HRRR on AWS Cookbook

In this Project Pythia Cookbook, you will access and create a map from archived data from NCEP’s High-Resolution Rapid Refresh (HRRR) model, which is served in an S3 bucket on AWS.

Motivation

This cookbook provides the essential materials to learning how to work with gridded NCEP model output that is served on AWS’ S3 buckets, in a data format called Zarr.

Once you go through this material, you will have mastered the following skills:

Understand what object store refers to, and how it relates to AWS’s S3 buckets
Familiarized yourself with the Zarr data representation model, and why it is an optimal format for data stored on S3
Access, analyze, and visualize gridded fields from the HRRR

Throughout this cookbook, we build on the core foundational Python material covered in the Foundations Book

Authors

Kevin Tyle

Contributors

Structure

This cookbook will have two main sections - “Foundations” and “Example Workflows.”

Foundations

Currently under development The foundational content will include:

NCEP Model data on AWS’ S3
- an overview of how to access NCEP’s real-time and archived NWP model output on AWS
- an introduction to the Zarr data format
- How to read in a Zarr-formatted HRRR grid with Xarray

Example Workflows

Here, we apply the lessons learned in the foundational material to various analysis workflows, including everything from reading in the data to plotting a beautiful visualization at the end. We include the additional dataset-specific details, focusing on building upon the foundational materials rather than duplicating previous content.

Plot a map of 2-meter temperature from a past HRRR run
Currently under development Plot a time series of wind speed from a past HRRR run
Currently under development Plot a sequence of forecast maps for the most recent run of the HRRR

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 Foundations book 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 Shift 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:

Clone the “HRRR-AWS-cookbook” repository

git clone https://github.com/ProjectPythia/HRRR-AWS-cookbook.git

Move into the HRRR-AWS-cookbook directory
```
cd HRRR-AWS-cookbook
```

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

conda env create -f environment.yml
conda activate HRRR-AWS-cookbook-dev

Move into the notebooks directory and start up Jupyterlab
```
cd notebooks/
jupyter lab
```

At this point, you can interact with the notebooks! Make sure to check out the “Getting Started with Jupyter” content from the Pythia Foundations material if you are new to Jupyter or need a refresher.