Here we provide the source code and required data sets to reproduce all results related to "Representing chemical history in ozone time-series predictions - a model experiment study building on the MLAir (v1.5) deep learning framework" by F. Kleinert, L. H. Leufen, A. Lupascu, T. Butler and M. G. Schultz, submitted to GMD(D) (gmd-2022-122).

This record contains the MLAir source code, reference (competitor) experiments and forecasts. As we could not make the full 4D (400x360x35) hourly wrf-chem model fields available for technical reasons, we decided to upload the subdomain of interest only. WRF-Chem model fields (YYYY-MM) are available from: 2009-01 to 2009-04: https://doi.org/10.34730/c799f04beb644e38a575fa20c2dd8d40 2009-05 to 2009-08: https://doi.org/10.34730/d5f34ae6a8e34d4c8ac33f75b993e8a9 2009-09 to 2009-12: https://doi.org/10.34730/a423ec9003194209989726a95a1a490c 2010-01 to 2010-03: https://doi.org/10.34730/718262bd2c894fd6aadce19a08040f69

This record contains the MLAir (v1.5) python framework used to conduct the experiments (mlair.tar.gz). The version is also available from the projects gitlab repository (tag Kleinert_etal_2022_initial_submission): https://gitlab.jsc.fz-juelich.de/esde/machine-learning/mlair/-/tree/Kleinert_etal_2022_initial_submission

Additionally, this record contains the competitor experiment directories (trained neural networks, input & target data, etc.) and our competitor models' forecasts. After extraction, you can use this forecast path as an external parameter in run_wrf_dh_sector3.py. Thus, MLAir can use the reference forecasts to calculate skill scores etc.

The coords.nc file contains the time-independent coordinates related to the model fields linked above and can be linked to MLAir by specifying the absolut path within the run_wrf_dh_sector3.py runscript (external_coords_file=coords.nc).