Articles | Volume 16, issue 4
Research article
21 Feb 2023
Research article |  | 21 Feb 2023

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Anne-Claire Billault-Roux, Gionata Ghiggi, Louis Jaffeux, Audrey Martini, Nicolas Viltard, and Alexis Berne

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Cited articles

Acquistapace, C., Kneifel, S., Löhnert, U., Kollias, P., Maahn, M., and Bauer-Pfundstein, M.: Optimizing observations of drizzle onset with millimeter-wavelength radars, Atmos. Meas. Tech., 10, 1783–1802,, 2017. a
Anderson, T. L., Covert, D. S., and Charlson, R. J.: Cloud droplet number studies with a counterflow virtual impactor, J. Geophys. Res., 99, 8249–8256,, 1994. a
Balduzzi, D., Frean, M., Leary, L., Lewis, J., Wan-Duo Ma, K., and Mcwilliams, B.: Shattered Gradients, Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia 6–11 August 2017,, 342–350, 2017. a
Barrett, A. I., Westbrook, C. D., Nicol, J. C., and Stein, T. H. M.: Rapid ice aggregation process revealed through triple-wavelength Doppler spectrum radar analysis, Atmos. Chem. Phys., 19, 5753–5769,, 2019. a, b, c
Battaglia, A., Tanelli, S., Tridon, F., Kneifel, S., Leinonen, J., and Kollias, P.: Triple-Frequency Radar Retrievals, Springer International Publishing, Cham, 211–229,, 2020. a

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Short summary
Better understanding and modeling snowfall properties and processes is relevant to many fields, ranging from weather forecasting to aircraft safety. Meteorological radars can be used to gain insights into the microphysics of snowfall. In this work, we propose a new method to retrieve snowfall properties from measurements of radars with different frequencies. It relies on an original deep-learning framework, which incorporates knowledge of the underlying physics, i.e., electromagnetic scattering.