Detection, Mechanism, and Forecasting of Lightning and Thunderstorms

1.   Introduction

Thunderstorms, as severe convective weather systems, are often accompanied by weather hazards, such as lightning strikes, heavy precipitation, hail fall, wind gust, and tornadoes, and thus pose a significant threat to human life and property. In the context of climate change, thunderstorms show significant regional differences, with some areas experiencing a significant increase in thunderstorm and lightning activity (Qie et al., 2022a, 2024b; Xu M. Y. et al., 2022, 2023). In addition to causing disasters in the troposphere, thunderstorms also induce transient luminous events in the upper atmosphere, such as red sprites, blue jets, and gigantic jets, affecting the stratospheric and ionospheric environments (Yang et al., 2008, 2020; Liu F. F. et al., 2021; Xu et al., 2023a, b).

In recent years, the advancement of Doppler and dual-polarization radar technology has greatly promoted the understanding of macro and micro characteristics and dynamical information in thunderstorms. Multiparameter radar can not only detect macro parameters of thunderstorms, such as convective intensity, structure, and height, but also microphysical parameters, such as hydrometeor particle phase (solid, liquid, mixed) and raindrop spectrum and dynamic parameters, such as radial wind, horizontal wind field, vertical air motion, and particle falling velocity (Wu et al., 2014, 2018; Zhao et al., 2019). Meanwhile, high-resolution lightning detection technologies have been rapidly developed. The application of these technologies has greatly enhanced the understanding of electrical characteristics of thunderstorms in China in the last two decades (Qie et al., 2014c, 2015, 2024a; Qie and Zhang, 2019; Lyu et al., 2023).

Lightning localization technology has been evolved from two-dimensional (2D) cloud-to-ground (CG) lightning detection to three-dimensional (3D) mapping with dynamical positioning ability of both intracloud (IC) and CG lightning channel, along with the capability to distinguish detailed discharge process, providing new insights into initiation and propagation characteristics of lightning channels. The understanding of entire process of lightning and mechanisms has been highly improved, consequently. Studies on lightning activity and thunderstorms have expanded from CG to total flashes, allowing investigation of lightning characteristics in different convective thunderstorms at different stages, as well as the relationship between lightning and convective structures. By combining 3D lightning location with Doppler and dual-polarization radar observations, the corresponding relationships between lightning and dynamical and microphysical processes have been widely investigated (Qie et al., 2021). Additionally, 3D thunderstorm models with electrification and lightning schemes have been developed based on mesoscale Weather Research and Forecasting (WRF) model and cloud-scale model, allowing numerical simulation of complex multiscale and multiprocesses inside the thunderstorm (Tan et al., 2014, 2017; Xu et al., 2016; Lian et al., 2020). The requirement for weather forecasting with high accuracy and the accumulation of high-quality lightning location data have promoted the establishment of lightning forecasting systems and lightning data assimilation methods which improved simulation and forecasting capabilities for thunderstorms.

In this paper, we will review the main progress in high-precision detection, mechanism, and forecasting of thunderstorm and lightning in China in the last decade, including high-resolution 3D lightning mapping technology and application, lightning in different thunderstorms and its relationship with cloud dynamical and microphysical processes, observation and simulation of thunderstorm charge structure, and lightning prediction and lightning data assimilation toward improvement of thunderstorm forecasting.

2.   High-accuracy 3D lightning mapping techniques and application

The localization of lightning activity, including time, location, intensity, and type of lightning, is an important basis for lightning research and lightning monitoring and warning (Qie et al., 2013). Through synchronously measuring electromagnetic (EM) signals generated by lightning in five or more stations, the spatiotemporal evolution of lightning processes can be mapped in 3D by using appropriate location algorithms (Wang et al., 2009; Zhang et al., 2010).

2.1   Lightning mapping techniques in radio frequency with discernible discharge channels

For scientific research, lightning location has been realized high-precisional 3D dynamic mapping for entire lightning process with distinguishable discharge channels in both low frequency (LF) and very high frequency (VHF) band. For long baseline mapping in the LF band, more than 5 LF lightning radiation detectors forming an array are usually used to determine the lightning source in 3D based on the time of arrival (TOA) of lightning signal at each station. The baseline between detectors can range from 10 to tens of kilometers (Li et al., 2022; Zhang et al., 2022; Qie et al., 2023).

Several regional 3D lightning mapping systems have been successively established, such as Beijing Broadband Lightning Network (BLNET; Wang Y. et al., 2016; Srivastava et al., 2017; Yuan et al., 2020), North China Lightning Location Network (NCLLN; Ma et al., 2021; Liu M. Y. et al., 2024), LF E-field Detection Array (LFEDA) in Guangdong, Hainan, and Naqu (Shi et al., 2017; Yin et al., 2019; Fan et al., 2021; Pan et al., 2023; Li et al., 2024b), Broadband Lightning System in Qinghai (Li et al., 2017; Wang Y. H. et al., 2021), and so on.

In order to accurately map lightning channels, Chen Z. F. et al. (2019) developed an algorithm combining time-reversal (TR) and TOA methods, achieving high-quality 3D localization even with a small number of stations, low signal-to-noise ratio and temporal accuracy, especially for long-range localization with weak signals. Liu B. et al. (2020) applied the TR frequency domain approach to LF mapping and also obtained promising results. Fan et al. (2021) applied the Empirical Mode Decomposition (EMD) technique in the LF mapping, and the improved signal filtering and noise reduction led to a more-than-five-fold increase in the number of mapped sources. Wang Y. H. et al. (2021) combined the Pearson correlation and the EMD methods to match the electric field pulses of lightning, and the mapped sources were almost seven times of those mapped using the pulse-peak matching. Liu et al. (2024) developed a high-gain dE/dt sensor in the NCLLN, enabling continuous detection and high-speed data storage of high-resolution signals over several hours. The location accuracy is found to be better than 100 m by comparing with rocket-triggered lightning.

