2006 Special issue: Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction

Authors:
Vladimir M. Krasnopolsky;Michael S. Fox-Rabinovitz
Affiliations:
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA and SAIC at EMC/NCEP/NOAA, 5200 Auth Road, Camp Springs MD, 20746-4304 USA;Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Venue:
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Year:
2006

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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 of developed HGCMs illustrate the feasibility and efficiency of the new approach for modeling complex multidimensional interdisciplinary systems.