Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7243-2021
https://doi.org/10.5194/amt-14-7243-2021
Research article
 | 
17 Nov 2021
Research article |  | 17 Nov 2021

Triple-frequency radar retrieval of microphysical properties of snow

Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde

Related authors

Advantages of G-band radar in multi-frequency, liquid phase microphysical retrievals
Benjamin Michael Courtier, Alessandro Battaglia, and Kamil Mroz
EGUsphere, https://doi.org/10.5194/egusphere-2024-205,https://doi.org/10.5194/egusphere-2024-205, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Enhancing Consistency of Microphysical Properties of Precipitation across the Melting Layer in the Dual-Frequency Precipitation Radar Data
Kamil Mroz, Alessandro Battaglia, and Ann M. Fridlind
EGUsphere, https://doi.org/10.5194/egusphere-2023-2117,https://doi.org/10.5194/egusphere-2023-2117, 2023
Short summary
In-orbit cross-calibration of millimeter conically scanning spaceborne radars
Alessandro Battaglia, Filippo Emilio Scarsi, Kamil Mroz, and Anthony Illingworth
Atmos. Meas. Tech., 16, 3283–3297, https://doi.org/10.5194/amt-16-3283-2023,https://doi.org/10.5194/amt-16-3283-2023, 2023
Short summary
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023,https://doi.org/10.5194/amt-16-2865-2023, 2023
Short summary
Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021,https://doi.org/10.5194/amt-14-511-2021, 2021
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024,https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024,https://doi.org/10.5194/amt-17-999-2024, 2024
Short summary
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024,https://doi.org/10.5194/amt-17-961-2024, 2024
Short summary
The EarthCARE mission: science data processing chain overview
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024,https://doi.org/10.5194/amt-17-839-2024, 2024
Short summary
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024,https://doi.org/10.5194/amt-17-261-2024, 2024
Short summary

Cited articles

Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K., Grecu, M., Lebsock, M., Watters, D., Mroz, K., Heymsfield, G., Li, L., and Furukawa, K.: Spaceborne Cloud and Precipitation Radars: Status, Challenges, and Ways Forward, Rev. Geophys., 58, e2019RG000686, https://doi.org/10.1029/2019RG000686, 2020a. a
Battaglia, A., Tanelli, S., Tridon, F., Kneifel, S., Leinonen, J., and Kollias, P.: Satellite Precipitation Measurement, Advances in Global Change Research, Vol. 67, Springer, Cham, ISBN: 978-3-030-24567-2, 2020b. a
Delene, D. and Poellot, M. R.: GPM GROUND VALIDATION UND CITATION CLOUD MICROPHYSICS MC3E, NASA Global Hydrology Resource Center DAAC [data set], Huntsville, Alabama, U.S.A., https://doi.org/10.5067/GPMGV/MC3E/MULTIPLE/DATA201, 2012. a
Ekelund, R., Eriksson, P., and Kahnert, M.: Microwave single-scattering properties of non-spheroidal raindrops, Atmos. Meas. Tech., 13, 6933–6944, https://doi.org/10.5194/amt-13-6933-2020, 2020a. a
Ekelund, R., Brath, M., Mendrok, J., and Eriksson, P.: ARTS Microwave Single Scattering Properties Database (1.1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4646605, 2020b. a
Download
Short summary
A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.