World Wide Lightning Location Network Data Research Articles

Performance of Lightning Potential Index, Lightning Threat Index, and the Product of CAPE and Precipitation in the WRF Model

AbstractLightning is a naturally occurring phenomenon with significant socioeconomic and environmental impacts, underlining the need for the application of advanced methods to predict it. This study aims to evaluate the performance of the Weather Research and Forecasting (WRF) model in the prediction of the lightning potential index (LPI), the lightning threat index (LTI), and the product of convective available potential energy (CAPE) and precipitation (CAPE × P). Based on the intensity of convection and the number of lightning flashes, we simulated seven thundercloud events that occurred in Tehran between 2008 and 2013. The simulated LPI, LTI, and CAPE × P values are compared both qualitatively and quantitatively against ground‐based lighting data obtained from the world wide lightning location network (WWLLN). LPI, LTI, and CAPE × P predict the location of lightning with relatively good accuracy, although LPI outperforms LTI and CAPE × P. We also compared the simulated area‐averaged LPI, LTI, and CAPE × P against the number of lightning flashes from the WWLLN data, based on which LPI shows a better performance. Overall, LPI can be used as an effective index for the prediction of lightning. As LPI is based on cloud microphysics and LTI and CAPE × P are thermodynamic‐based methods, a better performance of LPI implies that a method based on the microphysical approach can better predict lightning. The performance of LPI, LTI, and CAPE × P is also sensitive to the horizontal resolution of model simulations, with a considerable improvement in simulations with higher horizontal resolutions.

Trends and Features of Thunderstorms and Lightning Activity in the Upper Volga Region

An increase in the frequency of atmospheric hazards in a changing climate has attracted interest in the study of regional features of mesoscale convective systems and trends of lightning activity. Severe convective storms are the most destructive weather events causing substantial damage and fatalities. In this paper, we analyze general trends in the lightning activity in the Upper Volga region and identify the particular features of a severe thunderstorm on 13–14 July 2020. The analysis of the annual variability of the lightning activity in the Upper Volga region, carried out according to the World-Wide Lightning Location Network (WWLLN) data, has shown that the total number of discharges during the convective seasons of 2016–2021 increased. For the convective season of 2020, the daily number of discharges in the neighborhood of Nizhny Novgorod (the center of the region) is calculated according to the WWLLN data and the electric field mill (EFM) measurements. It is revealed that the most powerful (per convective season) thunderstorms have a number of similarities both according to observations and numerical simulation. The thunderstorm on 13–14 July was numerically simulated using the Weather Research and Forecasting (WRF) model in combination with calculations of the electric parameters (such as electric potential and intracloud electric field). The simulations were carried out using two parametrizations of microphysical processes to reveal the features associated with allowance for aerosol particles. The data from the meteorological radar and WWLLN were used to validate the radar reflectivity simulation results of the WRF model. Some features of the thunderstorm evolution characteristic for each of the microphysical parametrizations were identified. In general, the non-aerosol parametrization gives a more correct description of the development of severe thunderstorms in the Upper Volga region (with the exception of the final stage of the convective system development). For a convective event to have the required duration (more than 6 h), aerosol particles should be taken into account.

Diurnal and Monthly Variability of Lightning Observation Over West Africa

This study examines in detail lightning activities over West Africa using the worldwide lightning location network (WWLLN). The Worldwide Lightning Location Network (WWLLN; operated by the University of Washington) is a developing, experimental lightning detection network increasingly used for applications. In West Africa, there are many studies on the diurnal process of lightning activity at the regional level, but there is a shortage of literature examining the diurnal variation of lightning observation in this region. Therefore the main objective of the study is to examine the lightning activities over West Africa by presenting an evaluation of the WWLLN data as ground truth to improve the knowledge of ground-based lightning location system. For this study, the diurnal variability of flash counts (frequency) was measured over West Africa for the three climatic sub-region (Guinea, Savannah and Sahel) sub-regions using a line graph to plot the flash counts (frequency) over time. All regions exhibit a strong diurnal variation, with lightning activity peaking in the late afternoon between 1500 and 1700 WAT and decreased in the late morning between 0900 and 1100 WAT. The diurnal amplitude varies by location throughout West Africa.

