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Dong Zheng, Yijun Zhang, Qing Meng, Luwen Chen, and Jianru Dan

Abstract

The first climatological comparison of small-current cloud-to-ground (SCCG; peak current ≤50 kA) and large-current cloud-to-ground (LCCG; peak current >50 kA, >75 kA, and >100 kA) lightning flashes is presented for southern China. The LCCG lightning exhibits an apparent preference to occur over the sea. The percentage of positive LCCG lightning during the nonrainy season was more than twice that during the rainy season, while the percentage of positive SCCG lightning showed small seasonal differences. Positive cloud-to-ground (PCG) lightning was more likely to feature a large peak current than was negative cloud-to-ground (NCG) lightning, especially during the nonrainy season and over land. Distinct geographical differences are found between SCCG and LCCG lightning densities and between their own positive and negative discharges. Furthermore, the percentages of positive lightning from LCCG and SCCG lightning exhibit distinctly different geographical and seasonal (rain and nonrainy season) distributions. The diurnal variations in SCCG and LCCG lightning are clearly different over the sea but similar over land. Diurnal variations in the percentage of positive lightning are functions of the peak current and underlying Earth’s surface. In combination with the University of Utah precipitation feature (PF) dataset, it is revealed that thunderstorms with relatively weak convection and large precipitation areas are more likely to produce the LCCG lightning, and the positive LCCG lightning is well correlated with mesoscale convective systems in the spatial distribution during nonrainy season.

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Kanghui Zhou, Yongguang Zheng, Wansheng Dong, and Tingbo Wang

Abstract

Precise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures (T B) of six infrared bands from the Himawari-8 satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet’s nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts.

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Wenjuan Zhang, Yijun Zhang, Dong Zheng, and Xiuji Zhou

Abstract

Cloud-to-ground lightning data and storm intensity data (winds and central pressure) for 33 northwest Pacific tropical cyclones were used to analyze lightning distributions during the period of landfall in China. Lightning activities varied enormously from storm to storm with an average flash rate over 500 km of radius from 3 to 3201 flashes per hour, and no obvious relationship between average intensity and average flash rate occurred. The maximum flash density shifted from the eyewall region (0–60 km) to outer rainbands (180–500 km) as the intensity level increased. The average ratio of flash density in the eyewall to outer rainband was highest (1:0.5) for storms with the level of a tropical storm (17.2–24.4 m s−1) and lowest (1:8.6) for severe typhoons (41.5–50.9 m s−1). After storm landfall, flash density in the rainband decreased more rapidly in severe typhoons than in severe tropical storms (24.5–32.6 m s−1) and typhoons, but increased in tropical depressions (10.8–17.1 m s−1) and tropical storms. With the strength of intensity level, lightning in the outer rainband gradually weakened after the storm landfall.

Lightning outbreaks were identified in a consistent manner for all tropical cyclones to inspect the relationship of eyewall flashes to the changes of structure and intensity. Eyewall flash outbreaks were found during the period of intensity change (15% of outbreaks in intensification and 43% in weaken), and the period of maximum intensity (15% of outbreaks) of storms. A new result of this analysis found that 10% of the outbreaks occurred prior to and during periods of storm turning, which is potentially important for the trajectory change forecasting of tropical cyclones.

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Yanchen Zheng, Jianzhu Li, Lixin Dong, Youtong Rong, Aiqing Kang, and Ping Feng

Abstract

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

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Fan Wu, Xiaopeng Cui, Da-Lin Zhang, Dongxia Liu, and Dong Zheng

Abstract

In this study, the spatiotemporal characteristics of cloud-to-ground (CG) and intracloud (IC) lightning flashes observed by Surveillance et Alerte Foudre par Interférometrie Radioélectrique (SAFIR)-3000 over the Beijing metropolitan region (BMR) during 2005–07 were investigated. The results showed the presence of 299 lightning days with 241 688 flashes, most of which were IC lightning flashes. Only 19% of the total flashes were CG lightning flashes; 14% of these CG flashes were positive. Most lightning activity occurred during the summer months (June–August), with a major diurnal peak around 1900 Beijing standard time (BST) and a secondary peak around 2300 BST. Spatial variations in flash density and lightning days both exhibited an obvious southeastwardly increasing pattern, with higher flash densities or more lightning days occurring in the southeastern plains and lower values distributed on the northwestern mountains. The Z ratio (IC/CG lightning flashes) exhibited a similar spatial pattern, but the percentage of positive CG lightning flashes showed an almost opposite pattern. The results also showed significant topographic effects on the spatiotemporal variations in lightning activity. That is, flash counts on the northeastern and southwestern mountains peaked in the afternoon, whereas those on the southeastern plains peaked in the late night to early morning, which could be attributed to the propagation of thunderstorms from the mountains to the plains. The results showed that the SAFIR-3000 lightning data are more useful than CG lightning data alone for forecasting the development and propagation of thunderstorms over the BMR.

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Luwen Chen, Yijun Zhang, Weitao Lu, Dong Zheng, Yang Zhang, Shaodong Chen, and Zhihui Huang

Abstract

Performance evaluation for the lightning location system (LLS) of the power grid in Guangdong Province, China, was conducted based on observation data of the triggered lightning flashes obtained in Conghua, Guangzhou, during 2007–11 and natural lightning flashes to tall structures obtained in Guangzhou during 2009–11. The results show that the flash detection efficiency and stroke detection efficiency were about 94% (58/62) and 60% (97/162), respectively. The arithmetic mean and median values for location error were estimated to be about 710 and 489 m, respectively, when more than two reporting sensors were involved in the location retrieval (based on 87 samples). After eliminating one obviously abnormal sample, the absolute percentage errors of peak current estimation were within 0.4%–42%, with arithmetic mean and median values of about 16.3% and 19.1%, respectively (based on 21 samples).

