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  • View in gallery

    Map showing the part of the TP where the lightning activity is considered. The rectangle with the red dashed boundary encloses the region in which the cloud-to-ground (CG) lightning detected by a local CG lightning location system is compared with the WWLLN and LIS lightning, as discussed in section 3c.

  • View in gallery

    Yearly variation in the number of flashes detected by the WWLLN over the TP from 2010 to 2018.

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    Spatial distributions of the lightning density obtained from the (a) WWLLN and (b) LIS over the TP. The statistical grids of the WWLLN and LIS data are 1° × 1° and 0.5° × 0.5°, respectively. The WWLLN data were adjusted using the relative detection efficiency of the WWLLN over the TP.

  • View in gallery

    Spatial distribution of occurrence probability of TBB ≤ −52°C (denoted as OPTBB−52°C) over the TP.

  • View in gallery

    Spatial distribution of annual average thunderstorm days over the TP. The asterisk symbols label the locations of the weather stations that are providing the thunderstorm day data.

  • View in gallery

    Ten-day spatial distributions of lightning density over the TP from May to October obtained from WWLLN data. Each month from May to October was divided into early, middle, and late periods, corresponding to the images from (a) to (r) in sequence. The WWLLN data were adjusted using the relative detection efficiency of the WWLLN over the TP.

  • View in gallery

    Monthly (blue) and 10-day (red) variations in the lightning activity over the TP obtained from (a) WWLLN and (b) LIS data. The left and right axes correspond to the monthly and 10-day number of flashes, respectively.

  • View in gallery

    Monthly (blue) and 10-day (red) variations in the cumulative number of the FY-2E pixels with TBB of ≤−52°C (denoted as NTBB−52°C) over the TP.

  • View in gallery

    Monthly (blue) and 10-day (red) variations in the (a) WWLLN lightning from 2013 to 2016 and (b) LIS lightning from 1998 to 2014 over the midsouthern TP (the rectangular outlined region shown in Fig. 1).

  • View in gallery

    Monthly (blue) and 10-day (red) variations in the flash number of (a) all CG lightning and CG lightning with peak currents of greater than (b) 50, (c) 75, and (d) 100 kA observed by the CGLLS over the midsouthern TP (rectangular outlined region shown in Fig. 1) from 2013 to 2016.

  • View in gallery

    Diurnal variation in the WWLLN lightning data; the left and right axes are related to the diurnal variation in the lightning activity throughout the year and in every month of April–October, respectively.

  • View in gallery

    Spatial distribution of the peak time of the diurnal variation in the lightning density over the TP (calculated with a 2° × 2° grid).

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Spatiotemporal Lightning Activity Detected by WWLLN over the Tibetan Plateau and Its Comparison with LIS Lightning

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  • 1 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
  • | 2 Laboratory of Lightning Physics and Protection Engineering, Chinese Academy of Meteorological Sciences, Beijing, China
  • | 3 Institute of Atmospheric Sciences, Fudan University, Shanghai, China
  • | 4 Naqu Meteorological Service, Tibet, China
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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 that is not suggested by the LIS. Furthermore, the WWLLN lightning peaks in August in terms of monthly variation and in late August in terms of 10-day variation, unlike the corresponding LIS lightning peaks of July and late June, respectively. Other observation data were also introduced into the comparison. The blackbody 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 are more analogous to the LIS data, whereas the cloud-to-ground (CG) lightning data from a local CG lightning location system are 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.

These authors contributed equally to this work; they are the co–first authors.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dong Zheng, zhengdong@cma.gov.cn

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 that is not suggested by the LIS. Furthermore, the WWLLN lightning peaks in August in terms of monthly variation and in late August in terms of 10-day variation, unlike the corresponding LIS lightning peaks of July and late June, respectively. Other observation data were also introduced into the comparison. The blackbody 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 are more analogous to the LIS data, whereas the cloud-to-ground (CG) lightning data from a local CG lightning location system are 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.

These authors contributed equally to this work; they are the co–first authors.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dong Zheng, zhengdong@cma.gov.cn

1. Introduction

Known as the “roof of the world,” the Tibetan Plateau (TP) is the highest region in the world. It has a very important impact on the atmospheric circulation and climate changes in East Asia and the world because of the special thermal and dynamic effects of towering terrain entering the middle troposphere and the large area (Ye et al. 1957; Ye and Wu 1998; Duan and Wu 2005; Wu et al. 2007). Therefore, the TP has attracted considerable attention in atmospheric science research.

The TP is among the regions with the most active convections in China. In summer, the meso- and microscale turbulence and convective systems are usually well developed over the TP (Fu et al. 2020). The convective activity over the TP plays a significant role in the exchange of matter and energy between the troposphere and stratosphere, and strongly influences climate change (Fu et al. 2006; Bian et al. 2020). Currently, it is still difficult to carry out continuous observations of the convective clouds over the TP because of the harsh and sparsely populated natural environment and the small scale and short life of convective clouds (Jiang et al. 1996; Li et al. 2008; Qie et al. 2014; Fu et al. 2020). In this case, the observation and analysis of lightning activity may play a unique role in indicating the convection activity over the TP.

