Training and evaluation data for machine learning models emulating the RTE+RRTMGP radiation scheme or its components

Data associated with an upcoming paper on the use of neural networks (NN) to emulate a radiation parameterization. CAMS_* = pre-processed NetCDF files consisting of CAMS reanalysis profiles that can be used as input to the RTE+RRTGMP code to generate NN training data (the other files in the repository). The Fortran program and instructions for doing this can be found at https://github.com/peterukk/rte-rrtmgp-nn/tree/nn_dev/examples/emulator-training The other files are ready-to-be-used input-output data for training machine learning models using the Python scripts found at https://github.com/peterukk/rte-rrtmgp-nn/tree/nn_dev/examples/emulator-training/scripts: RADSCHEME_* = data to train NN emulators for the whole RTE+RRTMGP radiation scheme in the shortwave REFTRANS_* = data to train NN emulators for the shortwave reflectance-transmittance computations in RTE RRTMGP_* = data to train NN emulators for RRTMGP shortwave gas optics

Related Organizations

Danish Meteorological Institute
Denmark

Keywords

machine learning emulators, radiation parameterization, neural networks

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