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A tropical cyclone rainfall model for python

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📚 Overview

pyTCR is a physics-based model developed in python to estimate rainfall induced by tropical cyclones (TCs). It is largely based on the TCR model described by Zhu et al., 2013 and Lu et al., 2018. PyTCR simulates convective TC rainfall by correlating the precipitation rate with the total upward velocity within the TC vortex. It integrates seamlessly with outputs from a tropical cyclone downscaling model (see Lin et al., 2023) to generate detailed spatial-temporal rainfall patterns that align with hurricane tracks.

🔧 Installation

It is recommended to use mamba for a faster and more reliable installation process. If you haven't installed mamba yet, you can do so by running:

  1. Install mamba

    conda install -n base -c conda-forge mamba
  2. Clone the pyTCR repository

    git clone https://github.com/levuvietphong/pyTCR.git
  3. Navigate to the pyTCR directory and create a conda virtual environment named 'pyTCR' using the provided environment.yml file

    mamba env create -f environment.yml
  4. Activate the environment:

    conda activate pyTCR
  5. Install the PyTCR package:

    pip install -e .

▶️ Getting Started

This repository provides a collection of jupyter notebooks designed to facilitate the use of PyTCR for generating and visualizing rainfall and wind speed induced by TCs. These notebooks cover various use cases and data sources, providing comprehensive guidance for users.

  1. Downloading Tropical Cyclone Data: illustrates the process of downloading CMIP6 tropical cyclone tracks that have undergone downscaling using the tropical cyclone downscaling model.

  2. Visualizing Tropical Cyclone Tracks: showcases the visualization of tropical cyclone tracks and their density, utilizing both observational data and downscaled CMIP6 outputs.

  3. Rainfall Simulation: uses outputs from the tropical cyclone downscaling model to simulate rainfall driven by tropical cyclones. This includes rainfall at a specific location and over the entire domain of the tropical cyclone tracks.

  4. Wind Speed Generation: demonstrates the generation of spatially and temporally varying wind speeds from downscaled CMIP6 outputs.

  5. Rainfall within a Polygon: outlines the process of generating total rainfall within polygons loaded from shapefiles.

  6. Multiple Rainfall Events within a Polygon: generates multiple total rainfall events from multiple tropical cyclone tracks within a polygon.

Important

Downscaled tropical cyclone tracks for various CMIP6 models (including historical and ssp585 experiments) are available for download here.

🖼️ Gallery

The following figures illustrate the inputs used in pyTCR and results obtained from PyTCR:

  • Tropical cyclone tracks: The figure below shows the tracks of tropical cyclones downscaled from outputs from E3SM v1.0 model, Reanalysis ERA5, and best track observations.

  • Spatial Distribution of Total Rainfall: The image below shows the spatial distribution of total rainfall along three storm tracks that made landfall in the Southeast Texas (SETx) region.

  • Time Series of Precipitation: The figure below depicts the time series of precipitation at a specific location (29°N, 95°W) within the SETx region.

📄 License

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

🫂 Acknowledgments

This work was supported by the Southeast Texas Urban Integrated Field Lab project, one of four Urban Integrated Field Laboratories (UIFLs). The software was mostly developed at . We extend our gratitude to the SETx-UIFL team for their support and collaboration.

📫 Contacts

Collaborators and contributions are very welcome! For questions and feedback, please contact:


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A tropical cyclone rainfall model for python

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