07-09, 14:35–15:05 (US/Pacific), Room 318
Tracking and Object-Based Analysis of Clouds (tobac) is a Python package that enables researchers to identify, track, and perform object-based analyses of phenomena in large atmospheric datasets. Over the past four years, tobac’s userbase has grown within atmospheric science, and the package has transitioned from its original life as a small, focused package with few maintainers to a larger package with more robust governance and structure. In this presentation, we will discuss the challenges and lessons learned during the transition to robust governance structures and the future of tobac as we incorporate new techniques for using multiple variables and scales to track the same system.
The identification and tracking of atmospheric phenomena such as clouds has been desired since the first satellite images were collected. Although automated tracking techniques have been around since at least the 1980s, these techniques were generally slow and only worked on the datasets they were originally designed for. The Tracking and Object-Based Analysis of Clouds (tobac) package, an open-source Python package, was designed to allow researchers to identify, track, and perform object-based analyses of atmospheric phenomena on any input variable and on any input grid. This means that identification and tracking can be performed on any user-specified variable, whether from a weather satellite, radar, numerical model, or other data source. The flexible and modular design of tobac allows it to be used on any gridded dataset. For example, tobac has already been used to track features using brightness temperature, vertical velocity, radar reflectivity, dust concentration, trace gases, and lightning.
After tobac’s original release in 2019, the original developers moved on to other projects, necessitating a revitalization effort and for formal governance structures to be set up and the structure modernized to enable use and growth in the future. Development of tobac is done with open science principles in mind, with nearly all code reviewed similar to scientific peer review through pull requests rather than direct commits to the repository. Recent updates to tobac have focused on enabling the use of even larger and more diverse datasets and a pathway toward the identification and tracking of clouds and other atmospheric phenomena using multiple variables simultaneously.
Sean Freeman is an Assistant Professor of Atmospheric and Earth Science at The University of Alabama in Huntsville (UAH), having started that appointment in Spring 2023. Before coming to UAH, he received undergraduate degrees in Computer Science and Meteorology from Florida State University and MS and PhD degrees in Atmospheric Science from Colorado State University. Sean's research interests are primarily in clouds and storms, in particular, understanding the kinds of environments that support cloud development and severe weather. He uses numerical weather modeling and advanced data science tools such as cloud tracking to uncover the basic building blocks of convection from models and observations, as well as new measurements of convective inflows and outflows with drones. He has served as a tobac lead developer since 2021 and has chaired the tobac steering committee since 2023. Outside of work, he enjoys photography, hiking, traveling, baking, curling, and watching college football.