Compared to LF signals, VHF signals show much more pronounced impulses, allowing a more accurate dynamic evolution in lightning channels. According to the differences in baseline length and mapping algorithms, the VHF mapping can be divided into two main categories, including long baseline systems using the TOA method (Zhang et al., 2010) and short baseline interferometer. The interferometer maps the azimuth and elevation of radiation sources in 2D, capable of high-temporal resolution tracking of lightning discharges. Dual-station synchronization can also achieve 3D channel positioning (Liu et al., 2016; Sun et al., 2022). In recent years, hardware and algorithms have been significantly improved with signal acquisition evolving from segmented triggering to continuous acquisition, allowing a radiation source mapping in sub-microsecond even nanosecond resolution (Sun et al., 2014; Wang T. et al., 2020; Li et al., 2021a; Fan et al., 2023; Yuan et al., 2023).

2.2   New insight into lightning mechanism based on high-precision lightning mapping technologies

The rapid development of high-precision lightning detection technology, especially the channel distinguishable 3D mapping, has laid an important foundation for the study of lightning physics and mechanism. By combining with high-speed camera, current and near-field EM detection, understanding of initiation, propagation, and striking mechanism of lightning has been significantly put forwarded.

Positive lightning leader has long been believed to propagate in a continuous manner. However, more and more observations have suggested its stepping propagation. Jiang et al. (2013) found the discontinuous propagating features of upward positive leaders in the initial stage of rocket-triggered lightning. Wang Z. C. et al. (2016) captured an upward positive leader initiated from the 325-m meteorological tower using a high-speed camera operating at 150 kpf, and the sustained development of discharge channel showed clear stepping features with intermittent pauses and jumps of the channel tip (Fig. 1), which was considered to be the most detailed observational evidence of positive leader stepping (Kostinskiy et al., 2018). Some scholars once argued that the stepping behavior of positive leader may simply be an epiphenomenon in response to the approaching negative leader (Visacro et al., 2017). However, new observations have been available in China in recent years, leading to more detailed understanding of the positive leader stepping development. Srivastava et al. (2019) found that although both downward negative and upward positive leaders showed stepwise propagation as they approaching each other, their stepping behaviors showed significant spatiotemporal inconsistency, indicating that the positive leader stepping is determined by its intrinsic physical properties. Gao et al. (2020) confirmed the stepwise characteristics of positive leader based on optical images for the 365-m tower-initiated flash, with step length between 0.5 and 3 m. Based on a high-sensitivity LF magnetic antenna, Fan et al. (2018, 2022) and Wang et al. (2023) observed the discontinuous development of upward positive leaders in rocket-triggered lightning and natural lightning, respectively. Jiang et al. (2020) found a kind of luminous crown residual structure in front of the positive leader head during the leader pause and suggested its similarity to the space stem/leader in the negative leader stepping, which results in the step forward by connecting with the leader head.

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Fig  1.  Stepping propagation characteristics of upward positive leader based on high speed camera. The time interval between two frames is 6.67 µs, and the red dashed line marks the leader intermittent development featured with abrupt growth of the streamer system of the channel head [adapted from Wang Z. C. et al. (2016)].

The lightning attachment process has been a long-standing concern for both lightning physics and protection design, but with limited understanding due to the difficulty of its distinguishable detection. Considering tall buildings are vulnerable to lightning, observations have focused on the top of the building where the lightning connection process tends to take place. Jiang R. B. et al. (2021) found that within the common streamer zone between the connecting leaders, the dominant growth route established by the earliest interconnected streamers determines the hot plasma channel of the following return stroke. Lu et al. (2013, 2015) found diversity of the connection process between downward negative leader and upward positive connecting leader. The 2D distance between the heads of positive and negative leaders was 13 m in the frame before the attachment process completed (Qi et al., 2019). Qi et al. (2023) found upward negative leader connected to the lateral surface of the downward positive leader in a +CG flash. Liu M. Z. et al. (2020) found a special circuitous attachment process in an altitude-triggered flash striking at a 30-m tower.

The multiple return strokes of +CG flashes are usually believed to be generated by bifurcation and grounding of the lower positive leader of the bidirectional leader in the cloud. However, Yuan et al. (2020) found that the downward positive leader can originate from the opposite end of extinguished or developing negative leader channel, and eventually contact to the ground to form a positive return stroke by using the 3D lightning mapping (Fig. 2). Two triggering mechanisms for +CG flashes were then proposed: intracloud flash initiation and preliminary breakdown initiation (Yuan et al., 2021a). Jiang R. B. et al. (2021) observed frequent recoil leaders in the upward −CG lightning bridged the positively charged areas some distance away to participate in the lightning discharge, ultimately leading to grounding of the downward positive leader. Zhu et al. (2024) found that the downward positive leaders around the negative leader channel is facilitated through multiple side discharges, which play a crucial role in +CG lightning. High speed cameras and high-resolution lightning mapping have also revealed two mechanisms for −CG lightning with multiple grounding-points, one resulted from channel branches within the cloud that propagated toward and striking at ground, and the other is related to grounding of the lower leader branch channels (Qie and Kong, 2007; Jiang et al., 2015; Sun et al., 2016).

