Evaluation of WRF Microphysics Schemes for Simulating Lightning and Rainfall over the Complex Terrain of the Western Himalayan Region

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Umakanth, Alok Sagar Gautam, Swapnil S. Potdar, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9085936/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lightning causes substantial loss of life and infrastructure damage each year. This study presents the first numerical simulation of a severe pre-monsoon thunderstorm that occurred on 23 May 2023 over the complex western Himalayan region (WHR). We evaluate the performance of four WRF microphysics (MP) schemes in simulating lightning and rainfall using ground-based lightning observations and satellite-observed rainfall data. All MP schemes capture the observed temporal variability of lightning, but with notable differences in magnitude, timing, and variability. The Morrison scheme reproduces the timing of peak lightning activity at 15:00 UTC but substantially overestimates flash rates, whereas the Thompson, WDM5, and WSM6 schemes simulate the peak 1–2 hours earlier. Spatial analysis reveals dominant lightning activity along the Himalayan foothills, which is reasonably represented by all MP schemes. Morrisonve phase, Morrison and Thompson simulate enhanced cloud water mixing ratios, supporting latent heat release and sustained convection. Elevated mixing ratios of rainwater, snow, and ice further indicate active mixed-phase processes favorable for cloud electrification. Temporal and spatial evaluations show that Thompson provides the closest agreement with observed lightning variability, while Morrison exhibits higher variability and false detections. Rainfall evaluation indicates improved detection skill for WSM6 and Morrison. Overall, the results demonstrate that no single microphysics scheme consistently outperforms others for both lightning and rainfall, highlighting non-uniform model skill across processes. These findings emphasize the need for region-specific and process-oriented tuning of model physics to improve lightning and rainfall simulations over complex mountainous terrain. Lightning simulation Cloud microphysics WRF model Western Himalaya Mixed-phase processes Extreme convection Figures Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 9 1 Introduction Lightning events are a common atmospheric phenomenon that poses a major natural hazard during the pre-monsoon season in India (Halder et al. 2015 ; Siingh et al. 2015 ; Kumar et al. 2017; Kamra and Kumar, 2020 ). Each year, lightning strikes result in significant loss of human life and damage to infrastructure. Globally, lightning is responsible for an estimated 6000 to 24000 fatalities annually (Cooper and Holle, 2018 ). In India, Mishra et al. ( 2024 ) analyzed lightning-related fatalities using National Crime Records Bureau data and reported nearly 100000 deaths between 1967 and 2020, with an average of ~ 1876 fatalities per year. Lightning predominantly originates within cumulonimbus clouds and is govern by thermodynamic, dynamical, and microphysical processes, including land-surface thermal contrasts, frontal lifting, orographic forcing, cloud microphysics, and land-use & land-cover variability (Williams, 1992; Kamra and Kumar, 2020 ; Kumar et al. 2024 ; Potdar et al. 2025 ; Potdar et al. 2026 ). Numerous laboratory and field experiments have demonstrated that microphysical interactions between graupel and ice crystals play a critical role in charge separation and lightning initiation within the mixed-phase regions of thunderstorms (Takahashi, 1978 ; Berdeklis and List, 2001 ; Adamo et al. 2007 ; Oulkar et al. 2019 ; Zhao et al. 2024 ; Biswasharma et al. 2025b ). The coexistence of ice particles, graupel, and supercooled liquid water leads to intense collisions that facilitate charge separation and cloud electrification via non-inductive charging Mechanism (Williams et al. 2002; Saunders, 2008 ; Lund et al. 2009 ; Siingh et al. 2008 , 2011 , 2012 , 2023 ; Ghoshal Chowdhury et al. 2025). Therefore, realistic representation of cloud microphysics (MP) is fundamental for accurate simulation of lightning in numerical weather prediction models. The Weather Research and Forecasting (WRF) model has been widely employed for lightning simulation owing to its ability to resolve mesoscale convective processes (Price and Rind, 1992 ; Yair et al. 2010 ; Zepka et al. 2014 ). The Price-Rind (PR92) lightning parameterization relates lightning flash rates to storm height, while subsequent developments incorporate hydrometeor-based inputs to distinguish intra-cloud and cloud-to-ground lightning (Boccippio et al. 2002; Wong et al. 2013 ; Giannaros et al. 2015 ; Dafis et al. 2018 ; Wang et al. 2018 ; Choudhury et al. 2020 ; Mohan et al. 2021 ; Vani et al. 2022 ; Kumar et al. 2022 ). Several studies have demonstrated reasonable skill of the WRF model in simulating lightning and rainfall over India (Halder and Mukhopadhyay, 2016 ; Choudhury et al. 2020 ; Mohan et al. 2021 ; Kumar et al. 2023 ). However, statistical evaluation metrics such as Probability of Detection (POD), False Alarm Ratio (FAR), Bias Score (BIAS), and Critical Success Index (CSI) consistently indicate strong sensitivity to selection of MP scheme, cumulus parameterization, and model resolution (Hazra et al. 2013 ; Kumar et al. 2023 ). Different MP schemes represent mixed-phase processes, ice growth, and hydrometeor interactions in fundamentally different ways, directly influencing charge separation, lightning production, and precipitation efficiency. For example, the Morrison scheme incorporates a more detailed treatment of ice-phase and riming processes, whereas WSM6 employs a simpler bulk representation, leading to systematic differences in simulated electrification and rainfall outcomes (Morrison et al. 2009 ; Hong and Lim, 2006). As a result, schemes optimized for lightning generation do not necessarily yield superior rainfall simulations, and vice versa. The WHR, encompassing Uttarakhand, Himachal Pradesh, Jammu and Kashmir, and Ladakh, is among the most meteorologically vulnerable regions of India. Complex topography and active synoptic systems make the region highly prone to extreme weather events, including heavy rainfall, cloudbursts, hailstorms, and intense lightning, often resulting in landslides and societal impacts (Penki and Kamra, 2013 ; Dimri et al. 2017 ; Kashyap and Behera, 2023 ; Kumar et al. 2024 ; Lohan et al. 2025 ). Lightning activity exhibits pronounced variability during the pre-monsoon and retreating monsoon phases, whereas extreme precipitation is more frequent during the summer monsoon and western-disturbance-dominated periods (Bookhagen et al. 2005 ; Dimri et al. 2017 ; Kumar et al. 2024 ). The interaction of western disturbances, cyclonic circulations, and strong orographic forcing provides favorable conditions for deep convection concurrent lightning and rainfall events (Agnihotri and Singh, 1982 ; Gangane et al. 2025 ; Hunt et al. 2025 ). Despite the high societal exposure and recurring impacts, comprehensive modeling studies examining lightning characteristics over the complex terrain of the WHR remain scarce. Over complex mountainous terrain, sharp gradients in elevation, moisture convergence, and vertical motion introduce additional uncertainty in the representation of cloud microphysics and storm electrification, making lightning simulation particularly sensitive to model physics and resolution. Moreover, although many studies have evaluated lightning and rainfall separately, the combined and comparative performance of WRF microphysics schemes in simulating both processes has not been systematically investigated. In this study, we present the first modeling-based analysis of a severe lightning event over Uttarakhand and surrounding areas on 23 May 2023, characterized by high lightning activity observed by both ISS-LIS and the Indian Lightning Location Network (ILLN), and associated human fatalities. Insights from this study are relevant for understanding microphysics-lightning-rainfall interactions in other mountainous and orographically complex regions. The objective of this study is to diagnose the sensitivity of lightning and rainfall simulations to four WRF microphysics schemes (WSM6, Thompson, Morrison, and WDM5) over a complex mountainous environment. Specifically, we address the following research questions: (1) How well do different microphysics schemes in WRF simulate the temporal and spatial characteristics and storm dynamics of a severe lightning event over the western Himalaya? (2) Do the same microphysics schemes perform similarly for both lightning and rainfall simulations? 2 Materials and Methods 2.1 Study region The WHR, encompassing Uttarakhand, Himachal Pradesh, Ladakh, and Jammu and Kashmir, is among the most meteorologically vulnerable regions in India. Its complex terrain, characterized by sharp elevation gradients and deep valleys, together with frequent synoptic-scale disturbances, makes the region highly prone to extreme weather events, including heavy rainfall, cloudbursts, hailstorms, and severe lightning, which frequently trigger landslides and societal impacts (Penki and Kamra, 2013 ; Dimri et al. 2017 ; Kashyap and Behera, 2023 ; Kumar et al. 2024 ; Lohan et al. 2025 ). Lightning activity over the WHR exhibits pronounced variability, with enhanced occurrence during the pre-monsoon and retreating monsoon phases, while extreme precipitation is more common during the summer monsoon and western disturbance (WD) dominated periods (Bookhagen et al. 2005 ; Dimri et al. 2017 ; Kumar et al. 2024 ). The interaction of WDs, cyclonic circulations, and strong orographic forcing creates favorable conditions for deep convection, leading to concurrent episodes of severe lightning and intense rainfall (Agnihotri and Singh, 1982 ; Gangane et al. 2025 ; Hunt et al. 2025 ). Given the frequent co-occurrence of multiple high-impact hazards and the associated societal vulnerability, we employ a combination of observational datasets and numerical modeling to examine the characteristics of a severe convective event over WHR in detail. 