The charging process mainly occurs between collision-rebound ice-phase particles (Takahashi 1978). Therefore, the lightning activity, convection intensity, and ice-phase particle content of the cloud are related (Deierling and Petersen 2008; Zheng et al. 2010). Baker et al. (1999) proposed the use of lightning data as a proxy for the content of ice-phase particles transported to the upper troposphere. Price (2000) showed that global lightning activity and upper-tropospheric water-vapor variability are closely linked. The increase in lightning frequency indicates an increase in the concentration of water vapor at the top of the tropopause, which affects the transport of water vapor in the troposphere and the middle atmosphere, potentially causing climate warming. Therefore, lightning activity can indicate the features of convection activity and ice phase processes in the upper troposphere over the TP. In addition, lightning is considered to be the primary source of nitrogen oxides (NOx) over the TP. By affecting the NOx concentration over the TP, lightning can affect atmospheric chemical processes, such as ozone generation (Guo et al. 2017, 2019), thereby influencing the atmospheric composition and climate change. Furthermore, the lightning activity itself is one of the most powerful natural events over the TP, and it seriously threatens the livelihood of people living there and the unique humanity landscape in the TP region. Hence, comprehensive studies on lightning activity over the TP are also necessary from the perspective of lightning protection.

However, because of the harsh climate and geographical conditions of the TP, observations using lightning location systems (LLSs) over the entire TP have not been made (Fan et al. 2018), and the local observations that have been made cannot reflect the overall TP lightning activity. Previous studies on the lightning activity over the TP were mainly based on lightning data derived from spaceborne optical sensors. Qie et al. (2003a,b) studied the lightning activity over the TP using lightning data observed from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite. They found that the lightning activity over the TP had continental climate characteristics; it was mainly concentrated between June and August and features obvious diurnal variations with the peak occurring in the afternoon. Furthermore, they found that, in the summer, the lightning activity showed positive correlations with the surface Bowen ratio and sensible heat flux. They also suggested that the lightning activity over the TP was affected by the thermal and dynamic effects as a result of the special terrain. Based on LIS data, Qi et al. (2016) analyzed the lightning and precipitation activities over the TP and reported that high-density lightning activities could be found over the middle and northeastern part of the TP, while the most intense precipitation occurred in the southeastern TP. They also found that during the warm season, frequent lightning activity first advanced from east to west and then retreated from west to east.

Satellite-based lightning data were the first sources that allowed us to understand the spatial and temporal distributions of the lightning activity over the TP. As a ground-based LLS, the World Wide Lightning Location Network (WWLLN) is another effective tool that can provide lightning observations covering the entire TP. In this study, we investigated the spatiotemporal distribution patterns of the lightning activity over the TP based on the WWLLN data from 2010 to 2018 and compared them with those suggested by the TRMM/LIS data. Some auxiliary observation data on cloud and cloud-to-ground (CG) lightning were introduced into the comparison in order to test the differences between the WWLLN lightning and LIS lightning. The results of this study may enrich our understanding of the characteristics of lightning activity over the TP.

2. Study area and lightning data source

a. Study area

This study focuses on the region delineated by the thick black solid line shown in Fig. 1. The boundary data were obtained from Zhang et al. (2002); the boundary encloses the part of the TP in China by referring to the 4000-m isoheight.

Fig. 1.
Fig. 1.

Map showing the part of the TP where the lightning activity is considered. The rectangle with the red dashed boundary encloses the region in which the cloud-to-ground (CG) lightning detected by a local CG lightning location system is compared with the WWLLN and LIS lightning, as discussed in section 3c.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

b. Data and methods

In addition to WWLLN and TRMM/LIS lightning data, we considered CG data provided by a local ground-based CG lightning location system (CGLLS) and blackbody temperature (TBB) data from the Fengyun-2E (FY-2E) geostationary meteorological satellite for comparison.

1) WWLLN data

The WWLLN is managed by the University of Washington along with a consortium of worldwide universities and can continuously locate global lightning activity. It runs at a very low frequency (VLF) of 3–30 kHz and locates the lightning discharge events whose VLF signals are synchronously recorded by at least five sensors using the time of the group arrival algorithm (Dowden et al. 2002, 2008).

The WWLLN is typically more sensitive to strong lightning strokes, and generally has a relatively low and uneven-spatial-distribution detection efficiency (Rodger et al. 2005, 2006; Hutchins et al. 2012; Rudlosky and Shea 2013; Bürgesser 2017). Fan et al. (2018) analyzed the performance of the WWLLN over the TP in the 2013–15 period. Their results showed that the average location accuracy of the WWLLN over the TP was approximately 10 km. In the midsouthern TP, the detection efficiency of the WWLLN for CG lightning and the total lightning were approximately 9.37% and 2.58%, respectively. By matching and comparing the WWLLN lightning data with the CG lightning data from the CGLLS, they found that, among the lightning flashes observed by the WWLLN, CG lightning flashes accounted for approximately 71.98%, and the mean peak currents of the positive and negative CG lightning detected by the WWLLN in the midsouthern TP were approximately 62.43 and −56.74 kA, respectively. By matching and comparing the WWLLN and TRMM/LIS lightning data, they found that the duration, footprint, and radiance of the lightning flashes detected by both the LIS and WWLLN were approximately 1.27, 2.65, and 4.38 times those detected by only the LIS, respectively.

