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The Linear Meta-Model optimization (LiMMo) is a Python-tool designed for the objective tuning of regional climate models (RCM) and numerical weather prediction models (NWPM) to gridded observational datasets.

In this approach, the surface 2D output of the RCM/NWPM is approximated using regression method (linear, piecewise-linear, quadratic). A user-defined error norm, which quantifies the difference between the regression approximation and the observations, is then minimized using a gradient-based optimization method.

Source code for Multiphase Python Repository by HZDR

https://helmholtz.cloud

Data management tool that organizes and visualizes data from various loggers, allowing to create own extendable automatic pipelines and manually process your data.

Website to store presentations, projects and other materials

The generated documentation can be found here: https://hifis.net/doc

A basic template for generating a helm chart that is customized to the openshift workflows at Hereon.

See also the Kubernetes docs