2.2 Model configuration and evaluation To perform lightning simulations over WHR, we have used the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model, version 4.5. WRF model has been developed by the National Centre for Atmospheric Research (NCAR). It is built on the Arakawa-C grid and have terrain-following hydrostatic pressure coordinate system with Coriolis and curvature terms. Model integration has performed using a third-order Runge-Kutta time integration scheme (Skamarock et al. 2008). For this study, a three-domain nested configuration was used as D1: 27 km resolution, D2: 9 km resolution and D3: 3 km resolution (Fig. 1 ). The ILLN observation shows some limitation of detection efficiency over the upper portion of the D3 domain (Biswasharma et al. 2025b ). Thus for the analysis, we have focused on a smaller target domain (TD dashed inner box in Fig. 1 ). Lightning, associated hydrometeors and rainfall were simulated using four cloud microphysics (MP) schemes: WRF Single-Moment 6-class (WSM6), Thompson, Morrison two-moment, and WRF Double-Moment 5-class (WDM5), together with the Kain-Fritsch cumulus parameterization scheme. A detailed summary of the WRF model configuration is provided in Table 1 . Table 1 Description of WRF-ARW model configuration Parameters Description References Domains Size 27km, 9km, 3km No. of vertical levels 40 Cloud Microphysics (MP) Options Scheme Reference 1) mp_physics option = 8 2) mp_physics option = 10 3) mp_physics option =14 4) mp_physics option = 6 Thompson Morrison 2-mom WRF Double-moment 5-class (WDM5) WRF Single-moment 6-class (WSM6) Thompson et al. (2008) Morrison et al. (2009) Lim and Hong, ( 2010 ) Hong and Lim, (2006) Cumulus Options Scheme Reference Cumulus physics cu_physics = 1 Kain-Fritsch Kain, (2004) Land Surface, boundary layer and radiation model Options Scheme Reference Boundary layer scheme bl_pbl_physics = 1 Yonsei University Scheme Hong et al. (2006) Choudhury et al. (2020) Land surface model sf_surface_physics = 2 sf_sfclay_physics = 1 Noah LSM Niu et al. (2011) Yang et al. (2011) Radiation ra_la_physics = 4 ra_sw_physics = 4 Rapid Radiative transfer model Iacono et al. (2008) Time Steps 15 Sec Choudhury et al. (2020) Lightning Options Scheme Reference Lightning_option = 1 Initial boundary condition Price and Rind NCEP FNL data (1° × 1° spatial resolution) Price and Rind ( 1992 ) Choudhury et al. (2020) Vani et al. (2022) The PR92 is one of the widely used parametrization schemes used for the prediction of flash rate (FR) based on the storm height and specially designed for the ocean and land configurations (Price and Rind 1992 ). The PR92 formulation expresses flash rate as: $${\varvec{F}}_{\varvec{r}\varvec{c}}=3.44\times{10}^{-5}{\varvec{H}}_{\varvec{s}}^{4.9}$$ 1 Where, \({\varvec{F}}_{\varvec{r}\varvec{c}}\) : FR (Flash min − 1 ), and \({H}_{s}\) : Strom Height Also, PR92 scheme can be upgraded to calculate the Flash rate by using maximum vertical velocity $${\varvec{W}}_{\varvec{m}\varvec{a}\varvec{x}}=1.49\times{\varvec{H}}_{\varvec{s}}^{1.09}$$ 2 and the corresponding flash rate is calculated as $${\varvec{F}}_{\varvec{r}\varvec{c}}=5\times{10}^{-6}{\varvec{W}}_{\varvec{m}\varvec{a}\varvec{x}}^{4.54}$$ 3 The performance of the WRF model have evaluated using contingency table based skill scores (Table S1), including (i) Probability of Detection (POD), (ii) False Alarm Ratio (FAR), (iii) Bias Score (BIAS), (iv) Critical Success Index (CSI), and (v) Accuracy (Wilks, 2011; Vani et al. 2022 ). Additional details on fractional skill score (FSS) metrics are provided in Kumar et al. ( 2022 ) and references therein (Table S2). 2.3 Data We used ground-based lightning data from the ILLN to assess the model performance. The details regarding the principle and working of IILN are given in Biswasharma et al. 2025b and reference therein. The network operates within a frequency band of 1 Hz to 12 MHz, where the lower frequencies are used to detect CG lightning, and higher frequencies are used to detect IC lightning (Vani et al. 2022 ). The ILLN has an estimated detection efficiency of 90% for CG lightning flashes and 50% for IC flashes (Biswasharma et al. 2025b ). In India, the WRF model has been used over the NE, western ghats (WG) and Indo-Gangetic plains to simulate lightning events and the performance of the model has been evaluated by using ILLN data (Choudhury et al. 2020 ; Mohan et al. 2021 ; Kumar et al. 2022 ; Vani et al. 2022 ). For the Initial and boundary conditions (IC/BC), we have used Final (FNL) Operational Global Analysis Data, as well as geographic data (elevation, LULC, etc) for the model, which were obtained from the National Centers for Environmental Prediction (NCEP). The rainfall data is obtained from Global Precipitation Measurement (GPM). 3 Result and Discussion 3.1 Event description Figure S1 represents the monthly lightning flash counts along with daily flash counts in May 2023 observed by ISS-LIS over TD. Relatively higher lightning flash counts (1607) were observed in May month particularly on 23 May 2023. Observation shows 769 flashes in the span of 24 hours. Due to temporal limitations of ISS-LIS, we have further carried out the analysis based on ILLN data. To understand the synaptic condition, we analyzed using geopotential height (m) and wind vectors (m s⁻¹) at 500 hPa on over India and adjacent areas as shown in Fig. 2 . A well-marked active WD over northwest (NW) India with a mid-tropospheric trough extending from Iran and Pakistan into northwestern India. The southward dip in geopotential height and cyclonic curvature of westerlies over NE India indicate strong dynamical forcing and upper-level divergence associated with the disturbance. Concurrently, a cyclonic circulation at lower levels cantered over Arabian sea with moist southwesterly flow provided a continuous moisture supply into the WHR. Which promoted increased instability and vertical uplift over WHR, leading to the development of deep convective clouds. The Indian Meteorological Department (IMD) bulletin for the same day also reported thunderstorms, hailstorms, gusty winds (50–60 km h⁻¹), and heavy rainfall over the region between 23 and 25 May (Daily IMD bulletin 23 May 2023). The combination of mid-tropospheric trough, low-level moisture advection, orographic lifting possibly created an ideal environment for the observed severe lightning and rainfall over WHR on 23 May 2023. 3.2 Temporal variations Figure 3 compares the ground-based ILLN observations with WRF model-simulated lightning flash rates (FR). The case study spans a 36-hours window, covering six hours before and six hours after the event day. This period is divided into three phases as initial (22-May-2023 18:00 UTC to 23-May-2023 07:00 UTC), active (23-May-2023 08:00 UTC to 23-May-2023 20:00 UTC), and decay phase (23-May-2023 21:00 UTC to 24-May-2023 06:00 UTC). During the initial phase, FR was low in both observations and simulations. All MP schemes generally followed the observed variation, although Morrison exhibited some overestimation. In the active phase, lightning activity increased sharply before reaching the peak and then gradually declined. We have reported peak lightning at 15:00 UTC with the highest FR upto 1661 flashes hour − 1 . The Morrison successfully captured the peak lightning at 15:00 UTC with 2238 flashes hour − 1 and overestimation of 577 flashes hour − 1 . Thomson, WDM5 and WSM6 also captured the peak lightning closely with a lead time of 1–2 hours as compared to ILLN. In the decay phase, Thompson and WDM5 showed trends like those of WSM6 and Morrison. Furthermore, we have applied cross-correlation analysis to identify the lead-leg pair with the highest correlation. WSM6 and WDM5 show 3 hours lead with high (r = 0.76 & 0.80) correlations, and Thompson and Morrison show only 1 hourly lead, with slightly lower correlation (r = 0.73, 0.60). Vani et al. ( 2022 ) reported notable FSS for lightning activity over the WG, and also reported instances of both overestimation and underestimation. For example, in 81.25% of the cases, the model showed a lead or lag of 1–3 hours and in 18.75% of cases, the shift was up to 6 hours. 3.3 Spatial and longitudinal variations Figure 4 depicts the intercomparison between the model simulated and ILLN observed FR spatial distribution. As the two platform have different range of variations, for a more meaningful comparison, we have calculated the Normalized Flash Rate (NFR). The NFR is derived by converting flash points into 3 km grids and then dividing by the maximum FR. The ILLN observations indicate the accumulation of high NFR (0.8 to 1.0) over the foothills of the Himalayas in Himachal Pradesh, Uttarakhand, northern Uttar Pradesh and Nepal as described in Fig. 4 a (Kumar et al. 2024 ; Gautam et al. 2022 ). The WSM6 captures lightning activity over the foothills but weakly captured NFR over south-eastern region. However, it effectively rejected non-lightning events (Fig. 4 b). The Thompson MP scheme captures slightly more lightning activity over the foothills with fewer false detection as shown in Fig. 4 c. It should be noted that, Morrison detects more lightning event but suffers from false detection and comparatively fails to reject non-lightning events (Fig. 4 d). The WDM5 also captures lightning activity better as compared to the WSM6 across the entire foothill region, however, generates a significant false detection (Fig. 4 e). Overall, all MP schemes demonstrate the ability to reject non-lightning events and identify lightning hotspots. To understand the shift in lightning distribution, we plotted the average longitudinal variation (Fig. 4 f). The MP schemes showed overestimation compared to the ILLN between 75° to 77°E and Thompson showed the least longitudinal deviation (only 0.27°E) from the observed lightning peak. The WSM6 had a deviation of 0.58°E and aligned well with observations, producing stable results. However, the Morrison and WDM5 showed larger deviations ranging from 2.95° to 3.19°E, suggesting significantly high overestimation. Recently, Mohan et al. ( 2021 ) reported strong agreement between ILLN and simulated FR with a high spatial correlation of 82.7%. Similarly, Vani et al. ( 2022 ) reported that WDM5 performed better than Thompson with a spatial shift in isolated lightning events over WG region. The more details of spatial performance of MP schemes have also discussed in section 3.7 –3.8. 3.4 Evolution of simulated convective parameters Convection plays a critical role in cloud microphysical processes and lightning generation. Convective Available Potential Energy (CAPE) serving as a key indicator of convective activities (Kamra and Kumar, 2020 ). We have examined the dynamics of simulated CAPE at 850 hPa during the initial, active, and dissipation phases (Fig. S2). During initial phase, low CAPE (< 250 Jkg -1 ) dominates the most region, but due to localized convective high convection zones (HCZ) particularly over Rajasthan, Haryana, Punjab, and Uttar Pradesh and show relatively high CAPE ( 2000 Jkg -1 ). The Morrison and WDM5 well simulate the convection activities, and WDM5 reflects efficient transition phases. However, isolated small convective pockets can still support lightning. The Decay phase also depicts localized high convection, but stability dominates the region (< 250 Jkg -1 ). The spatial and temporal patterns of simulated CAPE align well FR (Figs. 3 and 4 ). In the WHR, especially over the higher-elevation regions, CAPE may not be a primary contributor for lightning activity and show lead relation with lightning FR density (Kumar et al. 2024 ; r = 0.34). Conversely, lightning in the NW Himalayan foothills is largely driven by solar heating, precipitable water, cloud clustering and orographic uplift (Kamra and Kumar, 2020 ). So, foothills may contribute to the initiation of the strong convection (surface-based convective lifting), and hilly terrain redistributes them, where orographic lifting may be important (Biswasharma 2025a, c). 