the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets
Abstract. The Tracking Algorithm for Mesoscale Convective Systems (TAMS) is a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems (MCSs). TAMS was initially developed to analyze MCSs over Africa and their relation to African easterly waves using satellite-derived datasets. This paper describes TAMS v2.0, an open-source MCS tracking and classifying Python-based package that can be used to study both observed and simulated MCSs. Each step of the algorithm is described with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this MCS tracker is its support for unstructured grids in the MCS identification stage and grid-independent tracking of MCSs, enabling application across various native modeling grids and satellite-derived products. A description of the available settings and helper functions is also provided. Finally, we share some of the current development goals for TAMS.
- Preprint
(3544 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 07 May 2024)
-
RC1: 'Comment on egusphere-2024-259', Julia Kukulies, 30 Mar 2024
reply
This paper describes a novel tracking algorithm that was originally developed to track MCSs over Africa,
and has been enhanced to a more general tool to track MCSs in large observational and model datasets.
The manuscript is well-written and the tracking steps are easy to understand based on the figures
presented. Another strength of the paper is that many specific examples are given which help the reader
to set the technical features into a scientific context and better understand the implications of different
tracking options. An outstanding feature of the TAMS algorithm is its capability to work with unstructured
grids, which has become more and more important with the advent of global k-scale models. In addition,
TAMS provides the possibility to combine tracked MCS features with any other variable or dataset, which
is useful to better understand the processes of convective organization from multiple angles. The tracking
tool should therefore be of high interest to the weather and climate research community. I recommend the
publication of this manuscript after addressing some minor issues (see my detailed comments and suggestions in the attached document)
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
151 | 40 | 16 | 207 | 11 | 9 |
- HTML: 151
- PDF: 40
- XML: 16
- Total: 207
- BibTeX: 11
- EndNote: 9
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1