Application of Ensemble Empirical Mode Decomposition in Low-Frequency Lightning Electric Field Signal Analysis and Lightning Location

Fan, Xiangpeng; Zhang, Yijun; Krehbiel, P. R.; Zhang, Yang; Zheng, Dong; Yao, Wen; Xu, Liangtao; Liu, Hengyi; Lyu, Weitao

doi:10.1109/tgrs.2020.2991724

Cited by 32 publications

(15 citation statements)

References 36 publications

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“…Similar to the flash‐initiating NBE in Figure 5c, the NBE‐like perturbation was initially spike like and became more gradual with time, and its onset was closely correlated with the VHF onset. We further separated the NBE‐like sferic from the EIP sferic by utilizing Ensemble Empirical Mode Decomposition (EEMD) (Fan et al, 2020), shown in Figure A7 of the Appendix, and determined the peak current moment (−26.1 kA‐km) and charge‐moment change (0.62 C‐km) of the NBE‐like event, which is consistent with other NBE observations (Liu et al, 2019; Rison et al, 2016; Tilles et al, 2019). The resulting filtered EIP sferic gives a slightly higher peak current moment (−298.3 kA‐km) and smaller charge‐moment change (13.1 C‐km) than the unfiltered values given in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…Figure A7. The same as Figure A6 except utilizing Ensemble Empirical Mode Decomposition (EEMD) (Fan et al, 2020) to extract the NBE-like perturbation from the EIP sferic, in a manner that illustrates the true amplitude of the initial part of the sferic. The remaining (red) waveform is that of the electron avalanching produced by the relativistic feedback process.…”

Section: Discussionmentioning

confidence: 99%

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Radio Interferometer Observations of an Energetic in‐Cloud Pulse Reveal Large Currents Generated by Relativistic Discharges

et al. 2020

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The production mechanism for terrestrial gamma ray flashes (TGFs) is not entirely understood, and details of the corresponding lightning activity and thunderstorm charge structure have yet to be fully characterized. Here we examine sub-microsecond VHF (14-88 MHz) radio interferometer observations of a 247-kA peak-current EIP, or energetic in-cloud pulse, a reliable radio signature of a subset of TGFs. The EIP consisted of three high-amplitude sferic pulses lasting ≃60 μs in total, which peaked during the second (main) pulse. The EIP occurred during a normal-polarity intracloud lightning flash that was highly unusual, in that the initial upward negative leader was particularly fast propagating and discharged a highly concentrated region of upper-positive storm charge. The flash was initiated by a high-power (46 kW) narrow bipolar event (NBE), and the EIP occurred about 3 ms later after ≃3 km upward flash development. The EIP was preceded ≃200 μs by a fast 6 × 10 6 m/s upward negative breakdown and immediately preceded and accompanied by repeated sequences of fast (10 7-10 8 m/s) downward then upward streamer events each lasting 10 to 20 μs, which repeatedly discharged a large volume of positive charge. Although the repeated streamer sequences appeared to be a characteristic feature of the EIP and were presumably involved in initiating it, the EIP sferic evolved independently of VHF-producing activity, supporting the idea that the sferic was produced by relativistic discharge currents. Moreover, the relativistic currents during the main sferic pulse initiated a strong NBE-like event comparable in VHF power (115 kW) to the highest-power NBEs.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Radio Interferometer Observations of an Energetic in‐Cloud Pulse Reveal Large Currents Generated by Relativistic Discharges

et al. 2020

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“…HHT is fully adaptive by initially introducing the intrinsic mode functions (IMFs), which is unlike the Wavelet Transform or Fourier Transform that needs a pre-determined basis. The brief mathematical process of the EMD can be found in the literature (Zhou et al, 2014;Fan et al, 2020). However, few of these IMFs may contain dramatic oscillations of different scales called "mode mixing" (Chen et al, 2021).…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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Accurate thunderstorm frequency (TSF) prediction is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed a novel hybrid machine learning model through hybridization of data pre-processing Ensemble Empirical Mode Decomposition (EEMD) with two state-of-arts models namely artificial neural network (EEMD-ANN), support vector machine (EEMD-SVM) for TSF prediction at three categories of yearly frequencies over Bangladesh. We were demarcated the yearly TSF datasets into three categories for the period 1981-2016 recorded at 28 sites; high (March-June), moderate (July-October), and low (November-February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared 2 with classical ANN, SVM, Autoregressive Integrated Moving Average (ARIMA). EEMD-ANN and EEMD-SVM hybrid models showed 8.02%-22.48% higher performance precision in terms of root mean square error (RMSE) compared to other models at high, moderate and low-frequency categories. Eleven out of 21 input parameters were selected based on the Random Forest (RF) variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid model developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world.

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“…The experimental base is composed of three main functional areas. The first functional area is the artificially triggered lightning experiment site, which is 1.9 km away from the Guangliancun (GLC) station of the low-frequency E-field detection array (LFEDA) system [21]- [23]. It is mainly used for triggering artificial lightning and conducting comprehensive observations.…”

Section: Experiments and The Data Of Artificially Triggered Lightmentioning

confidence: 99%

Parametric Reconstruction Method for the Long Time-Series Return-Stroke Current of Triggered Lightning Based on the Particle Swarm Optimization Algorithm

et al. 2020

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The lightning research group of the Chinese Academy of Meteorological Sciences has carried out observations and experiments with artificially triggered lightning for more than ten years, and it accumulated thousands of examples of channel base current data on the return stroke of artificially triggered lightning prior to 2020. Based on the current data, this paper explores ways of improving the constructed function of a long time-series (400) return stroke and the parametric reconstruction of the current waveform. The long time-series current can be divided into three components: the breakdown pulse current, corona current and quasi-uniform current. The corona current and quasi-uniform current are constructed by a Heidler function, while the breakdown pulse current is determined by the waveform characteristics of the current peak, which can be divided into the Heidler type and high-order exponential type. According to the three-component model of the return-stroke current, a two-step parametric reconstruction method for the long time-series lightning return-stroke current based on the particle swarm optimization (PSO) algorithm is proposed. In this paper, the results of the parameterized reconstruction of 14 return strokes are given, and the results of the multidimensional error analysis of 13 long time-series return strokes are given to illustrate the accuracy of the improved parameterized model of the return-stroke current and the effectiveness of the two-step reconstruction method based on the PSO algorithm.

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Application of Ensemble Empirical Mode Decomposition in Low-Frequency Lightning Electric Field Signal Analysis and Lightning Location

Cited by 32 publications

References 36 publications

Radio Interferometer Observations of an Energetic in‐Cloud Pulse Reveal Large Currents Generated by Relativistic Discharges

Radio Interferometer Observations of an Energetic in‐Cloud Pulse Reveal Large Currents Generated by Relativistic Discharges

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Parametric Reconstruction Method for the Long Time-Series Return-Stroke Current of Triggered Lightning Based on the Particle Swarm Optimization Algorithm

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