Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5471-2018
https://doi.org/10.5194/amt-11-5471-2018
Research article
 | 
05 Oct 2018
Research article |  | 05 Oct 2018

Retrieval of snowflake microphysical properties from multifrequency radar observations

Jussi Leinonen, Matthew D. Lebsock, Simone Tanelli, Ousmane O. Sy, Brenda Dolan, Randy J. Chase, Joseph A. Finlon, Annakaisa von Lerber, and Dmitri Moisseev

Related authors

Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024,https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance
Jussi Leinonen, Ulrich Hamann, Urs Germann, and John R. Mecikalski
Nat. Hazards Earth Syst. Sci., 22, 577–597, https://doi.org/10.5194/nhess-22-577-2022,https://doi.org/10.5194/nhess-22-577-2022, 2022
Short summary
Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) images
Jussi Leinonen, Jacopo Grazioli, and Alexis Berne
Atmos. Meas. Tech., 14, 6851–6866, https://doi.org/10.5194/amt-14-6851-2021,https://doi.org/10.5194/amt-14-6851-2021, 2021
Short summary
Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification
Jussi Leinonen and Alexis Berne
Atmos. Meas. Tech., 13, 2949–2964, https://doi.org/10.5194/amt-13-2949-2020,https://doi.org/10.5194/amt-13-2949-2020, 2020
Short summary
Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer
Mark Richardson, Jussi Leinonen, Heather Q. Cronk, James McDuffie, Matthew D. Lebsock, and Graeme L. Stephens
Atmos. Meas. Tech., 12, 1717–1737, https://doi.org/10.5194/amt-12-1717-2019,https://doi.org/10.5194/amt-12-1717-2019, 2019
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
An improved geolocation methodology for spaceborne radar and lidar systems
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024,https://doi.org/10.5194/amt-17-6301-2024, 2024
Short summary
Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024,https://doi.org/10.5194/amt-17-6223-2024, 2024
Short summary
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024,https://doi.org/10.5194/amt-17-6025-2024, 2024
Short summary
Global-scale gravity wave analysis methodology for the ESA Earth Explorer 11 candidate CAIRT
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
Atmos. Meas. Tech., 17, 5785–5819, https://doi.org/10.5194/amt-17-5785-2024,https://doi.org/10.5194/amt-17-5785-2024, 2024
Short summary
Retrieval of pseudo-BRDF-adjusted surface reflectance at 440 nm from the Geostationary Environmental Monitoring Spectrometer (GEMS)
Suyoung Sim, Sungwon Choi, Daeseong Jung, Jongho Woo, Nayeon Kim, Sungwoo Park, Honghee Kim, Ukkyo Jeong, Hyunkee​​​​​​​ Hong, and Kyung-Soo Han
Atmos. Meas. Tech., 17, 5601–5618, https://doi.org/10.5194/amt-17-5601-2024,https://doi.org/10.5194/amt-17-5601-2024, 2024
Short summary

Cited articles

Bailey, M. P. and Hallett, J.: A Comprehensive Habit Diagram for Atmospheric Ice Crystals: Confirmation from the Laboratory, AIRS II, and Other Field Studies, J. Atmos. Sci., 66, 2888–2899, https://doi.org/10.1175/2009JAS2883.1, 2009. a
Beyer, W. H.: CRC Handbook of Mathematical Sciences, CRC Press, Boca Raton, Florida, USA, 1987. a
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small Particles, John Wiley & Sons, Inc., New York, USA, 1983. a
Botta, G., Aydin, K., Verlinde, J., Avramov, A. E., Ackerman, A. S., Fridlind, A. M., McFarquhar, G. M., and Wolde, M.: Millimeter wave scattering from ice crystals and their aggregates: Comparing cloud model simulations with X-and Ka-band radar measurements, J. Geophys. Res., 116, D00T04, https://doi.org/10.1029/2011JD015909, 2011. a
Delanoë, J. M. E., Heymsfield, A. J., Protat, A., Bansemer, A., and Hogan, R. J.: Normalized particle size distribution for remote sensing application, J. Geophys. Res.-Atmos., 119, 4204–4227, https://doi.org/10.1002/2013JD020700, 2014. a
Download
Short summary
We developed a technique for inferring the physical properties (amount, size and density) of falling snow from radar observations made using multiple different frequencies. We tested this method using measurements from airborne radar and compared the results to direct measurements from another aircraft, as well as ground-based radar. The results demonstrate that multifrequency radars have significant advantages over those with a single frequency in determining the snow size and density.