  In Earth Systems Science, many complex data pipelines combine different data
sources and apply data filtering and analysis steps. Typically, such data
analysis processes are historically grown and implemented with many
sequentially executed scripts. Scientific workflow management systems (SWMS)
allow scientists to use their existing scripts and provide support for
parallelization, reusability, monitoring, or failure handling. However, many
scientists still rely on their sequentially called scripts and do not profit
from the out-of-the-box advantages a SWMS can provide. In this work, we
transform the data analysis processes of a Machine Learning-based approach to
calibrate the platform magnetometers of non-dedicated satellites utilizing
neural networks into a workflow called Macaw (MAgnetometer CAlibration
Workflow). We provide details on the workflow and the steps needed to port
these scripts to a scientific workflow. Our experimental evaluation compares
the original sequential script executions on the original HPC cluster with our
workflow implementation on a commodity cluster. Our results show that through
porting, our implementation decreased the allocated CPU hours by 50.2% and the
memory hours by 59.5%, leading to significantly less resource wastage. Further,
through parallelizing single tasks, we reduced the runtime by 17.5%.