Assessing the Impact of Lightning NOx Emissions in CMAQ Using Lightning Flash Data from WWLLN over the Contiguous United States

Comparison of lightning flash data from the National Lightning Detection Network (NLDN) and from the World Wide Lightning Location Network (WWLLN) over the contiguous United States (CONUS) for the 2016–2018 period reveals temporally and spatially varying flash rates that would influence lightning NOx (LNOx) production due to known detection efficiency differences especially during summer months over land (versus over ocean). However, the lightning flash density differences between the two networks show persistent seasonal patterns over geographical regions (e.g., land versus ocean). Since the NLDN data are considered to have higher accuracy (lightning detection with >95% efficiency), we developed scaling factors for the WWLLN flash data based on the ratios of WWLLN to NLDN flash data over time (months of year) and space. In this study, sensitivity simulations using the Community Multiscale Air Quality (CMAQ) model are performed utilizing the original data sets (both NLDN and WWLLN) and the scaled WWLLN flash data for LNOx production over the CONUS. The model performance of using the different lightning flash datasets for ambient O3 and NOx mixing ratios that are directly impacted by LNOx emissions and the wet and dry deposition of oxidized nitrogen species that are indirectly impacted by LNOx emissions is assessed based on comparisons with ground-based observations, vertical profile measurements, and satellite products. During summer months, the original WWLLN data produced less LNOx emissions (due to its lower lightning detection efficiency) compared to the NLDN data, which resulted in less improvement in model performance than the simulation using NLDN data as compared to the simulation without any LNOx emissions. However, the scaled WWLLN data produced LNOx estimates and model performance comparable with the NLDN data, suggesting that scaled WWLLN may be used as a substitute for the NLDN data to provide LNOx estimates in air quality models when the NLDN data are not available (e.g., due to prohibitive cost or lack of spatial coverage).

Parameterization and global distribution of sprites based on the WWLLN data

This study of the global distribution of sprites is based on the new parameterization and WWLLN (World Wide Lightning Location Network) data for 2016. The information on the position, time, and power (peak current) for cloud-to-ground discharges is taken from the archive of the WWLLN. Parameterizations of the sprite initiation probability are proposed for positive and negative discharges. In the calculations, only nighttime events are considered and the probabilistic nature of the sprite initiation, depending on the impulse charge moment change, is taken into account. It is shown that on average, 870 nighttime sprites per twenty-four hours can be initiated globally, 41.4% of them over land and 58.6% over ocean. On the basis of the proposed parameterization, the regional distribution of sprites is analyzed, as well as seasonal and monthly distribution over land and ocean. The annual dynamics of the number of sprites over land and ocean is investigated, revealing the maximum number of events in August and the minimum in January and February.

First study of cloud to ground lightning discharges using ground-based observations over Indian subcontinent and its possible relationship with carbon dioxide and aerosols

We report the first estimation of Cloud to Ground (CG) lightning activity over the Indian subcontinent. Initial results from the analysis of ground-based data of worldwide lightning location network (WWLLN) over the Indian region are discussed. The CG lightning density is analysed from the quality controlled WWLLN data for three years during 2009–2011. The lightning flash density (LFD) analysis is shown with high and low LFD regions spatially. The LFD is enhanced by 20% in year 2010 and 13% in year 2011 in comparison to LFD in the year 2009 over the eastern sector. Over the west coast (around geo latitude 200 E) there is a 30% increase in LFD in 2010 and 17% increase in 2011. Over the Bay of Bengal, there is an increase of up to 66% LFD at few places in both the years. Aerosols and carbon dioxide (CO2) being the two most significant species in the Earth atmosphere responsible for atmospheric heating and convection are studied for the variations in their concentration during this period. There is an enhancement of 4 ppm in the CO2 concentration over the study region in 2010 and 7 ppm in 2011 corresponding to enhancement in LFD. The Aerosols concentration over Indian mainland has increased up to 50%. Over the Arabian Sea and Bay of Bengal the aerosol optical depth (AOD) is found to decrease 25% where lightning flash density is found to reduce from the previous year. We find positive correlation between LFD-aerosols and LFD-CO2. Overall, there is an increasing trend of atmospheric temperature, LFD, aerosols and CO2. The cloud microphysical effect that may have been impacted due to increase in air temperature, AOD and in concentrations of CO2 in the atmosphere is investigated with this study of lightning discharges.