3.5 Cloud microphysical properties The MP scheme plays a crucial role in governing lightning activity, interaction between graupel and cloud ice particles through collisions enhances cloud electrification, ultimately leading to severe lightning (Adamo et al. 2007 ; Yair et al. 2010 ; Hazra et al. 2013 ; Williams et al. 2002; Choudhury et al. 2020 ). Therefore, understanding and evaluation of MP schemes are essential for improving lightning prediction and representing in the model. In the present study, we have investigated the temporal and vertical distribution of Cloud Water Mixing Ratio (q c ), Rainwater Mixing Ratio (q r ), Snow Mixing Ratio (q s ), and Ice Mixing Ratio (q i ) simulated over the domain (Figs. 5 – 6 ). The q c has a significant contribution in lightning activity, as the presence of high q c indicates more supercooled liquid droplets available for hydrometeor growth (Bovalo et al. 2019 ). Updraughts transport these droplets to higher altitudes, facilitating the conversion into ice and snow particles through the Wegener-Bergeron-Findeisen process (WBFP) in mixed phase region (MPR) of thundercloud (Storelvmo & Tan, 2015 ). The time-height variations of the domain-averaged q c obtained from different MP schemes are presented in Fig. 5 (i). The q c shows a significant vertical extent between 5.5 and 8.5 km, which can be converted into ice and snow particles via accretions and riming process (Korolev, 2007). The presence of hydrometeor significantly contributes to charge separation (Mansell et al. 2005 ). Thompson and Morrison show stronger evidence of elevated q c (> 2.5 × 10⁻⁵ kg kg - 1 ) and correspond well with FR variation during the active phase (Figs. 5 (i)b & 5(i)c). The WSM6 and WDM5 also generate high q c levels > 2.5 × 10⁻⁵ kg kg - 1 during active phase (Figs. 5 (i)a, d), with scattered q c structures and coinciding with FR trend (Fig. 3 ). Which is indicating the favourable condition for the hydrometeor growth, charge separation, electrification and lightning. The q r simulation shows significantly high values (5 × 10⁻⁵ kg kg- 1 ) between active phase with a vertical extent from the surface up to 4 km in the WSM6, Thompson, and WDM5 (Fig. 5 (ii) a, b & d). In contrast, the Morrison exhibits peak q r during the decay phase, with maximum values exceeding 2.5 × 10⁻⁵ kg kg- 1 (Fig. 5 (ii)c). The presence of high q r values in active phase suggesting the collision and coalescence processes to initiate the warm rain processes (WRP) in shallow MPR later at 15 UTC (Rosenfeld and Lensky 1998 ). The effect of WRP in thunderstorm has been observed and it is dissipating due to significant contribution of downdraughts generated by dominance of high gridded average rainfall up to 1.3 mm at 18 UTC as observed by the GPM observations. The q s clearly indicates dominance during both the active and decay phases of FR, with significant vertical extension from 4–14 km (Fig. 6 a-d). The Thompson and WDM5 microphysics exhibit high q s values exceeding 7.5 × 10⁻⁵ kg kg- 1 within MPR and above MPR between 5.5 and 12 km well as shown in the Fig. 6 (i)b, d. A high snow content zone persists for several hours (11–21 UTC), suggesting active depositional growth and riming, which are crucial for graupel and snow production, ultimately leading to severe lightning. It should be noted that, in Thompson MP ice is transfer to the snow therefore Thompson shows relatively high snow but weak ice production (Ko et al. 2022 ). Furthermore, Choudhury et al. ( 2020 ) highlighted that snow formation is affected by strong updraughts, induced by latent heat release and regulates auto-conversion of cloud ice to snow at high altitudes. Which support the riming process, charge segregation, cloud electrification and thus contribute severe lightning. However, the WSM6 and Morrison show relatively lower snow production (Fig. 6 (i)a, c). Figure 6 (ii)a-d describes the q i , which is a crucial hydrometeor for understanding the microphysical characteristics of ice-phase cloud development and its role in subsequent precipitation and lightning generation. The WSM6, Morrison, and WDM5 shows a widespread and elevated q i > 2.5 × 10⁻⁵ kg kg − 1 between approximately 8–12 km altitude. The Morrison exhibits a broader peak with highest q i upto 5×10⁻⁵ kg kg − 1 between 9–13 km, suggesting active ice production and dominance of cold cloud processes. However, this enhanced ice-phase development does not directly translate to strong surface rainfall, possibly due to weak WRP. The weakest q i is generated by the Thompson as compared to WSM6 and WDM5 MP Schemes. Weak ice production in Thompson MP schemes is due to generation of relatively smaller ice particles (Ko et al. 2020; Bao et al. 2021 ). Understanding such critical differences is crucial for improving cloud microphysics, cloud-radiation interactions, thunderstorm dynamics. Several past studies support these observations, Hazra et al. ( 2013 ) investigated the role of cloud ice in lightning and heavy rainfall using WRF simulations and in-situ measurements from an ice nuclei spectrometer. They found that the Fletcher, 1962 or F1962 MP scheme generated hydrometeors more favourable for lightning (Fletcher, 1962). Similarly, Unnikrishnan et al. ( 2021 ) reported a strong spatial analogy between cloud ice water content and lightning frequency over the NE and NW Indian regions. The vertical moisture transport, combined with the WBFP and cold cloud processes initiates the growth of hydrometeors and condensational latent heat release. Which strengthens the further vertical updraughts and cloud development for severe lightning generation (Korolev, 2007; Hazra et al. 2013 ; Choudhury et al. 2020 ). Likewise, Biswasharma et al. ( 2021 ) highlighted the dominance of high concentrations of ice particle during thunderstorms over Rampurhat compared to Nagaland. In our study, the existence of hydrometeors reinforces the critical role in shaping severe lightning episodes over WHR. 3.6 Temporal evaluation of lightning The temporal performance of MP schemes has been evaluated by employing Taylor diagrams, which provide critical performance statistics such as correlation coefficient (r), standard deviation (SD), and root mean square error (RMSE) etc. (Taylor et al. 2001; Federico et al. 2022 ; Yadava et al. 2023 ; Biswasharma et al. 2024 ). Among MP schemes, Thompson indicates most favourable performance characteristics due to high (r = 0.63) and balance RMSE (429 flashes hour − 1 ) between ILLN observation and model generated FR. It means, Thompson successfully generates comparable magnitude and temporal behaviour of ILLN observed FR (Fig. 7 (i)). Morrison shows highest RMSE (518 flashes hour − 1 ) with a lower correlation (r = 0.53), which is indicating overestimation as compared to the observation. The WSM6 shows lowest performance characteristics, displaying slightly lower RMSE (458.2 flashes hour − 1 ) and weak correlation (r = 0.34), with performance metrics closely similar to WDM5 (r = 0.43, RMSE: 448.8 flashes hour − 1 ). Overall, Thompson demonstrates better skill, Morrison shows a reasonable correlation despite higher RMSE. Both WSM6 and WDM5 suggest similar, but weaker lightning simulation capabilities in temporal evaluation over WHR (Fig. 7 (i)). The spatial performance evaluation of the MP schemes has been carried out by preparation of spatial contingency table for each 12 km, 21 km and 51 km grid and then FSS has been computed over TD domain. The POD show increasing trend towards larger grid and MP schemes are producing stable results with low FAR and Bais. We have observed that Morrison had the highest detection skill (POD = 0.66, CSI = 0.39, Accuracy = 0.55), but it slightly overpredicted (Bias = 1.01) and suffers from high FAR (0.55) in 12 km grid (Fig. 7 (ii)a). WSM6, Thompson and Morrison show slightly better performance at 21 km grid in terms of POD, CSI, Accuracy. But due to low FAR and Bais, WDM5 shows slightly better Accuracy as shown in Fig. 7 (ii)b. In the largest 51 km grid, the performance of MP schemes shows significant enhancement. For example, Thompson, Morrison, WDM5 and WSM6 show high POD (0.82, 0.84, 0.81 & 0.78), CSI (0.72, 0.64, 0.69, 0.64), accuracy (0.76, 0.65, 0.73, 0.68) and low FAR (0.13, 0.26, 0.17) and Bias (0.95, 1.15, 0.98, 0.94) respectively (Fig. 7 (ii)c). Therefore, Thompson can predict the FR well while maintaining the lowest FAR and Bias over TD. Although Morrison has high POD, it has significant overestimation and high FAR with low Accuracy and CSI. The spatial performance of the MP Schemes also depends upon the grid resolution. For example, Vani et al. ( 2022 ) have selected sixteen lighting cases over the Maharashtra and evaluated the model performance. They have reported high FSS in 50 km grid in comparison with 10 km grid. Similarly, Kumar et al. ( 2022 , 2023 ) have simulated the extreme cases of lightning over the Bihar and Rajasthan and evaluated the WRF model performance. They observed POD is in the range of 0.5–0.69 by using different combinations of parameterization schemes. The FSS of our analysis in case of all MP schemes is comparable with these previous studies. 3.7 Spatial and temporal evaluation of rainfall Several studies have reported strong spatial and temporal associations between rainfall and lightning over different regions (Lal and Pawar, 2009 ; Petersen and Rutledge, 1998 ; Williams et al. 1992 ). In this context, we have also evaluated the model performance to examine whether microphysics schemes that perform well for the lightning prediction can exhibit similar skill for rainfall, or whether different model configurations and tuning are required to represent both the processes. The spatial and temporal performance of the model for predicting rainfall in comparison with GPM derived grid average rainfall shown in Fig. 8 (i). In temporal performance, GPM data shows a high SD (0.36 mm) as compared to the model simulated rainfall (0.02–0.3 mm). WSM6 shows the highest correlation (r = 0.74) with GPM data, while Thompson (r = 0.52) and Morrison (r = 0.45) show moderate correlations. WDM5 shows the lowest correlation (r = 0.26) with lowest SD (0.02 mm). It is to be noted that Thomson and Morrison schemes show the best performance for lightning simulation but not for rainfall over TD. The evaluation of spatial performance using FSS for four different thresholds (0.1 mm, 0.5 mm, 1 mm, and 2 mm) of rainfall, has been carried out at grid resolution of 12 km as shown in Fig. 8 (ii). The WSM6, Thompson, Morrison and WDM5 MP schemes were able to predict (0.1 mm threshold) rainfall with moderate to low POD (0.64, 0.37, 0.29, 0.25) and low FAR (0.02–0.04) as shown Fig. 8 (ii)a. The Morrison shows the highest Accuracy (0.63) but with high bias (0.68) at 0.1 mm threshold. Furthermore, we observed that the POD and CSI are decreasing while FAR is increasing from 0.1 to 2 mm threshold. It means that model could not capture rainfall at a higher threshold over the TD domain (Fig. 8 (ii)). In both lightning and rainfall, Morrison