PL-ExaESM
Pilot Lab Exascale Earth System Modelling
PL-ExaESM is a “Helmholtz-Inkubator Information & Data Science” project and explores specific concepts to enable exascale readiness of Earth System models and associated work flows in Earth System science.
The work is organized in five collaborative work packages, leveraging co-design between domain and computer scientists to address the computational and data challenges posed by future supercomputers. PL-EESM provides a new platform for scientists of the Helmholtz Association to develop scientific and technological concepts for future generation Earth System models and data analysis systems.
Even though extreme events can lead to disruptive changes in society and the environment, current generation models have limited skills particularly with respect to the simulation of these events. Reliable quantification of extreme events requires models with unprecedentedly high resolution and timely analysis of huge volumes of observational and simulation data, which drastically increase the demand on computing power as well as data storage and analysis capacities. At the same time, the unprecedented complexity and heterogeneity of exascale systems, will require new software paradigms for next generation Earth System models as well as fundamentally new concepts for the integration of models and data.
Specifically, novel solutions for the parallelisation and scheduling of model components, the handling and staging of huge data volumes and a seamless integration of information management strategies throughout the entire process-value chain from global Earth System simulations to local scale impact models will be developed in PL-ExaESM. The potential of machine learning to optimize these tasks will be investigated. At the end of the project, several program libraries and workflows will be available, which provide the basis for the development of next generation Earth System models.
GERICS will contribute to PL-ExaESM by optimizing nested regional downscaling workflows. The aim is to introduce an efficient way of diagnosing extreme events such as extreme precipitation in a coarse host model simulation and automatically explore uncertainties of these events by launching short-term high-resolution ensembles around the extreme event. This strategy allows an efficient and flexible usage of available computing resources with an improvement of statistics around single events at the same time.
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