Articles | Volume 13, issue 3
https://doi.org/10.5194/amt-13-1227-2020
https://doi.org/10.5194/amt-13-1227-2020
Research article
 | 
13 Mar 2020
Research article |  | 13 Mar 2020

Unsupervised classification of vertical profiles of dual polarization radar variables

Jussi Tiira and Dmitri Moisseev

Related authors

Towards the connection between snow microphysics and melting layer: insights from multifrequency and dual-polarization radar observations during BAECC
Haoran Li, Jussi Tiira, Annakaisa von Lerber, and Dmitri Moisseev
Atmos. Chem. Phys., 20, 9547–9562, https://doi.org/10.5194/acp-20-9547-2020,https://doi.org/10.5194/acp-20-9547-2020, 2020
Short summary
Disk and circumsolar radiances in the presence of ice clouds
Päivi Haapanala, Petri Räisänen, Greg M. McFarquhar, Jussi Tiira, Andreas Macke, Michael Kahnert, John DeVore, and Timo Nousiainen
Atmos. Chem. Phys., 17, 6865–6882, https://doi.org/10.5194/acp-17-6865-2017,https://doi.org/10.5194/acp-17-6865-2017, 2017
Short summary
Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland
Jussi Tiira, Dmitri N. Moisseev, Annakaisa von Lerber, Davide Ori, Ali Tokay, Larry F. Bliven, and Walter Petersen
Atmos. Meas. Tech., 9, 4825–4841, https://doi.org/10.5194/amt-9-4825-2016,https://doi.org/10.5194/amt-9-4825-2016, 2016
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Drone-based photogrammetry combined with deep learning to estimate hail size distributions and melting of hail on the ground
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024,https://doi.org/10.5194/amt-17-2539-2024, 2024
Short summary
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech., 17, 2195–2217, https://doi.org/10.5194/amt-17-2195-2024,https://doi.org/10.5194/amt-17-2195-2024, 2024
Short summary
Next-generation radiance unfiltering process for the Clouds and the Earth's Radiant Energy System instrument
Lusheng Liang, Wenying Su, Sergio Sejas, Zachary Eitzen, and Norman G. Loeb
Atmos. Meas. Tech., 17, 2147–2163, https://doi.org/10.5194/amt-17-2147-2024,https://doi.org/10.5194/amt-17-2147-2024, 2024
Short summary
Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data
Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala
Atmos. Meas. Tech., 17, 2165–2182, https://doi.org/10.5194/amt-17-2165-2024,https://doi.org/10.5194/amt-17-2165-2024, 2024
Short summary
A directional surface reflectance climatology determined from TROPOMI observations
Lieuwe G. Tilstra, Martin de Graaf, Victor J. H. Trees, Pavel Litvinov, Oleg Dubovik, and Piet Stammes
Atmos. Meas. Tech., 17, 2235–2256, https://doi.org/10.5194/amt-17-2235-2024,https://doi.org/10.5194/amt-17-2235-2024, 2024
Short summary

Cited articles

Andrić, J., Kumjian, M. R., Zrnić, D. S., Straka, J. M., and Melnikov, V. M.: Polarimetric Signatures above the Melting Layer in Winter Storms: An Observational and Modeling Study, J. Appl. Meteorol. Clim., 52, 682–700, https://doi.org/10.1175/JAMC-D-12-028.1, 2013. a
Arthur, D. and Vassilvitskii, S.: k-means++: The advantages of careful seeding, 1027–1035, Society for Industrial and Applied Mathematics, 2007. a
Bechini, R. and Chandrasekar, V.: A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications, J. Atmos. Ocean. Tech., 32, 22–47, https://doi.org/10.1175/JTECH-D-14-00097.1, 2015. a
Bechini, R., Baldini, L., and Chandrasekar, V.: Polarimetric Radar Observations in the Ice Region of Precipitating Clouds at C-Band and X-Band Radar Frequencies, J. Appl. Meteorol. Clim., 52, 1147–1169, https://doi.org/10.1175/JAMC-D-12-055.1, 2013. a, b
Cattell, R. B.: The Scree Test For The Number Of Factors, Multivar. Behav. Res., 1, 245–276, 1966. a
Download
Short summary
Modern weather radars are sensitive for properties of precipitating snow particles, such as their sizes, shapes and number concentration. Vertical profiles of such radar measurements can be used for studying the processes through which snow is formed. We created a profile classification method for this purpose, and we show how it can be used for automatic identification of snow growth processes. Being able to identify the processes is expected to improve radar-based precipitation estimation.