In this study, we use WWLLN data in the analysis region from 2010 to 2018. With the method reported by Fan et al. (2018), that is, adjacent strokes for one flash should be within an interval of 0.5 s and a distance of 30 km, we grouped the WWLLN strokes into lightning flashes. As a result, we obtained approximately 2.4 × 106 WWLLN lightning flashes in the analysis regions. Figure 2 shows the yearly variation in the number of flashes. The generally continuous increase in the number of flashes should be predominantly related to the increase in the detection efficiency of WWLLN because it is almost impossible for the amount of data in 2018 to be nearly 2 times that in 2010 as a result of the slow climate change. This indicates that the detection efficiency of the WWLLN over the TP is lower and higher than that reported by Fan et al. (2018) before 2013 and after 2015, respectively.

Fig. 2.
Fig. 2.

Yearly variation in the number of flashes detected by the WWLLN over the TP from 2010 to 2018.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

To avoid the possible impact on the spatial distribution pattern of WWLLN lightning activity due to the uneven spatial distribution of WWLLN detection efficiency over the TP, we introduced the 1° × 1° grid WWLLN relative detection efficiency (RDE; http://wwlln.net/deMaps) to adjust the WWLLN lightning density, and ensure that the WWLLN lightning activities in different places are comparable. The RDE of the WWLLN was obtained by the model developed by Hutchins et al. (2012). The model used only the data collected by the WWLLN network itself to estimate the RDE of the WWLLN everywhere compared to the best average WWLLN detection efficiency. By the way, in a preliminary comparison, the spatial distribution pattern of lightning activity based on the original WWLLN data is almost indistinguishable from that based on the WWLLN data adjusted based on the RDE, suggesting that the detection efficiency of the WWLLN over the TP is generally even, which is supported by the spatial distribution of RDE with little difference over the TP (the figure is omitted).

Because of the low and varying detection efficiency of WWLLN, in the following analysis, we will mainly focus on the spatiotemporal distribution patterns of the lightning activity and ignore the specific values.

2) TRMM/LIS data

The TRMM satellite was in a polar orbit between 35°N and 35°S. It was launched into a low orbit of approximately 350 km in November 1997 and boosted to approximately 400 km in August 2001. The LIS aboard the TRMM satellite was a solid-state optical detector. It detected lightning by measuring transient changes in the cloud brightness due to lightning with storm-scale resolution and recorded the time, position, and radiance of the lightning (Christian et al. 2000). The flash detection efficiency of the LIS was approximately 93% ± 4% during nighttime and 73% ± 11% during daytime (Boccippio et al. 2002). The field of view of the LIS was approximately 600 × 600 km2 of Earth’s surface. The LIS recorded the lightning activity between approximately 38°S and 38°N with a typical short continuous staring time of approximately 90 s for a fixed location in a given orbit. The TRMM satellite ended its mission in 2015.

In section 3a, the LIS 0.5° High Resolution Full Climatology (HRFC) dataset provided by the Global Hydrology Resource Center (GHRC) (Cecil et al. 2014) was used to analyze the spatial distribution of lightning over the TP. In section 3c, the LIS orbit data are used to analyze the monthly variation characteristics of lightning. There were a total of approximately 1.6 × 105 LIS flashes in the analysis region from 1998 to 2014.

3) CGLLS and CG data

The 4-yr (2013–16) ground-based CG lightning location data were provided by the CGLLS built by the State Grid Corporation of China (Fan et al. 2018). Following the data processing method used in previous studies (Cummins et al. 1998; Zheng et al. 2016a; Fan et al. 2018), the positive strokes with a current lower than 10 kA were removed from the dataset, and then the residual return strokes were grouped into CG flashes based on the criterion that adjacently located return strokes for one lightning flash should be within an interval of 0.5 s and a distance of 10 km. The location and current of the return stroke with maximum current in one CG flash were taken at the position and current of the CG flash. As reported by Fan et al. (2018), the CGLLS shows relatively higher detection efficiency in the midsouthern TP, roughly corresponding to the rectangular area located in the midsouthern TP, delineated by the red dashed lines in Fig. 1 (28.5°–33°N, 90°–95°E). In section 3c, we analyze the CG lightning in this region for comparison.

4) FY-2E and TBB data

The TBB data of the FY-2E geostationary satellite collected from 2010 to 2018 were also considered to investigate the spatiotemporal distribution of convections. The FY-2E satellite was launched in December 2008. It was first positioned over the equator at 104.5°E in February 2009 and then drifted to 86.5°E on 1 July 2015. The TBB measurement was provided once every hour, and the spatial resolution at nadir was approximately 5 km. The TBB value is inversely proportional to the cloud-top height.

5) Thunderstorm day data

The thunderstorm day data over the TP were collected from 130 weather stations over the analysis region. The data were manually recorded. When thunder was heard by the meteorological observer, the day on which it was heard was documented as a thunderstorm day. After 2010, the thunderstorm day observation was gradually cancelled in China. Here, the data generally cover the period from 1961 to 2014, but different weather stations may have had different observation periods. We converted the data into the average number of thunderstorm day per year.

