Nonlinearity plays a significant role in determining the formation of the spatial structure. According to Muraki , simple models as well as complex predictive models are needed to understand weather science. Complicated models don t give you much in terms of basic principles because they have everything, including the kitchen sink. It can answer how much . As an example, Muraki points to the basic asymmetry of low and high pressure weather systems.
Low pressure is generally a harbinger of bad weather while high pressure creates lovely blue skies.
Modern weather computers predict this asymmetry beautifully. But Muraki and his NCAR collaborators have some pretty good ideas. Originally from Scarborough, Ontario, Muraki recently moved here from New York where he was teaching at the Courant Institute of New York University. You still need the human being there.