A Machine Learning-based Planetary Boundary Layer Height Scheme for ICON-A Trained on Vertically Highly Resolved Simulations

Zenodo (CERN European Organization for Nuclear Research) 2026 Open access

Read full text
Authors: janisklamt
DOI: 10.5281/zenodo.17832769
Cited by: 0

Abstract

This repository contains the Jupyter notebooks as well as Python and Bash scripts required to reproduce the main results of the paper "A Machine Learning-based Planetary Boundary Layer Height Scheme for ICON-A Trained on Vertically Highly Resolved Simulations". Version v1.1 additionally includes the results and the training dataset "T250_df" used in this study.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
27 August 2026