It is noted that some data cover different periods. This is because the corresponding observations were conducted in different years, or the present study is restricted by some conditions for obtaining data. However, because climate change is slow, it is almost impossible for the spatiotemporal distribution patterns of lightning or thunderstorms to change drastically over a short term. For example, some studies involving the climatological lightning activity over the TP and using the lightning data from LIS or the spaceborne Optical Transient Detector (Boccippio et al. 2000) in different years suggest similar lightning activity characteristics (Qie et al. 2003a; Yuan and Qie 2004, 2005; Ma et al. 2005; Qi et al. 2016). Furthermore, we plotted the spatiotemporal distribution of the WWLLN lightning each year (the figures are omitted) and found that the distribution patterns are generally consistent. Therefore, it is believed that although some data do not coincide in the observation period, they can be compared in climatology.

3. Results

a. Spatial distribution of WWLLN and LIS lightning data

Figures 3a and 3b show the spatial distributions of the lightning density over the TP according to the WWLLN and LIS data, respectively. The spatial distribution of WWLLN lightning in each year was first adjusted using the WWLLN RDE of that year. Then the spatial distributions of lightning for all years were added together to obtain the spatial distribution pattern of WWLLN lightning during the analysis period. The distribution pattern of the LIS lightning data is the same as that obtained in previous studies based on TRMM/LIS data (Qie et al. 2003a; Qi et al. 2016), although there were some differences in the time periods of the selected data.

Fig. 3.
Fig. 3.

Spatial distributions of the lightning density obtained from the (a) WWLLN and (b) LIS over the TP. The statistical grids of the WWLLN and LIS data are 1° × 1° and 0.5° × 0.5°, respectively. The WWLLN data were adjusted using the relative detection efficiency of the WWLLN over the TP.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

Overall, the spatial distributions of the WWLLN and LIS lightning data have some similarities. For example, they indicate both strong lightning activity in the southeastern (approximately 30°N and 100°E) and midsouthern (approximately 32°N and 92.5°E) TP and low-frequency lightning in the north, west, southmost (close to the Himalaya Mountains), and south-by-east (approximately around 30°N and 95°E) parts of the TP. Meanwhile, they show different geographic distribution patterns in some strong lightning activity regions. First, the LIS lightning data show a very high lightning density in the northeastern plateau (approximately to the north of 34°N and to the east of 98°E), while the lightning density obtained from WWLLN in the same part is low. Second, unlike the LIS data, the WWLLN data suggest an independent center featuring intense lightning activity over the southwestern TP (approximately 30.5°N and 88°E). Furthermore, in the western TP (west of approximately 85°E), the LIS suggests more intense lightning activity than that suggested by the WWLLN.

The differences in the spatial distribution of the WWLLN and LIS lightning data prompted us to use other data for comparison. First, we considered the TBB product from the FY-2E satellite. The TBB value reflects the cloud height and, therefore, the convective intensity. Lightning is closely related to convection and can usually be found in areas featuring low TBB values (Ávila et al. 2010; Mattos and Machado 2011). In a study on summer convection activities in China, Zheng et al. (2007, 2008) suggested that the threshold value of TBB ≤−52°C is suitable for identifying convection clouds. In this study, we used this threshold to identify the cloud region with strong convection. Because most lightning flashes and deep convections over the TP occur in the warm season (Qie et al. 2003a, 2014; Fu et al. 2020; also see Fig. 7 in this paper) and to exclude the influence of the cold TP surface on the observation data in the cold season, we only used the TBB data from May to September. We calculated the occurrence probability of TBB ≤ 52°C (denoted as OPTBB−52°C) in each grid box and obtained its geographic distribution. The results are shown in Fig. 4, which indicates the spatial distribution of the deep-convection cloud bodies. We also show the geographic distribution of the annual average number of thunderstorm days and the referenced weather stations in Fig. 5.

Fig. 4.
Fig. 4.

Spatial distribution of occurrence probability of TBB ≤ −52°C (denoted as OPTBB−52°C) over the TP.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

Fig. 5.
Fig. 5.

Spatial distribution of annual average thunderstorm days over the TP. The asterisk symbols label the locations of the weather stations that are providing the thunderstorm day data.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

It is clear that the spatial distribution patterns of OPTBB−52°C (Fig. 4) and the thunderstorm days (Fig. 5) are more consistent with those of the WWLLN than those of the LIS lightning data. On one hand, the areas where the spatial distribution of WWLLN lightning data agree well with those of LIS lightning data are also well correlated with the spatial distributions of OPTBB−52°C and thunderstorm days. For example, the areas featuring low lightning densities according to both WWLLN and LIS data, including the north, west, southmost, and south-by-east parts of the TP, are also characterized by relatively small OPTBB−52°C and thunderstorm days. The midsouthern TP is characterized jointly by relatively large lightning densities, OPTBB−52°C and thunderstorm days. On the other hand, in the areas where the WWLLN and LIS suggest different intensities of lightning activity, the distributions of OPTBB−52°C and thunderstorm days support the data from the WWLLN more. First, OPTBB−52°C and thunderstorm days are relatively low over the northeastern TP, where the WWLLN data indicate low lightning density, while the LIS data indicate much greater lightning density. Second, the OPTBB−52°C indicates a center of convection activity over the south-by-west TP, which corresponds to the lightning activity center in the same place indicated by the WWLLN data. Meanwhile, the thunderstorm days have a “ridge” in this area (note: there is no weather station at the core of this area). Third, in the western TP, the OPTBB−52°C and number of thunderstorm days both significantly decrease, generally analogous to the WWLLN lightning in this region.

