A cornerstone of the daily weather forecast is the numerical weather prediction, where one uses large numerical models, running on high-performance systems. These models aim to represent the physical processes in the atmosphere together with its interactions with other parts of the earth system as realistically as possible. Even though significant progress has been achieved in the last 40 years, a central question remains: Did we already reach the fundamental limit of predictability due to the chaotic behavior of the atmosphere? Anwering this question is the overarching goal of the Transregional Collaboratice Research Center (TRR) "Waves to Weather" and the chosen approach is to understand, how errors propagate through the numerical model.
Apart from providing technical services as helping users to run their numerical models on our high performance system "Mogon", the subproject Z2 at the ZDV also has a research component. Physical processes in the atmosphere, which cannot be explicitely resolved by numerical models are represented using "subgrid parameterizations", which are designed to feedback the net-effect of the unresolved process back to the model. An important parameterized process is the formation and evolution of clouds. Using the technique of "Algorithmic Differentiation", well-known in computer science but significantly less known in many branches of meteorology, we aim to understand the uncertainty of parameters within cloud parameterizations.
- Group for Atmospheric Dynamics at LMU Munich
- Regionales Rechenzentrum an der Universität Hamburg
- Institute for Atmospheric Physics at JGU Mainz
- Zentrum für Datenverarbeitung
07/2019 - 06/2023
- M Hieronymus, M Baumgartner, A Miltenberger, and A Brinkmann. 2022. Algorithmic Differentiation for Sensitivity Analysis in Cloud Microphysics. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 14, 7. DOI Author/Publisher URL
- Manuel Baumgartner, Max Sagebaum, Nicolas R Gauger, Peter Spichtinger, and Andre Brinkmann. 2019. Algorithmic differentiation for cloud schemes (IFS Cy43r3) using CoDiPack (v1.8.1). GEOSCIENTIFIC MODEL DEVELOPMENT 12, 12: 5197–5212. DOI Author/Publisher URL