Staff
Franz Kanngießer
FB 1: Ozeanzirkulation und Klimadynamik
FE Maritime Meteorologie
- Phone:
- +49 431 600 2492
- Email:
- fkanngiesser(at)geomar.de
Address:
Wischhofstraße 1-3
24148 Kiel
research topics
- remote sensing of aerosol processes
- aerosol optics
- application of machine learning methods
- impact of mineral dust on air quality and climate system
curriculum vitae
2011 - 2014 studies in meteorology (BSc), Leipzig University
2014 - 2017 studies in meteorology (MSc), Leipzig University, thesis: "Observations of glories above arctic boundary layer clouds to identify cloud phase and derive its frequency" (original title in German)
2017 - 2022 doctoral researcher in radio- und space science, Chalmers University of Technology, Schweden
2020 fil. lic., thesis: "Modelling optical properties of morphological complex soot aerosol"
2022 PhD, thesis: "Modelling optical properties of morphological complex aerosol"
2022 - 2023 postdoctoral researcher, University of Cologne
since 2023 postdoctoral researcher, GEOMAR
selected publications (before May 2023)
- Michael Kahnert and Franz Kanngießer, "Optical properties of marine aerosol: modelling the transition from dry, irregularly shaped crystals to brine-coated, dissolving salt particles", J. Quant. Spectrosc. Radiat. Transfer, 295, 108408 (2023)
- Michael Kahnert, Franz Kanngießer, Emma Järvinen and Martin Schnaiter, "Aerosol-optics model for the backscatter depolarisation ratio of mineral dust particles", J. Quant. Spectrosc. Radiat. Transfer, 254, 107177 (2020)
- Franz Kanngiesser and Michael Kahnert, "Coating material-dependent differences in modelled lidar-measurable quantities for heavily coated soot particles," Opt. Express, 27, 36368-36387 (2019)
Articles in a Scientific Journal - peer-reviewed
(2024)
Optical Characterization of Marine Aerosols Using a Morphologically Realistic Model With Varying Water Content: Implications for Lidar Applications and Passive Polarimetric Remote Sensing.
Geophysical Research Letters, 51
(5).
Art.Nr. e2023GL107541.
DOI 10.1029/2023GL107541.
and Fiedler, S.
(2024)
“Seeing” Beneath the Clouds — Machine-Learning-Based Reconstruction of North African Dust Plumes.
AGU Advances, 5
(1).
Art.Nr. e2023AV001042.
DOI 10.1029/2023AV001042.