shows high POD but suffers from the FAR and Bais issues (Fig. 7 (ii), 8(ii)). Navale and Singh ( 2020 ) have explored the influence of topography on the performance of the WRF model for simulation of rainfall compared with IMD GPM over the NW Himalayan region, particularly over Uttarakhand and Himachal Pradesh. They found that WRF model simulates the rainfall well with considerable FSS such as Hit Rate (0.78–0.85), FAR (0.20–0.42), accuracy (0.70 − 0.60), CSI (0.70 − 0.52) for 0.1 mm threshold. Overall, WSM6 showed relatively better performance in capturing temporal variations while Morrison showed higher FSS for spatial variations. We noted that for lightning simulation, Morrison and Thompson consistently performed better than the other schemes. Consequently, no single microphysics scheme emerges as uniquely superior for simulating both rainfall and lightning, indicating scheme-dependent performance across processes. The vertical profiles of domain-averaged q c , q r, q sa and q i simulated using four MP schemes during the active phase of the thunderstorm shown in Fig. 9(i)a-d. All MP schemes show high q c between 3–5 km; however, Thompson and Morrison schemes maintain relatively higher q c and a deeper vertical extent reaching to mid-troposphere (5–8 km). This indicates that more supercooled liquid water stays higher in the cloud, which is essential for non-inductive charge separation through graupel-ice interactions (Takahashi, 1978 ; Tsenova and Mitzeva 2008 ). The WSM6 and WDM5 schemes produce shallow cloud water profiles, which favor faster conversion to precipitation. The q r peaks in the lower troposphere (2–4 km) across all schemes, but Morrison shows comparatively stronger rainwater production and a more coherent vertical structure near the surface. This suggests efficient warm-rain processes and rapid conversion of cloud water into rain causing high surface rainfall (King et al. 2015 ). In contrast, Thompson scheme shows decreasing rainwater, consistent with delayed precipitation formation due to prolonged liquid water retention aloft. q s dominate above ~ 6 km, with Thompson scheme showing high q s within the MPR. This increase in snow mass indicates active depositional growth and riming processes, which are closely linked to graupel formation and lightning electrification. The WSM6 scheme shows relatively lower snow aloft, reflecting weaker cold-cloud microphysical processes. The q i reveals marked scheme-dependent differences. The Thompson scheme produces slightly less but more uniform ice distribution due to a smaller ice particle spectrum within the MPR (Bao et al. 2021 ). The WSM6 scheme shows comparatively extensive ice profiles, consistent with its single-moment formulation and emphasis on precipitation efficiency rather than electrification. Overall, the vertical hydrometeor profiles indicate that Thompson, Morrison scheme increases mixed-phase processes which are critical for lightning generation, whereas WSM6 scheme favors efficient warm-rain production and surface rainfall. Figure 9(ii)a-d presents Contoured Frequency by Altitude Diagrams (CFADs) of simulated reflectivity, highlighting pronounced differences in storm vertical structure across microphysics schemes. WSM6 scheme shows high-frequency reflectivity exceeding 40 dBZ primarily below ~ 4 km, indicating efficient warm-rain processes and strong rainfall production, but weaker mixed-phase development aloft. In contrast, Thompson scheme shows moderate-to-high reflectivity extending coherently into the mixed-phase region (5.5–8.5 km), indicating active riming and graupel growth, which are favorable for charge separation and lightning generation. Morrison scheme shows large spread in reflectivity, supporting precipitation growth but with less organized mixed-phase structure, while WDM5 shows intermediate behavior. These differences demonstrate that the choice of microphysics strongly controls storm structure, with Thompson scheme favoring lightning-related processes while WSM6 scheme emphasizing rainfall production. Overall, the evaluation indicates that the WRF-simulated lightning and rainfall are substantially sensitive to the choice of MP scheme. The Thompson scheme shows better temporal skill for lightning, while Morrison performs better in spatial detection, and WSM6 captures rainfall variability more effectively. However, no single scheme consistently outperforms best for both lightning and rainfall, highlighting that WRF simulations are not uniformly reliable across processes. These contrasting performances underscore the need for further targeted tuning and region-specific optimization of model physics for improved simultaneous simulation of lightning and precipitation over complex terrain. The contrasting performance of microphysics schemes for lightning and rainfall arises from fundamental differences in how they represent mixed-phase cloud processes, hydrometeor growth, and precipitation conversion. Lightning production is strongly controlled by the coexistence of supercooled liquid water, graupel, and ice within the mixed-phase region, which promotes non-inductive charge separation through riming and collisions (Takahashi, 1978 ; Saunders, 2008 ; Williams et al. 2002). Schemes such as Thompson, which incorporate more detailed ice-phase physics and riming processes, tend to sustain hydrometeors within the charging zone for longer durations, thereby enhancing electrification and lightning activity (Thompson et al. 2008 ; Morrison et al. 2009 ; Choudhury et al. 2020 ). In contrast, rainfall simulation is more sensitive to warm-rain processes, auto-conversion rates, and melting efficiency, which are emphasized in simpler bulk schemes such as WSM6, leading to more efficient precipitation production near the surface (Hong and Lim, 2006; Halder and Mukhopadhyay, 2016 ; Kumar et al. 2023 ). As a result, schemes optimized for electrification processes do not necessarily maximize rainfall skill, and vice versa. Similar scheme-dependent behavior has been reported in earlier WRF-based lightning and precipitation studies over India and other convective regions (Mohan et al. 2021 ; Vani et al. 2022 ; Kumar et al. 2022 ; Haldar and Mukhopadhyay 2016), highlighting that no single microphysics formulation can simultaneously optimize both lightning and rainfall simulations, particularly over complex terrain. It is to be noted that the analysis is based on a single severe pre-monsoon lightning event, and therefore, the results may not be fully representative of the wide range of convective environments and storm types that occur over the western Himalaya. Model performance and microphysics sensitivity may vary under different synoptic conditions, seasons, and storm intensities. In addition, uncertainties associated with lightning parameterization, observational detection efficiency, and model resolution over complex terrain can influence the results. Future studies using a larger sample of events across multiple seasons and regions are needed to generalize the findings and further refine model physics for improved lightning and rainfall simulations. 4 Conclusion This study examined a severe pre-monsoon lightning event over the complex terrain of WHR on 23 May 2023 and evaluated the ability of the WRF model to simulate lightning and associated convective processes using four MP schemes (WSM6, Thompson, Morrison, and WDM5). Lightning simulations were evaluated against ILLN observations, while rainfall was assessed using GPM observations. The analysis focused on temporal evolution, spatial distribution, thermodynamical and microphysical characteristics. All MP schemes captured the broad temporal evolution of lightning activity, including the initial, active, and decay phases, but differed significantly in magnitude, timing, and variability. Morrison reproduced the timing of peak FR but with overestimated magnitude, whereas the Thompson scheme showed the closest agreement with observed lightning variability with reduced false detections. WSM6 and WDM5 showed larger timing offsets and higher variability. A pronounced increase in CAPE occurred from the initial phase (< 250 J kg⁻¹) to the active phase (250–750 J kg⁻¹) of thunderstorm, with some zones exceeding 2000 J kg⁻¹. Morrison effectively captured this transition. During the active phase, Thompson and Morrison simulated enhanced mid-level cloud water (q c ≳ 2 × 10⁻⁵ kg kg⁻¹) between 4–8 km, supporting sustained updrafts and latent heat release. Elevated snow mixing ratios (q s ≳ 7 × 10⁻⁵ kg kg⁻¹) extending into the mixed-phase region indicated active depositional and riming growth favorable for lightning. In contrast, WSM6 produced stronger low-level rainwater peaks (q r ≳ 5 × 10⁻⁵ kg kg⁻¹ below ~ 4 km), reflecting efficient WRP and enhanced surface precipitation during the decay phase of thunderstorm. These contrasting hydrometeor profiles explain the divergent performance of MP schemes for simulating lightning and rainfall. Schemes that retain supercooled liquid water and ice in the MPR favour charge separation and lightning, whereas schemes emphasizing rapid WRP enhances surface rainfall but limits electrification. Consequently, no single MP scheme consistently outperforms others for both lightning and rainfall over the WHR. Overall, WRF performance over the complex Himalayan terrain is strongly process- and scheme-dependent, indicating that microphysics configurations optimized for one atmospheric process may not be transferable to others. The contrasting behavior of MP schemes for lightning and rainfall highlights the need to treat electrification and precipitation as distinct yet interacting outcomes of storm microphysics, rather than assuming a single optimal setup. These findings have important implications for high-impact weather prediction over mountainous regions, where lightning, cloudbursts, and intense rainfall frequently co-occur. Improving model reliability therefore requires region-specific and process-aware tuning of microphysics schemes, guided by detailed observational evaluation. More broadly, the results emphasize that robust lightning prediction must be grounded in physically consistent representations of mixed-phase cloud processes, which are also relevant to other orographically influenced convective environments. Declarations Declarations Competing of interest: The authors declare that they have no financial or personal relationships that could have influenced the work reported in this manuscript. Use of AI Tools The authors used an AI-based language editing tool to improve the grammar, clarity, and readability of the manuscript. The AI tool was not used for scientific interpretation, data analysis, figure preparation, or the generation of research content. All scientific conclusions, analyses, and interpretations were developed and verified by the authors. Funding Not Applicable Author Contribution Sanjeev Kumar: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing-original draft, Writing-review & editing. N. Umakanth: Data curation, Formal