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

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.

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

10.5194/npg-2018-17 ◽

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.

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Singular Vector Structure and Evolution of a Recurving Tropical Cyclone

Monthly Weather Review ◽

10.1175/2008mwr2643.1 ◽

2009 ◽

Vol 137 (2) ◽

pp. 505-524 ◽

Cited By ~ 31

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.

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

Journal of the Atmospheric Sciences ◽

10.1175/jas3545.1 ◽

2005 ◽

Vol 62 (10) ◽

pp. 3825-3830 ◽

Cited By ~ 6

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

10.5194/npg-2019-24 ◽

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.

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QuikSCAT scatterometer wind data impact on tropical cyclone

MAUSAM ◽

10.54302/mausam.v57i1.463 ◽

2021 ◽

Vol 57 (1) ◽

pp. 41-150

Author(s):

COMMANDER G. RAMBABU

Keyword(s):

Tropical Cyclone ◽

Additional Data ◽

Mesoscale Model ◽

Positive Impact ◽

Wind Data ◽

Data Simulation ◽

Initial Field ◽

Fifth Generation ◽

Penn State ◽

The Impact

lkj & bl v/;;u esa eslksLdsy fun’kZ ¼,e- ,e- 5½ dk mi;ksx djrs gq, m".kdfVca/kh; pØokr fo’ys"k.kksa vkSj iwokZuqekuksa ij fDodLdsV LdsVªksehVj vk¡dM+ksa ds ldkjkRed izHkko dk mYys[k fd;k x;k gSA fDodLdsV ds vk¡dM+s fo’ks"k :i ls blfy, Hkh ewY;oku gSa D;kasfd os m".kdfVca/kh; pØokrksa ds eqf’dy ls izkIr gksus okys vk¡dM+ksa ds {ks=ksa esa gh ugha cfYd es?kkPNUu vkSj o"kkZ dh fLFkfr;ksa esa Hkh miyC/k jgrs gSaA bl v/;;u ds fy, mi;ksx fd;k x;k fun’kZ ,e- ,e- 5 ik¡poha ih<+h ds ,u- lh- ,- vkj- @ isu LVsV eslksLdsy fun’kZ ds uke ls tkuk tkrk gSA fDodLdsV LdsVªªksehVj iou vk¡dM+ksa ds izHkko dks le>us vkSj mldh tk¡p djus ds fy, 1999 ls 2003 dh vof/k ds nkSjku dqN m".kdfVca/kh; pØokrksa ds fy, LdsVªªksehVj vk¡dM+ksa dk lekos’ku lfgr vkSj fcuk lekos’ku ds izfr:i.k fd;k x;k gSA pØokrh fLFkfr gsrq ml le; fo|eku dqN iksrksa ij fy, x, vk¡dM+sa vkSj dqN rVh; vFkok }hiksa ds dsUnzksa ij izkIr fd, x, vk¡dM+sa gh miyC/k gSaA izs{k.k }kjk izkIr fd, x, vk¡dM+ksa dk ,e- ,e- 5 esa lfEefyr djus ds fy, vyx&vyx le;ksa ij fy, x, fDodLdsV ds dqN iklsa miyC/k gSaA vk¡dM+ksa dks lfEefyr djus ds fy, bu vfrfjDr vk¡dM+ksa ls izkjEHk esa fy, x, vk¡dMksa esa o`f) gqbZ gSA buls izkIr gq, ifj.kkeksa ls ;g irk pyk gS fd LdsVªªksehVj vk¡dM+ksa ds lekos’ku ls izkjfEHkd {ks= okLrfod fLFkfr ds vf/kd fudV FkkA iwokZuqeku tk¡p ls ;g Hkh irk pyk gS fd mixzg ls izkIr fd, x, vk¡dM+ksa ds lekos’ku ls 48 ?kaVs dh vof/k rd dk iwokZuqeku nsus esa lq/kkj gqvk gSA This study describes the positive impact of QuikSCAT Scatterometer data on tropical cyclone analyses and forecasts using a Mesoscale Model (MM5). QuikSCAT data is especially valuable because they are available in the data sparse genesis regions of tropical cyclones, and because they are available in cloudy and rainy conditions. The model used in the study, MM5 is known as fifth generation NCAR/Penn State Mesoscale model (MM5). In order to understand and investigate the impact of QuikSCAT Scatterometer wind data, simulation with and without assimilation of scatterometer data has been performed for a few tropical cyclone cases during the period 1999 to 2003. For a cyclonic situation, data of few ships of opportunity and of some coastal or island stations are only available. For the assimilation of observed data into MM5, a few passes of QuikSCAT at different times are available. These additional data strengthen the initial data for assimilation. The results showed that the initial field with the inclusion of scatterometer data was nearer to the actual situation. In the prediction experiment, it was also shown that the inclusion of satellite data improved the prediction up to 48 hrs.

