Learning machines seminars – previous seminars

Observing and measuring clouds and rain is essential for climate science, meteorology, and an increasing range of societal and economic activities. This is due to the importance of clouds and precipitation for the hydrological cycle and the weather and climate of the Earth. Since patterns of cloudiness and precipitation vary across continental scales, their study and monitoring require observations with global coverage, which currently can only be provided by satellite observations.

My research focuses on development of machine-learning-based methods to improve satellite measurements of hydrometeors, i. e., the particles that make up clouds and precipitation.

I have developed neural-network-based methods for the processing of satellite measurements that can handle the uncertainties caused by their inverse-problem character. These methods only require only minor changes to the training and architecture of the neural network and can be combined with any currently available architectures

The methods have been successfully applied to the processing of observations from the Global Precipitation Measurement, an international satellite mission led by NASA and the Japanese Space Agency, as well as near real-time measurements of rain over Brazil. In both applications, the novel methods lead to considerable improvements in the accuracy of the satellite measurements.
The key advantage of the methods is that they allow combining the power of modern, deep neural networks with the mathematically sound handling of uncertainties offered by conventional methods for solving inverse problems. The methods unlocked significant improvements in global measurements of precipitation and will therefore be used operationally in the upcoming processing scheme for the Global Precipitation Measurement mission.

Simon Pfreundschuh obtained a BSc in Computer Science from the University of Kiel and two MScs in Engineering Mathematics and Physics and Astronomy from Chalmers University of Technology. From 2017 until 2022 he was a graduate student at the Department of Space, Earth and Environment at Chalmers University of Technology. He was a visiting scholar at the Department of Atmospheric Science at Colorado State University for his work on the Global Precipitation Measurement satellite mission. He completed his graduate studies in May 2022 and will continue his work as a post-doctoral fellow at Chalmers University of Technology and associate scientist at the Swedish Meteorological and Hydrological Institute (SMHI).