A Method for Identifying the Sensitive Areas in Targeted Observations for Tropical Cyclone Prediction: Conditional Nonlinear Optimal Perturbation

2009 ◽  
Vol 137 (5) ◽  
pp. 1623-1639 ◽  
Author(s):  
Mu Mu ◽  
Feifan Zhou ◽  
Hongli Wang
Keyword(s):  
Tropical Cyclone ◽  
Natural Extension ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation ◽  
Sensitive Areas ◽  
Sensitivity Experiments ◽  
Adaptive Observation ◽  
Adaptive Observations ◽  
Cyclone Prediction

Abstract Conditional nonlinear optimal perturbation (CNOP), which is a natural extension of the linear singular vector into the nonlinear regime, is proposed in this study for the determination of sensitive areas in adaptive observations for tropical cyclone prediction. Three tropical cyclone cases, Mindulle (2004), Meari (2004), and Matsa (2005), are investigated. Using the metrics of kinetic and dry energies, CNOPs and the first singular vectors (FSVs) are obtained over a 24-h optimization interval. Their spatial structures, their energies, and their nonlinear evolutions as well as the induced humidity changes are compared. A series of sensitivity experiments are designed to find out what benefit can be obtained by reductions of CNOP-type errors versus FSV-type errors. It is found that the structures of CNOPs may differ much from those of FSVs depending on the constraint, metric, and the basic state. The CNOP-type errors have larger impact on the forecasts in the verification area as well as the tropical cyclones than the FSV-types errors. The results of sensitivity experiments indicate that reductions of CNOP-type errors in the initial states provide more benefits than reductions of FSV-type errors. These results suggest that it is worthwhile to use CNOP as a method to identify the sensitive areas in adaptive observation for tropical cyclone prediction.

scholarly journals A Novel Approach for Solving CNOP and its Application in Identifying Sensitive Regions of Tropical Cyclone Adaptive Observations

2018 ◽  
Author(s):  
Linlin Zhang ◽  
Bin Mu ◽  
Shijin Yuan ◽  
Feifan Zhou
Keyword(s):  
Tropical Cyclone ◽  
Mesoscale Model ◽  
Search Algorithm ◽  
Principal Component ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation ◽  
Fifth Generation ◽  
Novel Approach ◽  
Adaptive Observations ◽  
Parallel Particle Swarm Optimization

Abstract. In this paper, a novel approach is proposed for solving conditional nonlinear optimal perturbation (CNOP), named it adaptive cooperation co-evolution of parallel particle swarm optimization and wolf search algorithm (ACPW) based on principal component analysis. Taking Fitow (2013) and Matmo (2014) as two tropical cyclone (TC) cases, CNOP solved by ACPW is used to investigate the sensitive regions identification of TC adaptive observations with the fifth-generation mesoscale model (MM5). Meanwhile, the 60 km and 120 km resolutions are adopted. The adjoint-based method (short for the ADJ-method) is also applied to solve CNOP, and the result is used as a benchmark. To validate the validity of ACPW, the CNOPs obtained from the different methods are compared in terms of the patterns, energies, similarities and simulated TC tracks with perturbations. (1) The ACPW can capture similar CNOP patterns with the ADJ-method, and the patterns of TC Fitow are more similar than TC Matmo. (2) When using the 120 km resolution, similarities between CNOPs of the ADJ-method and ACPW are higher than those using the 60 km. (3) Compared to the ADJ-method, although the CNOPs of ACPW produce lower energies, they can obtain better benefits gained from the reduction of CNOPs, not only in the entire domain but also in the sensitive regions identified. (4) The sensitive regions identified by CNOPs-ACPW has the same influence on the improvements of the TC tracks forecast skills with those identified by CNOPs-ADJ-method. (5) The ACPW has a higher efficiency than the ADJ-method. All conclusions prove that ACPW is a meaningful and effective method for solving CNOP and can be used to identify sensitive regions of TC adaptive observations.

scholarly journals Conditional Nonlinear Optimal Perturbations: Adjoint-Free Calculation Method and Preliminary Test

2010 ◽  
Vol 138 (4) ◽  
pp. 1043-1049 ◽  
Author(s):  
Bin Wang ◽  
Xiaowei Tan
Keyword(s):  
Prediction Models ◽  
Natural Extension ◽  
Preliminary Test ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Growth Ratio ◽  
Optimal Perturbation ◽  
New Approach ◽  
Simple Theoretical Model ◽  
Simplex Search ◽  
Sensitivity Studies