analysis, Methodology, Software. Alok Sagar Gautam: Conceptualization, Investigation, Project administration, Validation. Rupraj Biswasharma: Conceptualization, Investigation, Methodology, Validation, Visualization, Writing-original draft, Writing-review & editing. Swapnil S. Potdar: Data curation, Methodology, Software, Writing-review & editing. Karan Singh: Data curation, Software. Devendraa Siingh: Investigation, Project administration, Resources, Supervision, Validation. R. P. Singh: Visualization, Writing - original draft. Acknowledgement Indian Institute of Tropical Meteorology, Pune, is funded by the Ministry of Earth Sciences, Government of India. We are sincerely thankful to Head of the Department of Physics; and the Vice-Chancellor of Hemvati Nandan Bahuguna Garhwal University, Srinagar, for providing the support and infrastructure necessary for this research. We gratefully acknowledge the data providers and modelling platforms that supported this study, including the Global Hydrometeorology Resource Center (GHRC), the European Centre for Medium-Range Weather Forecasts (ECMWF), NASA's Giovanni portal, and the National Centers for Environmental Prediction (NCEP) team, whose resources were crucial to the analysis and outcomes of this work. Data availability The Weather Research and Forecasting (WRF) Model version 4.5 used in this study is publicly available from NCAR at https://www.mmm.ucar.edu/models/wrf and via the official GitHub repository at https://github.com/wrf-model/WRF/tree/release-v4.5 . Lightning observations from the Indian Lightning Location Network (ILLN) are available upon reasonable request, subject to institutional data policies. The dataset supporting this study is publicly available at Zenodo (Kumar et al. 2026 ). The archived dataset includes lightning flash grids, rainfall time series, vertical hydrometeor profiles, and spatial rainfall fields used for model evaluation. The data can be accessed at https://doi.org/10.5281/zenodo.18747188 References Adamo C, Solomon R, Medaglia CM, Dietrich S, Mugnai A (2007) Cloud Microphysical Properties from Remote Sensing of Lightning within the Mediterranean. Measuring Precipitation From Space: EURAINSAT and the Future. 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Space Sci Rev 137:335–353. ttps://doi.org/10.1007/s11214-008-9345-0 Sharma S, Chen Y, Zhou X, Yang K, Li X, Niu X, Hu X, Khadka N (2020) Evaluation of GPMERA satellite precipitation products on the southern slopes of the Central Himalayas against rain gauge data. Remote Sens 12:1836. ttps://doi.org/10.3390/rs12111836 Siingh D, Singh AK, Patel RP, Singh R, Singh RP, Veenadhari B, Mukherjee M (2008) Thunderstorms, lightning, sprites and magnetospheric Whistler-Mode radio waves. Surv Geophys 29:499–551. ttps://doi.org/10.1007/s10712-008-9053-z Siingh D, Singh RP, Singh AK, Kulkarni MN, Gautam AS, Singh AK (2011) Solar activity, lightning and climate. Surv Geophys 32:659–703. ttps://doi.org/10.1007/s10712-011-9127-1 Siingh D, Singh RP, Singh AK, Kumar S, Kulkarni MN, Singh AK (2012) Discharges in the stratosphere and mesosphere. Space Sci Rev 169:73–121. ttps://doi.org/10.1007/s11214-012-9906-0 Siingh D, Singh R, Victor NJ, Kamra A (2023) The DC and AC global electric circuits and climate. Earth-Sci Rev 244:104542. ttps://doi.org/10.1016/j.earscirev.2023.104542 Siingh D, Buchunde P, Gandhi H, Singh R, Singh S, Patil M, Singh R (2015) Lightning and convective rain over Indian peninsula and Indo-China peninsula. Adv Space Res 55:1085–1103. ttps://doi.org/10.1016/j.asr.2014.11.014 Skamarock WC, Klemp JB (2008) A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227:3465–3485. ttps://doi.org/10.1016/j.jcp.2007.01.037 Storelvmo T, Tan I (2015) The Wegener-Bergeron-Findeisen process - Its discovery and vital importance for weather and climate. Meteorol Z 24:455–461. ttps://doi.org/10.1127/metz/2015/0626 Takahashi T (1978) Riming electrification as a charge generation mechanism in thunderstorms. 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Atmos Res 91:79–86. ttps://doi.org/10.1016/j.atmosres.2008.07.001 Unnikrishnan C, Pawar S, Gopalakrishnan V (2021) Satellite-observed lightning hotspots in India and lightning variability over tropical South India. Adv Space Res 68:1690–1705. ttps://doi.org/10.1016/j.asr.2021.04.009 Vani G, Mohan GM, Hazra A, Pawar SD, Pokhrel S, Chaudhari HS, Konwar M, Saha SK, Mallick C, Das SK, Deshpande S, Ghude SD, Domkawale M, Rao SA, Nanjundiah RS, Rajeevan M (2022) Evaluation and Usefulness of Lightning Forecasts Made with Lightning Parameterization Schemes Coupled with the WRF Model. Weather Forecast 37:709–726. ttps://doi.org/10.1175/waf-d-21-0080.1 Wang Y, Yang Y, Jin S (2018) Evaluation of lightning forecasting based on one lightning parameterization scheme and two diagnostic methods. Atmos 9:99. ttps://doi.org/10.3390/atmos9030099 Williams ER, Geotis SG, Renno N, Rutledge SA, Rasmussen E, Rickenbach T (1992) A radar and electrical study of tropical hot towers. J Atmos Sci 49:1386–1395. ttps://doi.org/10.1175/1520-0469(1992)0492.0.CO;2 Williams E, Stanfill S (2002) The physical origin of the land-ocean contrast in lightning activity. C R Phys 3:1277–1292. ttps://doi.org/10.1016/s1631-0705(02)01407-x Wong J, Barth MC, Noone D (2013) Evaluating a lightning parameterization based on cloud-top height for mesoscale numerical model simulations. Geosci Model Dev 6:429–443. ttps://doi.org/10.5194/gmd-6-429-2013 Yadava PK, Sharma A, Payra S, Mall RK, Verma S (2023) Influence of meteorological parameters on lightning flashes over Indian region. J Earth Syst Sci 132:179. ttps://doi.org/10.1007/s12040-023-02188-w Yair Y, Lynn B, Price C, Kotroni V, Lagouvardos K, Morin E, Mugnai A, Del Carmen Llasat M (2010) Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields. J Geophys Res Atmos 115:D04205. ttps://doi.org/10.1029/2008jd010868 Yang Z, Niu G, Mitchell KE, Chen F, Ek MB, Barlage M, Longuevergne L, Manning K, Niyogi D, Tewari M, Xia Y (2011) The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins. J Geophys Res Atmos 116:D12110. ttps://doi.org/10.1029/2010jd015140 Zepka G, Pinto O, Saraiva A (2014) Lightning forecasting in southeastern Brazil using the WRF model. Atmos Res 135–136:344–362. ttps://doi.org/10.1016/j.atmosres.2013.01.008 Zhao C, Zhang Y, Zheng D, Li H, Du S, Peng X, Liu X, Zha P, Zheng J, Shi J (2024) Technical note: On the ice microphysics of isolated thunderstorms and non-thunderstorms in southern China - a radar polarimetric perspective. Atmos Chem Phys 24:11637–11651. ttps://doi.org/10.5194/acp-24-11637-2024 Supplementary Fig and Tables Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9085936","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638754980,"identity":"8354852c-9303-46ea-b9d4-5d207f905e8c","order_by":0,"name":"Sanjeev Kumar","email":"","orcid":"","institution":"Indian Institute of Tropical Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Sanjeev","middleName":"","lastName":"Kumar","suffix":""},{"id":638754981,"identity":"a0f79b6e-fd54-4a86-8463-36c3568582f5","order_by":1,"name":"N. Umakanth","email":"","orcid":"","institution":"Indian Institute of Tropical Meteorology","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"","lastName":"Umakanth","suffix":""},{"id":638754982,"identity":"416e2540-6f85-490e-a9f5-9d75d6026aba","order_by":2,"name":"Alok Sagar Gautam","email":"","orcid":"","institution":"Hemwati Nandan Bahuguna Garhwal University","correspondingAuthor":false,"prefix":"","firstName":"Alok","middleName":"Sagar","lastName":"Gautam","suffix":""},{"id":638754983,"identity":"38c94621-2ada-412b-ada0-f9831f59d65e","order_by":3,"name":"Swapnil S. Potdar","email":"","orcid":"","institution":"Indian Institute of Tropical Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Swapnil","middleName":"S.","lastName":"Potdar","suffix":""},{"id":638754984,"identity":"fdba2006-4ccd-4265-be82-2749ceb775a1","order_by":4,"name":"Rupraj Biswasharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYNACHiBmbwASBhakaOE5ANIiQYpNEglgkrBC8xm5xz78kLFLnD/z+dUNPwokGPjbuxPwapG5kZc8s4cnOXHD7Zyymz1Ah0mcObsBv3MkcowZeHiYEzdI56Td4AFqMZDIJayF8Q9PPdBhZ9Ju/iFWCzMPz+HEhhvsx24TZwvPu2RmGZ7jxhvO5LDdljGQ4CHsF/bcw4xve6pl57cff3bzzR8bOf72XvxawNHI2ANmGEC5BAFIzQ8Qg/0BEapHwSgYBaNgJAIAb0BDMVMHEdAAAAAASUVORK5CYII=","orcid":"","institution":"Indian Institute of Tropical Meteorology","correspondingAuthor":true,"prefix":"","firstName":"Rupraj","middleName":"","lastName":"Biswasharma","suffix":""},{"id":638754985,"identity":"7afb33d8-e125-4c3d-80a6-b8b6e0a106a2","order_by":5,"name":"Karan Singh","email":"","orcid":"","institution":"Indian Institute of Tropical Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Karan","middleName":"","lastName":"Singh","suffix":""},{"id":638754986,"identity":"015b0a46-6e80-4727-af33-72e64e65cefc","order_by":6,"name":"Devendraa Siingh","email":"","orcid":"","institution":"Indian Institute of Tropical Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Devendraa","middleName":"","lastName":"Siingh","suffix":""},{"id":638754987,"identity":"5ff70203-b516-48ea-a606-ce9432f432d6","order_by":7,"name":"R P Singh","email":"","orcid":"","institution":"Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"R","middleName":"P","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2026-03-10 15:43:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9085936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9085936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109261416,"identity":"b2d39d95-6d04-4b08-99b0-e3aa42140246","added_by":"auto","created_at":"2026-05-14 11:31:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":724551,"visible":true,"origin":"","legend":"\u003cp\u003eThe geopotential height (m) and wind vectors (m s⁻¹) at 500 hPa on 23 May 2023 over India and adjacent areas\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/90e2dfcb08cddeb6ada5daa7.png"},{"id":109296254,"identity":"cf33b969-6366-4c7e-975c-a59e6078b8a3","added_by":"auto","created_at":"2026-05-15 08:46:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":983743,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variation\u0026nbsp;of\u0026nbsp;(a)\u0026nbsp;ILLN\u0026nbsp;observed and model simulated\u0026nbsp;(WSM6, Thompson, Morrison, and WDM 5)\u0026nbsp;flash rate (Flashes hour\u003csup\u003e-1\u003c/sup\u003e)\u0026nbsp;and\u0026nbsp;(b)\u0026nbsp;cross correlation analysis over the TD\u0026nbsp;domain\u0026nbsp;\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/1aef4d6170211a8ad1535e40.png"},{"id":109296244,"identity":"9cbb18ed-872b-4cca-9a7e-ae616a649e0d","added_by":"auto","created_at":"2026-05-15 08:46:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3504547,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in normalized flash rate from (a) ILLN, (b) WSM6, (c) Thompson, (d) Morrison, (e) WDM5 MP schemes and (f) longitudinal variation over the 3 km grid (TD)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/0274ca33909fb1eb7a47faf0.png"},{"id":109296530,"identity":"1867a229-4966-4875-b4c4-4d878add549b","added_by":"auto","created_at":"2026-05-15 08:47:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2186593,"visible":true,"origin":"","legend":"\u003cp\u003eTime-height distributions of (i) Cloud Water Mixing Ratio (q\u003csub\u003ec\u003c/sub\u003e) by using (a) WSM6, (b) Thompson, (c) Morrison and (d) WDM5 MP schemes. The solid Magenta line is the observed lightning flash rate by ILLN, and the Magenta dotted line represents the model-generated flash rate in specific MP schemes. Same for (ii) rainwater mixing ratio with GPM rainfall (solid lines) and Model (doted) accumulated rainfall.