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

Related authors

Characterisation of the melting layer variability in an Alpine valley based on polarimetric X-band radar scans
Floor van den Heuvel, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 11, 5181–5198,,, 2018
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Next-generation radiance unfiltering process for the Clouds and the Earth's Radiant Energy System instrument
Lusheng Liang, Wenying Su, Sergio Sejas, Zachary Eitzen, and Norman G. Loeb
Atmos. Meas. Tech., 17, 2147–2163,,, 2024
Short summary
Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data
Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala
Atmos. Meas. Tech., 17, 2165–2182,,, 2024
Short summary
Investigation of gravity waves using measurements from a sodium temperature/wind lidar operated in multi-direction mode
Bing Cao and Alan Z. Liu
Atmos. Meas. Tech., 17, 2123–2146,,, 2024
Short summary
An improved BRDF hotspot model and its use in VLIDORT for studying the impact of atmospheric scattering on hotspot directional signatures in the atmosphere
Xiaozhen Xiong, Xu Liu, Robert Spurr, Ming Zhao, Qiguang Yang, Wan Wu, and Liqiao Lei
Atmos. Meas. Tech., 17, 1965–1978,,, 2024
Short summary
A multi-decadal time series of upper stratospheric temperature profiles from Odin-OSIRIS limb-scattered spectra
Daniel Zawada, Kimberlee Dubé, Taran Warnock, Adam Bourassa, Susann Tegtmeier, and Douglas Degenstein
Atmos. Meas. Tech., 17, 1995–2010,,, 2024
Short summary

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.