The difference in the spatial distributions of OPTBB−52°C and WWLLN lightning is mainly reflected in the order of the central areas according to their values. The lightning density obtained from the WWLLN data is largest over the southeastern TP, followed, in order, by those over the midsouthern and southwestern TP. However, the geographic distribution shows a large area of high-value OPTBB−52°C over the southwestern TP, followed by those over the southeastern and midsouthern TP. We speculate that the reasons for the different comparisons may be the following. First, the frequency of lightning is not only related to the vertical development of the cloud, but also to the expansion of the cloud area. Generally, a cloud with larger vertical development and horizontal expansion is expected to produce more frequent lightning flashes. According to Zheng et al. (2020), the average horizontal expansion of thunderstorm clouds over the TP decreases from east to west. Furthermore, the southwestern part of the TP with a relatively high density of WWLLN lightning data is among the regions with the highest average height of thunderstorm clouds over the TP. In this case, the thunderstorms in the eastern plateau may produce more lightning flashes. Meanwhile, a large horizontal expansion makes it more likely that some lightning flashes occur in the regions where the TBB is higher than −52°C. This causes the lightning density and OPTBB−52°C in different regions to be not linearly proportional. Second, recent studies have revealed that lightning discharge intensity is affected by dynamic and microphysical processes in thunderstorms. In simple terms, small (large) charged regions tend to cause small (large) or weak-intensity (strong-intensity) lightning flashes, while the size of the charged regions is affected by the convection intensity and horizontal extent (Bruning and MacGorman 2013; Beirle et al. 2014; Peterson and Liu 2013; Wang et al. 2016; You et al. 2019; Zheng et al. 2018, 2019). The differences in the thunderstorm properties in different regions of the TP may lead to different intensities of the lightning discharge. While the WWLLN mainly detects strong-discharge lightning, the relationship between the actual lightning density and that determined from WWLLN observations may be different in different regions, which will further decrease the correspondence between lightning density obtained from WWLLN data and OPTBB−52°C.

b. Ten-day spatial distributions of WWLLN lightning data

The WWLLN data are then used to investigate the 10-day variation in the spatial distribution of the lightning activity from May to October. The spatial distribution of WWLLN lightning in each 10-day period in each year was first adjusted using the RDE of that period. Then the spatial distribution of lightning for a certain 10-day period for all years were added together to obtain the spatial distribution pattern of 10-day WWLLN lightning (Fig. 6).

Fig. 6.
Fig. 6.

Ten-day spatial distributions of lightning density over the TP from May to October obtained from WWLLN data. Each month from May to October was divided into early, middle, and late periods, corresponding to the images from (a) to (r) in sequence. The WWLLN data were adjusted using the relative detection efficiency of the WWLLN over the TP.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

According to Fig. 6, the areas of frequent lightning activity over the TP generally advance from east to west first, and then retreat from west to east. This is consistent with the result of the analysis of the monthly lightning activity over the TP based on TRMM/LIS data by Qi et al. (2016). Meanwhile, the 10-day variation in the spatial distribution of the lightning activity provides more detailed information on the occurrence and disappearance of the centers characterized by strong lightning activity. In early May (Fig. 6a), the lightning activity is mainly concentrated in the southeastern TP. In mid-May, a new strong lightning active center emerges in the midsouthern TP (Fig. 6b). In late May (Fig. 6c), these two regions are maintained, and the area with relatively intense lightning activity connect them; meanwhile, another weak center with relatively frequent lightning activity emerges in the southwestern plateau (Fig. 6c) and then extends (Fig. 6d). The aforementioned three centers with high-density lightning activities, as in Fig. 3a, initially appear in mid-June (Fig. 6e) and become more prominent in late June (Fig. 6f) and early July (Fig. 6g). The center over the midsouthern TP dims in mid- and late July (Figs. 6h,i). In August, intense lightning activities can still be observed in the aforementioned three centers (Figs. 6j–1). Meanwhile, a new region featuring frequent lightning emerges over the southwest of the Qinghai province (roughly centered at 34°N and 91°E) in mid-August (Fig. 6k) and is maintained in late August (Fig. 6l) and early September (Fig. 6m). The areas with relatively frequent lightning further expand westward and northward in July and August, and reach their westernmost and northernmost positions, respectively, in early September (Fig. 6m). Afterward, the areas with relatively frequent lightning begin to retreat eastward in mid-September (Fig. 6n). In late September, the lightning activity center over the southwestern TP vanishes (Fig. 6o). The lightning activity centers over the midsouthern and southeastern TP are still identifiable in late September and early October (Figs. 6o,p). After mid-October, the lightning activity over the TP is very low (Figs. 6q,r).

c. Seasonal variation of WWLLN and LIS lightning data

Figure 7 shows the monthly and 10-day variations in the lightning activity over the TP obtained from the WWLLN and LIS data. The lightning activity detected by WWLLN is predominantly concentrated in the warm season (Fig. 7a), with approximately 95% of lightning occurring between May and September. The WWLLN data suggest that lightning is most frequent in August and September. According to the 10-day variation in the WWLLN lightning data, the lightning is most active in late August and then in early September, and there is a prominent periodical peak appearing in late June.

