This repository contains Jupyter Notebooks for regridding CESM2 sea ice data between different grids using the xESMF Python package.

regrid_CESM2_seaice_to_1x1.ipynb → Example script to regrid CESM2 sea ice concentration to a regular 1x1 grid

plot_CESM2_siconc_regrid.ipynb → Script to plot the regridded sea ice concentration data using a polar stereographic projection

plot_CESM2_siconc_original_regrid.ncl → A NCL script that plots the original (since NCL does not require a regular grid for plotting) and regridded data side by side

regrid_env.yml → Python environment used to run scripts in this directory

To be able to run the different Python scripts in this repository, you'll need to install a few packages. I recommend creating a new, clean environment to avoid any dependency issues.

To do so using Conda:

• Create a new envrionment (in this case named regrid_env)

  conda create -n regrid_env python=3.7
  

• Activate your new environment

  conda activate regrid_env
  

• Install the xESMF package

  conda install -c conda-forge xesmf
  

• Install dask and netCDF4 to support all features in xESMF

  conda install -c conda-forge dask netCDF4
  

• Install plotting and notebook dependencies (optional)

  conda install -c conda-forge matplotlib cartopy jupyterlab
  

For more information, check out the xESMF installation webpage: https://xesmf.readthedocs.io/en/latest/installation.html

Alternatively, you can download the environment file regrid_env.yml from this repository and create a new environment from that file directly.

conda env create --name regrid_env --file=regrid_env.yml

Python is usually already installed on your machine. To check, type python --version in a terminal.

Here are 3 different methods to install Python:

1. Instally Python manually (https://www.python.org/downloads/):

   • This will get you the latest version of Python3
   • You will now need to use the command python3 instead of python (unless you create an alias by adding the line alias python=python3 in your .bash_profile)

2. Installing Python using Anaconda (https://www.anaconda.com/products/individual):

   • Anaconda is a data science platform by Continuum Analytics that comes with a lot of useful features right out of the box
   • Many people find that installing Python through Anaconda is much easier than doing so manually (see method #1 above)
   • Anaconda comes with Conda, which is Continuum's package, dependency and environment manager (analogous to pip for Python)
   • The libraries and packages included in Anaconda are usually related to data science (numpy, scipy, jupyter notebooks, etc.)
   • Benefits:
     • Simplify common problems
     • Great for use in the classroom as it provides each student with the same setup

3. Installing Python using Miniconda (https://docs.conda.io/en/latest/miniconda.html):

   • Miniconda is a free minimal installer for conda
   • Good if hard drive space is an issue for you
   • It is a small bootstrapped version of Anaconda that comes with the Python distribution, essential packages, and conda.