Recreating Observed Convection-Generated Gravity Waves From Weather Radar Observations via a Neural Network and a Dynamical Atmospheric Model

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 spatiotemporally sparse, limiting understanding and preventing constraints on parameterizations. Convection-permitting or -resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the CGW-forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full-physics convection-permitting Weather Research and Forecasting 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 closely resembled 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 approximation. 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.