Modeling typhoon wind power spectra near sea surface based on measurements in the South China sea

Li, Lixiao; Xiao, Yiqing; Kareem, Ahsan; Song, Lili; Peng, Qin

doi:10.1016/j.jweia.2012.04.005

Cited by 65 publications

(31 citation statements)

References 51 publications

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“…The data are compared with the spectral sub-ranges analyzed in the previous section. An earlier experimental study has been conducted to establish a data-driven model for velocity spectra in tropical cyclone winds using data obtained at Zhizai Island in tropical cyclone Hagupit (Li et al 2012).…”

Section: Observation Towers and Instrumentationsmentioning

confidence: 99%

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Turbulence Spectra for Boundary-Layer Winds in Tropical Cyclones: A Conceptual Framework and Field Measurements at Coastlines

Kareem

Hunt

et al. 2014

Boundary-Layer Meteorol

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A conceptual model is proposed for the characteristic sub-ranges in the velocity and temperature spectra in the boundary layer of tropical cyclones (hurricanes or typhoons). The model is based on observations and computation of radial and vertical profiles of the mean flow and turbulence, and on the interpretation of eddy mechanisms determined by shear (namely roll and streak structures near the surface), convection, rotation, blocking and sheltering effects at the ground/sea surface and in internal shear layers. The significant subranges, as the frequency increases, are associated with larger energy containing eddies, shear and blocking, inertial transfer between large and small scales, and intense small-scale eddies generated near the surface caused by waves, coastal roughness change, and the buoyancy force associated with the evaporation of spray droplets. These sub-ranges vary with the locations at which the spectra are measured, i.e. the level z in relation to the height z max of the peak mean velocity and the depth h of the boundary layer, and the radius r in relation to the eyewall radius R ew and the outer-vortex radius R ov . For two tropical cyclones (Nuri and Hagupit), experimental data were analyzed. Spectra were measured where r is near to R ew and R ov using four 1-h long datasets at coastal towers, at 10-and 60-m heights for tropical cyclone Nuri, and at 60-m height for tropical cyclone Hagupit at the south China coast. The field measurements of spectra within the boundary layer show significant sub-ranges of self-similar energy spectra (lying between the length scale 1,000 m and the smallest scales less than 40 m) that are consistent with the above conceptual model of the surface layer. However, with very high wind speeds near the eyewall, the energy of the independently 123 L. Li et al. generated intense surface eddy motions, associated with surface waves and water droplets in the airflow, greatly exceeds the energies of the small scales in the inertial sub-range of the boundary layer, over scales less than about 3-40 m depending on the height z and the radius r . This rise in the small-scale frequency weighted spectra (nS u (n), where n is natural frequency, and S u (n) is the energy spectrum of the longitudinal wind component) is consistent with the hypothesis that these processes are only weakly correlated with the main boundary-layer turbulence.

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Section: Observation Towers and Instrumentationsmentioning

confidence: 99%

“…The boundary-layer rolls also magnify the ratio of the variation of longitudinal fluctuating velocity to friction velocity in tropical cyclones (Masters et al 2010;Li et al 2012) and other flows with convective turbulence (Smedman et al 2007). …”

Section: Introductionmentioning

confidence: 99%

Turbulence Spectra for Boundary-Layer Winds in Tropical Cyclones: A Conceptual Framework and Field Measurements at Coastlines

Kareem

Hunt

et al. 2014

Boundary-Layer Meteorol

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“…In addition, numerous studies have employed artificial intelligence to predict wind speed. For example, Li et al [10] proposed a data-driven model for determining a typhoon's wind power spectrum according to analytical considerations and field measurements of typhoons in the South China Sea. Wei [11] adopted two types of neural network-an adaptive-network-based fuzzy inference system (ANFIS) and a multilayer perceptron neural network-to predict the effect on wind speed in the Penghu Islands from typhoons of various tracks.…”

