed features (and known issues).

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Contributing

Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

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Contact

Willi Schimmel - @KarlJohnsonnn - [email protected]

Project Link: https://github.com/remsens-lim/Voodoo

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Acknowledgments

Special thanks for templates and help during implementation.

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remsens-lim
Leipzig Institute for Meteorology Remote-Sensing and the Arctic Climate Systems
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