Data and codes for JAMES article on "Recreating observed convection-generated gravity waves from weather radar observations"

This dataset includes a subset of data and all codes used in the JAMES article titled 'Recreating convection-generated gravity waves from weather radar observations via a neural network and a dynamical atmospheric model'. All files included here are compressed (*.tar.gz) NetCDF files of idealized Weather Research and Forecasting (WRF) model output at 10-minute output frequency. The run archived is that forced by a neural network trained on the Darwin and Florida runs (i.e. the DAFLNN-forced idealized WRF run) described in the paper. Additionally, all the Bramberger et al. 2020 look-up table, training data, time-averaged 3-D output of the DAFLNN-forced WRF run, the trained NNs, and all Python scripts and WRF source codes used in the paper are included in "everything_else.tar.gz". Finally, a README is contained as well, which provides further description of the contents here.
Paper Abstract:
Convection-generated gravity waves (CGWs) transport momentum and energy, and this momentum is a dominant driver of global features of Earth’s atmosphere’s general circulation (e.g. the quasi-biennial oscillation, the pole-to-pole mesospheric circulation). As CGWs are not generally resolved by global weather and climate models, their effects on the circulation need to be parameterized. However, quality observations of GWs are spa24
tiotemporally sparse, limiting understanding and preventing constraints on parameter izations. Convection-permitting or -resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full-physics convection-permitting Weather Research and Forecasting (WRF) simulations, is predicted from weather radar observations using neural networks and a previously developed look-up table. These heating rates are then used to force an idealized GW-resolving dynamical model. Simulated CGWs forced in this way did closely resemble those observed by the Atmospheric InfraRed Sounder in the upper stratosphere. CGW drag in these validated simulations extends 100s of kilometers away from the convective sources, highlighting errors in current gravity wave drag parameterizations due to the use of the ubiquitous single-column approx36
imation. Such validatable simulations have significant potential to be used to further basic understanding of CGWs, improve their parameterizations physically, and provide more restrictive constraints on tuning with confidence.