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 A novel approach for solving CNOPs and its application in identifying sensitive regions of tropical cyclone adaptive observations

2018 ◽  
Vol 25 (3) ◽  
pp. 693-712
Author(s):  
Linlin Zhang ◽  
Bin Mu ◽  
Shijin Yuan ◽  
Feifan Zhou
Keyword(s):  
Tropical Cyclone ◽  
Mesoscale Model ◽  
Search Algorithm ◽  
Principal Component ◽  
Cooperative Coevolution ◽  
Hybrid Strategy ◽  
Fifth Generation ◽  
Novel Approach ◽  
Adaptive Observations ◽  
Parallel Particle Swarm Optimization

Abstract. In this paper, a novel approach is proposed for solving conditional nonlinear optimal perturbations (CNOPs), called the adaptive cooperative coevolution of parallel particle swarm optimization (PSO) and the Wolf Search algorithm (WSA) based on principal component analysis (ACPW). Taking Fitow (2013) and Matmo (2014), two tropical cyclone (TC) cases, CNOPs solved by the ACPW algorithm are used to investigate the sensitive regions identified by TC adaptive observations with the fifth-generation Mesoscale Model (MM5). Meanwhile, the 60 and 120 km resolutions are adopted. The adjoint-based method (short for the ADJ method) is also applied to solve CNOPs, and the result is used as a benchmark. To evaluate the advantages of the ACPW algorithm, we run the PSO, WSA and ACPW programs 10 times and then compare the maximum, minimum and mean objective values as well as the RMSEs. The analysis results prove that the hybrid strategy and cooperative coevolution are useful and effective. To validate the ACPW algorithm, the CNOPs obtained from the different methods are compared in terms of the patterns, energies, similarities and simulated TC tracks with perturbations. The results of our study may be summarized as follows: The ACPW algorithm can capture similar CNOP patterns as the ADJ method, and the patterns of TC Fitow are more similar than TC Matmo. At the 120 km resolution, similarities between the CNOPs of the ADJ method and the ACPW algorithm are more than those at the 60 km resolution. Compared to the ADJ method, although the CNOPs of the ACPW method produce lower energies, they can have improved benefits gained from the reduction of the CNOPs not only across the entire domain but also in the identified sensitive regions. The sensitive regions identified by the CNOPs from the ACPW algorithm have the same influence on the improvements of the skill of TC-track forecasting as those identified by the CNOPs from the ADJ method. The ACPW method is more efficient than the ADJ method. All conclusions prove that the ACPW algorithm is a meaningful and effective method for solving CNOPs and can be used to identify sensitive regions of TC adaptive observations.

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 Singular Vector Structure and Evolution of a Recurving Tropical Cyclone

2009 ◽  
Vol 137 (2) ◽  
pp. 505-524 ◽  
Author(s):  
Hyun Mee Kim ◽  
Byoung-Joo Jung
Keyword(s):  
Tropical Cyclone ◽  
Mesoscale Model ◽  
Lower Troposphere ◽  
Development Stage ◽  
State University ◽  
Lanczos Algorithm ◽  
Pennsylvania State University ◽  
Fifth Generation ◽  
Adaptive Observations ◽  
Vector Structure

Abstract In this study, the structure and evolution of total energy singular vectors (SVs) of Typhoon Usagi (2007) are evaluated using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) and its tangent linear and adjoint models with a Lanczos algorithm. Horizontal structures of the initial SVs following the tropical cyclone (TC) evolution suggest that, relatively far from the region of TC recurvature, SVs near the TC center have larger magnitudes than those in the midlatitude trough. The SVs in the midlatitude trough region become dominant as the TC passes by the region of recurvature. Increasing magnitude of the SVs over the midlatitude trough regions is associated with the extratropical transition of the TC. While the SV sensitivities near the TC center are mostly associated with warming in the midtroposphere and inflow toward the TC along the edge of the subtropical high, the SV sensitivities in the midlatitude are located under the upper trough with upshear-tilted structures and associated with strong baroclinicity and frontogenesis in the lower troposphere. Given the results in this study, sensitive regions for adaptive observations of TCs may be different following the TC development stage. Far from the TC recurvature, sensitive regions near TC center may be important. Closer to the TC recurvature, effects of the midlatitude trough become dominant and the vertical structures of the SVs in the midlatitude are basically similar to those of extratropical cyclones.

