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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Floortje van den Heuvel on behalf of the Authors (30 Mar 2020)  Author's response   Manuscript 
ED: Publish as is (31 Mar 2020) by Gianfranco Vulpiani
AR by Floortje van den Heuvel on behalf of the Authors (09 Apr 2020)
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.