Applications of Conditional Nonlinear Optimal
Perturbation to the Predictability Studies
Professor Mu Mu
Fudan University, Shanghai
23 May, 10:30 am, in 2155
In this presentation, I will introduce a nonlinear optimization
approach to the predictability studies in atmosphere and oceans, which
is conditional nonlinear optimal perturbation (CNOP). For initial
perturbation problem, CNOP satisfies a given constraint and has the
largest nonlinear evolution at the prediction time, which is a natural
generalization of the linear singular vector to the nonlinear regime.
When considering perturbations of model parameters, CNOP causes the
largest departure from a given reference state at prediction time.
The physical meaning of CNOP depends on the problems, which could
represent the optimal precursors for a weather or climate event onset,
for example, the precursors of blockings, north Atlantic oscillation
(NAO), and ENSO events, etc. In predictability studies, CNOP stands for
the initial error, or parameter errors, that has the largest negative
effect on prediction, and in sensitivity analysis, CNOP is the most
unstable (sensitive) mode.
I will briefly present some applications of CNOP to the ENSO spring
predictability barrier, Indian Ocean dipole, North Atlantic oscillation
(NAO) onset, and ocean circulation of Kuroshio path variations. An
interesting phenomenon that the similarities between optimal precursors
and optimally growing initial errors will be shown, and related
targeted observations issues will be discussed.
The challenges, related to the calculations of CNOP, to the targeted
observations of tropical cyclones, and to ensemble forecasts, will be