Clouds are important players for the Earth’s atmosphere. They significantly influence the hydrological cycle as well as the energy budget of the atmosphere, and in turn also atmospheric flows due to diabatic heating (e.g. latent heat and/or interaction with radiation). Although clouds are composed by a myriad of small water particles, they can form macroscopic pattern (cloud structures) in which diabatic heat sources are concentrated. This effect might additionally affect atmospheric flows.

In this working group we investigate clouds from a more theoretical point of view. We use modern methods from physics, mathematics and computer sciences to address the following basic problems of cloud research in an interdisciplinary way:

What are the dominant processes for formation and evolution of clouds, especially of clouds containing ice particles?
How do processes on different scales interact to form pattern on larger scales?
How can cloud processes formulated in a mathematically and physically consistent way? How can multiple scale models of clouds be formulated?
How can the impact of clouds on larger scales be parameterised in coarse resolution models (e.g. for numerical weather prediction or climate projection)?

Big Data in Atmospheric Physics (BINARY) is an interdisciplinary project, involving the research fields Atmospheric Physics and Computer Sciences. Researcher from the Institutes of Atmospheric Physics and Computer Sciences at the Johannes Gutenberg University Mainz investigate important scientific questions in Atmospheric Physics applying modern machine learning methods for big data sets.

Projections of climate change rely on an adequate representation of UTLS processes and their feedbacks in climate models. In the Collaborative Research Centre TPChange this will be addressed by a combination of field measurements, laboratory studies, theoretical approaches, and multiscale numerical modelling. Based on an improved understanding of relevant processes at different scales, we will develop parameterisations to improve state-of-the-art climate models. Our goal is to specify the impact of UTLS processes on composition, dynamics and ultimately on future climate and climate variability.

Köhler, D., Reutter, P. and Spichtinger, P.

2024

Niebler, S., B. Schmidt, P. Spichtinger, H. Tost

2024

Krüger, M., A. Mishra, P. Spichtinger, U. Pöschl, T. Berkemeier

2024

Chertock, A., A. Kurganov, M. Lukáčová-Medviďová, P. Spichtinger, B. Wiebe

2023

Atmosphärische Thermodynamik
Instructor: Dr. Philipp Reutter
Methodenkenntnis
Instructor: Dr. Heiko Bozem; Univ.-Prof. Dr. Peter Hoor; Dr. Daniel Kunkel; Dr. Franziska Köllner; Jun.-Prof. Dr. Annette Miltenberger; Dr. Philipp Reutter; Dr. Michael Riemer; Dr. Miklos Szakall; Univ.-Prof. Dr. Holger Tost; Prof. Dr. Thomas Wagner; Dr. Ralf Weigel; Univ.-Prof. Dr. Volkmar Wirth
Modellierung mit partiellen Differentialgleichungen
Instructor: Univ.-Prof. Dr. Peter Spichtinger
Planetenatmosphären
Instructor: Dr. Philipp Reutter
Projekt Umweltwissenschaften
Instructor: Dr. Heiko Bozem; Univ.-Prof. Dr. Peter Hoor; Dr. Daniel Kunkel; Dr. Franziska Köllner; Jun.-Prof. Dr. Annette Miltenberger; Dr. Philipp Reutter; Dr. Michael Riemer; Univ.-Prof. Dr. Peter Spichtinger; Dr. Miklos Szakall; Univ.-Prof. Dr. Holger Tost; Dr. Ralf Weigel; Univ.-Prof. Dr. Volkmar Wirth
Stochastische Modellierung
Instructor: Univ.-Prof. Dr. Peter Spichtinger
Format: online

SoSe 2026

For general inquiries regarding open research positions please contact spichtin@uni-mainz.de

Modellierung mit partiellen Differentialgleichungen