During work with NASA, I developed a library of Python-based code to build a data set of tracked precipitation features from a global satellite-derived precipitation data set. The code is published in Github, data set published in Zenodo, and the work is published in the Geoscience Data Journal and JGR: Atmospheres.

Over a number of years, I have also built a Python library of Python functions for atmospheric science data analysis for use in a variety of research applications.

The graphic shows a depiction of a number of tracked precipitation features from the data set above. It also shows a graph depicting the calculation of the area, volumetric rain rate, and lifetime stage of those precipitation features, developed with code from the atmospheric science functions described.

I have also developed a library of Google Colab-based Python notebooks that can download a plethora of publicly available observational (e.g. satellite and radar) and model forecast data (e.g. GFS and HRRR), and visualize those data on maps and soundings. An example is included where I developed a script that produces tropical GOES imagery centered on a tropical cyclone given the date and time and storm name.