This repository contains lessons in the form of Jupyter notebooks focused on understanding the basic functionality of WRF-Hydro.

The easiest and recommended way to run these lessons is via the wrfhydro/training Docker container, which has all software dependencies and data pre-installed.

• Docker >= v.17.12
• Web browser (Google Chrome recommended)

If you have general questions about Docker, there are ample online resourves including the excellent Docker documentation at https://docs.docker.com.

If you have questions about WRF-Hydro or these lessons please use the contact form on our website: https://ral.ucar.edu/projects/wrf_hydro/contact.

If you have found a bug in these lessons please log an issue on the Issues page of the GitHub repository at https://github.com/NCAR/wrf_hydro_training/issues.

Make sure you have Docker installed and that it can access your localhost ports. Most out-of-the-box Docker installations accepting all defaults will have this configuration.

Step 1: Open a terminal or PowerShell session

Step 2: Pull the wrfhydro/training Docker container for the desired code version Each training container is specific to a release version of the WRF-Hydro source code, which can be found at https://github.com/NCAR/wrf_hydro_nwm_public/releases.

Issue the following command in your terminal to pull a specific version of the training corresponding to your code release version.

docker pull wrfhydro/training:v5.2.0-rc1

Step 3: Start the training container Issue the following commnand in your terminal session to start the training Docker container.

docker run --name wrf-hydro-training -p 8888:8888 -it wrfhydro/training:v5.2.0-rc1

Note: If you have already started the training once you will need to remove the previous container using the command docker rm wrf-hydro-training

The container will start and perform a number of actions before starting the training.

1. The container will pull the model code
2. The container will pull an example test case
3. The container will launch a Jupyter Lab server and echo the address to your terminal

Note: Port forwarding is setup with the -p 8888:8888 argument, which maps your localhost port to the container port. If you already have sometihng running on port 8888 on your localhost you will need to change this number