Here are some resources related to mesoscale meteorology and simulation and analysis, and the science of remote sensing and using radar data for weather and climate applications:
Python resources for data analysis
Tutorial on Installing and using anaconda python on keeling – getting anaconda linux up and running on keeling (DAS’s linux computing cluster) and installing Py-ART. UofI DAS folks, be sure to request a keeling account from SESE computing help if you haven’t already. keeling documentation is here.
Tutorial on Using jupyter notebook, and using it remotely – Instructions and resources about jupyter notebook, a powerful interactive python interactive development environment (IDE). Also, how to use jupyter notebook from DAS’s linux cluster keeling on your local machine for improved responsiveness. Directions should apply to remotely display to a local machine (i.e. a laptop) from a remote machine (i.e. a linux workstation or cluster).
Here are instructions on using jupyter notebook on a high-performance computing cluster.
Radar data analysis
Tutorial on installing processing radar data with Radx – how to use NCAR software for converting radar to CfRadial (a common netCDF radar format) and gridding radar data to Cartesian coordinates
Installing Py-ART and other pythonic radar software on keeling (Python ARM Radar Toolkit) – maintained by Argonne National Labs – an open source toolkit for reading, writing, perusing, and performing retrievals on radar data in the python programming language, including data in CfRadial format.
Mesoscale models and data analysis using python
I have course materials linked to a page called Mesoscale modeling with the Weather Research and Forecasting (WRF) model, which introduces the NCAR/NCEP Weather Research and Forecasting (WRF) model, as well as WRF output analysis using our pymesomodel package on github.