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Clément Guilloteau and Efi Foufoula-Georgiou

for deep learning algorithms). It is also independent of the distance metric used to compute the distances between the TB vectors. The only way to reduce this uncertainty is to add supplementary information to the vector of observed TBs. This may be achieved by using ancillary datasets, as for example environmental variables from reanalyses ( Ferraro et al. 2005 ; Ringerud et al. 2015 ; Kidd et al. 2016 ; Petković et al. 2018 ; Takbiri et al. 2019 ). While the current state

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

for Operational Hydrology and Water Management (H SAF) program, is a frozen-precipitation-only retrieval algorithm based on machine learning, primarily designed for the GMI. SLALOM inputs all 13 GMI channels together with ancillary variables describing the atmospheric conditions (e.g., ERA-Interim T2m, TPW, humidity profiles). In contrast to the GPROF algorithm, SLALOM does not consider any background information on the surface type. SLALOM is composed of four modules: (i) the snowfall detection

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