Development of neural network convection parameterizations for climate and NWP models using Cloud Resolving Model simulations

A novel approach based on the neural network (NN) technique is formulated and used for development of a NN ensemble stochastic convection parameterization for climate models. This fast parameterization is built based on data from Cloud Resolving Model (CRM) simulations initialized with and driven by the TOGA-COARE data available for the 4-month winter season from November 1992 to February 1993. CRM simulated data were averaged and projected onto the GCM space of atmospheric states to implicitly define a stochastic convection parameterization. This parameterization is emulated using an ensemble of neural networks (NNs). The developed NNs are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in a diagnostic mode of NCAR CAM. It produced reasonable and promising climate simulations for the TOGA-COARE winter. This paper is devoted to discussion of the concept, methodology, initial results,and the major challenges of development of NN convection parameterizations for climate and numerical weather prediction (NWR) models. V.M. Krasnopolsky, Michael Fox-Rabinovitz, Alexei Belochitski, Philip J.Rasch, Peter Blossey, and Yefim Kogan. "MMAB Contribution No. 293." "November 2, 2011." System requirements: Adobe Acrobat Reader. Includes bibliographical references (pages 67-70). 2011 NWS (National Weather Service) NCEP (National Centers for Environmental Prediction) Library Public Domain 2285