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Fig  2.  Very low frequency (VLF) mapping on a whole +CG process containing preceding intracloud (IC) discharge. (a) Waveform of very high frequency (VHF) radiation, fast electric field change (FEC), and slow electric field change (SEC); (b) altitude variation of lightning sources with time; (c) vertical view of the whole discharge process from south to north; (d) plan view of the whole discharge process; and (e) the 3D view of the flash [adapted from Yuan et al. (2020)]. The color gradient from dark blue to dark red represents the temporal sequence of the lightning channel’s development and propagation.

In contrast to lightning leaders in virgin air showing mainly stepwise propagation characteristics, leaders developing in preconditioned channels show diversity. Dart leaders occurring before subsequent return strokes sometimes exhibit characteristics of irregular pulse train and propagate rapidly with strong EM radiation may accompanying with X-ray bursts (Pu et al., 2019). Jiang et al. (2022) found long recoil leader activated the negative-end breakdown with a regular EM pulse train, and a back-and-forth interaction occurred between opposite ends of the channel. Wu et al. (2019) found that a recoil leader initiating near the extremity of positive end of the preceding attempted leader reached ground and induced a return stroke. Li et al. (2021b) found that the recoil leader developed towards the channel cut-off point and activated negative leader to develop outwards and eventually resulted in charge transfer to ground with discharge polarity reversal from negative to positive. Rare positive recoil leaders were found in the reactivation of preconditioned negative channel associated bidirectional development (Jiang et al., 2014; Qie et al., 2017).

Lightning generally breaks down air around the channel head to create new channels. Advanced lightning detection technology is helping to reveal new lateral breakdown process. Yuan et al. (2019) found that bidirectional leaders were induced at approximately 200 m from the developing positive leader channel, with the positive end propagating away while the negative end towards and eventually connecting with the main positive channel. This results in a new branch of the positive leader, acting as a new form of positive leader branching, in addition to the direct splitting of the channel at its head and the connection of the recoil leader to the existing channel. Yuan et al. (2021b) found that two types of lateral positive polarity breakdowns occur around the negative leader channel, that is, reactivation of the distinguished branches and the induced positive leader breakdown. Wu et al. (2022) found that during the continuing current stage after a +CG stroke, the opposite polarity charges injected by the stroke process caused flickering negative needles on the side of horizontal channel. The needles exhibited a variety of modes such as bidirectional recoil leader and stepwise development, and lateral breakdown of the lightning channel is often associated with reactivation of the extinguished channel (Wu et al., 2023). Using a VHF lightning interferometer with nanosecond time resolution, Fan et al. (2024) found that the propagation speed of the regular pulse burst sequence of lightning is approximately 0.6–1.8 × 10⁸ m s⁻¹, which is significantly faster than expected, and the inferred channel current is 6–18 kA.

2.3   Performance evaluation and applications of the FY4A-LMI

Performance of Lightning Mapping Imager (LMI) on Fengyun-4A (FY-4A), the first lightning detection payload on space platforms in China, has been widely evaluated through various methods. Zhang et al. (2020) found that LMI exhibits relatively high efficiency and accuracy in detecting IC lightning associated with severe convective weather, such as typhoons. Cao et al. (2021) found that the density peak distribution is consistent with that of the Lightning Imaging Sensor on the International Space Station, although LMI detects fewer lightning events with shorter duration. Chen et al. (2021) found that LMI effectively captures variations in cloud-top radiation resulting from discharges in weak thunderstorm cells, although its overall detection efficiency is relatively low and exhibits considerable fluctuations depending on thunderstorm characteristics. Hui et al. (2020) found lower detection efficiency of LMI in the Qinghai–Xizang Plateau (QXP) due to the weak radiation, short life cycle, and small size of lightning. Liu Y. et al. (2021) suggested that LMI’s detection efficiency is dependent on lightning current magnitude, with a horizontal positioning error of approximately 15 km for isolated thunderstorms. Zou et al. (2024) found that LMI’s detection efficiency was significantly higher at nighttime than daytime, while the spatial accuracy was better in daytime.

Zhang et al. (2023) established an Ellipsoidal Cloud Top Height Parallax Correction Model (ECPC) to improve the lightning localization with FY-4A LMI. By compensating for parallax effects caused by varying cloud top heights, the model reduced LMI positioning errors to 7–12 km from larger than 20–30 km, thereby enhanced the accuracy and reliability of satellite lightning localization data.

3.   Lightning in different thunderstorms and its relationship with cloud dynamical and microphysical processes

The dynamical structure, microphysical processes, and lightning activity in thunderstorm are intricately related to and interact with each other. Due to different dynamical and microphysical characteristics in different thunderstorms, lightning activity and charge structure may exhibit significant differences.

3.1   Lightning activity in different type of thunderstorms

Based on weather radar, lightning, and ground-based observational data, the characteristics of lightning activity during the evolution of thunderstorms are widely investigated. Liu et al. (2024b) analyzed different types of thunderstorms over Beijing area and found that lightning frequency was the highest in linear mesoscale convective systems (MCSs), while single cell was the most common type of thunderstorms, and the +CG lightning ratio was the highest in weak convective systems. Furthermore, the parallel linear convective systems (PS) generated the highest ratio of both CG and +CG lightning, followed by leading stratiform (LS) and trailing stratiform (TS) of MCSs (Liu et al., 2021b). Lightning is predominantly concentrated in the leading line convective region of TS-MCS within the strong radar reflectivity, especially within the height range of 6–11 km where the radar reflectivity exceeds 30 dBZ. Although the temporal evolution of total lightning rate and the volume of high radar reflectivity are basically similar, there exists discrepancies in the timing of the peaks (Yu et al., 2022). The lightning sources occurring in the trailing stratiform region exhibited relatively sparse and showed a distinct inclination toward the precipitation region of the trailing stratiform region, suggesting that the charges there are transferred from the convective region through transition zone (Xu et al., 2018; Sun et al., 2019; Wang D. F. et al., 2020).

