Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 11
Articles | Volume 17, issue 11
https://doi.org/10.5194/amt-17-3377-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3377-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 11
Research article
 | 
03 Jun 2024
Research article |  | 03 Jun 2024

Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms

Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller

Data sets

Northeast US Regional NEXRAD radar mosaics of winter storms from 1996-2023, part 2 Laura Tomkins et al. https://doi.org/10.5061/dryad.rbnzs7hj9

Model code and software

The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language (https://arm-doe.github.io/pyart/API/generated/ pyart.retrieve.feature_detection.html) Jonathan J. Helmus and Scott M. Collis https://doi.org/10.5334/jors.119

Video supplement

07 February 2021 feature detection example Laura Tomkins https://doi.org/10.5446/63170

17 December 2019 feature detection example Laura Tomkins https://doi.org/10.5446/63172

17 December 2020 feature detection example Laura Tomkins https://doi.org/10.5446/63171

7 February 2020 feature detection example Laura Tomkins https://doi.org/10.5446/63168

Download
Short summary
We have created a new method to better identify enhanced features in radar data from winter storms. Unlike the clear-cut features seen in warm-season storms, features in winter storms are often fuzzier with softer edges. Our technique is unique because it uses two adaptive thresholds that change based on the background radar values. It can identify both strong and subtle features in the radar data and takes into account uncertainties in the detection process.
Read more