Assessment of the Uncertainty for the Spatial Distribution of Lightning Discharge Density Based on the Smoothed Bootstrap Procedure and WWLLN Data: a Case Study

Thunderstorm activity is the most common natural cause of forest fires. Such forest fires can affect the functioning of industrial and infrastructure facilities. An effective application of the deterministic-probabilistic approach is possible if there is a set of initial data, including cloud-to-ground lightning discharges. It is especially important to understand the boundaries of uncertainty in determining the spatial distribution of the density of lightning discharges in a controlled forest-covered area. The aim of this work is to develop a procedure for assessing the uncertainty of the spatial distribution of lightning discharge density using a smoothed bootstrap algorithm and World Wide Lightning Location Network (WWLLN) data. The novelty of the work is due to the use of WWLLN data in combination with bootstrap algorithm in order to assess uncertainty of spatial distribution of lightning activity. The main findings are: 1) algorithm of bootstrap procedure to assess uncertainty in lightning discharge density within spatial distribution; 2) program realization of this algorithm using QGIS and GRASS software; 3) case study results for the typical boreal zone, namely, Republic of Buryatia (Russian Federation). Conclusions based on the results of bootstrap assessment of lightning discharge density uncertainty are presented regarding spatial distribution of lightning discharges. These results are applicable for forest fire danger prediction in lightning activity conditions.

Comparison of ground-based and satellite data on spatiotemporal distribution of lightning discharges under solar minimum

Worldwide maps of lightning activity have been obtained from the ground-based World Wide Lightning Location Network (WWLLN) for 2007–2009. We have compiled these maps separately for different seasons and UT periods, using WWLLN data on the time and coordinates of each of the recorded lightning. The total number of flashes of lightning in WWLLN data is by an order of magnitude smaller than in satellite data from Optical Transient Detector and the Lightning Imaging Sensor satellites. However, the key features of the spatial distribution and seasonal trends coincide well. The main difference observed is the absence of diurnal variation (similar to Carnegie curve) in WWLLN data against the satellite one. This concerns the global lightning number as well as its density in major thunderstorm regions. The solar local time dependence is also weak in WWLLN data. We show that in 2007–2009 the mean latitude of lightning observation is shifted to the summer hemisphere up to 10° from the annual mean value. From the beginning of 2007 to the end of 2009, the global monthly average number of flashes of lightning increased threefold. We attribute this fact primarily to improved processing techniques in WWLLN. The constructed maps are necessary for numerical simulation of the Global Electric Circuit.

Сравнение наземных и спутниковых данных о пространственно-временном распределении грозовых разрядов при низкой солнечной активности

Worldwide maps of lightning activity have been obtained from the ground-based World Wide Lightning Location Network (WWLLN) for 2007–2009. We have compiled these maps separately for different seasons and UT periods, using WWLLN data on the time and coordinates of each of the recorded lightning. The total number of flashes of lightning in WWLLN data is by an order of magnitude smaller than in satellite data from Optical Transient Detector and the Lightning Imaging Sensor satellites. However, the key features of the spatial distribution and seasonal trends coincide well. The main difference observed is the absence of diurnal variation (similar to Carnegie curve) in WWLLN data against the satellite one. This concerns the global lightning number as well as its density in major thunderstorm regions. The solar local time dependence is also weak in WWLLN data. We show that in 2007–2009 the mean latitude of lightning observation is shifted to the summer hemisphere up to 10° from the annual mean value. From the beginning of 2007 to the end of 2009, the global monthly average number of flashes of lightning increased threefold. We attribute this fact primarily to improved processing techniques in WWLLN. The constructed maps are necessary for numerical simulation of the Global Electric Circuit.

Сравнение данных глобальной сети локализации молний WWLLN и стандартных наблюдений на метеостанциях юга Дальнего Востока России

Standard observations at 20 weather stations were compared with data of the World Wide Lightning Location Network (WWLLN) in the south of the Russian Far East for the period of 2009-2018. Using two statistical methods, the estimates are obtained of the radii of the circular region of the WWLLN data sample, at which the best consistency of the WWLLN data with the number of days with thunderstorms according to weather station data is observed. It is shown that these radii are in the range from 12 to 36 km for all stations, with an average value of 23 km. Daytime radii are smaller than nighttime ones, the average values for all stations are equal to 21 and 26 km, respectively. It is demonstrated that an increase/decrease in the radius of the WWLLN data sample by 1 km leads to an increase/decrease in the average annual number of days with WWLLN lightnings relative to weather reports by ~1 day.