The merging between different convective cells within a thunderstorm is essential for maintaining convective development and intensification. Lu et al. (2021) found that total lightning rate increased rapidly during the merging process and reached its peak when the merger finished. The merging process usually weakens the back cell, and feeds and enhances the front cell, resulting in an increase in lightning activity and its sources height in the front cell (Fig. 3). Yi et al. (2017) found that after the start of convective cell merging, the lightning rate showed jump increase, even reaching a peak, and the height of the main positive charge region involved in discharge decreased by 1–4 km.

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Fig  3.  Radar echoes (color; dBZ) and lightning sources [black dots for IC; blue “+” for positive cloud-to-ground lightning (PCG); light blue “×” for negative CG lightning (NCG)] during the merging process of mature convective cells (A) and newly formed cells (B). (b) and (c) are vertical radar profiles along the black line in (a), showing the merger process of cells A and B, and the distribution of radiation sources within 0.1° latitude around the profile [adapted from Lu et al. (2021)].

Generally, the positive charge layer associated with lightning in the stratiform region of thunderstorms is predominantly located at higher levels. However, Wang Y. et al. (2016) found that CG lightning in the region often occurs around or at the edge of the bright band echo. The vigorous convective systems are capable of forming a sufficiently strong positive charge layer near the melting layer, which is often involved in discharges in the stratiform region (Wang et al., 2019b, 2020; Wang T. et al., 2020). Yuan et al. (2017) found that +CG occurred in the stratiform region can induce upward lightning from tall towers, corresponding to weak echo areas of 30–45 dBZ and cloud top heights of 6–9 km. The spontaneous upward lightning corresponded to lower ambient temperature and higher wind speed reaching 14 m s⁻¹.

Lightning activity of supercells exhibits distinct characteristics compared to other severe convective systems. Liu D. X. et al. (2020) found significant variations in lightning rate before and after hail events, with a notably higher ratio of +CG lightning during these periods in a typical supercell. The corresponding charge structure underwent a significant transformation from an inverted tripolar to a normal tripolar charge structure before and after the hail event. Chen et al. (2020b) found that the lightning production increased much faster than the storm volume growth in a supercell embedded in MCS with complex charge structure, and merges of convective cells enhanced the overall complexity of the charge structure and increased local electric field and lightning flashes.

Lightning serves as an effective indicator of strong convection, providing an essential reference for the convective intensity variations associated with typhoons. Lightning activity varies significantly between different typhoon systems, with lightning more likely to occur in tropical depressions and tropical storms (Wang F. et al., 2017). Lightning occurs usually in the extensive outer rainbands and the eyewall of typhoon, and almost absent in the inner rainbands (Pan et al., 2014; Xu et al., 2017; Wang et al., 2018; Zhang et al., 2019). During the maximum intensity phase of typhoon, lightning rate usually reaches the peak or secondary peak, and therefore, lightning peaks can potentially be used to predict the typhoon intensification (Zhang et al., 2015; Kong et al., 2021).

3.2   Relationship between lightning and thunderstorm dynamical–microphysical processes

Lightning is resulted from combined effects of the dynamic (such as wind fields, shear, and turbulence) and microphysical processes (such as phase transition for different particles, particle morphology, and growth and distribution of hydrometeors) of thunderstorms, and the evolution of storm convective structure directly affects the characteristics of lightning activity. Zheng et al. (2019b) found that ice-phase particles are concentrated at low altitudes with the primary charge region situated near the −10°C isotherm when the convective intensity is weak. For the strong convection, ice-phase particles could be transported to higher levels, leading to formation of primary charge regions at both higher and lower levels. Liu Z. et al. (2020) found that when the electrification rate in the original region is lower than the rate of charge moving away from the region, the charge density in the original region remains low, leading to an increased lightning activity in localized areas far from the original charge region. Lu et al. (2022) indicated that the strong convergence caused by the cell merging results in more water vapor being carried up from the lower to the upper levels, facilitating the formation of ice-phase particles and thereby enhancing the non-inductive charging and lightning.

Lightning initiations are often associated with local airflow characteristics. Zheng et al. (2016) found that ice-phase particles are difficult to remain for long period in the updraft regions of supercell, thereby forming an obvious low lightning zone compared to the surrounding areas, referred as a “lightning hole.” Han et al. (2020) found that the vertical velocity was distributed around −2 to 2 m s⁻¹ and −10 to 10 m s⁻¹ in the 0°C and −20°C layers over the CG flash grounding points, respectively. Li et al. (2024) found that the correlation coefficients between flash rate and hail and ice crystal in a hailstorm were 0.76 and 0.83, respectively. The initiation and channel extension of lightning are typically associated with hydrometeor particles such as graupel, ice crystals, and dry snow (Liu Z. et al., 2020; Zhao et al., 2021b) and sometimes near the echo top of the high radar reflectivity (Zhang et al., 2017).

The incloud turbulence may cause differences in velocity of the hydrometer particles with different masses and inertia, thereby affecting their collision velocity in the mixed phase region. Zhao et al. (2021b, 2022) found that the eddy dissipation rate (EDR) and the achievable height of turbulence in thunderstorms were both greater than those in non-thunderstorms, and their characteristics of liquid/ice particles at different height layers in the first radar echo also showed differences. Li Y. R. et al. (2021, 2022, 2024) found that lightning with complex channel morphology often appeared in areas with large EDR (Fig. 4), and lightning channels that extend within a certain height range without obvious branching or turning tend to propagate in the direction of decreasing EDR, while channel branching and turning usually occur in areas with large radial velocity gradients and large EDR (Zheng et al., 2018, 2019a, b).

