LiMMo (Linear Meta-Model optimization for regional climate model)

The Linear-MetaModel 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.

Version 1.0 of LiMMo focuses on tuning the parameters of the ICON-CLM model (with 12km resolution) over the EURO-CORDEX domain, using the EOBS (temperature, precipitation, pressure, short-wave radiation) and HOAPS (latent heat flux over the sea) datasets for the period 2003-2008.

If you are interested in using LiMMo for your applications, please contact: Dr. Sergei Petrov (sergei.petrov@hereon.de, chuckchaness@gmail.com)