Objectives

The objective of this applied course is to provide the theoretical and technical context of terrestrial modeling in high-performance scientific computing (HPSC) environments utilizing a stand-alone land surface model, and a coupled approach with a subsurface and atmospheric model.

The course will focus on using the Community Land Model (eCLM) and the Terrestrial Systems Modelling Platform (TSMP2). We will focus on modelling and simulation of land surface processes and land surface modeling, and coupled modeling involving the atmosphere and subsurface. The simulations will be carried out with these models running on high-performance computing infrastructure. The course will also pay attention to data science aspects of land surface and terrestrial systems modelling, in particular focusing on data assimilation. The main course topics are:

- setting up a land surface model and performing simulations in massively parallel supercomputer environments at the Jülich Supercomputing Centre,
- land surface model simulations involving biogeochemical cycles and dynamic simulation of vegetation states,
- configuring and running coupled atmosphere-land surface-subsurface simulation with the Terrestrial Systems Modelling Platform version 2.
- parallel data assimilation using TSMP-PDAF (Parallel Data Assimilation Framework),
- post-processing and visualization in the age of big data,

Learning Outcomes

Completion of the course will provide the participants with the generic capabilities of land surface modeling including biogeochemical cycles in supercomputing environments with a focus on eCLM. The course will also guide the participants through coupled modelling of the atmosphere-land surface- subsurface, from configuration to operation and analysis. Data science in combination with such models, like the handling of very large data sets in the analyses and visualization process, and data assimilation methods, is part as well of the course.

Target Audience and Prerequisites

Master or PhD students, PostDocs with a deep interest in terrestrial modeling (hydrology, land surface, atmosphere)
Basic knowledge of LINUX/UNIX and programming languages such as R, Python, C/C++, or FORTRAN as well as data formats such as NetCDF is an advantage