Simon Pfreundschuh

Doctoral Student at Geoscience and Remote Sensing 2

Simon Pfreundschuh is a PhD student in the Microwave and Optical Remote Sensing division of the department of Space, Earth and Environment. His research focuses on the observation of clouds using satellites. In particular he is involved in the development of methods and software for the processing of the data recorded by the satellites. His work aims to improve the measurement of cloud properties and advance the understanding of their influence on weather and the climate system.

Source: chalmers.se
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Showing 9 publications

2021

Can machine learning correct microwave humidity radiances for the influence of clouds?

Inderpreet Kaur, Patrick Eriksson, Simon Pfreundschuh et al
Atmospheric Measurement Techniques. Vol. 14 (4), p. 2957-2979
Journal article
2020

First measurements of tides in the stratosphere and lower mesosphere by ground-based Doppler microwave wind radiometry

Jonas Hagen, Klemens Hocke, Gunter Stober et al
Atmospheric Chemistry and Physics. Vol. 20 (4), p. 2367-2386
Journal article
2020

Study to support the definition of Arctic Weather Satellite (AWS) high frequency channels

Patrick Eriksson, Inderpreet Kaur, Simon Pfreundschuh
Report - European Organisation for the Exploitation of Meteorological Satellites
2020

Using passive and active observations at microwave and sub-millimetre wavelengths to constrain ice particle models

Robin Nils Ekelund, Patrick Eriksson, Simon Pfreundschuh
Atmospheric Measurement Techniques. Vol. 13 (2), p. 501-520
Journal article
2020

Synergistic radar and radiometer retrievals of ice hydrometeors

Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler et al
Atmospheric Measurement Techniques. Vol. 13 (8), p. 4219-4245
Journal article
2019

An experimental 2D-Var retrieval using AMSR2

David Duncan, Patrick Eriksson, Simon Pfreundschuh
Atmospheric Measurement Techniques. Vol. 12 (12), p. 6341-6359
Journal article
2019

On the distinctiveness of observed oceanic raindrop distributions

David Duncan, Patrick Eriksson, Simon Pfreundschuh et al
Atmospheric Chemistry and Physics. Vol. 19 (10), p. 6969-6984
Journal article
2018

A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems

Simon Pfreundschuh, Patrick Eriksson, David Duncan et al
Atmospheric Measurement Techniques. Vol. 11 (8), p. 4627-4643
Journal article

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