Articles | Volume 13, issue 5
Research article
19 May 2020
Research article |  | 19 May 2020

Learning about the vertical structure of radar reflectivity using hydrometeor classes and neural networks in the Swiss Alps

Floor van den Heuvel, Loris Foresti, Marco Gabella, Urs Germann, and Alexis Berne

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Cited articles

Bell, C.: Detection of the Riming Process with a Vertically Pointing Radar, PhD thesis, McGill University, Montreal, Quebec, 2000. a
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445,, 2016. a
Besic, N., Gehring, J., Praz, C., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Unraveling hydrometeor mixtures in polarimetric radar measurements, Atmos. Meas. Tech., 11, 4847–4866,, 2018. a
Boodoo, S., Hudak, D., Donaldson, N., and Leduc, M.: Application of dual-polarization radar melting-layer detection algorithm, J. Appl. Meteorol. Clim, 49, 1779–1793,, 2010. a
Bowler, N. E., Arribas, A., Mylne, K., Robertson, K., and Neare, S.: The MOGREPS short-range EPS, Q. J. Roy. Meteor. Soc., 133, 937–948,, 2007. a
Short summary
In areas with reduced visibility at the ground level, radar precipitation measurements higher up in the atmosphere need to be extrapolated to the ground and be corrected for the vertical change (i.e. growth and transformation) of precipitation. This study proposes a method based on hydrometeor proportions and machine learning (ML) to apply these corrections at smaller spatiotemporal scales. In comparison with existing techniques, the ML methods can make predictions from higher altitudes.