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/2f1c657efc21939cf07ce893.png"},{"id":109261419,"identity":"c7cf2ae8-3279-4eb4-8651-f18fc9477809","added_by":"auto","created_at":"2026-05-14 11:31:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2646910,"visible":true,"origin":"","legend":"\u003cp\u003eVertical distribution of (i) snow mixing ratio by using (a) WSM6, (b) Thompson, (c) Morrison and (d) WDM5 MP scheme and the same for (ii) ice mixing ratio. Horizontal dashed lines represent the mixed-phase regions (approximately) in snow mixing ratio\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/a96c33f15bdc599fceff2ad4.png"},{"id":109261420,"identity":"9a4cc37c-c191-4c9b-81f0-2fe81e761105","added_by":"auto","created_at":"2026-05-14 11:31:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":760035,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of (i) Temporal Model Performance by Taylor diagram analysis. \u0026nbsp;The radial distance in black shows standard deviation and green doted concentric circles show RMSE. The angular position represents the correlation coefficient. (ii) Comparison of different MP schemes and their performance for lightning over TD domain by using (a) 15 km, (b) 21 km, (c) 51 km grid\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/5873807764416f7e17b231df.png"},{"id":109261422,"identity":"51ddfc9b-ae24-4494-933f-1a97d929926a","added_by":"auto","created_at":"2026-05-14 11:31:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1310853,"visible":true,"origin":"","legend":"\u003cp\u003eVertical profiles of domain-averaged (i) mixing ratios of (a) cloud water, (b) rainwater, (c) snow, and (d) ice simulated using the WSM6, Thompson, Morrison, and WDM5 microphysics schemes during the active phase of the thunderstorm. The profiles highlight scheme-dependent differences in the vertical distribution of hydrometeors, with Thompson showing enhanced mixed-phase hydrometeors that favor lightning generation, while WSM6 shows stronger low-level rainwater production associated with more efficient rainfall processes. \u0026nbsp;(ii) Contoured Frequency by Altitude Diagrams (CFADs) of simulated radar reflectivity for (a) WSM6, (b) Thompson, (c) Morrison, and (d) WDM5 microphysics schemes during the active convective phase. The CFADs illustrate scheme-dependent differences in storm vertical structure, with Thompson exhibiting deeper and more coherent reflectivity cores in the mixed-phase region, favorable for lightning generation, while WSM6 shows stronger low-level reflectivity associated with efficient rainfall production.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/b0517ba058eec6896089522d.png"},{"id":109296336,"identity":"8c30db93-925f-4b71-894a-2d0680521f0d","added_by":"auto","created_at":"2026-05-15 08:46:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8807086,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/42a5fa49-5933-40f9-9ae9-ab1dbc4316f7.pdf"},{"id":109261413,"identity":"4916e5fc-9741-4e4f-8c8c-50e61802cfc0","added_by":"auto","created_at":"2026-05-14 11:31:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":657459,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigtables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9085936/v1/832b25b30e3f61b4a6ffdfa7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of WRF Microphysics Schemes for Simulating Lightning and Rainfall over the Complex Terrain of the Western Himalayan Region","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLightning events are a common atmospheric phenomenon that poses a major natural hazard during the pre-monsoon season in India (Halder et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Siingh et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kumar et al. 2017; Kamra and Kumar, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Each year, lightning strikes result in significant loss of human life and damage to infrastructure. Globally, lightning is responsible for an estimated 6000 to 24000 fatalities annually (Cooper and Holle, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In India, Mishra et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) analyzed lightning-related fatalities using National Crime Records Bureau data and reported nearly 100000 deaths between 1967 and 2020, with an average of ~\u0026thinsp;1876 fatalities per year. Lightning predominantly originates within cumulonimbus clouds and is govern by thermodynamic, dynamical, and microphysical processes, including land-surface thermal contrasts, frontal lifting, orographic forcing, cloud microphysics, and land-use \u0026amp; land-cover variability (Williams, 1992; Kamra and Kumar, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Potdar et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Potdar et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Numerous laboratory and field experiments have demonstrated that microphysical interactions between graupel and ice crystals play a critical role in charge separation and lightning initiation within the mixed-phase regions of thunderstorms (Takahashi, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Berdeklis and List, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Adamo et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Oulkar et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Biswasharma et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). The coexistence of ice particles, graupel, and supercooled liquid water leads to intense collisions that facilitate charge separation and cloud electrification via non-inductive charging Mechanism (Williams et al. 2002; Saunders, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lund et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Siingh et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ghoshal Chowdhury et al. 2025). Therefore, realistic representation of cloud microphysics (MP) is fundamental for accurate simulation of lightning in numerical weather prediction models. The Weather Research and Forecasting (WRF) model has been widely employed for lightning simulation owing to its ability to resolve mesoscale convective processes (Price and Rind, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Yair et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zepka et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The Price-Rind (PR92) lightning parameterization relates lightning flash rates to storm height, while subsequent developments incorporate hydrometeor-based inputs to distinguish intra-cloud and cloud-to-ground lightning (Boccippio et al. 2002; Wong et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Giannaros et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dafis et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Choudhury et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mohan et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vani et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Several studies have demonstrated reasonable skill of the WRF model in simulating lightning and rainfall over India (Halder and Mukhopadhyay, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Choudhury et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mohan et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, statistical evaluation metrics such as Probability of Detection (POD), False Alarm Ratio (FAR), Bias Score (BIAS), and Critical Success Index (CSI) consistently indicate strong sensitivity to selection of MP scheme, cumulus parameterization, and model resolution (Hazra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Different MP schemes represent mixed-phase processes, ice growth, and hydrometeor interactions in fundamentally different ways, directly influencing charge separation, lightning production, and precipitation efficiency. For example, the Morrison scheme incorporates a more detailed treatment of ice-phase and riming processes, whereas WSM6 employs a simpler bulk representation, leading to systematic differences in simulated electrification and rainfall outcomes (Morrison et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hong and Lim, 2006). As a result, schemes optimized for lightning generation do not necessarily yield superior rainfall simulations, and vice versa. The WHR, encompassing Uttarakhand, Himachal Pradesh, Jammu and Kashmir, and Ladakh, is among the most meteorologically vulnerable regions of India. Complex topography and active synoptic systems make the region highly prone to extreme weather events, including heavy rainfall, cloudbursts, hailstorms, and intense lightning, often resulting in landslides and societal impacts (Penki and Kamra, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dimri et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kashyap and Behera, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lohan et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Lightning activity exhibits pronounced variability during the pre-monsoon and retreating monsoon phases, whereas extreme precipitation is more frequent during the summer monsoon and western-disturbance-dominated periods (Bookhagen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dimri et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The interaction of western disturbances, cyclonic circulations, and strong orographic forcing provides favorable conditions for deep convection concurrent lightning and rainfall events (Agnihotri and Singh, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Gangane et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hunt et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the high societal exposure and recurring impacts, comprehensive modeling studies examining lightning characteristics over the complex terrain of the WHR remain scarce. Over complex mountainous terrain, sharp gradients in elevation, moisture convergence, and vertical motion introduce additional uncertainty in the representation of cloud microphysics and storm electrification, making lightning simulation particularly sensitive to model physics and resolution. Moreover, although many studies have evaluated lightning and rainfall separately, the combined and comparative performance of WRF microphysics schemes in simulating both processes has not been systematically investigated. In this study, we present the first modeling-based analysis of a severe lightning event over Uttarakhand and surrounding areas on 23 May 2023, characterized by high lightning activity observed by both ISS-LIS and the Indian Lightning Location Network (ILLN), and associated human fatalities. Insights from this study are relevant for understanding microphysics-lightning-rainfall interactions in other mountainous and orographically complex regions. The objective of this study is to diagnose the sensitivity of lightning and rainfall simulations to four WRF microphysics schemes (WSM6, Thompson, Morrison, and WDM5) over a complex mountainous environment. Specifically, we address the following research questions:\u003c/p\u003e \u003cp\u003e(1) How well do different microphysics schemes in WRF simulate the temporal and spatial characteristics and storm dynamics of a severe lightning event over the western Himalaya?