Section: Introductionmentioning

confidence: 99%

Regional Forecasting of Wind Speeds during Typhoon Landfall in Taiwan: A Case Study of Westward-Moving Typhoons

et al. 2018

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Abstract:Taiwan is located on a route where typhoons often strike. Each year, the strong winds accompanying typhoons are a substantial threat and cause significant damage. However, because the terrains of high mountains in Taiwan vary greatly, when a typhoon passes the Central Mountain Range (CMR), the wind speed of typhoons becomes difficult to predict. This research had two primary objectives: (1) to develop data-driven techniques and a powerful artificial neural network (ANN) model to predict the highly complex nonlinear wind systems in western Taiwan; and, (2) to investigate the accuracy of wind speed predictions at various locations and for various durations in western Taiwan when the track of westward typhoons is affected by the complex geographical shelters and disturbances of the CMR. This study developed a typhoon wind speed prediction model that evaluated various typhoon tracks (covering Type 2, Type 3, and Type 4 tracks, as defined by the Central Weather Bureau), and evaluated the prediction accuracy at Hsinchu, Wuqi, and Kaohsiung Stations in western Taiwan. Back propagation neural networks (BPNNs) were employed to establish wind speed prediction models, and a linear regression model was adopted as the benchmark to evaluate the strengths and weaknesses of the BPNNs. The results were as follows: (1) The BPNNs generally had favorable performance in predicting wind speeds and their performances were superior to linear regressions; (2) when absolute errors were adopted to evaluate the prediction performances, the predictions at Hsinchu Station were the most accurate, whereas those at Wuqi Station were the least accurate; however, when relative errors were adopted, the predictions at Hsinchu Station were again the most accurate, whereas those at Kaohsiung were the least accurate; and, (3) regarding the relative error rates for the maximum wind speed of Types 2, 3, and 4 typhoons, Wuqi, Kaohsiung, and Wuqi had the most accurate performance, respectively; as for maximum wind time error (ET M ) for Types 2, 3, and 4 typhoons, Kaohsiung, Wuqi, and Wuqi correspondingly performed the most favorably.

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“…Many scholars [23][24][25][26][27][28][29][30] have done some research on fitting the wind pressure autospectra data with empirical formulas. The universal model of wind velocity autospectra has been described by Olesen et al [31] and Tieleman [32] as follows:…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [29] found that coefficient parameters, , , and , are related to the location of the autospectral curves and index parameters, , , and , are related to the shape of the curve. Sun [26] and Pan [27] applied (1) in empirical formula of wind pressure autospectral model with identified parameters = 1, = 2, and = 1 in Sun [26] and = 10/3, = 1, and = 1 in Pan [27], respectively.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Non‐Gaussian Characteristics of Wind Pressure for Rigid and Flexible Structures

Jiang

2018

Shock and Vibration

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This paper summarizes a comprehensive study for non-Gaussian properties of wind pressure. The field measurements are implemented on the structure surface of a rigid structure, while a large-span membrane structure is selected as the flexible structure wind pressure contrast group. The non-Gaussian characteristics of measured pressure data were analyzed and discussed through probability density distribution, characteristic statistical parameters, power spectral density function, and the correlations, respectively. In general, the non-Gaussian characteristics of wind pressure present depending on tap location, wind direction, and structure geometry. In this study, the fluctuating wind pressures on the windward side and leeward side of the structures show obvious different degrees of non-Gaussian properties; that is, the surface of rigid structure shows strong non-Gaussian property in leeward, while the roof of flexible structure shows obvious non-Gaussian property in windward. Finally, this paper utilizes the present autospectrum empirical formula to fit the wind pressure power spectrum density of the measured data and obtains the conclusion that the existing empirical formula is not ideal for fitting of the flexible structure.

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Modeling typhoon wind power spectra near sea surface based on measurements in the South China sea

Cited by 65 publications

References 51 publications

Turbulence Spectra for Boundary-Layer Winds in Tropical Cyclones: A Conceptual Framework and Field Measurements at Coastlines

Turbulence Spectra for Boundary-Layer Winds in Tropical Cyclones: A Conceptual Framework and Field Measurements at Coastlines

Regional Forecasting of Wind Speeds during Typhoon Landfall in Taiwan: A Case Study of Westward-Moving Typhoons

Comparative Study on Non‐Gaussian Characteristics of Wind Pressure for Rigid and Flexible Structures

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