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Fig  4.  The superposition of lightning radiation sources (black dots) and radial velocity (colored shadows), with white boxes indicating the fork of the lightning channel [adapted from Li Y. R. et al. (2024)].

Lightning rate in thunderstorm is also related to the size of thunderstorm. Zheng and Zhang (2021) found that thunderstorms in QXP exhibited weak convection, resulting in lower lightning rates and smaller effective charging regions by comparing with those over nearby regions. The deep convective systems over the QXP are significantly compressed vertically and have smaller horizontal scales (Qie et al., 2014a). The complex topography of the QXP plays a significant role in modulating the convective structure and intensity of thunderstorms (Wu et al., 2016). Qie et al. (2022b) found that thunderstorms in the eastern QXP generated the highest rate, which correlated with increased ice-phase particle content, cloud top height, and maximum volumetric convective precipitation rates. In contrast, the small-horizontal scale and lower-vertical convection in the western QXP showed lower lightning rate. Cui et al. (2022) found that morphological information of lightning channels can improve the stability of the quantitative relationship between lightning activity and convective structures of thunderstorms. By introducing parameters that characterize the spatial extent of lightning channels, Zhang et al. (2017) found a significant linear relationship between lightning rate and area of radar echoes exceeding 30 dBZ at an altitude of 5 km.

4.   Observation and numerical simulation of charge structure in thunderstorm

It is well accepted that the primary electrification mechanism within a thunderstorm is the non-inductive mechanism (Takahashi, 1978; Saunders and Peck, 1998), where collisions between graupel and ice crystals in the mixed-phase region result in charge transfer in between, and the polarity of which is related to ambient temperature and supercooled water content. Once the thunderstorm is electrified via the non-inductive mechanism, different phase hydrometeor particles when collision are also charged by inductive charging mechanism under the environment of electric field. The particles carrying different polarities of charges are transported by airflow to form the charge structure inside thunderstorm with alternating positive and negative charge regions. As the electric field exceeding the threshold of air breakdown, lightning discharge initiates.

4.1   Observation of charge structure inside thunderstorm

The charge structure within a thunderstorm can be observed by two main approaches, electric field sounding and retrieve from location of lightning charge source. The electric field sounding allows for the direct measurement of 3D or vertical electric fields, and the charge distribution along the sounding path can be calculated by using Gauss’s law (Zhao et al., 2010; Zhang et al., 2021a). The lightning charge retrieval, from multistation electric field change (Qie et al., 2013) or radiation source mapping, can show charge regions and their horizontal distributions involved in lightning discharge, which overcomes the limitation of sounding path restrictions. By combining two methods, the vertical charge structure of thunderstorm and the horizontal extension of main charge regions can be obtained simultaneously.

Zhang et al. (2021a) developed a 3D electric field sonde integrated with meteorologic radiosonde and observed five stacked charge regions with alternating polarity in the stratiform region of a decaying thunderstorm, including a weak negative shielding charge region near the cloud top, main positive, main negative, positive charge region, and the weakest negative charge region at the bottom (Zhang et al., 2021b). Using the strong electric field sonde developed by Zhao et al. (2010), six and seven charge regions with alternating polarity were found in thunderstorm and typhoon eyewall by Yu et al. (2021) and Zhang T. L. et al. (2021), respectively.

Li et al. (2017) found that a thunderstorm in northeastern QXP showed stable inverted dipole charge structure with upper negative and lower positive charge regions during its initial and mature stages by using VHF 3D lightning mapping. During the dissipation stage, due to the merging of two convective cells, different regions of the thunderstorm exhibited positive dipole, negative dipole, and tripolar charge structures, respectively. Based on high-accuracy VHF lightning interferometer, Liu et al. (2024a) showed charge structure evolution of summer thunderstorms in central QXP evolved from an inverted dipole to a special tripolar charge structure with a large lower positive charge center (LPCC) in the bottom, and the LPCC in the lower level was mainly contributed by graupel particles carrying positive charges, confirming the lower-level dominant charge structure (bottom heavy) of thunderstorms proposed by Qie et al. (2005), and further showed new insight into the evolution of the charge structure.

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Fig  5.  Evolution of charge structure in thunderclouds over the QXP based on VHF interferometer. Blue “−” and deep red “+” represent negative and positive charge regions, red (blue) represents positive (negative) lightning channels, and black dotted lines for ambient temperature, respectively [adapted from Liu et al. (2024)].

Li Y. R. et al. (2022) suggested that lightning channel morphology can reflect more detailed horizontal charge structure characteristics in thunderstorms. The areas where lightning channel branching and turning occur have higher charge density, while the areas where direct extension occurs have lower density. Zheng et al. (2019a) found that the charge regions involved in the discharge included quadripolar, tripolar, positive dipole, and inverted dipole and tripolar, with an average distance of about 1.3 km between the core of adjacent charge regions in the winter thunderstorms of Japan. All above observation results indicate that the charge structure in thunderstorms is more complex than the commonly simplified assumed tripole charge structure.

4.2   Numerical simulation on the electrification and microphysics of thunderstorm

Due to the limitations of current detection technology, it is not yet possible to achieve fine-scale observations of the complex dynamic–microphysical–electrical processes of thunderstorms. Therefore, high spatial resolution numerical models on the scale of 500 m or 1 km have become an important approach to conduct relevant related research.