\u003c/p\u003e \u003cp\u003e(2) Do the same microphysics schemes perform similarly for both lightning and rainfall simulations?\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study region\u003c/h2\u003e\n \u003cp\u003eThe WHR, encompassing Uttarakhand, Himachal Pradesh, Ladakh, and Jammu and Kashmir, is among the most meteorologically vulnerable regions in India. Its complex terrain, characterized by sharp elevation gradients and deep valleys, together with frequent synoptic-scale disturbances, makes the region highly prone to extreme weather events, including heavy rainfall, cloudbursts, hailstorms, and severe lightning, which frequently trigger landslides and societal impacts (Penki and Kamra, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dimri et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kashyap and Behera, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kumar et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lohan et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Lightning activity over the WHR exhibits pronounced variability, with enhanced occurrence during the pre-monsoon and retreating monsoon phases, while extreme precipitation is more common during the summer monsoon and western disturbance (WD) dominated periods (Bookhagen et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dimri et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kumar et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The interaction of WDs, cyclonic circulations, and strong orographic forcing creates favorable conditions for deep convection, leading to concurrent episodes of severe lightning and intense rainfall (Agnihotri and Singh, \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e; Gangane et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hunt et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Given the frequent co-occurrence of multiple high-impact hazards and the associated societal vulnerability, we employ a combination of observational datasets and numerical modeling to examine the characteristics of a severe convective event over WHR in detail.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Model configuration and evaluation\u003c/h2\u003e\n \u003cp\u003eTo perform lightning simulations over WHR, we have used the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model, version 4.5. WRF model has been developed by the National Centre for Atmospheric Research (NCAR). It is built on the Arakawa-C grid and have terrain-following hydrostatic pressure coordinate system with Coriolis and curvature terms. Model integration has performed using a third-order Runge-Kutta time integration scheme (Skamarock et al. 2008). For this study, a three-domain nested configuration was used as D1: 27 km resolution, D2: 9 km resolution and D3: 3 km resolution (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The ILLN observation shows some limitation of detection efficiency over the upper portion of the D3 domain (Biswasharma et al. \u003cspan class=\"CitationRef\"\u003e2025b\u003c/span\u003e). Thus for the analysis, we have focused on a smaller target domain (TD dashed inner box in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Lightning, associated hydrometeors and rainfall were simulated using four cloud microphysics (MP) schemes: WRF Single-Moment 6-class (WSM6), Thompson, Morrison two-moment, and WRF Double-Moment 5-class (WDM5), together with the Kain-Fritsch cumulus parameterization scheme. A detailed summary of the WRF model configuration is provided in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of WRF-ARW model configuration\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDomains Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27km, 9km, 3km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of vertical\u0026nbsp;levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCloud Microphysics (MP) Options\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1)\u0026nbsp;mp_physics\u0026nbsp;option\u0026thinsp;=\u0026thinsp;8\u003c/p\u003e\n \u003cp\u003e2)\u0026nbsp;mp_physics\u0026nbsp;option\u0026nbsp;= 10\u003c/p\u003e\n \u003cp\u003e3)\u0026nbsp;mp_physics\u0026nbsp;option\u0026nbsp;=14\u003c/p\u003e\n \u003cp\u003e4)\u0026nbsp;mp_physics\u0026nbsp;option\u0026nbsp;= 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThompson\u003c/p\u003e\n \u003cp\u003eMorrison 2-mom WRF Double-moment 5-class (WDM5)\u003c/p\u003e\n \u003cp\u003eWRF\u0026nbsp;Single-moment 6-class (WSM6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThompson\u0026nbsp;et al.\u0026nbsp;(2008)\u003c/p\u003e\n \u003cp\u003eMorrison\u0026nbsp;et al.\u0026nbsp;(2009)\u003c/p\u003e\n \u003cp\u003eLim and Hong, (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eHong and Lim, (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulus Options\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulus physics\u003c/p\u003e\n \u003cp\u003ecu_physics\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKain-Fritsch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKain, (2004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand Surface,\u0026nbsp;boundary\u0026nbsp;layer and radiation model Options\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoundary layer scheme\u003c/p\u003e\n \u003cp\u003ebl_pbl_physics\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYonsei University Scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHong\u0026nbsp;et al.\u0026nbsp;(2006)\u003c/p\u003e\n \u003cp\u003eChoudhury\u0026nbsp;et al.\u0026nbsp;(2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLand surface model\u003c/p\u003e\n \u003cp\u003esf_surface_physics\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\n \u003cp\u003esf_sfclay_physics\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNoah LSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNiu\u0026nbsp;et al.\u0026nbsp;(2011)\u003c/p\u003e\n \u003cp\u003eYang\u0026nbsp;et al.\u0026nbsp;(2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadiation\u003c/p\u003e\n \u003cp\u003era_la_physics\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e\n \u003cp\u003era_sw_physics\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRapid Radiative transfer model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIacono\u0026nbsp;et al.\u0026nbsp;(2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime Steps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 Sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChoudhury\u0026nbsp;et al.\u0026nbsp;(2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLightning Options\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScheme\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLightning_option\u0026nbsp;= 1\u003c/p\u003e\n \u003cp\u003eInitial boundary condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrice and Rind\u003c/p\u003e\n \u003cp\u003eNCEP\u0026nbsp;FNL data (1\u0026deg; \u0026times; 1\u0026deg; spatial resolution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrice and Rind (\u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e) Choudhury\u0026nbsp;et al.\u0026nbsp;(2020)\u003c/p\u003e\n \u003cp\u003eVani\u0026nbsp;et al.\u0026nbsp;(2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eThe PR92 is one of the widely used parametrization schemes used for the prediction of flash rate (FR) based on the storm height and specially designed for the ocean and land configurations (Price and Rind \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e). The PR92 formulation expresses flash rate as:\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$${\\varvec{F}}_{\\varvec{r}\\varvec{c}}=3.44\\times{10}^{-5}{\\varvec{H}}_{\\varvec{s}}^{4.9}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{F}}_{\\varvec{r}\\varvec{c}}\\)\u003c/span\u003e\u003c/span\u003e: FR (Flash min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{s}\\)\u003c/span\u003e\u003c/span\u003e : Strom Height\u003c/p\u003e\n \u003cp\u003eAlso, PR92 scheme can be upgraded to calculate the Flash rate by using maximum vertical velocity\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$${\\varvec{W}}_{\\varvec{m}\\varvec{a}\\varvec{x}}=1.49\\times{\\varvec{H}}_{\\varvec{s}}^{1.09}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eand the corresponding flash rate is calculated as\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$${\\varvec{F}}_{\\varvec{r}\\varvec{c}}=5\\times{10}^{-6}{\\varvec{W}}_{\\varvec{m}\\varvec{a}\\varvec{x}}^{4.54}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe performance of the WRF model have evaluated using contingency table based skill scores (Table S1), including (i) Probability of Detection (POD), (ii) False Alarm Ratio (FAR), (iii) Bias Score (BIAS), (iv) Critical Success Index (CSI), and (v) Accuracy (Wilks, 2011; Vani et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additional details on fractional skill score (FSS) metrics are provided in Kumar et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and references therein (Table S2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Data\u003c/h2\u003e\n \u003cp\u003eWe used ground-based lightning data from the ILLN to assess the model performance. The details regarding the principle and working of IILN are given in Biswasharma et al. \u003cspan class=\"CitationRef\"\u003e2025b\u003c/span\u003e and reference therein. The network operates within a frequency band of 1 Hz to 12 MHz, where the lower frequencies are used to detect CG lightning, and higher frequencies are used to detect IC lightning (Vani et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The ILLN has an estimated detection efficiency of 90% for CG lightning flashes and 50% for IC flashes (Biswasharma et al. \u003cspan class=\"CitationRef\"\u003e2025b\u003c/span\u003e). In India, the WRF model has been used over the NE, western ghats (WG) and Indo-Gangetic plains to simulate lightning events and the performance of the model has been evaluated by using ILLN data (Choudhury et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mohan et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vani et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). For the Initial and boundary conditions (IC/BC), we have used Final (FNL) Operational Global Analysis Data, as well as geographic data (elevation, LULC, etc) for the model, which were obtained from the National Centers for Environmental Prediction (NCEP). The rainfall data is obtained from Global Precipitation Measurement (GPM).