Abstract An ensemble-based approach is proposed to obtain conditional nonlinear optimal perturbation (CNOP), which is a natural extension of linear singular vector to a nonlinear regime. The new approach avoids the use of adjoint technique during maximization and is thus more attractive. Comparisons among CNOPs of a simple theoretical model generated by the ensemble-based, adjoint-based, and simplex-search methods, respectively, not only show potential equivalence of the first two approaches in application according to their very similar spatial structures and time evolutions of the CNOPs, but also reveal the limited performance of the third measure, an existing adjoint-free algorithm, due to its inconsistent spatial distribution and weak net growth ratio of norm square of CNOP comparing with the results of the first two methods. Because of its attractive features, the new approach is likely to make it easier to apply CNOP in predictability or sensitivity studies using operational prediction models.

scholarly journals A Study on the Reduction of Forecast Error Variance by Three Adaptive Observation Approaches for Tropical Cyclone Prediction

2011 ◽  
Vol 139 (7) ◽  
pp. 2218-2232 ◽  
Author(s):  
Xiaohao Qin ◽  
Mu Mu
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Forecast Error ◽  
Error Variance ◽  
Northwest Pacific ◽  
Optimal Perturbation ◽  
Observational Network ◽  
Steering Flow ◽  
Forecast Error Variance ◽  
Cyclone Prediction

Abstract Three adaptive approaches for tropical cyclone prediction are compared in this study: the conditional nonlinear optimal perturbation (CNOP) method, the first singular vector (FSV) method, and the ensemble transform Kalman filter (ETKF) method. These approaches are compared for 36-h forecasts of three northwest Pacific tropical cyclones (TCs): Matsa (2005), Nock-Ten (2004), and Morakot (2009). The sensitive regions identified by each method are obtained. The CNOPs form an annulus around the storm at the targeting time, the FSV targets areas north of the storm, and the ETKF closely targets the typhoon location itself. The sensitive results of both the CNOPs and FSV collocate well with the steering flow between the subtropical high and the TCs. Furthermore, the regions where the convection is strong are targeted by the CNOPs. Relatively speaking, the ETKF sensitive results reflect the large-scale flow. To identify the most effective adaptive observational network, numerous probes or flights were tested arbitrarily for the ETKF method or according to the calculated sensitive regions of the CNOP and FSV methods. The results show that the sensitive regions identified by these three methods are more effective for adaptive observations than the other regions. In all three cases, the optimal adaptive observational network identified by the CNOP and ETKF methods results in similar forecast improvements in the verification region at the verification time, while the improvement using the FSV method is minor.

The time and regime dependencies of sensitive areas for tropical cyclone prediction using the CNOP method

2012 ◽  
Vol 29 (4) ◽  
pp. 705-716 ◽  
Author(s):  
Feifan Zhou ◽  
Mu Mu
Keyword(s):  
Tropical Cyclone ◽  
Sensitive Areas ◽  
Cyclone Prediction

Target observation of mesoscale eddies in the ocean

2020 ◽  
Author(s):  
Lin Jiang ◽  
Wansuo Duan
Keyword(s):  
Ocean Circulation ◽  
Mesoscale Eddies ◽  
Global Ocean ◽  
Sensitive Area ◽  
Ocean Energy ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation ◽  
Sensitive Areas ◽  
Grid Points ◽  
Better Than

<p>Previous studies show that the kinetic energy of mesoscale eddies (MEs) accounts for more than 80% of the global ocean energy. The theoretical study and numerical simulation of MEs will enable us to better understand the dynamics of ocean circulation. Weiss and Grooms (2017) found that assimilating uniform observations taken over MEs is much better than assimilating a subset of observations on a regular grid for improving prediction skill of SSH associated with ocean state. In the present study, we use a conditional nonlinear optimal perturbation (CNOP) approach to investigate the sensitivity of the ocean state sea surface height (SSH) predictions on MEs with a two-layer quasi-geostrophic model and show the optimal assimilating scheme. In the study, the CNOPs of SSH predictions are first computed. It is found that, if one regards the regions covered by the grid points with large values of CNOPs as sensitive area of SSH predictions, the sensitive areas are mainly located on MEs. Furthermore, the stronger the MEs, the more the MEs grid points covered by the sensitive area. Especially, these grid points associated with sensitive areas are not uniformly distributed over the MEs. It is obvious that the predictions of SSH are quite sensitive to the initialization of MEs (especially that of the particular region of large values of CNOPs for strong MEs, rather than of the uniformly distributed grid points over MEs). Therefore, an appropriate initialization of MEs is much helpful for improving the prediction accuracy of SSH. And the CNOPs of SSH prediction here may provide useful information on how to improve initialization of MEs.</p>

CNOP-based sensitive areas identification for tropical cyclone adaptive observations with PCAGA method

2017 ◽  
Vol 53 (1) ◽  
pp. 63-73 ◽  
Author(s):  
Lin-Lin Zhang ◽  
Shi-Jin Yuan ◽  
Bin Mu ◽  
Fei-Fan Zhou
Keyword(s):  
Tropical Cyclone ◽  
Sensitive Areas ◽  
Adaptive Observations
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