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

Data sets

ICE GENESIS: data catalogue A.-C. Billault-Roux, J. Grazioli, J. Delanoë, S. Jorquera, N. Pauwels, N. Viltard, A. Martini, V. Mariage, C. Le Gac, C. Caudoux, C. Aubry, F. Bertrand, A. Schwarzenboeck, L. Jaffeux, P. Coutris, G. Febvre, J. M. Pichon, F. Dezitter, J. Gehring, A. Untersee, C. Calas, J. Figueras i Ventura, B. Vie, A. Peyrat, V. Curat, S. Rebouissoux, and A. Berne

Model code and software

annecbroux/DeepSpectralRetrieval: v1.0.0- DeepSpectralRetrieval A.-C. Billault-Roux


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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.