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MetPy Skew-T Cookbook

nightly-build Binder

This Project Pythia Cookbook covers creating various styles of Skew-T Plots using MetPy.

Motivation

This cookbook will walkthrough creating a simple Skew-T plot with MetPy. This simple Skew-T will serve as a base to add different features on top of with MetPy.

Authors

Erin Rhoades

Contributors

Structure

This cookbook is broken up into two main sections - "Foundations" and "Example Workflows."

This cookbook consists of multiple notebooks. The initial notebook provides a comprehensive tutorial on constructing a basic sounding. Subsequent notebooks showcase example workflows with advanced features that build upon the fundamental sounding code.

Foundational Sounding

This notebook is an indepth tutorial going over creating a Skew-T. This will build our base code that is used in the Example Workflows section.

Example Workflows

Example workflows include:

  • Sounding with Advanced Features
  • Skew-T Analysis
  • Skew-T with Hodograph Inset
  • Skew-T with Separate Hodograph
  • Sounding with Xarray Dataset
  • Sounding Plotter
  • Common Sounding Calculations

Running the Notebooks

You can either run the notebook using Binder or on your local machine.

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. The details of how this works are not important for now. All you need to know is how to launch a Pythia Cookbooks chapter via Binder. Simply navigate your mouse to the top right corner of the book chapter you are viewing and click on the rocket ship icon, (see figure below), and be sure to select “launch Binder”. After a moment you should be presented with a notebook that you can interact with. I.e. you’ll be able to execute and even change the example programs. You’ll see that the code cells have no output at first, until you execute them by pressing {kbd}Shift+{kbd}Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

  1. Clone the https://github.com/ProjectPythia/skew-t-cb repository:

     git clone https://github.com/ProjectPythia/skew-t-cb.git

  2. Move into the skew-t-cb directory

    cd skew-t-cb

  3. Create and activate your conda environment from the environment.yml file

    conda env create -f environment.yml
conda activate skew-t-cb

  4. Move into the notebooks directory and start up Jupyterlab

    cd notebooks/
jupyter lab