3.4 TINT—TINT Is Not TITAN. Easy-to-Use Tracking Code Based on TITAN—Details and Uses

Monday, 8 January 2018: 11:15 AM
Room 8 ABC (ACC) (Austin, Texas)
Mark H. Picel, ANL, Argonne, IL; and S. Collis, B. Raut, S. Carani, R. Jackson, M. van Lier-Walqui, and A. M. Fridlind

For meteorologists interested in studying storms from a Lagrangian frame of reference (i.e., following moving parcels rather than fixed points), some method of storm cell tracking is necessary. The TITAN methodology by Dixon and Wiener has been a popular means to this end, but the associated software can be difficult to set up and use. TINT aims to provide a simpler alternative to TITAN for Python users. The package makes use of the Python-ARM Radar Toolkit (Py-ART), as well as packages from the SciPy ecosystem including NumPy and Pandas. TINT takes iterables of pyart grid objects as input and outputs pandas dataframes containing object characteristics such as area, volume, height, isolation, coordinates, etc. These dataframes along with other cell metrics and metadata are stored as attributes in TINT tracks objects. Tracks objects can be dynamically updated with new iterables of pyart grids. FFT phase correlation is used to estimate the movement of cells between radar scans. As in TITAN, objects are matched between scans based on a disparity metric and the Hungarian algorithm. Tracking is currently based on reflectivity, but other fields like specific differential phase are being tested. The package will also feature visualization tools such as a Lagrangian viewer which centers on a selected cell in a scan and follows it through subsequent scans. TINT setup and usage will be discussed along with future plans and development.
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