Fig. 7.
Fig. 7.

Monthly (blue) and 10-day (red) variations in the lightning activity over the TP obtained from (a) WWLLN and (b) LIS data. The left and right axes correspond to the monthly and 10-day number of flashes, respectively.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

Figure 7b shows the monthly and 10-day variations in the lightning activity over the TP from 1998 to 2014 obtained from the LIS data. The seasonal variation pattern is consistent with the results of previous studies based on LIS data (Qie et al. 2003a,b; Qi et al. 2016). Similar to the WWLLN, the LIS suggests that the lightning activity is concentrated in the warm season, with approximately 95% of LIS lightning flashes occurring during May–September. However, the LIS indicates different peaks in the seasonal variation from those indicated by the WWLLN. According to the monthly variation, the lightning obtained from LIS data peaks in July and then June, and, according to the 10-day variation, it peaks in late June and then early August. Further, in order to avoid the possible impact of WWLLN lightning data in some specific years on the final results, we analyzed the seasonal variation in the lightning activity obtained from WWLLN data for each year from 2010 to 2018 and found that the peak lightning activity always occurred in August or September (figures are omitted), confirming that the difference between WWLLN and LIS data in their seasonal variation peaks is real.

The TBB product from the FY-2E satellite was then introduced for comparison. In Fig. 8, we show the seasonal distribution of the cumulative number of the FY-2E pixels with TBB of ≤−52°C (denoted as NTBB−52°C) for each month and 10-day period from May to September. The NTBB−52°C peaks in July, which agrees well with the peak month of LIS lightning. In August, when the WWLLN lightning peaks, the NTBB−52°C corresponds to the second-highest peak. In September, the LIS lightning and NTBB−52°C both show much smaller values than those in July and August, whereas the WWLLN lightning hits the second-highest peak value. In general, the seasonal variation of NTBB−52°C supports that of LIS lightning more.

Fig. 8.
Fig. 8.

Monthly (blue) and 10-day (red) variations in the cumulative number of the FY-2E pixels with TBB of ≤−52°C (denoted as NTBB−52°C) over the TP.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

The CGLLS CG lightning data were further introduced into the comparison. According to Fan et al. (2018), the CGLLS achieves relatively high detection efficiency in the rectangular area delineated by the red dashed lines in Fig. 1. This region approximately encloses the midsouthern lightning activity center suggested by both the WWLLN and LIS. Figures 9a, 9b, and 10 show the seasonal variations of the WWLLN lightning, LIS lightning, and CGLLS CG lightning in this rectangular area, respectively. Among them, the WWLLN data in Fig. 9a and CGLLS data in Fig. 10 are both taken during 2013–16, for absolute consistency in time. In an additional investigation, we found that in this area, the seasonal change pattern of the WWLLN lightning from 2013 to 2016 is consistent with that of all the WWLLN lightning from 2010 to 2018 (the figure is not shown). The LIS data in Fig. 9b cover 1998–2014 out of their observation period. In Fig. 10, in addition to the seasonal variation of total CG lightning (Fig. 10a), those of CG lightning flashes with peak currents larger than 50 (Fig. 10b), 75 (Fig. 10c), and 100 (Fig. 10d) kA (they are called large-current lightning) are also investigated, considering that the WWLLN is more sensitive to strong-discharge lightning.

Fig. 9.
Fig. 9.

Monthly (blue) and 10-day (red) variations in the (a) WWLLN lightning from 2013 to 2016 and (b) LIS lightning from 1998 to 2014 over the midsouthern TP (the rectangular outlined region shown in Fig. 1).

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

Fig. 10.
Fig. 10.

Monthly (blue) and 10-day (red) variations in the flash number of (a) all CG lightning and CG lightning with peak currents of greater than (b) 50, (c) 75, and (d) 100 kA observed by the CGLLS over the midsouthern TP (rectangular outlined region shown in Fig. 1) from 2013 to 2016.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

The WWLLN lightning and LIS lightning still show significant differences in their seasonal changes in this relatively small area. According to the WWLLN data (Fig. 9a), the peak lightning activity occurs in September in terms of monthly variation and in early September in terms of 10-day variations. The corresponding peaks of the LIS lightning are in June and late June, respectively (Fig. 9b). According to Fig. 10, the CGLLS CG and large-current CG lightning flashes all peak in August in terms of monthly variation. Although the CG lightning is different from both the WWLLN and LIS lightning in terms of the seasonal variation pattern, its main peak month corresponds to the second peak month of the WWLLN lightning, and its second peak month corresponds to the main peak month of the WWLLN lightning. Furthermore, the large-current CG lightning shows a peak in early September in terms of 10-day variation, similar to that of the WWLLN lightning. Generally, the seasonal variation pattern of CG lightning is closer to that of the WWLLN lightning than to that of the LIS lightning, which should be associated with that the WWLLN data are mainly composed of CG lightning with strong strokes (Fan et al. 2018). On the other hand, some intracloud flashes with strong strokes are also included in the WWLLN data, which may be one of the reasons for the difference in seasonal variation pattern between the WWLLN lightning and CG lightning.

