Table of Contents
Advances in Artificial Neural Systems
Volume 2013 (2013), Article ID 485913, 13 pages
Research Article

Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

1National Centers for Environmental Prediction, NOAA, College Park, MD 20740, USA
2Earth System Sciences Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
3Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ 08540, USA
4Brookhaven National Laboratory, Upton, NY 11973, USA

Received 28 November 2012; Accepted 11 March 2013

Academic Editor: Ozgur Kisi

Copyright © 2013 Vladimir M. Krasnopolsky et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel approach based on the neural network (NN) ensemble technique is formulated and used for development of a NN stochastic convection parameterization for climate and numerical weather prediction (NWP) models. This fast parameterization is built based on learning from data simulated by a cloud-resolving model (CRM) initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members 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 National Center of Atmospheric Research (NCAR) Community Atmospheric Model (CAM). It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models.