Publications

Minimising forecast error requires accurately specifying the initial state from which 
the forecast is made by optimally using available observing resources to obtain the 
most accurate possible analysis. The Kalman filter accomplishes this for linear sys...

Understanding of the stability of deterministic and stochastic dynamical systems 
has evolved recently from a traditional grounding in the system’s normal modes 
to a more comprehensive foundation in the system’s propagator and especially in 
an...

Temporally distributed deterministic and stochastic excitation of the tangent linear forecast system gov-
erning forecast error growth and the tangent linear observer system governing assimilation error growth is 
examined. The method used is to determine...

Synoptic-scale eddy variance and fluxes of heat and momentum in midlatitude jets are sensitive to small changes in mean jet velocity, dissipation, and static stability. In this work the change in the jet producing the greatest increase in variance or flux...

Perturbation growth in uncertain systems is examined and related to previous work in which linear stability 
concepts were generalized from a perspective based on the nonnormality of the underlying linear operator. In 
this previous work the linear...

Perturbation growth in uncertain systems associated with fluid flow is examined concentrating on deriving, solving, and interpreting equations governing the ensemble mean covariance. Covariance evolution equations are obtained for fluctuating operators...

In studies of perturbation dynamics in physical systems, certain specification of the gov-
erning perturbation dynamical system is generally lacking, either because the perturba-
tion system is imperfectly known or because its specification is...

Methods for approximating a stable linear autonomous dynamical system of lower order are examined. Reducing the order of a dynamical system is useful theoretically in identifying the irreducible dimension of the dynamics and in isolating the dominant...

Minimizing forecast error requires accurately specifying the initial state from which the forecast is made by 
optimally using available observing resources to obtain the most accurate possible analysis. The Kalman filter 
accomplishes this for a wide...

forecast reliability is known to be the highly variable and this variability can be traced in part to differences in the innate predictability of atmospheric flow regimes. These differences in turn have traditionally been ascribed to variation in the...