Xu et al. (2016) utilized a mesoscale WRF model coupled with an electrical process to simulate an actual hailstorm and pointed out that the generation of inverted tripolar charge structures is due to dynamic transport effects on normal polarity charging scenario, with graupel particles carrying negative charges and ice crystals carrying positive charges. Guo et al. (2017) simulated a supercell with tornado emergence and found that the inverted charge structure formed during the hail falling was due to the positively charged graupel particles being lifted into the central region by strong updrafts. Wang et al. (2015a) found that electrification efficiency of non-inductive charging is high when the maximum vertical velocity ranges from 5 to 20 m s⁻¹. However, the center of electrification efficiency does not coincide with the center of updraft. The relatively stable region of vertical motion with velocity between −1 and 1 m s⁻¹ is the primary location for charge separation. The current outflow rate of precipitation particles either promotes or inhibits the charge-neutralization rate during lightning activities (Wang et al., 2015b). Wang et al. (2019a) found that, in addition to the non-induction electrification, the induction mechanism also plays an important role in the formation of the strong LPCC in thunderstorms over the northeastern QXP.

Sun et al. (2018) added the influence of electric field force on the final velocity of hail and graupel particles in the cloud microphysics scheme of the WRF-Elec model (Mansell et al., 2010; Fierro et al., 2013), and the simulation suggested that the feedback effect of electric field forces on electrification and charge structure within thunderstorms cannot be ignored. The simulation by Lian et al. (2020) also found that the lightning process can affect the electric field in thundercloud, thereby affecting the final velocity of water condensation, which in turn affects the dynamic structure, precipitation, and microphysical characteristics.

Xu et al. (2019) used two non-inductive charge schemes based on liquid water content (LWC) and riming accretion rate (RAR) to simulate the charge structure of thunderstorm, and found that the RAR-based scheme resulted in an inverted tripolar structure in the convective region. Strong updraft, high LWC, and high RAR in the layer above −20°C were identified as the environmental conditions for the formation of upper positive charge region. Lu et al. (2022) found that the merging of convective cells significantly affected the charge structure, which evolved from a typical triple to a five-layer charge structure with the upper being positive. The reduction in distance between charged regions of different polarity caused by cell merging or strong dynamic processes can increase lightning activity (Chen Z. X. et al., 2019). Xu et al. (2020) found that the melting of ice crystals and graupel particles contribute differently to the formation of charge structures. The melting of ice crystals can create a significant positive charge region at the lower level of the stratiform region, while the melting of graupel particles further strengthens the positive charge region in the lower level of the convective region.

Tan et al. (2017) simulated the effects of three ice crystal nucleation schemes on lightning behavior and showed that ice crystal nucleation schemes have a certain impact on the microphysical characteristics, electrification of ice crystals, and discharge processes in thundercloud. Huang et al. (2024) found that different secondary ice crystal production processes have an impact on the simulated thunderstorm charge structure and lightning activity. Zheng et al. (2022) established a parameterization scheme for lightning simulation that takes into account the nonlinear electrical parameters of the lightning channel and the overall electrical neutrality, achieving the simulation of the reactivation phenomenon of the lightning channel after extinction. Zhang et al. (2024) established a self-sustaining electrically neutral lightning parameterization scheme that can reproduce the process of leader truncation and re-breakdown, and the simulated intracloud flashes showed good consistency with some observations in terms of channel structure, leader truncation and reactivation, and polarity asymmetry of positive and negative leaders.

Aerosols impact the microphysical characteristics of thunderstorm and lightning acting as cloud condensation nuclei (CCN). Sun et al. (2021) found that increase in aerosol concentration leads to larger cloud droplet concentrations, resulting in larger cloud water content and smaller droplet sizes (Zhao et al., 2015; Jiang et al., 2017). A simulation on charge structure of supercell showed that graupel particles carry positive charges by non-inductive charging mechanism under higher supercooled water content, forming a dominant middle-level positive charge region and resulting in a higher proportion of +CG flashes (Sun et al., 2024). The water vapor content can influence the aerosol effect by the growth rate of ice-phase particles. Sufficient water vapor ensures the growth of graupel and ice crystals, leading to more ice-phase particles and enhanced non-inductive charging processes (Lin et al., 2021).

Dust aerosols, acting as CCN and ice nuclei (IN), participate in cloud microphysical processes and impact electrification. Sun C. F. et al. (2023) found that a thunderstorm initiated under a dust-storm environment was dominated by PCG flashes and inferred that thunderstorm under the influence of dust aerosols exhibited inverted charge structure. Sun M. Y. et al. (2023) found that in low convective available potential energy (CAPE) environments, the increase of aerosol concentration leads to a higher collision efficiency between graupel particles with increased radius and other ice-phase particles, despite low content of ice crystals. Shi et al. (2023) found that the enhancement of IN concentration favored the enhancement of heterogeneous nucleation, leading to a gradual decrease in the positive non-inductive charging rate and a gradual increase in the negative non-inductive charging rate.

5.   Lightning prediction and lightning data assimilation for thunderstorm forecasting

With the growing social demand and the continuous progress in accurate weather forecasting, lightning forecasting technologies have been innovatively developed (Meng et al., 2019; Qie et al., 2023). The rapid development of artificial intelligence (AI) recently has effectively improved the accuracy and timeliness of lightning forecasting. Meanwhile, the development and application of lightning data assimilation methods has also improved the thunderstorm forecasting.

