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 M. Courtier, Alessandro Battaglia, and Kamil Mroz
Atmos. Meas. Tech., 17, 6875–6888, https://doi.org/10.5194/amt-17-6875-2024,https://doi.org/10.5194/amt-17-6875-2024, 2024
Short summary
Enhancing consistency of microphysical properties of precipitation across the melting layer in dual-frequency precipitation radar data
Kamil Mroz, Alessandro Battaglia, and Ann M. Fridlind
Atmos. Meas. Tech., 17, 1577–1597, https://doi.org/10.5194/amt-17-1577-2024,https://doi.org/10.5194/amt-17-1577-2024, 2024
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
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
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025,https://doi.org/10.5194/amt-18-1325-2025, 2025
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.
Share