d. Diurnal variation in WWLLN lightning data

Figure 11 shows the diurnal variation in the WWLLN lightning data over the whole year and over the months with relatively frequent lightning activity (April–October). For all the WWLLN lightning data, most of the lightning flashes occur in the afternoon, local time (LT); the maximum and minimum occur at 1600–1700 LT and 0700–0800 LT, respectively. The higher lightning activity over the TP in the afternoon, according to the WWLLN data, is consistent with that obtained from LIS data reported in previous studies (Qie et al. 2003a,b, 2014; Li et al. 2008). This indicates that solar radiation and surface heating are the key factors promoting thunderstorms. There are some differences in the peak hours of the lightning activities in different months. The peak value of lightning activity in May occurs at 1500–1600 LT, while in June and the other months analyzed, it occurs at 1700–1800 and 1600–1700 LT, respectively.

Fig. 11.
Fig. 11.

Diurnal variation in the WWLLN lightning data; the left and right axes are related to the diurnal variation in the lightning activity throughout the year and in every month of April–October, respectively.

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

The ratio of the maximum flash number to the minimum flash number (max/min) reflects the degree of diurnal variation in the lightning activity. From Table 1, it can be seen that in the investigated months from April to October, the diurnal variations in the lightning activity in August and October are the most and least significant, with a max/min of approximately 110 and 33, respectively. Over the whole year, the max/min value is approximately 78, which is much higher than that in other regions. For example, Xia et al. (2015) analyzed the diurnal variation of CG lightning in northern China, the Yangtze River basin (China), southern China, and the Sichuan basin (China) and its surrounding mountainous areas. According to their Fig. 9, we roughly estimated that the max/min changed between 6 and 16. Zheng et al. (2016b) reported that the max/min of the diurnal variation in the CG lightning activity in Guangdong, which is one of the provinces with the most frequent lightning activity in China, was approximately 6.82. The comparisons indicate that the convection activity over the TP is more sensitive to solar radiation and surface heating than the other regions.

Table 1.

Ratio of the maximum flash number to the minimum flash number in the diurnal variation of the WWLLN lightning data over the TP.

Table 1.

We further investigated the spatial distribution of the peak time of the lightning activity over the TP based on the WWLLN data, and the results are shown in Fig. 12. In general, the peak times of lightning activity in the eastern and southern TP are typically after 1600 LT. The central, northwestern, and northernmost parts of the TP generally feature lightning activity peak times between 1200 and 1600 LT. The regional difference in the peak time of the lightning activity over the TP may be associated with the different local terrain, temperature, and humidity conditions.

Fig. 12.
Fig. 12.

Spatial distribution of the peak time of the diurnal variation in the lightning density over the TP (calculated with a 2° × 2° grid).

Citation: Journal of Atmospheric and Oceanic Technology 38, 3; 10.1175/JTECH-D-20-0080.1

4. Discussion

Our previous understanding of the climatological lightning activity over the TP mainly came from studies based on the lightning data derived from spaceborne lightning detectors, especially the TRMM/LIS (Qie et al. 2003a,b; Qi et al. 2016). Our investigation revealed that the spatiotemporal distribution patterns of the lightning activity over the TP obtained from the WWLLN data are partly different from those obtained from the LIS data.

It should be noted that the LIS and WWLLN feature different observation modes. The LIS was mounted on a polar orbit satellite, having a much lower position-visiting frequency, with a fixed region being visited approximately twice a day for approximately 90 s each time. Furthermore, the LIS is more sensitive to the lightning occurring in the upper part of the clouds. The WWLLN predominantly locates lightning with strong strokes and achieves a relatively low detection efficiency for total lightning, particularly intracloud lightning (Fan et al. 2018). The difference between the observation modes of the WWLLN and LIS enables their observation data to be dominated by different types of lightning flashes. For example, the LIS data may include more intracloud lightning flashes occurring in the upper part of the cloud, and the WWLLN data may include more lightning flashes with strong strokes, in which CG lightning flashes account for the most.

The electrical properties of thunderstorms over the TP may also play a role in impacting the LIS and WWLLN lightning detection. Some studies reported that thunderstorms over the TP tend to have a dominant lower positive charge region due to the relatively weak updraft of the TP convection clouds, and the lightning flashes initiated and propagating between the lower two charge regions may account for a relatively larger ratio (Zhang et al. 2004; Qie et al. 2005; Wang et al. 2019). This is different from the behavior of typical thunderstorms, which feature the dominant upper positive charge region, and most of the lightning flashes are associated with the upper positive charge region and middle negative charge region. That is, in TP thunderstorms, lightning may be more likely to occur at lower altitudes, which may cause a decrease in the LIS detection efficiency. On the other hand, some previous studies reported that lightning flashes over the TP feature a relatively smaller horizontal extent and weaker optical radiance, relative to those featured by the lightning flashes over other places (Beirle et al. 2014; You et al. 2019). This may cause the detection efficiency of the WWLLN and LIS to be relatively lower over the TP than that in other places.

