Euclidean distance based permutation methods in atmospheric science

The majority of existing statistical methods inherently involve complex non metric analysis spaces due to their least squares regression origin; conseq uently, the analysis space of such statistical methods is not consistent wi th the simple metric Euclidean geometry of the data space in question. The statistical methods presented in this paper are consistent with the data sp aces in question. These alternative methods depend on exact and approximate permutation procedures for univariate and multivariate data involving cycl ic phenomena, autoregressive patterns, covariate residual analyses includin g most linear model based experimental designs, and linear and nonlinear pr ediction model evaluations. Specific atmospheric science applications inclu de climate change, Atlantic basin seasonal tropical cyclone predictions, an alyses of weather modification experiments, and numerical model evaluations for phenomena such as cumulus clouds, clear-sky surface energy budgets, an d mesoscale atmospheric predictions.