tobac: Xarray Implementation and Nontraditional Uses for Object-Based Analysis of Clouds

Tracking and Object-Based Analysis of Clouds (tobac) is a Python package that enables researchers to identify, track, and perform object-based analyses of deep and shallow clouds and related phenomena in large atmospheric datasets. tobac is designed from the ground up to be variable-agnostic, meaning identification and tracking can be performed on any user-specified variable, whether from a satellite, radar, model, or other data source. The flexible and modular design of tobac allows it to be used on any gridded dataset. For exampleSpecifically, tobac has already been used for tracking features in brightness temperature, vertical velocity, radar reflectivity, dust concentration, trace gasses, and lightning. tobac is being used by the NASA AOS and INCUS missions both as a research and development tool prior to launch. Further applications include field campaigns such as TRACER and the MCS tracking intercomparison project for km-scale global climate models. Development of tobac is done with open science principles in mind, and the code and development process are fully open-source and available on GitHub. Recent updates to tobac have focused on enabling the use of even larger and more diverse datasets, a wider variety of earth observation and model data, and advancements in detection and tracking methodology. In this presentation, we will describe the recent updates made to tobac relevant for employing it on large datasets, including the switch to xarray as the internal data storage library with version 1.6. Transition to xarray improves data handling by providing multidimensional arrays with named dimensions, lazy loading and improving interoperability with other data formats. We will also highlight how components of tobac, specifically feature detection and segmentation, are used as independent analysis tools, leveraging open-source tools to serve research needs more broadly.