Estimation of thunderstorms occurrence from lightning cluster recorded by WWLLN and its comparison with the ‘universal’ Carnegie curve

Continuous global monitoring of lightning has been important in recent years to study a possible relationship with global warming. Consequently, several networks to detect lightning have been installed at various spatial scales (regional and global). One of these networks is the World Wide Lightning Location Network (WWLLN), which has been monitoring lightning since 2003. It is also important to monitor the occurrence of thunderstorms and this can be roughly archived by detecting lightning clusters. In this work, we propose a lightning grouping methodology, in order to estimate the global number of thunderstorms. Our methodology consists of grouping the WWLLN data into a density matrix with a spatial resolution of 0.1° x 0.1° (1 pixel) and a temporal resolution of one hour, then the algorithm identifies the pixels with lightning and groups these pixels with the adjacent pixels to form the thunderstorm. Then, we calculate monthly, seasonal and annual averaged daily curves of the number of thunderstorms. The data set under study includes years 2012 and 2013. Our methodology is validated by calculating the linear correlation coefficient (R) between the annual daily thunderstorm curve and the “universal” Carnegie curve (R = 0.97) and with the Vostok electric field measurements (R = 0.98). Additionally, we found higher correlation in September–October–November months (R = 0.98) compared with June–July–August months (R = 0.75) for Carnegie and for Vostok station (R=0.99 and R=0.88, respectively).

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

AbstractHerein, 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.

Prediction of lightning activity using WRF-ELEC model: Impact of initial and boundary conditions

The impact of initial conditions in predicting lightning activity using the Weather Research and Forecasting (WRF) with the electrification (ELEC) extra package, known as WRF-ELEC model, has been investigated. The severity, frequency, and some physical and dynamical properties of lightning occurred during 11 years (2004–2014) are considered. Four thundercloud events with distinctive characteristics over the Tehran area were chosen for detailed study. The selection process was made based on the observational data received from the Iranian Meteorological Organization (IRIMO), ground-based lightning data from the World Wide Lightning Location Network (WWLLN) and satellite-based lightning data from the Lightning Imaging Sensor (LIS). The WRF-ELEC simulations initialized with the European Centre for Medium-Range Weather Forecasts-ERA Interim (ECMWF-ERA Interim), National Centers for Environmental Prediction Final Analysis (NCEP-FNL) and the NCEP operational Global Forecast System (GFS) data have been conducted to obtain the Number Of Lightning flash density (NOL) associated with each case study. The numerical simulations are compared qualitatively and quantitatively with the observations. Several statistical metrics are used in order to determine the performance of the WRF-ELEC model initialized by various IBCs (initial and boundary conditions) for lightning prediction purposes. The results show that in the most of the studied cases, there is a good agreement between the simulated time-averaged horizontal patterns of the Lightning Potential Index (LPI) obtained from the ERA-Interim-based experiments and the locations of lightning occurrence of WWLLN data as well as LIS observations. Furthermore, GFS-based simulations have a better quantitative performance in the NOL prediction than FNL and ERA-Interim-based simulations regarding the values of Standard Deviation (SD), and centered Root Mean Square Error (RMSE). The results of further statistical analysis using different metrics reveal that ERA-Interim initialization has the best performance of lightning activity prediction.

