Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction

Neural Networks : the Official Journal of the International Neural Network Society
Vladimir M Krasnopolsky, Michael S Fox-Rabinovitz

Abstract

A new practical application of neural network (NN) techniques to environmental numerical modeling has been developed. Namely, a new type of numerical model, a complex hybrid environmental model based on a synergetic combination of deterministic and machine learning model components, has been introduced. Conceptual and practical possibilities of developing hybrid models are discussed in this paper for applications to climate modeling and weather prediction. The approach presented here uses NN as a statistical or machine learning technique to develop highly accurate and fast emulations for time consuming model physics components (model physics parameterizations). The NN emulations of the most time consuming model physics components, short and long wave radiation parameterizations or full model radiation, presented in this paper are combined with the remaining deterministic components (like model dynamics) of the original complex environmental model--a general circulation model or global climate model (GCM)--to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The speed-up of model calculations opens the opportunity for model improvement. Examples o...Continue Reading

References

Apr 4, 2003·Neural Networks : the Official Journal of the International Neural Network Society·Vladimir M Krasnopolsky, Helmut Schiller
Feb 7, 2008·IEEE Transactions on Neural Networks·F AiresN Scott
Jan 1, 1992·IEEE Transactions on Neural Networks·L A Wessels, E Barnard

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Citations

Jan 1, 2013·Neural Networks : the Official Journal of the International Neural Network Society·Laurent BourgoisMohammed Benjelloun
May 25, 2007·Neural Networks : the Official Journal of the International Neural Network Society·Vladimir M Krasnopolsky
Feb 1, 2008·Neural Networks : the Official Journal of the International Neural Network Society·Vladimir M KrasnopolskyAlexei A Belochitski
Jun 7, 2020·Sensors·Petros KarvelisChrysostomos Stylios
Feb 16, 2021·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·A J Geer
Feb 16, 2021·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Menno A VeermanChiel C van Heerwaarden
Feb 16, 2021·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·M G SchultzS Stadtler

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