scholarly journals CNOP based on ACPW for Identifying Sensitive Regions of Typhoon Target Observations with WRF Model

2019 ◽  
Author(s):  
Bin Mu ◽  
Linlin Zhang ◽  
Shijin Yuan ◽  
Wansuo Duan
Keyword(s):  
Positive Impact ◽  
Search Algorithm ◽  
Wrf Model ◽  
Principal Component ◽  
Horizontal Resolution ◽  
Optimal Perturbation ◽  
Arw Model ◽  
Parallel Particle Swarm Optimization ◽  
Typhoon Tracks ◽  
Energy Similarity

Abstract. In this paper, we rewrite the ACPW (adaptive cooperation co-evolution of parallel particle swarm optimization and wolf search algorithm based on principal component analysis) and applied it to solve conditional nonlinear optimal perturbation (CNOP) in the WRF-ARW for identifying sensitive areas of typhoon target observations, which is proposed by us in the study of Zhang et al. (2018), to investigate its feasibility and effectiveness in the WRF-ARW model. Fitow (2013) and Matmo (2014) are taken as two typhoon cases, and simulated with the 60 km horizontal resolution. The total dry energy is adopted as the objective function. The CNOP is also calculated by the method based on the adjoint model (ADJ-method) as a benchmark. To evaluate the ACPW-CNOP, five aspects are analysed, such as the pattern, energy, similarity, benefits from the CNOPs reduced in the whole domain and the sensitive regions identified, and the simulated typhoon tracks. The experimental results show that the temperature and wind patterns of ACPW-CNOP is similar to those of the ADJ-CNOP in all typhoons. And the similarity values of ADJ-CNOP and ACPW-CNOP of two typhoon cases are more than 0.5. When reducing CNOPs in the sensitive regions, the forecast income of ACPW-CNOP is greater than that of ADJ-CNOP in all typhoons. Moreover, the sensitive regions identified by the ACPW-CNOP has the similar influence with the ADJ-CNOP on the simulation of typhoon tracks, sometimes the ACPW-CNOP has more positive impact on the simulation of typhoon tracks. The ACPW is more efficient than the ADJ-method in this paper.

scholarly journals A Parallel Hybrid Intelligence Algorithm for Solving Conditional Nonlinear Optimal Perturbation to Identify Optimal Precursors of North Atlantic Oscillation

2019 ◽  
Author(s):  
Bin Mu ◽  
Jing Li ◽  
Shijin Yuan ◽  
Xiaodan Luo ◽  
Guokun Dai
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Hybrid Algorithm ◽  
Message Passing Interface ◽  
Numerical Models ◽  
Initial Perturbation ◽  
Principal Component ◽  
Local Optimum ◽  
Conditional Nonlinear Optimal Perturbation ◽  
Optimal Perturbation

Abstract. The North Atlantic Oscillation (NAO) is the most prominent atmospheric seesaw phenomenon in North Atlantic Ocean. It has a profound influence on the strength of westerly winds as well as the storm tracks in North Atlantic, thus affecting winter climate in Northern Hemisphere. Therefore, it is necessary to investigate the mechanism related with the NAO events. In this paper, conditional nonlinear optimal perturbation (CNOP), which has been widely used in research on the optimal precursor (OPR) of climatic event, is adopted to investigate which kind of initial perturbation is most likely to trigger the NAO anomaly pattern with the Community Earth System Model (CESM). Since CESM does not have an adjoint model, we propose an adjoint-free parallel principal component analysis (PCA) based genetic algorithm (GA) and particle swarm optimization (PSO) hybrid algorithm (PGAPSO) to solve CNOP in such a high dimensional numerical model. The results demonstrate that the OPRs obtained by CNOP trigger the reference flow into typical NAO mode, which provide the theoretical underpinning in observation and prediction. Furthermore, the hybrid algorithm can accelerate convergence and avoid falling into a local optimum. After parallelization with Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA), the PGAPSO algorithm achieves a speed-up of 40× compared with its serial version. The results as mentioned above indicate that the proposed algorithm can efficiently and effectively acquire CNOP and can also be generalized to other complex numerical models.