5.1   Deep learning-based lightning prediction

Studies on lightning prediction based on numerical forecasting models have shown that the forecast performance is largely dependent on the parameterization scheme adopted, and the results usually deviate from the predicted convective events in both spatial and temporal domains (Xu L. T. et al., 2022). In operational numerical models, there is still a large room for improvement in the accuracy of cloud simulation, while the lightning short-term forecasting method based on numerical models has an essential dependence on the accuracy of the model itself. In recent years, with the significant improvement of computing power and the rise of big data, machine learning and AI have been successfully applied in lightning forecasting.

Geng et al. (2021) used AI techniques to explore the complementary information of observational and numerical model data, and applied to the post-model bias correction by AI techniques. By integrating the historical observational data (lightning, automatic weather stations, etc.) before the onset of the reporting moment with numerical model products (ice, snow, graupel content, and vertical updraft) in the dimension of spatial and temporal conjunction, a method of fusion of observational data and numerical model products has been established based on convolutional spatial and temporal coding. The “LightNet” simulation–observation grid-point data fusion forecast model has been developed for 0–6-h lightning forecast. Unlike the traditional prediction method based on numerical weather prediction, LightNet uses a dual encoder to extract spatial and temporal characteristics of WRF model data and nearby lightning observations in an attempt to calibrate the model and assist in forecasting. The experimental evaluation of the North China lightning dataset shows that the equitable threat scores (ETSs) of LightNet in 0–6-h prediction are greatly improved compared with the traditional lightning diagnostic methods. Furthermore, a simulation–observation heterogeneous multisource data fusion forecasting model of “LightNet+” shows that the forecast effect is significantly better than LightNet with more sources and higher quality of meteorological observation data, as well as better forecast performance. Zhou et al. (2021) developed a short-term prediction method for lightning striking area that utilizes deep learning to integrate high-resolution numerical models and multisource observation data (Fig. 6). They constructed a dual input and single output deep learning semantic segmentation model (LightningNet-NWP), which achieved better lightning strike prediction from 0 to 6 h compared to only using multisource observation data or high-resolution numerical model data. The longer the prediction time, the more obvious the advantage of the integrating method.

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Fig  6.  Schematic diagram of a deep learning model for lightning strike area prediction using multisource observation data and high-resolution numerical model prediction data [adapted from Zhou et al. (2021)].

For longer timeliness of forecasting, Lin et al. (2019) proposed an attention-based dual-source spatiotemporal neural network (ADSNet) for 0–12-h lightning forecasting. The model takes advantage of the recurrent neural network (RNN) encoder–translator structure and deploys a channel attention mechanism on the model to adaptively enhance the valuable information carried by different types of data during the forecasting process. The model not only improves the 6–12-h lightning forecasting performance, but also gives the model interpretability in terms of contributing to various input meteorological parameters. Zhou et al. (2022) redesigned a bidirectional non-local fusion prediction model, which introduces a bidirectional spatiotemporal propagator to encode the forward and backward trend information of the pattern data, different from the classical convolutional and recursive neural blocks that deal with one local neighborhood at a time. This bi-directional spatio-temporal design is capable of correcting negative effects of errors due to time delay or advance between simulation and observation.

5.2   Lightning data assimilation for improving thunderstorm forecasting

Numerical weather prediction (NWP) model is an important tool for convective weather forecasting. Effectively incorporating observational data into the models can enhance the initial conditions with adding more small- and mesoscale information. Lightning data with high resolution are less affected by distance and terrain, so as can be good data source for assimilation. In some thunderstorms, lightning peaks occur several to tens of minutes before severe convection is detected by weather radar (Wu et al., 2017; Tian et al., 2019, 2022). Therefore, lightning data assimilation may provide additional increments on the basis of radar data assimilation. However, since lightning parameters are not model-resolvable variables, it is necessary to establish the relationship between lightning and model variables before assimilation.

5.2.1   Hydrometeor microphysical–thermal field-based lightning data assimilation

In severe convective thunderstorm, the content of ice-phase particles, especially graupel and ice crystals, significantly contributes to non-inductive charging and lightning in thunderstorms. Qie et al. (2014b) constructed the relationship between flash rate and mixing ratios of snow, ice crystal, and graupel in the temperature range of 0 to −20°C, and the simulation with WRF effectively improved the forecasts of convection and precipitation. Chen et al. (2017) developed an observational operator that simultaneously adjusts water vapor and ice-phase particles by lightning observation. The simulations on two severe convective weather systems showed that the simulated convection adopting this lightning assimilation persisted longer and precipitation forecasts were more accurate (Chen Z. X. et al., 2019). Wang H. L. et al. (2017) used a linear relationship between lightning rate and column-integrated graupel mass and adjusted the temperature increments of latent heat release based on changes in the mixing ratio of graupel, thereby regulating ice-phase particles and thermodynamic fields, and the lightning and precipitation proximity forecasting were much improved. Furthermore, by using a function of graupel mixing ratio as an observational operator, they improved the forecast of reflectivity and precipitation by increasing the water vapor mixing ratio and RH in the 0 to −20°C range (Wang H. L. et al., 2017).

Liu P. et al. (2020) used lightning data to increase the background RH to 90% within the range from lifting condensation level to cloud top height. They found that adjusting the water vapor, wind, and hydrometeors field based on lightning extended the duration of the predicted convection and suppressed the spurious convection cells. Pan et al. (2022) modified the RH between cloud base and 650 hPa to 65%–95% in areas with lightning and improved the ability to forecast precipitation within 3 h, as well as the ability to forecast the path and intensity of thunderstorm. Using lightning as a proxy for radar reflectivity, Yang et al. (2015) found that the assimilated reflectivity in the first 6 h was closer to observations with adjusting cloud droplets, hydrometeors, and humidity in the cloud analysis.

