Visualization

End-to-end Shell Scripts

Our shell scripts tie together ClimateMachine.jl and VizCLIMA.jl for an end-to-end analysis pipeline, enabling performing a model run (or several) and plotting the results with one command. These are available under Bash-Run-Scripts

VizCLIMA Post-processing

VizCLIMA can generate notebooks from Julia scripts, which can be adapted for quick testing and visualization of ClimateMachine output. Below we explain how to do this for various use cases.

VizCLIMA notebooks on a remote server using a local browser

• On both local and remote machines:

  • ensure JupyterLab is installed on both local and remote machines (check version of Python/Julia and necessary packages for your notebook)

• Remote host:

  • git clone ClimateMachine.jl, install and run the model to generate data (instructions)
  • git clone VizCLIMA.jl
  • cd VizCLIMA.jl
  • jupyter notebook --no-browser --port=XXXX

• Local host:

  • ssh -N -f -L YYYY:localhost:XXXX <remoteuser>@<remote-cluster-node>
  • In your local browser type localhost:YYYY
  • You may get a prompt to authenticate using a token (printed when launched the notebook on the remote host), then you're good to go!

VizCLIMA on Caltech HPC Cluster's OnDemand Environment

• Set up your HPC environment as in here
• Set up Caltech OnDemand as in here
• Once your Central Desktop is running, you need to setup your Julia environment

Available VizCLIMA notebooks for post-processing:

• Default moist LES notebook
• Default dry GCM notebook
• Please contribute with your own notebooks to VizCLIMA!

Interactive Visualization

• There are various third party software packages that enable instant 3D interactive visualization, slicing and animations of our data (NetCDF format by default), as well as conversion to other data formats. Examples include VisIt and ParaView. Panoply is especially useful for summarizing geospatial information of the global geospatial data.