Explore projects

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

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.

This is a prototype written in Bash. The actual implementation can be found here: https://gitlab.hzdr.de/fwcc/gitlab-hpc-driver

Status of Sample Calculator at the beginning of the second iteration.

Example project used in this workshop. It is an adapted version from another workshop. The original project can be found here.