University of Reading, NCAS Seminar Series, Friday 5th October 10am, Met GU01
TITLE
Stochastically Generated Skew: Implications for Forecast Probabilities
ABSTRACT
The salient non-Gaussian features of observed climatological distributions are captured by so-called stochastically generated skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise and as such represent the simplest physically based prototypes of the observed distributions. As is true in most dynamical systems, an ensemble-mean forecast based on this process is generally not the most likely forecast, and the forecast uncertainty depends on the initial conditions, even when the initial conditions are perfect. Further, the heavy tails associated with SGS distributions have large ramifications for prediction of extremes. In this talk, I will discuss these and other annoyances.
