A. Trevisan et R. Legnani, TRANSIENT ERROR GROWTH AND LOCAL PREDICTABILITY - A STUDY IN THE LORENZ SYSTEM, Tellus. Series A, Dynamic meteorology and oceanography, 47(1), 1995, pp. 103-117
Lorenz's three-variable convective model is used as a prototypical cha
otic system in order to develop concepts related to finite time local
predictability. Local predictability measures can be represented by gl
obal measures only if the instability properties of the attractor are
homogeneous in phase space. More precisely, there are two sources of v
ariability of predictability in chaotic attractors. The first depends
on the direction of the initial error vector, and its dependence is li
mited to an initial transient period. IF the attractor has homogeneous
predictability properties, this is the only source of variability of
error growth rate and, after the transient has elapsed, all initial pe
rturbations grow at the same rate, given by the first (global) Lyapuno
v exponent. The second is related to the local instability properties
in phase space. If the predictability properties of the attractor are
not homogeneous, this additional source of variability affects both th
e transient and post-transient phases of error growth. After the trans
ient phase all initial perturbations of a particular initial condition
grow at the same rate, given in this case by the first local Lyapunov
exponent. We consider various currently used indexes to quantify fini
te time local predictability. The probability distributions of the dif
ferent indexes are examined during and after the transient phase. By c
omparing their statistics it is possible to discriminate the relative
importance of the two sources of variability of predictability and to
determine the most appropriate measure of predictability for a given f
orecast time. It is found that a necessary premise for choosing a rele
vant local predictability index for a specific system is the study of
the characteristics of its transient. The consequences for the problem
of forecasting forecast skill in operational models are discussed.