Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts

Qin, Xiangjing; Mu, M.

doi:10.1002/qj.902

Cited by 54 publications

(40 citation statements)

References 38 publications

(34 reference statements)

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“…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.…”

Section: The Target Observationmentioning

confidence: 99%

“…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).…”

Section: The Target Observationmentioning

confidence: 99%

“…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). However, the observations for ENSO is such a complex project with large investment and time consuming that we expect it can provide information for more than just one specific El Niño prediction.…”

Section: The Target Observationmentioning

confidence: 99%

See 2 more Smart Citations

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

Duan

Tian

2018

Clim Dyn

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This paper investigates the optimal observational array for improving the initialization of El Niño-Southern Oscillation predictions by exploring the sensitive areas for target observations of two types of El Niño predictions. The sensitive areas are identified by calculating the optimally growing errors (OGEs) of the Zebiak-Cane model, as corrected by the optimal forcing vector that is determined by assimilating the observed sea surface temperature anomalies (SSTAs). It is found that although the OGEs have similar structures for different start months of predictions, the regions covered by much large errors for the SSTA component tend to locate at different zonal positions and depends on the start months. Furthermore, these regions are also in difference between two types of El Niño events. The regions covered by large errors of OGEs represent the sensitive areas for target observations. Considering the dependence of the sensitive areas on related El Niño types and the start months of predictions, the present study propose a quantitative frequency method to determine the sensitive areas for target observations associated with two types of El Niño predictions, which is expected to be applicable for both types of El Niño predictions with different start months. As a result, the sensitive areas that describe the array of target observations are presented with a reversal triangle-like shape locating in the eastern Pacific, specifically the area of 120°W-85°W, 0°S-11°S, and an extension to the west along the equator and then gathering at the 180° longitude and the western boundary. "Hindcast" experiments demonstrated that such observational array is very useful in distinguishing two types of El Niño and superior to the TAO/TRITON array. It is therefore suggested that the observational array provided in the present study is towards the optimal one and the original TAO/TRITON array should be further optimized when applied to predictions of the diversities of El Niño events.

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Section: The Target Observationmentioning

confidence: 99%

Section: The Target Observationmentioning

confidence: 99%

Section: The Target Observationmentioning

confidence: 99%

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

Duan

Tian

2018

Clim Dyn

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“…At present, targeted observations, also called adaptive observations, determine certain special areas (called sensitive areas) that cause large uncertainty in forecast results. These additional observations in these sensitive areas supply more reliable initial states for the model and thus a more accurate prediction is expected (Palmer et al 1998;Bishop et al 2001;Wu et al 2005;Qin and Mu 2011). Mu (2013) proposed a new idea to address the second predictability problem by generalizing targeted observations from geographical space to phase space for model parameters.…”

Section: Sensitivity Experiments To Identify the Five Most Important Pmentioning

confidence: 99%

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

Sun

2016

Theor Appl Climatol

Self Cite

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An important source of uncertainty, which causes further uncertainty in numerical simulations, is that residing in the parameters describing physical processes in numerical models. Therefore, finding a subset among numerous physical parameters in numerical models in the atmospheric and oceanic sciences, which are relatively more sensitive and important parameters, and reducing the errors in the physical parameters in this subset would be a far more efficient way to reduce the uncertainties involved in simulations. In this context, we present a new approach based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) method. The approach provides a framework to ascertain the subset of those relatively more sensitive and important parameters among the physical parameters. The Lund-Potsdam-Jena (LPJ) dynamical global vegetation model was utilized to test the validity of the new approach in China. The results imply that nonlinear interactions among parameters play a key role in the identification of sensitive parameters in arid and semiarid regions of China compared to those in northern, northeastern, and southern China. The uncertainties in the numerical simulations were reduced considerably by reducing the errors of the subset of relatively more sensitive and important parameters. The results demonstrate that our approach not only offers a new route to identify relatively more sensitive and important physical parameters but also that it is viable to then apply Btarget observations^to reduce the uncertainties in model parameters.

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“…These methods are particularly useful to determine regions of sensitivities to ocean initial conditions (Montani et al 1999;Leutbecher et al 2002). They have been shown to be extremely efficient in improving typhoon forecasts (Qin and Mu 2011;Zhou and Mu 2011), for instance. Hence, in the context of the AMOC, Wunsch (2010) and Heimbach et al (2011) suggested the use of these methods to assess regions where enhanced sampling strategy can improve prediction.…”

Section: Introductionmentioning

confidence: 99%

Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

et al. 2017

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In this study, the relation between two approaches to assess the ocean predictability on interannual to decadal time scales is investigated. The first pragmatic approach consists of sampling the initial condition uncertainty and assess the predictability through the divergence of this ensemble in time. The second approach is provided by a theoretical framework to determine error growth by estimating optimal linear growing modes. In this paper, it is shown that under the assumption of linearized dynamics and normal distributions of the uncertainty, the exact quantitative spread of ensemble can be determined from the theoretical framework. This spread is at least an order of magnitude less expensive to compute than the approximate solution given by the pragmatic approach. This result is applied to a state-of-the-art Ocean General Circulation Model to assess the predictability in the North Atlantic of four typical oceanic metrics: the strength of the Atlantic Meridional Overturning Circulation (AMOC), the intensity of its heat transport, the two-dimensional spatially-averaged Sea Surface Temperature (SST) over the North Atlantic, and the three-dimensional spatially-averaged temperature in the North Atlantic. For all tested metrics, except for SST, ∼ 75% of the total uncertainty on interannual time scales can be attributed to oceanic initial condition uncertainty rather than atmospheric stochastic forcing. The theoretical method also provide the sensitivity pattern to the initial condition uncertainty, allowing for targeted measurements to improve the skill of the prediction. It is suggested that a relatively small fleet of several autonomous underwater vehicles can reduce the uncertainty in AMOC strength prediction by 70% for 1-5 years lead times.

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Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts

Cited by 54 publications

References 38 publications

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

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

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

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