Cloud-resolving model for machine learning buoyant cloudy updraught

<p>Atmospheric model data from simulations carried out using the Unified Model nesting suite u-cj161.</p> <p>This repository includes scripts to perform all the required steps.</p> <p>Retrieval of the model output files from the archive:&nbsp;ml_cape_pdf_1_retrieve_lam_files.py&nbsp;</p> <p>Coarse-graining of the 1.5 km atmospheric model data to a series of different "global model" resolutions by calculating mean profiles of key thermodynamic variables and the calculation of the fraction of that coarse-volume in which the 1.5 km pixels meet the criterion of being bouyant cloudy updraughts: ml_cape_pdf_2d_coarse_grain_bcu.py&nbsp;</p> <p>Subsampling to rebalance the data set since so much of the raw data consists of examples of no convectie activity: ml_cape_pdf_3e_sub_sample_bcu.py&nbsp;</p> <p>There was too much data to process all the raw data in one go, so it was done is several steps. There is then a need to join multiple files together: ml_cape_pdf_3g_stitch_together_bcu_files.py&nbsp;</p> <p>The data is on the Unified Model L70 grid (counting from the bottom up), but the goal is to deploy the machine-learnt algorithm on the E3SM grid (72 levels counting from the top down), so some regirddign is required: ml_cape_pdf_3h_regrid_um_to_e3sm.py&nbsp;</p> <p>Training of 1 two-headed 1d CNN is done using: ml_cape_pdf_4c_bcu_cnn.py&nbsp;</p> <p>Evaluation against witheld data is done using: ml_cape_pdf_5b_validate_BCU.py&nbsp;</p> <p>&nbsp;</p>