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
Joint Authors
Belochitski, Alexei A.
Krasnopolsky, Vladimir M.
Fox-Rabinovitz, Michael S.
Source
Advances in Artificial Neural Systems
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-07
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Krasnopolsky, Vladimir M.& Fox-Rabinovitz, Michael S.& Belochitski, Alexei A.. 2013. 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. Advances in Artificial Neural Systems،Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-475424
Modern Language Association (MLA)
Krasnopolsky, Vladimir M.…[et al.]. 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. Advances in Artificial Neural Systems No. 2013 (2013), pp.1-13.
https://search.emarefa.net/detail/BIM-475424
American Medical Association (AMA)
Krasnopolsky, Vladimir M.& Fox-Rabinovitz, Michael S.& Belochitski, Alexei A.. 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. Advances in Artificial Neural Systems. 2013. Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-475424
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-475424