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Ruiyang Ma, Dong Zheng, Yijun Zhang, Wen Yao, Wenjuan Zhang, and Deqing Cuomu

Abstract

Herein, we compared data on the spatiotemporal distribution of lightning activity obtained from the World Wide Lightning Location Network (WWLLN) with that from the Lightning Imaging Sensor (LIS). The WWLLN and LIS both suggest intense lightning activity over the central and southeastern Tibetan Plateau (TP) during May–September. Meanwhile, the WWLLN indicates relatively weak lightning activity over the northeastern TP, where the LIS suggests very intense lightning activity, and it also indicates a high-density lightning center over the southwestern TP, not suggested by the LIS. Furthermore, the WWLLN lightning peaks in August in terms of monthly variation and in late August in terms of ten-day variation, unlike the corresponding LIS lightning peaks of July and late June, respectively. Other observation data were also introduced into the comparison. The black body temperature (TBB) data from the Fengyun-2E geostationary satellite (as a proxy of deep convection) and thunderstorm day data support the spatial distribution of the WWLLN lightning more. Meanwhile, for seasonal variation, the TBB data is more analogous to the LIS data, while the cloud-to-ground (CG) lightning data from a local CG lightning location system is closer to the WWLLN data. It is speculated that the different WWLLN and LIS observation modes may cause their data to represent different dominant types of lightning, thereby leading to differences in the spatiotemporal distributions of their data. The results may further imply that there exist regional differences and seasonal variations in the electrical properties of thunderstorms over the TP.

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Penglei Fan, Dong Zheng, Yijun Zhang, Shanqiang Gu, Wenjuan Zhang, Wen Yao, Biwu Yan, and Yongbin Xu

Abstract

A systematic evaluation of the performance of the World Wide Lightning Location Network (WWLLN) over the Tibetan Plateau is conducted using data from the Cloud-to-Ground Lightning Location System (CGLLS) developed by the State Grid Corporation of China for 2013–15 and lightning data from the satellite-based Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) for 2014–15. The average spatial location separation magnitudes in the midsouthern Tibetan Plateau (MSTP) region between matched WWLLN and CGLLS strokes and over the whole Tibetan Plateau between matched WWLLN and LIS flashes were 9.97 and 10.93 km, respectively. The detection efficiency (DE) of the WWLLN rose markedly with increasing stroke peak current, and the mean stroke peak currents of positive and negative cloud-to-ground (CG) lightning detected by the WWLLN in the MSTP region were 62.43 and −56.74 kA, respectively. The duration, area, and radiance of the LIS flashes that were also detected by the WWLLN were 1.27, 2.65, and 4.38 times those not detected by the WWLLN. The DE of the WWLLN in the MSTP region was 9.37% for CG lightning and 2.58% for total lightning. Over the Tibetan Plateau, the DE of the WWLLN for total lightning was 2.03%. In the MSTP region, the CG flash data made up 71.98% of all WWLLN flash data. Based on the abovementioned results, the ratio of intracloud (IC) lightning to CG lightning in the MSTP region was estimated to be 4.05.

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J. Li, J. Steppeler, F. Fang, C. C. Pain, J. Zhu, X. Peng, L. Dong, Y. Li, L. Tao, W. Leng, Y. Wang, and J. Zheng
Open access
Ping Zhao, Xiangde Xu, Fei Chen, Xueliang Guo, Xiangdong Zheng, Liping Liu, Yang Hong, Yueqing Li, Zuo La, Hao Peng, Linzhi Zhong, Yaoming Ma, Shihao Tang, Yimin Liu, Huizhi Liu, Yaohui Li, Qiang Zhang, Zeyong Hu, Jihua Sun, Shengjun Zhang, Lixin Dong, Hezhen Zhang, Yang Zhao, Xiaolu Yan, An Xiao, Wei Wan, Yu Liu, Junming Chen, Ge Liu, Yangzong Zhaxi, and Xiuji Zhou

Abstract

This paper presents the background, scientific objectives, experimental design, and preliminary achievements of the Third Tibetan Plateau (TP) Atmospheric Scientific Experiment (TIPEX-III) for 8–10 years. It began in 2013 and has expanded plateau-scale observation networks by adding observation stations in data-scarce areas; executed integrated observation missions for the land surface, planetary boundary layer, cloud–precipitation, and troposphere–stratosphere exchange processes by coordinating ground-based, air-based, and satellite facilities; and achieved noticeable progress in data applications. A new estimation gives a smaller bulk transfer coefficient of surface sensible heat over the TP, which results in a reduction of the possibly overestimated heat intensity found in previous studies. Summer cloud–precipitation microphysical characteristics and cloud radiative effects over the TP are distinguished from those over the downstream plains. Warm rain processes play important roles in the development of cloud and precipitation over the TP. The lower-tropospheric ozone maximum over the northeastern TP is attributed to the regional photochemistry and long-range ozone transports, and the heterogeneous chemical processes of depleting ozone near the tropopause might not be a dominant mechanism for the summer upper-tropospheric–lower-stratospheric ozone valley over the southeastern TP. The TP thermodynamic function not only affects the local atmospheric water maintenance and the downstream precipitation and haze events but also modifies extratropical atmospheric teleconnections like the Asia–Pacific Oscillation, subtropical anticyclones over the North Pacific and Atlantic, and temperature and precipitation over Africa, Asia, and North America. These findings provide new insights into understanding land–atmosphere coupled processes over the TP and their effects, improving model parameterization schemes, and enhancing weather and climate forecast skills.

Open access