FY-4A LMI Observed Lightning Activity in Super Typhoon Mangkhut (2018) in Comparison with WWLLN Data

Using lightning observations from the Fengyun-4A Lightning Mapping Imager (FY-4A LMI), best-track data from the Shanghai Typhoon Institute, bright temperature (TBB) data from Himawari-8 satellite, and composite reflectivity (CR) data from the South China radar network, we investigate the spatiotemporal distribution of lightning activity and convective evolution during the landfall of Super Typhoon Mangkhut, the strongest landing typhoon in China in 2018. Three stages of active total lightning are observed, and differences of lightning characteristics between the inner core and the outer rainbands are present. The onset of inner-core lightning outbreak is about 4 h ahead of the maximum intensity of the storm, providing indicative information on the change of typhoon intensity. Lightning rates in the outer rainbands increase rapidly 12 h before the landfall, and lightning activity is mainly confined in the outer rainbands after the landfall. A good correlation in hourly variation is shown between lightning rates from the LMI and TBBs from the satellite. The averaged TBB within the inner core reaches its minimum (–80°C) when the inner-core lightning outbreak occurs, indicating the occurrence and enhancement of deep convection there. Lightning locations observed by the LMI has a good spatial correspondence with regions of low TBBs and high CRs, revealing the monitoring capability of the LMI to lightning activity and deep convection in landing typhoons. Comparisons between the World Wide Lightning Location Network (WWLLN) and the LMI reveal that the spatial distribution, temporal evolution, and radial pattern of lightning activity in Mangkhut observed by the two systems are consistent. Furthermore, due to the detection capability of total lightning, the LMI has advantages in revealing the higher ratio of intra-cloud lightning within the inner core in typhoon. The continuous and real-time observation of FY-4A LMI provides an unprecedented platform for monitoring total lightning and deep convection in landing typhoons in China, which will promote the generation of new research and applications in the future.

Thunderstorm Activity in the Primorsky Krai

The statistical analysis of thunderstorm activity in the Primorsky krai is carried out using data of weather stations and World Wide Lightning Location Network (WWLLN) for the period of 2009–2018. The WWLLN data reveal that the seasonal variability of thunderstorm activity is characterized by the maxima in June and August and the daily variability is characterized by the maximum at 14:30 local time (04:30 UTC). It is shown that the average lightning density in the Primorsky krai is 210 discharge/(100 km2 year) and varies from 30 to 560 discharge/(100 km2 year) in the north of the Sea of Japan coast and in the Khanka Lowland, respectively. The comparison of thunderstorm activity characteristics based on weather station and WWLLN data exhibited high correlation between them.

Analysis of lightning discharges activity within the territory of Buryatia in 2010-2016 based on WWLLN data

Thunderstorm activity is the main natural cause of forest fires. The development of new or modernization of existing systems for monitoring and forest fires forecasting requires the involvement of data on the spatial and temporal distribution of thunderstorms in monitorable forest areas. This article discusses the territory of the Republic of Buryatia (Russian Federation). The study of the spatial and temporal components of thunderstorm activity was carried out based on the World Wide Lightning Location Network (WWLLN) data. WWLLN data on thunderstorms locations for 2010-2016 were used. The network registers lightning discharges during the year. The thunderstorm season lasts from May to September. Archival WWLLN data for this period were selected. The resulting dataset allowed us to distinguish areas with different lightning activity. A high degree of spatial nonuniformity of thunderstorm activity in Buryatia was established. The number of lightning discharges begins to increase in May, the maximum is observed in July, and a recession occurs in August. Two daily maximum were revealed: the first maximum of number of lightning discharges is in the interval from 12 to o’clock and the second is about 19 o’clock of local time.

Characteristics of Typhoon Eyewalls According to World Wide Lightning Location Network Data

Abstract Methods for the estimation of typhoon eyewall characteristics (the center location, the radius and the width, and radii of inner and outer boundaries) based on World Wide Lightning Location Network (WWLLN) data are presented and discussed in this work. The center locations, the eyewall radii, and inner boundary radii estimated from WWLLN data for the typhoons of the northwestern Pacific from 2011 to 2015 were compared with the typhoon centers, radii of maximum winds, and the radii of the eyes obtained from Advanced Scatterometer (ASCAT) wind data, the Japan Meteorological Agency (JMA) archives, and the Joint Typhoon Warning Center (JTWC) archives. It is shown that the eyewall characteristics estimates based on the lightning discharge data are most closely related to characteristics of the ASCAT wind speed fields, and the radii of the eyewalls and their inner boundaries are linearly related to the radii of maximum winds and the radii of the eyes, with correlation coefficients reaching approximately 0.9 and 0.8, respectively. It was shown that the distances between locations of the eyewalls and typhoon centers estimated according to the WWLLN and those of the ASCAT, JMA, and JTWC data on average were 19, 16, and 17 km, respectively. The eyewall widths varied from 15 to 69 km, with an average of ~30 km.