Assuming that the LIS and WWLLN data are both reliable for the types of lightning they represent, the different spatiotemporal distribution patterns of WWLLN and LIS lightning may imply that the electrical properties of the thunderstorm over the TP have obvious regional differences and seasonal variations. For example, intense lightning activity is suggested by the LIS data in the northeastern part of the plateau, but not by the WWLLN data, OPTBB−52°C, and thunderstorm days, which may mean that the thunderstorms in this area tend to yield more lightning flashes in the upper part of clouds and a lower percentage of strong-discharge lightning flashes relative to those of other areas of the TP. This is possible, considering some recent studies suggested that the thunderstorms with strong convection could yield high-frequency lightning flashes (they are typically associated with the upper two main charge regions), but the flash size and energy (expressed by the radiation, peak current, etc.) tend to be small because strong convection and turbulence could cause the charge regions in the cloud to be small in size (Bruning and MacGorman 2013; Bruning and Thomas 2015; Wang et al. 2016; Zhang et al. 2017; Zheng et al. 2018, 2019; You et al. 2019). The LIS data and NTBB−52°C suggest earlier peaks than those of the WWLLN data and CGLLS data in terms of their seasonal variations, which may mean that there exists significant seasonal change in the thunderstorm property in the proportions of CG lightning and strong-discharge lightning to the total lightning and the dominant position at which the lightning occurs in the vertical direction (associated with the variation in the main positive charge region, determined by the convection intensity). These speculations are worth paying attention to in future studies.

5. Conclusions

In this study, we analyzed the spatiotemporal distribution of the lightning activity over the TP obtained from WWLLN and compared it with that obtained from the LIS. The FY-2E TBB data, thunderstorm day data, and CG lightning data observed by a local CGLLS were introduced into the comparison as third-part data.

Overall, the WWLLN and LIS both indicate strong lightning activity in the southeastern and midsouthern TP and low-frequency lightning in the north, west, southmost, and south-by-east parts of the TP. However, they show different geographic distribution patterns in some regions. While the LIS indicates a strong lightning activity over the northeastern TP, WWLLN indicates relatively weak lightning activity over the region. Unlike the LIS data, the WWLLN data indicate a high-density lightning center over the southwestern TP. The geographic distribution pattern of the lightning activity obtained from the WWLLN data corresponds better to the strong convection activity indicated by the FY-2E OPTBB−52°C and the thunderstorm days. The 10-day spatial distributions of the lightning activity over the TP from May to September indicate that the areas with strong lightning activity first advance from east to west and then retreat from west to east, and the conversion occurs in mid-September.

The WWLLN and LIS both highlight that the lightning activity over the TP is predominantly concentrated in the period from May to September, but different peaks are observed in terms of the monthly and 10-day variations. The WWLLN data show the main peak in August and the secondary peak in September. In contrast, the corresponding peaks indicated by the LIS data were in July and June. Furthermore, the WWLLN data indicate that the 10-day variations in the lightning activity peaked in late August and the second peak in early September. In contrast, the corresponding peaks indicated by the LIS data were in late June and early August. In terms of the monthly variation, the NTBB−52°C, which indicates the active level of strong convection activity, supports the LIS data more, also showing peaking in July. In the comparison of seasonal variation of lightning activity in the midsouthern plateau, the WWLLN lightning still shows a significant difference from the LIS lightning; meanwhile, the WWLLN lightning is more analogous to the CG lightning.

The diurnal variation in the lightning activity obtained from WWLLN data is similar to that obtained in previous studies based on LIS data, which indicates a predominant lightning activity in the afternoon. The lightning activity peaks at 1600–1700 LT and reaches a minimum at 0800–0900 LT. The max/min value of the WWLLN lightning in the diurnal variation is approximately 78, indicating that the TP thunderstorm activity is significantly sensitive to solar radiation and surface heating. The peak hours of the lightning over the southern and eastern TP are observed after 1600 LT, and that over the central, northeastern, and northernmost TP is observed between 1200 and 1600 LT.

We speculate that the differences in the spatial and temporal distribution patterns of the lightning activities observed by the WWLLN and LIS are due to the different detection modes of WWLLN and LIS, and the resulting dominant representation of their data for different types of lightning (such as strong-discharge lightning, particularly CG lightning for WWLLN data and lightning flashes occurring in the upper part of clouds for the LIS data). The comparison of the WWLLN and LIS data may imply regional differences and seasonal variations in the electrical properties of thunderstorms over the TP.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (41675005), the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (2019QZKK0104), and the Basic Research Fund of the Chinese Academy of Meteorological Sciences (2020Z009). We are grateful to the institutions and organizations that provided the data. The CGLLS CG data were provided by Wuhan NARI Company, Ltd., of the State Grid Electric Power Research Institute. The WWLLN lightning data (http://wwlln.net) were contributed by a collaboration among over 50 universities and institutions. The TRMM/LIS data were a part of TRMM dataset. TRMM is an international project jointly sponsored by the Japan National Space Development Agency (NASDA) and the National Aeronautics and Space Administration (NASA) Office of Earth Science.

Data availability statement

The data associated with this paper can be accessed online [https://pan.baidu.com/s/1jtW90do1mJ5JbIlHDlLHog (with password jaot)] or from the corresponding author.

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