[Objective] The research aimed to analyze characteristics of atmospheric electric field in polluted area. [Method] By using data of atmospheric electric field meter in Taiyuan in 2009, daily and annual changes of atmo...[Objective] The research aimed to analyze characteristics of atmospheric electric field in polluted area. [Method] By using data of atmospheric electric field meter in Taiyuan in 2009, daily and annual changes of atmospheric electric field in Taiyuan were analyzed. [ Result] Atmospheric electric field intensity in Taiyuan was higher than that in other areas. Daily change was double-peak double-valley type. The first highest value appeared at nightfall, and the second highest value was before the noon. The first lowest value appeared in early morning, while the second low- est value appeared after the noon. Annual change was single-peak single-valley type. The maximum appeared in winter, while the minimum ap- peared in summer. [ Conclusion] When forecasting thunderstorm, in the area with higher aerosol content, alarm threshold of electric field should be adjusted via comparison with actual observation, which had very strong regional characteristics. By analyzing real-time monitoring data of atmospheric electric field, thunderstorm forecast could be realized, and timeliness and accuracy of warning and forecast could be increased.展开更多
Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge localization.To solve this problem,we propose a data complementary method based on the atmospheric electric...Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge localization.To solve this problem,we propose a data complementary method based on the atmospheric electric field apparatus array group.The electric field component measurement model of the atmospheric electric field apparatus is established,and the orientation parameters of the thunderstorm point charge are defined.Based on the mirror method,the thunderstorm point charge coordinates are obtained by using the potential distribution formulas.To test the validity of the basic algorithm,the electric field component measurement error and the localization accuracy are studied.Besides the azimuth angle and the elevation angle,the localization parameters also include the distance from the apparatus to the thunderstorm cloud.Based on a primary electric field apparatus,we establish the array group of apparatuses.Based on this,the data measured by each apparatus is complementarily processed to regain the thunderstorm point charge position.The results show that,compared with the radar map data,this method can accurately reflect the location of the thunderstorm point charge,and has a better localization effect.Additionally,several observation results during thunderstorm weather have been presented.展开更多
The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm detection.This paper proposes a method for thunderstorm point charge path recovery.Based on the relation-ship b...The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm detection.This paper proposes a method for thunderstorm point charge path recovery.Based on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization model.Generally,point charge movement paths are obtained after fitting time series localization results.However,AEF data losses make it difficult to fit and visualize paths.Therefore,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost AEF.As paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered AEF.Specifically,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are acquired.The recovered AEF is then input into the fixed SAE-XGBoost model.Whether paths need to be fitted is determined by the interval to which the output PV belongs.The results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.展开更多
为了解决大气电场数据预报雷暴虚警率高的问题,将集成经验模态分解（EEMD）方法和二阶差分法结合应用于大气电场资料的分析,提出了一种雷电预警分析方法。该方法先用EEMD分解出晴天天气和雷暴天气大气电场的不同时间尺度变化分量,然后...为了解决大气电场数据预报雷暴虚警率高的问题,将集成经验模态分解（EEMD）方法和二阶差分法结合应用于大气电场资料的分析,提出了一种雷电预警分析方法。该方法先用EEMD分解出晴天天气和雷暴天气大气电场的不同时间尺度变化分量,然后对包含雷电信号的高频模态分量IMF1（本征模态函数）进行二阶差分分析。晴天无雷暴发生时,地面大气电场的差分值集中在-0.5-0.5 k V/m3之间;雷暴过程中,差分大气电场出现剧烈变化,雷暴发生前,IMF1二阶差分量的增幅会明显变大,所对应的电场频率在0.016 5-0.045 5 Hz之间跳跃。经过仿真试验,结合雷达回波资料进行验证,得到雷电探测概率（probability of detection,POD）为85.1%,预警平均时间为30.2 min。展开更多
文摘[Objective] The research aimed to analyze characteristics of atmospheric electric field in polluted area. [Method] By using data of atmospheric electric field meter in Taiyuan in 2009, daily and annual changes of atmospheric electric field in Taiyuan were analyzed. [ Result] Atmospheric electric field intensity in Taiyuan was higher than that in other areas. Daily change was double-peak double-valley type. The first highest value appeared at nightfall, and the second highest value was before the noon. The first lowest value appeared in early morning, while the second low- est value appeared after the noon. Annual change was single-peak single-valley type. The maximum appeared in winter, while the minimum ap- peared in summer. [ Conclusion] When forecasting thunderstorm, in the area with higher aerosol content, alarm threshold of electric field should be adjusted via comparison with actual observation, which had very strong regional characteristics. By analyzing real-time monitoring data of atmospheric electric field, thunderstorm forecast could be realized, and timeliness and accuracy of warning and forecast could be increased.
基金This work is supported by the National Key Research and Development Program of China(Grant No.2021YFE0105500)the National Natural Science Foundation of China(Grant No.61671248)+2 种基金the Key Research and Development Plan of Jiangsu Province,China(Grant No.BE2018719)Postgraduate Research and Practice Innovation Program of Jiangsu Province(Grant No.SJCX19_0309)the Advantage Discipline Information and Communication Engineering of Jiangsu Province,China.
文摘Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge localization.To solve this problem,we propose a data complementary method based on the atmospheric electric field apparatus array group.The electric field component measurement model of the atmospheric electric field apparatus is established,and the orientation parameters of the thunderstorm point charge are defined.Based on the mirror method,the thunderstorm point charge coordinates are obtained by using the potential distribution formulas.To test the validity of the basic algorithm,the electric field component measurement error and the localization accuracy are studied.Besides the azimuth angle and the elevation angle,the localization parameters also include the distance from the apparatus to the thunderstorm cloud.Based on a primary electric field apparatus,we establish the array group of apparatuses.Based on this,the data measured by each apparatus is complementarily processed to regain the thunderstorm point charge position.The results show that,compared with the radar map data,this method can accurately reflect the location of the thunderstorm point charge,and has a better localization effect.Additionally,several observation results during thunderstorm weather have been presented.
基金supported by a grant from State Key Laboratory of Resources and Environmental Information System,the National Natural Science Foundation of China,Grant Number 42201053the Program of China Scholarship Council,Grant Number 202209040027the Postgraduate Research&Practice Innovation Program of Jiangsu Province,Grant Number KYCX21_1000,which are highly appreciated by the authors.
文摘The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm detection.This paper proposes a method for thunderstorm point charge path recovery.Based on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization model.Generally,point charge movement paths are obtained after fitting time series localization results.However,AEF data losses make it difficult to fit and visualize paths.Therefore,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost AEF.As paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered AEF.Specifically,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are acquired.The recovered AEF is then input into the fixed SAE-XGBoost model.Whether paths need to be fitted is determined by the interval to which the output PV belongs.The results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.
文摘为了解决大气电场数据预报雷暴虚警率高的问题,将集成经验模态分解（EEMD）方法和二阶差分法结合应用于大气电场资料的分析,提出了一种雷电预警分析方法。该方法先用EEMD分解出晴天天气和雷暴天气大气电场的不同时间尺度变化分量,然后对包含雷电信号的高频模态分量IMF1（本征模态函数）进行二阶差分分析。晴天无雷暴发生时,地面大气电场的差分值集中在-0.5-0.5 k V/m3之间;雷暴过程中,差分大气电场出现剧烈变化,雷暴发生前,IMF1二阶差分量的增幅会明显变大,所对应的电场频率在0.016 5-0.045 5 Hz之间跳跃。经过仿真试验,结合雷达回波资料进行验证,得到雷电探测概率（probability of detection,POD）为85.1%,预警平均时间为30.2 min。
