STARCH compuets regional extreme storm physical characteristics and moisture balance based on spatiotemporal precipitation data from reanalysis or climate model data.

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Deep Learningstarch
Overview

STARCH (Storm Tracking And Regional CHaracterization)

STARCH computes regional extreme storm physical and moisture balance characteristics based on spatiotemporal precipitation data from reanlaysis or climate model data. The algorithm is proposed and implemented in (paper) to identify extreme storms and analyze their moisture balance in the Mississippi Basin based on ERA5 reanlaysis data. Detailed description and implementation of the algorithm can be found in the Jupyter Notebook "Example.ipynb".

Installation

git clone https://github.com/lorenliu13/starch.git

Dependencies

Name Version
geographiclib 1.52
matplotlib 3.4.2
mpu 0.23.1
numpy 1.20.3
pandas 1.3.3
Pillow 9.0.0
scikit_image 0.18.1
scikit_learn 1.0.2
scipy 1.6.3
skimage 0.0
tqdm 4.62.0

cdsapi and urllib3 are necesary when using the code "ERA5_single_levels_download.py" to group download ERA5 data from the ECMWF data center. Basemap Matplotlib Toolkit is necessary when using the plotting function "sequence_strom_plot_basemap()" in the Example.ipynb to visualize the storm tracking results.

Usage

Introduction and implementation of the codes can be found in "Example.ipynb".

Contributing

Feel free to open an issue for bugs and feature requests.

License

STARCH is released under the MIT License.

Authors

Acknowledgements

  • Guo Yu - Feature improvement
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Comments
  • Consider combination, division, and deformation of the storm objects in tracking

    Consider combination, division, and deformation of the storm objects in tracking

    The track function may be improved following the figure in the study of Zhou et al. (2018) to deal with storm split and merge: image

    Zhou, Yang, Hyemi Kim, and Bin Guan. “Life Cycle of Atmospheric Rivers: Identification and Climatological Characteristics.” Journal of Geophysical Research: Atmospheres 123, no. 22 (2018): 12,715-12,725. https://doi.org/10.1029/2018JD029180.

    Another idea of dealing with spliting and merging of storm objects can be found in Guan et al. (2019), which gives more detailed cases: image

    Guan, Bin, and Duane E. Waliser. “Tracking Atmospheric Rivers Globally: Spatial Distributions and Temporal Evolution of Life Cycle Characteristics.” Journal of Geophysical Research: Atmospheres 124, no. 23 (2019): 12523–52. https://doi.org/10.1029/2019JD031205.

    opened by lorenliu13 0
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