Introduction¶
Creating a recipe is almost the same as working on a Jupyter notebook. There are a few differences that you need to be aware of. In the first cell, you need to add the metadata for the recipe. You can see an example of this in the template.ipynb file. The metadata includes the title of the recipe, the author(s)'s id, and any downloads that you want to include (it is ok to leave this blank while you work on the content).
The other main difference is that you will need to add a lot more information along with the code. Think of the recipe as a tutorial for someone that is trying to learn a new method or technique for the first time. You can format the text using markdown, add external images, and include links to other resources.
In the introduction, make sure you explain the purpose of the recipe and what the reader will learn from it. You can also include any background information that is necessary to understand the recipe. If you need to cite papers, you can use this format [Author et al., Year](https://doi.org/10.1029/2026GL124031). This will create a link to the paper and will also include the citation in the references section at the end of the recipe. For example you could cite Reeder el al 2015 who intruduced the method of using rapid temperature falls to detect cold fronts.
# Import all the libraries you will use at the beginning
import glob
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrsData¶
Explain the data that you are using in the recipe. You can include information about where the data comes from and any preprocessing that you did. You can also mention any limitations of doing the analysis with this small dataset and provide guidance on how to get the full dataset if available.
# The data should be read from `data/`
# Always use relative paths
pattern = "data/am3a.pb*.nc"
files = glob.glob(pattern)
ds = xr.open_mfdataset(
files,
combine="by_coords",
)Analysis¶
In this section add all the code and explanations for the analysis. You can include plots, tables, and any other visualizations that help explain the results. Make sure to explain what each step of the code is doing and why it is important for the analysis.
# Some code that calculates things # More code that creates a figureLearn more¶
This sections is optional, you can include links to other resources. It also a good idea to mention any paper that extends this analysis or that uses this methods in a formal analysis.
- Reeder, M. J., Spengler, T., & Musgrave, R. (2015). Rossby waves, extreme fronts, and wildfires in southeastern Australia. Geophysical Research Letters, 42(6), 2015–2023. 10.1002/2015gl063125