Articles | Volume 16, issue 19
https://doi.org/10.5194/amt-16-4571-2023
https://doi.org/10.5194/amt-16-4571-2023
Research article
 | 
12 Oct 2023
Research article |  | 12 Oct 2023

Segmentation of polarimetric radar imagery using statistical texture

Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan

Related authors

Supercooled liquid water cloud classification using lidar backscatter peak properties
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024,https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022,https://doi.org/10.5194/amt-15-3663-2022, 2022
Short summary
Effect of disdrometer type on rain drop size distribution characterisation: a new dataset for south-eastern Australia
Adrien Guyot, Jayaram Pudashine, Alain Protat, Remko Uijlenhoet, Valentijn R. N. Pauwels, Alan Seed, and Jeffrey P. Walker
Hydrol. Earth Syst. Sci., 23, 4737–4761, https://doi.org/10.5194/hess-23-4737-2019,https://doi.org/10.5194/hess-23-4737-2019, 2019
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025,https://doi.org/10.5194/amt-18-1927-2025, 2025
Short summary
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025,https://doi.org/10.5194/amt-18-1859-2025, 2025
Short summary
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025,https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025,https://doi.org/10.5194/amt-18-1659-2025, 2025
Short summary
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025,https://doi.org/10.5194/amt-18-1621-2025, 2025
Short summary

Cited articles

Berenguer, M., Sempere-Torres, D., Corral, C., and Sánchez-Diezma, R.: A Fuzzy Logic Technique for Identifying Nonprecipitating Echoes in Radar Scans, J. Atmos. Ocean. Tech., 23, 1157–1180, https://doi.org/10.1175/JTECH1914.1, 2006. 
Brook, J. P., Protat, A., Soderholm, J. S., Warren, R. A., and McGowan, H.: A Variational Interpolation Method for Gridding Weather Radar Data, J. Atmos. Ocean. Tech., 39 1633–1654, https://doi.org/10.1175/JTECH-D-22-0015.1, 2022. 
Chandrasekar, V., Keränen, R., Lim, S., and Moisseev, D.: Recent advances in classification of observations from dual polarization weather radars, Atmos. Res., 119, 97–111, https://doi.org/10.1016/j.atmosres.2011.08.014, 2013. 
Chitalia, R. D. and Kontos, D.: Role of texture analysis in breast MRI as a cancer biomarker: a review, J. Magn. Reson. Imaging, 49, 927–938, https://doi.org/10.1002/jmri.26556, 2019. 
Clausi, D. A. and Jernigan, M. E.: A fast method to determine co-occurrence texture features, IEEE T. Geosci. Remote, 36, 298–300, https://doi.org/10.1109/36.655338, 1998. 
Download
Short summary
We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Share