Thunderstorm activity in typhoon Lionrock (2016)

On Aug. 29, 2016, the tropical cyclone Lionrock was reported to turn into an extra-tropical storm and go to the shore of the Primorsky Region as monitoring shows, which brought about significant precipitation growth recorded in the north of the coast. Many surveys show the tropical cyclones in this area to transform into extra-tropical ones and become powerful mid-latitude storms with significant damage to coastal and maritime areas. The paper uses WWLLN (World Wide Lightning Location Network) data to study the thunderstorm activity while the tropical cyclone reaches the typhoon level with the maximum wind velocity. This made it possible to identify parameters’ variations of thunderstorm activity during atmospheric vortex phenomena. It also enabled us to determine the emergency probability due to a thunderstorm.

Numerical study of performance of two lightning prediction methods based on: Lightning Potential Index (LPI) and electric POTential difference (POT) over Tehran area

The electric POTential difference (POT) and the Lightning Potential Index (LPI) performance in predicting the lightning activity is investigated and the probable relationship between them is examined. These two indices have a similar dependency on microphysical variables such as ice, graupel mixing ratios, and also updraft characteristics within the cloud. Regardless of this similarity, the LPI directly calculated from WRF model is a more favorable parameter for predicting the lightning events in comparison with the POT, which requires an extra package (ELEC) model. Ten years' available data over the Tehran area were reviewed and four thundercloud cases with distinct characteristics (CAPE, time-frequency, intensity) were selected. In order to acquire the associated physical properties, four simulations have been done using the WRF-ELEC model, which is initialized with ERA-Interim data.The assessment conducted within both quantitative and qualitative themes to verify the potential probability of predicting lightning events. In qualitative evaluation framework, the horizontal distribution of LPI and POT were compared to the locations of lightning occurrence detected by WWLLN (The World Wide Lightning Location Network) data as well as the total lightning data obtained from LIS (Lightning Imaging Sensor) in the innermost simulation domain. This evaluation shows that the horizontal patterns of LPI and POT are well consistent with the locations of lightning occurrence. Moreover, in accordance with acquired values of correlation coefficients, it could be inferred that LPI has a better performance in the Number Of Lightning flashes (NOL) prediction than POT. Statistical review of our simulated data (LPI and POT) is performed to study the variation of LPI and POT which resulted in a good correlation between them and therefore, any information about lightning event using LPI values with less computational cost is preferable in the considered domain. Another comparison was carried out based on the calculated correlation coefficient between the simulated NOL and the NOL recorded by WWLLN that does not show any significant correlation between them for none of the cases. Since WWLLN has no observatory in the studied area, inconsistency of the simulated NOL with real atmosphere is justifiable.

Characteristics of cloud-to-ground lightning and its relationship with climate change in Muli, Sichuan province, China

Along with climate change, cloud-to-ground lightning (CG)-caused forest fires are becoming increasingly pronounced. This study employed the Chinese lightning location system data (2009–2015) and worldwide lightning location network data (2005–2015) to jointly analyze CG characteristics and study the correlation between CG and climate change. The Muli county in southwest China was taken as research area. The CG number showed a clear increasing trend on yearly timescale. At the monthly timescale, CG occurred from March to October, with a peak in June. At the daily timescale, 15:00–23:00 (local time) and 00:00–05:00 both had a high CG frequency, and the peak was at 18:00–19:00. We divided CG electric current intensity into six grades and found that negative CG accounted for more than 90% of total CG and, among these, the 3rd and 4th intensity grades accounted for about 70%. To examine the spatial distribution, we focused on lightning-caused forest fires high occurrence seasons. In spring, CG distribution changed from the initial southeast to the northwest, gradually spreading to the whole area of Muli, whereas the CG area gradually moved back to the southeast in autumn. Our research suggested that minimum temperature was the most sensitive temperature to CG change. In March, the relationship between CG and minimum temperature showed a strong positive correlation. Considered jointly, we suggest the CG and related lightning-caused forest fires could increase in the future under an increased minimum temperature and decreased precipitation, especially in March.