The Impact of Assimilating Dropwindsonde Data Deployed at Different Sites on Typhoon Track Forecasts

2013 ◽  
Vol 141 (8) ◽  
pp. 2669-2682 ◽  
Author(s):  
Boyu Chen ◽  
Mu Mu ◽  
Qin Xiaohao
Keyword(s):  
Mesoscale Model ◽  
Three Dimensional ◽  
Weather Research And Forecasting ◽  
Surveillance Program ◽  
State University ◽  
Optimal Perturbation ◽  
Fifth Generation ◽  
Typhoon Track ◽  
Adaptive Observation ◽  
The Impact

Abstract This study investigates the impacts on typhoon track forecasting by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and its three-dimensional variational data assimilation (3DVAR) system of assimilating dropwindsonde observational data acquired from different sites. All of the sonde data were obtained between 2004 and 2009 in the typhoon surveillance program Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). Experiments were conducted to test the model's response to five scenarios involving differing dropwindsonde data inputs: 1) no dropwindsonde data, 2) all available dropwindsonde data, 3) data gathered in sensitive regions identified by the conditional nonlinear optimal perturbation (CNOP) approach, 4) data gathered in sensitive regions identified by the first singular vector (FSV) approach, and 5) several sondes selected at random. The results show that using dropwindsonde data based on CNOP sensitivity can lead to improvements in typhoon track forecasting similar to, and occasionally better than, those achieved by assimilating all of the available data. Both approaches offered greater benefits than the other three alternatives averagely. It is proposed that CNOP provides a suitable approach to determining sensitive regions during adaptive observation of typhoons. Similar results may be obtained if the sensitivity products developed using MM5 are employed in the Weather Research and Forecasting Model (WRF), suggesting that it is applicable to utilize sensitivity produced by MM5 in WRF.

scholarly journals The Effects of the Full Coriolis Force on the Structure and Motion of a Tropical Cyclone. Part I: Effects due to Vertical Motion

2005 ◽  
Vol 62 (10) ◽  
pp. 3825-3830 ◽  
Author(s):  
Xudong Liang ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
Coriolis Force ◽  
Mesoscale Model ◽  
Vertical Motion ◽  
Momentum Equation ◽  
Scale Analysis ◽  
State University ◽  
Structure And Motion ◽  
Fifth Generation ◽  
Wind Structure

Abstract In most dynamical studies of synoptic-scale phenomena, only the components of the Coriolis force contributed by the horizontal motion are considered, and only in the horizontal momentum equation. The other components are neglected based on a scale analysis. However, it is shown that such an analysis may not be fully valid in a tropical cyclone (TC) and that these terms should be included. The two neglected terms are 1) ew, the Coriolis force in the x-momentum equation due to vertical motion, and 2) we, the Coriolis force in the vertical equation of motion due to the zonal wind. In this paper, effects of the first term (i.e., ew) on the structure and motion of a TC are investigated through numerical simulations using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). The results suggest that after the ew term has been included, the structure of a TC even on an f plane is changed. A southwestward displacement of a TC center with a speed of ∼1 km h−1 is found in the f-plane experiment. On a β plane, inclusion of the ew term gives a vortex track that is generally west to southwest of the inherent northwestward track (due to the β effect). A scale analysis suggests that the ew term can be as large as half the magnitude of the horizontal acceleration. This term generates an asymmetric wind structure with a generally easterly flow near the center, which therefore causes the vortex to displace toward the southwest. A rainfall asymmetry consistent with the convergence associated with the wind asymmetry is also found and accounts for 10%–20% of the symmetric parts.

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