Derivation of time-varying mean for non-stationary downburst winds

Su, Yanwen; Huang, Gangliang; Xu, You Lin

doi:10.1016/j.jweia.2015.02.008

Cited by 69 publications

(22 citation statements)

References 23 publications

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“…We selected these two methods since the DWT with higher orders of Daubechies wavelets (e.g. db10) and the EEMD can extract a reasonable and physically meaningful time-varying mean (Su et al, 2015). Each step for revealing the 3D E field pattern is described in detail in the following.…”

Section: Discussionmentioning

confidence: 99%

Effects of 3D electric field on saltation during dust storms: an observational and numerical study

Zhang

Zhou

2020

Atmos. Chem. Phys.

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Abstract. Particle triboelectric charging, being ubiquitous in nature and industry, potentially plays a key role in dust events, including the lifting and transport of sand and dust particles. However, the properties of the electric field (E field) and its influences on saltation during dust storms remain obscure as the high complexity of dust storms and the existing numerical studies are mainly limited to the 1D E field. Here, we quantify the effects of the real 3D E field on saltation during dust storms through a combination of field observations and numerical modelling. The 3D E fields in the sub-metre layer from 0.05 to 0.7 m above the ground during a dust storm are measured at the Qingtu Lake Observation Array site. The time-varying means of the E field series over a certain timescale are extracted by the discrete wavelet transform and ensemble empirical mode decomposition methods. The measured results show that each component of the 3D E field data roughly collapses on a single third-order polynomial curve when normalized. Such 3D E field data within a few centimetres of the ground have never been reported and formulated before. Using the discrete element method, we then develop a comprehensive saltation model in which the triboelectric charging between particle–particle midair collisions is explicitly accounted for, allowing us to evaluate the triboelectric charging in saltation during dust storms properly. By combining the results of measurements and modelling, we find that, although the vertical component of the E field (i.e. 1D E field) inhibits sand transport, the 3D E field enhances sand transport substantially. Furthermore, the model predicts that the 3D E field enhances the total mass flux and saltation height by up to 20 % and 15 %, respectively. This suggests that a 3D E field consideration is necessary if one is to explain precisely how the E field affects saltation during dust storms. These results further improve our understanding of particle triboelectric charging in saltation and help to provide more accurate characterizations of sand and dust transport during dust storms.

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Section: Discussionmentioning

confidence: 99%

Effects of 3D electric field on saltation during dust storms: an observational and numerical study

Zhang

Zhou

2020

Atmos. Chem. Phys.

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“…The other three hybrid models DWT-PACF-ELM, EEMD-PACF-ELM, and AVMD-PACF-ELM used PCAF to determine the inputs. Specifically, the Daubechies 10 is selected as the mother wavelet mother function for DWT according to Ref [43]. The EEMD method is employed with the ensemble number of 100 and the white noise amplitude of 0.2 times standard deviation [12].…”

Section: B Parameter Settingsmentioning

confidence: 99%

A Composite Uncertainty Forecasting Model for Unstable Time Series: Application of Wind Speed and Streamflow Forecasting

et al. 2020

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Wind speed and streamflow series always are nonlinear and unstable because the effects of chaotic weather systems. These inherent features make them difficult to forecast, especially in a changing environment. To improve forecasting accuracy, an innovation uncertainty forecasting architecture is developed by coupling data decomposition method, feature selection, multiple artificial intelligence (AI) techniques and composite strategy to do unstable time series forecasting. In the designed architecture, the AVMD (adaptive variational mode decomposition) is first applied to excavate implicit information from the original time series. Then, the random forest is utilized to select the suitable inputs for each mode. After that, the GPR (Gaussian Process Regression), a very famous probabilistic AI technique, is driven by various neural networks (ELM (Extreme Learning Machine), BP (Back Propagation Neural Networks), GRNN(Generalized Regression Neural Networks) and RBF (Radial Basis Function Neural Networks)) to produce both deterministic and probabilistic forecasting results in a nonlinear manner to play strengths of each other. The effectiveness and applicability of the proposed approach is verified by unstable wind speed data and streamflow data, and also compared with eleven related models. Results indicate that the proposed model not only improves the forecasting accuracy for deterministic predictions, but also provides more probabilistic information for decision making. The proposed method achieves significantly better performance than the traditional forecasting models both on wind speed forecasting and streamflow forecasting with at least 50% average performance promotion over all the eleven competitors. Comprehensive comparisons demonstrate the superior performance of the proposed method than the involved models as a powerful tool for unstable series forecasting.

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“…However, the selection of decomposition levels depends on users’ judgment, which puts forward a requirement for a straightforward treatment of this procedure. Su et al [ 20 ] proposed a scheme to derive a reasonable time-varying mean, but the procedure is relatively complicated since the estimation of EPSD and structural response computation is included. Tao et al [ 21 ] developed a self-adaptive WT-based approach to extract the time-varying mean according the stationarity of the signal.…”

Section: Mean Wind Characteristicsmentioning

confidence: 99%

Measurement of Non-Stationary Characteristics of a Landfall Typhoon at the Jiangyin Bridge Site

Qin

Tao

et al. 2017

Sensors

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The wind-sensitive long-span suspension bridge is a vital element in land transportation. Understanding the wind characteristics at the bridge site is thus of great significance to the wind- resistant analysis of such a flexible structure. In this study, a strong wind event from a landfall typhoon called Soudelor recorded at the Jiangyin Bridge site with the anemometer is taken as the research object. As inherent time-varying trends are frequently captured in typhoon events, the wind characteristics of Soudelor are analyzed in a non-stationary perspective. The time-varying mean is first extracted with the wavelet-based self-adaptive method. Then, the non-stationary turbulent wind characteristics, e.g.; turbulence intensity, gust factor, turbulence integral scale, and power spectral density, are investigated and compared with the results from the stationary analysis. The comparison highlights the importance of non-stationary considerations of typhoon events, and a transition from stationarity to non-stationarity for the analysis of wind effects. The analytical results could help enrich the database of non-stationary wind characteristics, and are expected to provide references for the wind-resistant analysis of engineering structures in similar areas.

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Derivation of time-varying mean for non-stationary downburst winds

Cited by 69 publications

References 23 publications

Effects of 3D electric field on saltation during dust storms: an observational and numerical study

Effects of 3D electric field on saltation during dust storms: an observational and numerical study

A Composite Uncertainty Forecasting Model for Unstable Time Series: Application of Wind Speed and Streamflow Forecasting

Measurement of Non-Stationary Characteristics of a Landfall Typhoon at the Jiangyin Bridge Site

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