The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation

Abstract

[1] The links between optimal precursor (OPR) and optimally growing initial error (OGIE) in the predictability studies of Kuroshio large meander (LM) are investigated using the Conditional Nonlinear Optimal Perturbation approach within a 1.5-layer shallow-water model. The OPR is a kind of initial anomaly that is the easiest to cause the occurrence of Kuroshio LM path. The OGIE refers to another kind of initial perturbation that has the largest effects on the prediction of the LM path. Numerical results show that the spatial structures of OPR and OGIEs are similar and their dominant amplitudes are localized upstream of the occurrence region of Kuroshio LM, i.e., southeast of Kyushu. Their spatial patterns are closely related to the potential vorticity distribution of the reference states. The main features of the nonlinear evolutions of both OPR and OGIEs are also similar, which implies that they have a similar development mechanism. The similarities between OPR and OGIEs indicate that targeted observations may be implemented to improve the prediction of the Kuroshio LM path. We define sensitive areas using the spatial patterns of OGIEs and investigate their usefulness. The results show that the initial errors in sensitive areas have greater impacts on the prediction of the Kuroshio LM path than those in other randomly selected areas. In addition, the prediction is more significantly improved when eliminating the part of initial error in the sensitive area than in other areas.Citation: Wang, Q., M. Mu, and H. A. Dijkstra (2013), The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation,

Optimal nonlinear excitation of decadal variability of the North Atlantic thermohaline circulation

“…This method has been used in predictability of ENSO events (Duan et al, 2004;Mu et al, 2007a, b;Duan et al, 2008Duan et al, , 2012Yu et al, 2012a, b), predictability of the Kuroshio Large Meander (Wang et al, 2011(Wang et al, , 2013, adaptive observations for tropical cyclones Qin and Mu, 2011a, b), sensitivity and decadal variability of THC in box models Sun et al, 2005;Wu and Mu, 2009), ecosystem sensitivity (Sun and Mu, 2011), and the study of ensemble forecast ( Jiang et al, 2009). For a review of the application of CNOP, we refer to Duan and Mu (2009).…”

Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño

“…Generally speaking, these additional observations would be assimilated by a data assimilation system to provide the numerical model a more reliable initial state. The idea of the target observation has been applied to some weather and climate events forecasting, such as Fronts and Atlantic Storm-Track Experiment (FASTEX; Synder 1996), North Pacific Experiment (NORPEX; Langland et al 1999), tropical cyclone (TC; Qin et al 2013;Qin and Mu 2012;Zhou and Mu 2011), Kuroshio large meander (KLM; Wang et al 2013), India Ocean Dipole (IOD; Feng et al 2014), ENSO Hu and Duan 2016), etc.…”

“…Recently, the nonlinear approach of CNOP has been successfully used to determine the sensitive areas for targeting in TC, IOD, KLM forecasting (Qin et al 2013;Qin and Mu 2012;Zhou and Mu 2011;Feng et al 2014;Wang et al 2013). For the TC forecasting, the CNOP is computed case by case and the sensitive areas for targeting observation are case dependent (Qin et al 2013;Qin and Mu 2012;Zhou and Mu 2011).…”

Sensitivity and resilience of the climate system: A conditional nonlinear optimization approach

“…The CNOP [12] methodology has been applied to derive nonlinear stability boundaries in many idealized fluid flows, such as pipe flows [13,14]. It has also been much used to study the predictability of properties in geophysical flows [12,15].…”