Alexis Berne
Associate Professor
EPFL ENAC IIE LTE
GR C2 564 (Bâtiment GR)
Station 2
CH-1015 Lausanne
Web site: Web site: https://lte.epfl.ch
EPFL ENAC SSIE-GE
GR A2 392 (Bâtiment GR)
Station 2
CH-1015 Lausanne
Web site: Web site: https://ssie.epfl.ch/
Fields of expertise
Radar meteorology
Precipitation microphysics
Polar precipitation
Geostatistics
Precipitation microphysics
Polar precipitation
Geostatistics
Mission
Reserch interests: radar remote sensing and variability of precipitation in mountainous regions.Professional course
Associate Professor
LTE
EPFL
2013 -
Assistant Professor
LTE
EPFL
2006 - 2013
Post-doctoral researcher
LTHE
INPG, Grenoble, France
03/2006 - 09/2006
Post-doctoral researcher
HWQ
Wageningen University, The Netherlands
2003 - 2006
Teaching & PhD
Teaching
Environmental Sciences and Engineering
PhD Programs
PhD Students
Erb Sophie Catherine, Ghiggi Gionata, Liernur Adrien Francis Olivier,Past EPFL PhD Students
Bianchi Blandine , Billault--Roux Anne-Claire Marie , Feldmann Monika , Ferrone Alfonso , Gehring Josué Etienne , Grazioli Jacopo , Jaffrain Joël , Raupach Timothy Hugh , Schleiss Marc , Schwärzel Marc Damien , Van den Heuvel Floortje Elisabeth Maria , Wolfensberger Daniel ,Courses
Remote sensing
This course aims at exposing the students to the main concepts, instruments and techniques of environmental remote sensing. The interactions between waves and matter, different types of sensors and image-processing techniques are presented.
Atmospheric processes: from cloud to global scales
The main objective is to present important atmospheric processes from the local to global scales. The course will start with cloud processes, continue to synoptic phenomena like extratropical cyclones and fronts, to finally cover numerical modeling at the regional and global scales.
Sensing and spatial modeling for earth observation
Students get acquainted with the process of mapping from images (orthophoto and DEM), as well as with methods for monitoring the Earth surface using remotely sensed data. Methods will span from machine learning to geostatistics and model the spatiotemporal variability of processes.