5.2.2   Vertical velocity field-based lightning data assimilation

Lightning is closely related to the vertical development of thunderstorm. Based on the relationship between lightning flash rate and maximum updraft velocity (w_max) established by Price and Rind (1992), Chen et al. (2020a) and Xiao et al. (2021a) created observational operators for lightning data assimilation in their models. They implemented the assimilation using 3D variational (3DVAR) and 4DVAR techniques in the WRF and Variational Doppler Radar Analysis System (VDRAS) models, respectively. Using the empirical relationship between lightning flash rate and w_max to adjust model-based vertical velocity profile, they found that lightning data assimilation enhanced convergence and upward airflow within and around the convective system, and effectively improved the precipitation forecasting and suppressed some spurious convection cells as well, consequently (Xiao et al., 2021a, b).

Considering the regional adaptation of quantitative relationships and the resolution of satellite lightning data, Chen et al. (2020a) obtained relationships between lightning event (extent) density (LED) from the FY4-LMI and w_max. On the basis of radar radial velocity assimilation, lightning data assimilation provides increments with reducing wind forecast errors and enhancing squall line convergence. The mixed-phase convection and the forecast of radar reflectivity and accumulated precipitation are also improved (Fig. 7). Zhang et al. (2023) used Chen et al. (2020a)’s lightning data assimilation scheme and showed that the assimilated vertical speeds from lightning data effectively corrected precipitation intensity and location. The joint assimilation of lightning and radar data significantly improved short-term convection forecast accuracy. Since lightning data assimilation adjusting the dynamic field effectively enhances severe convective weather forecast, Chen et al. (2020a)’s scheme has been incorporated in the latest released WRF model (WRF-V4.6; https://github.com/wrf-model/WRF/releases) as the first official lightning data assimilation scheme (da_lightning), marking a major advance in integrating lightning observations into short-term severe convective forecasting.

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Fig  7.  Scores for strong and weak precipitation by lightning assimilation scheme during a severe convection process. CTR for control run, RA for radar data assimilation, LN for lightning data assimilation, and RALN for both radar and lightning data assimilation. (a, b) Fractions skill score (FSS) for hourly cumulative precipitation, (c, d) performance of precipitation forecast in the first three hours. The magenta lines represent the critical success index (CSI), the black dashed lines represent the frequency bias (FR), the numbers inside the solid circles represent the forecast hour, and FAR means the false alarm rate (Chen et al., 2020b).

Currently, various types of real-time observational data are used for assimilation to improve convection forecast accuracy. While most previous studies only assimilated a single type of observational data, integrating multiple types into the assimilation can further enhance forecasts for severe convective weather. For example, the joint assimilation of radar and lightning data can capture small-scale convergence and suppress the spurious convection (Xiao et al., 2021a; Zhang et al., 2023). Therefore, when combined with the radar data assimilation schemes in current models, lightning data assimilation scheme can provide additional convective observational information for the model’s initial fields, thereby improving the forecasting of severe convection.

6.   Summary and prospects

In recent years, with the development of high-resolution lightning mapping technologies, significant progress has been made in the study of mechanism and forecasting of thunderstorm and lightning.

Based on comprehensive observations, including advanced 3D dynamic lightning mapping technology with channel-resolvable capabilities, high-speed photography, Doppler and dual-polarization radar, and electric field sounding, significant advances have been made in understanding the development, propagation characteristics, and mechanisms of lightning, as well as the thunderstorm physics. The observations have revealed the differences in lightning activity across various regions and types of thunderstorms, leading to a deeper understanding of the relationship between lightning and convective structures. By integrating observations with numerical simulation, some new insights into lightning activity, charge structures, and their formation mechanisms have been obtained. Combining observational data with numerical simulation results, machine learning-based lightning prediction methods have been developed. Novel methods for lightning data assimilation have also been created, which stimulated the application of lightning data in meteorological operations.

Although substantial progress has been made in the characteristics, mechanisms, and forecasting of thunderstorm and lightning, due to the electrification and lightning activity within thunderstorm strongly rely on the thunderstorm dynamical and microphysical processes, which are highly complex and difficult to measure directly, issues such as the mutual influence between dynamics, microphysics, and electrical processes within thunderstorm, and the charge generation between different phases of hydrometeor particles to initiate lightning discharge, and so on, are still not fully understood important scientific questions. Therefore, in order to better understand the mechanism of severe convective thunderstorms and improve disaster prevention capabilities, it is necessary to further strengthen observation experiments and mechanism research on thunderstorm dynamical, microphysical, and electrical processes, mainly including the following aspects.

(1) Comprehensive observations on typical thunderstorm systems based on advanced multiwavelength and multifrequency radars, multifrequency 3D lightning mapping technologies, and incloud electric fields and meteorological soundings to obtain detailed characteristics of convective structures, cloud dynamical and microphysical features, as well as lightning activity, to further reveal the interrelationships between dynamics, microphysics, and electrical processes in thunderstorms.

(2) The microphysical characteristics within thunderclouds, particularly the ice-phase microphysical processes and their impact on the mechanism of cloud electrification, as well as the explicit expression of the microphysical mechanisms of electrification in numerical models.

(3) The multidimensional physical and geometric characteristics of lightning discharge development, and their correlation with dynamic processes, microphysical field characteristics, and convective structure of the thunderstorm.

(4) The application of deep learning to the mining of multisource observational data such as lightning, radar, satellite data, and model results and its application to thunderstorms and lightning forecasting.

(5) Establishment of lightning data assimilation methods and observation operators for numerical prediction models incorporating multidimensional lightning information, and application of effective lightning data assimilation schemes in different numerical prediction models.