EGU22-10138, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Simulating Convective GWs forced by Radar-Based, Neural-Network-Predicted Diabatic Heating

Christopher Kruse1, M. Joan Alexander1, Martina Bramberger1, Padram Hassanzadeh2, Ashesh Chattopadhyay2, Brian Green3, and Alison Grimsdell1
Christopher Kruse et al.
  • 1NorthWest Research Associates, Boulder, Colorado, USA (
  • 2Rice University, Houston, Texas, USA
  • 3Stanford University, Stanford, California, USA

Convection, both observed and modeled, generates gravity waves (GWs) that significantly impact large-scale circulations in the stratosphere and above. However, models that permit convection and resolve the GWs they generate cannot reproduce the timing, location, and intensity of the actual convective cells that generate the observed convective GWs. This issue prevents comparison of observed and modeled convective GWs and model validation/evaluation. 

Here, convective latent heating is predicted based on radar observations and provided to an idealized version of WRF, allowing WRF’s dynamics to generate convective updrafts/downdrafts and generated convective GWs both mechanically and diabatically. Two methods are used to predict convective latent heating: the composited lookup table of Bramberger et al. 2020 and neural networks (NNs) using the same, and additional, input variables. Offline performance of the NN-predicted latent heating can be improved over the previous method when more input variables are used. Preliminary comparisons of modeled and observed (via superpressure-balloon and satellite) convective GWs will be presented. 

How to cite: Kruse, C., Alexander, M. J., Bramberger, M., Hassanzadeh, P., Chattopadhyay, A., Green, B., and Grimsdell, A.: Simulating Convective GWs forced by Radar-Based, Neural-Network-Predicted Diabatic Heating, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10138,, 2022.

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