Development of neural network convection parameterizations for numerical climate and weather prediction models using cloud resolving model simulations

Abstract

A novel approach based on the neural network (NN) technique is formulated and used for development of a NN ensemble stochastic convection parameterization for numerical climate and weather prediction models. This fast parameterization is built based on data from Cloud Resolving Model (CRM) simulations initialized with TOGA-COARE data. CRM emulated data are averaged and projected onto the General Circulation Model (GCM) space of atmospheric states to implicitly define a stochastic convection parameterization. This parameterization is comprised as an ensemble of neural networks. The developed NNs are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived in such a way is estimated. The major challenges of development of stochastic NN parameterizations are discussed based on our initial results.