Neural network applications to developing hybrid atmospheric and oceanic numerical models

Abstract

The past several decades revealed a well pronounced trend in geosciences. This trend marks a transition from investigating simpler linear or weakly nonlinear single-disciplinary systems like simplified atmospheric or oceanic systems that include a limited description of the physical processes, to studying complex nonlinear multidisciplinary systems like coupled atmospheric-oceanic climate systems that take into account atmospheric physics, chemistry, land-surface interactions, etc. The most important property of a complex interdisciplinary system is that it consists of subsystems that, by themselves, are complex systems. Accordingly, the scientific and practical significance of interdisciplinary complex geophysical/environmental numerical models has increased tremendously during the last few decades, due to improvements in their quality via developments in numerical modeling and computing capabilities. © 2009 Springer Netherlands.