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A Comparison of Two Tropical Cyclone Bogussing Schemes

Weather and Forecasting ◽

10.1175/2007waf2006094.1 ◽

2008 ◽

Vol 23 (1) ◽

pp. 194-204 ◽

Cited By ~ 11

Author(s):

Dongliang Wang ◽

Xudong Liang ◽

Ying Zhao ◽

Bin Wang

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Vortex Structure ◽

Mesoscale Model ◽

Initial Conditions ◽

Negative Impact ◽

Initial Structure ◽

State University ◽

Fifth Generation ◽

The Impact

Abstract The impact of two bogussing schemes on tropical cyclone (TC) forecasts is compared. One scheme for bogussing TCs into the initial conditions of the nonhydrostatic version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is proposed by NCAR and the Air Force Weather Agency (AFWA), and four-dimensional variational data assimilation technology is employed for the other bogus data assimilation (BDA) scheme. The initial vortex structure adjusted by the NCAR–AFWA (N–A) scheme is more physically realistic, while the BDA scheme produces an initial vortex structure that is more consistent with the model. The results from 41 forecasts of TCs occurring over the western North Pacific (WNP) in 2002 suggest that the adjustment of the initial structure in the BDA scheme produces a greater benefit to the subsequent track and intensity forecasts, and the improvements in the track and intensity forecasts are significant using the BDA scheme. It seems that when using a model with 45-km grid length, the N–A scheme has a negative impact on the track forecasts for the recurving TCs and on the intensity predictions after 24 h.

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Interactions between Simulated Tropical Cyclones and an Environment with a Variable Coriolis Parameter

Monthly Weather Review ◽

10.1175/mwr3359.1 ◽

2007 ◽

Vol 135 (5) ◽

pp. 1889-1905 ◽

Cited By ~ 37

Author(s):

Elizabeth A. Ritchie ◽

William M. Frank

Keyword(s):

Tropical Cyclone ◽

Tropical Cyclones ◽

Large Scale ◽

Inner Core ◽

Mesoscale Model ◽

Vertical Wind Shear ◽

State University ◽

Coriolis Parameter ◽

Fine Mesh ◽

Fifth Generation

Abstract Numerical simulations of tropical cyclones are performed to examine the effects of a variable Coriolis parameter on the structure and intensity of hurricanes. The simulations are performed using the nonhydrostatic fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model using a 5-km fine mesh and fully explicit representation of moist processes. When a variable Conolis parameter ( f ) environment is applied to a mature tropical cyclone, a persistent north-northwesterly shear develops over the storm center as a result of an interaction between the primary circulation of the storm and the gradient in absolute vorticity. As a result, the variable-f storm quickly develops a persistent wavenumber-1 asymmetry in its inner-core structure with upward motion and rainfall concentrated on the left side of the shear looking downshear, in agreement with earlier studies. In comparison, the constant-f storm develops weak transient asymmetries in structure that are only partially related to a weak vertical wind shear. As a result, it is found that the tropical cyclone with variable f intensifies slightly more slowly than that with constant f, and reaches a final intensity that is about 5 mb weaker. It is argued that this “beta shear” is not adequately represented in large-scale analyses and so does not figure into calculations of environmental shear. Although the effect of the beta shear on the tropical cyclone intensity seems small by itself, when combined with the environmental shear it can produce a large net shear or it can reduce an environmental shear below the apparent threshold to impact storm intensity. If this result proves to be generally true, then the presence of an additional overlooked beta shear may well explain differences in the response of tropical cyclone intensification to westerly versus easterly shear regimes.

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Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

Mathematics ◽

10.3390/math8111970 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1970

Author(s):

Jun-Kyu Park ◽

Suwoong Lee ◽

Aaron Park ◽

Sung-June Baek

Keyword(s):

Search Algorithm ◽

Principal Component ◽

Search Method ◽

Reference Spectrum ◽

Search Range ◽

Fast Search ◽

Input Spectrum ◽

Computationally Intensive ◽

Multiple Clusters ◽

Full Search

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

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Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5

Weather and Forecasting ◽

10.1175/waf-d-11-00004.1 ◽

2012 ◽

Vol 27 (2) ◽

pp. 438-450 ◽

Cited By ~ 29

Author(s):

Chih-Chiang Wei

Keyword(s):

Statistical Models ◽

Mesoscale Model ◽

Gaussian Kernel ◽

Support Vector ◽

State University ◽

Rainfall Prediction ◽

Fifth Generation ◽

Shihmen Reservoir ◽

Reservoir Watershed ◽

Lag Times

Abstract This study presents two support vector machine (SVM) based models for forecasting hourly precipitation during tropical cyclone (typhoon) events. The two SVM-based models are the traditional Gaussian kernel SVMs (GSVMs) and the advanced wavelet kernel SVMs (WSVMs). A comparison between the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and statistical models, including SVM-based models and linear regressions (regression), was made in terms of performance of rainfall prediction at the Shihmen Reservoir watershed in Taiwan. Data from 73 typhoons affecting the Shihmen Reservoir watershed were included in the analysis. This study designed six attribute combinations with different lag times for the forecast target. The modified RMSE, bias, and estimated threat score (ETS) results were employed to assess the predicted outcomes. Results show that better attribute combinations for typhoon climatologic characteristics and typhoon precipitation predictions occurred at 0-h lag time with modified RMSE values of 0.288, 0.257, and 0.296 in GSVM, WSVM, and the regression, respectively. Moreover, WSVM having average bias and ETS values close to 1.0 gave better predictions than did the GSVM and regression models. In addition, Typhoons Zeb (1998) and Nari (2001) were selected for comparison between the MM5 model output and the developed statistical models. Results showed that the MM5 tended to overestimate the peak and cumulative rainfall amounts while the statistical models were inclined to yield underestimations.

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Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

Weather and Forecasting ◽

10.1175/waf940.1 ◽

2006 ◽

Vol 21 (4) ◽

pp. 663-669 ◽

Cited By ~ 11

Author(s):

Dongliang Wang ◽

Xudong Liang ◽

Yihong Duan ◽

Johnny C. L. Chan

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Mesoscale Model ◽

State University ◽

Motion Vectors ◽

Geostationary Meteorological Satellite ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors ◽

Data Assimilation Technique ◽

The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

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