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Result and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Event description\u003c/h2\u003e \u003cp\u003eFigure S1 represents the monthly lightning flash counts along with daily flash counts in May 2023 observed by ISS-LIS over TD. Relatively higher lightning flash counts (1607) were observed in May month particularly on 23 May 2023. Observation shows 769 flashes in the span of 24 hours. Due to temporal limitations of ISS-LIS, we have further carried out the analysis based on ILLN data. To understand the synaptic condition, we analyzed using geopotential height (m) and wind vectors (m s⁻\u0026sup1;) at 500 hPa on over India and adjacent areas as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A well-marked active WD over northwest (NW) India with a mid-tropospheric trough extending from Iran and Pakistan into northwestern India. The southward dip in geopotential height and cyclonic curvature of westerlies over NE India indicate strong dynamical forcing and upper-level divergence associated with the disturbance. Concurrently, a cyclonic circulation at lower levels cantered over Arabian sea with moist southwesterly flow provided a continuous moisture supply into the WHR. Which promoted increased instability and vertical uplift over WHR, leading to the development of deep convective clouds. The Indian Meteorological Department (IMD) bulletin for the same day also reported thunderstorms, hailstorms, gusty winds (50\u0026ndash;60 km h⁻\u0026sup1;), and heavy rainfall over the region between 23 and 25 May (Daily IMD bulletin 23 May 2023). The combination of mid-tropospheric trough, low-level moisture advection, orographic lifting possibly created an ideal environment for the observed severe lightning and rainfall over WHR on 23 May 2023.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temporal variations\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e compares the ground-based ILLN observations with WRF model-simulated lightning flash rates (FR). The case study spans a 36-hours window, covering six hours before and six hours after the event day. This period is divided into three phases as initial (22-May-2023 18:00 UTC to 23-May-2023 07:00 UTC), active (23-May-2023 08:00 UTC to 23-May-2023 20:00 UTC), and decay phase (23-May-2023 21:00 UTC to 24-May-2023 06:00 UTC). During the initial phase, FR was low in both observations and simulations. All MP schemes generally followed the observed variation, although Morrison exhibited some overestimation. In the active phase, lightning activity increased sharply before reaching the peak and then gradually declined. We have reported peak lightning at 15:00 UTC with the highest FR upto 1661 flashes hour\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The Morrison successfully captured the peak lightning at 15:00 UTC with 2238 flashes hour\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and overestimation of 577 flashes hour\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Thomson, WDM5 and WSM6 also captured the peak lightning closely with a lead time of 1\u0026ndash;2 hours as compared to ILLN. In the decay phase, Thompson and WDM5 showed trends like those of WSM6 and Morrison. Furthermore, we have applied cross-correlation analysis to identify the lead-leg pair with the highest correlation. WSM6 and WDM5 show 3 hours lead with high (r\u0026thinsp;=\u0026thinsp;0.76 \u0026amp; 0.80) correlations, and Thompson and Morrison show only 1 hourly lead, with slightly lower correlation (r\u0026thinsp;=\u0026thinsp;0.73, 0.60). Vani et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported notable FSS for lightning activity over the WG, and also reported instances of both overestimation and underestimation. For example, in 81.25% of the cases, the model showed a lead or lag of 1\u0026ndash;3 hours and in 18.75% of cases, the shift was up to 6 hours.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spatial and longitudinal variations\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the intercomparison between the model simulated and ILLN observed FR spatial distribution. As the two platform have different range of variations, for a more meaningful comparison, we have calculated the Normalized Flash Rate (NFR). The NFR is derived by converting flash points into 3 km grids and then dividing by the maximum FR. The ILLN observations indicate the accumulation of high NFR (0.8 to 1.0) over the foothills of the Himalayas in Himachal Pradesh, Uttarakhand, northern Uttar Pradesh and Nepal as described in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea (Kumar et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gautam et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The WSM6 captures lightning activity over the foothills but weakly captured NFR over south-eastern region. However, it effectively rejected non-lightning events (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The Thompson MP scheme captures slightly more lightning activity over the foothills with fewer false detection as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ec. It should be noted that, Morrison detects more lightning event but suffers from false detection and comparatively fails to reject non-lightning events (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The WDM5 also captures lightning activity better as compared to the WSM6 across the entire foothill region, however, generates a significant false detection (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Overall, all MP schemes demonstrate the ability to reject non-lightning events and identify lightning hotspots.\u003c/p\u003e \u003cp\u003eTo understand the shift in lightning distribution, we plotted the average longitudinal variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). The MP schemes showed overestimation compared to the ILLN between 75\u0026deg; to 77\u0026deg;E and Thompson showed the least longitudinal deviation (only 0.27\u0026deg;E) from the observed lightning peak. The WSM6 had a deviation of 0.58\u0026deg;E and aligned well with observations, producing stable results. However, the Morrison and WDM5 showed larger deviations ranging from 2.95\u0026deg; to 3.19\u0026deg;E, suggesting significantly high overestimation. Recently, Mohan et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported strong agreement between ILLN and simulated FR with a high spatial correlation of 82.7%. Similarly, Vani et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that WDM5 performed better than Thompson with a spatial shift in isolated lightning events over WG region. The more details of spatial performance of MP schemes have also discussed in section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.7\u003c/span\u003e\u0026ndash;3.8.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Evolution of simulated convective parameters\u003c/h2\u003e \u003cp\u003eConvection plays a critical role in cloud microphysical processes and lightning generation. Convective Available Potential Energy (CAPE) serving as a key indicator of convective activities (Kamra and Kumar, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We have examined the dynamics of simulated CAPE at 850 hPa during the initial, active, and dissipation phases (Fig. S2). During initial phase, low CAPE (\u0026lt;\u0026thinsp;250 Jkg\u003csup\u003e-1\u003c/sup\u003e) dominates the most region, but due to localized convective high convection zones (HCZ) particularly over Rajasthan, Haryana, Punjab, and Uttar Pradesh and show relatively high CAPE (\u0026lt;\u0026thinsp;750 Jkg\u003csup\u003e-1\u003c/sup\u003e). During the active phase, most of the region is covered by moderate CAPE (250\u0026ndash;750 Jkg\u003csup\u003e-1\u003c/sup\u003e) and HCZ exhibits high CAPE (750 and \u0026gt;\u0026thinsp;2000 Jkg\u003csup\u003e-1\u003c/sup\u003e). The Morrison and WDM5 well simulate the convection activities, and WDM5 reflects efficient transition phases. However, isolated small convective pockets can still support lightning. The Decay phase also depicts localized high convection, but stability dominates the region (\u0026lt;\u0026thinsp;250 Jkg\u003csup\u003e-1\u003c/sup\u003e). The spatial and temporal patterns of simulated CAPE align well FR (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the WHR, especially over the higher-elevation regions, CAPE may not be a primary contributor for lightning activity and show lead relation with lightning FR density (Kumar et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; r\u0026thinsp;=\u0026thinsp;0.34). Conversely, lightning in the NW Himalayan foothills is largely driven by solar heating, precipitable water, cloud clustering and orographic uplift (Kamra and Kumar, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). So, foothills may contribute to the initiation of the strong convection (surface-based convective lifting), and hilly terrain redistributes them, where orographic lifting may be important (Biswasharma 2025a, c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Cloud microphysical properties\u003c/h2\u003e \u003cp\u003eThe MP scheme plays a crucial role in governing lightning activity, interaction between graupel and cloud ice particles through collisions enhances cloud electrification, ultimately leading to severe lightning (Adamo et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yair et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hazra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Williams et al. 2002; Choudhury et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, understanding and evaluation of MP schemes are essential for improving lightning prediction and representing in the model. In the present study, we have investigated the temporal and vertical distribution of Cloud Water Mixing Ratio (q\u003csub\u003ec\u003c/sub\u003e), Rainwater Mixing Ratio (q\u003csub\u003er\u003c/sub\u003e), Snow Mixing Ratio (q\u003csub\u003es\u003c/sub\u003e), and Ice Mixing Ratio (q\u003csub\u003ei\u003c/sub\u003e) simulated over the domain (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The q\u003csub\u003ec\u003c/sub\u003e has a significant contribution in lightning activity, as the presence of high q\u003csub\u003ec\u003c/sub\u003e indicates more supercooled liquid droplets available for hydrometeor growth (Bovalo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Updraughts transport these droplets to higher altitudes, facilitating the conversion into ice and snow particles through the Wegener-Bergeron-Findeisen process (WBFP) in mixed phase region (MPR) of thundercloud (Storelvmo \u0026amp; Tan, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The time-height variations of the domain-averaged q\u003csub\u003ec\u003c/sub\u003e obtained from different MP schemes are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e(i). The q\u003csub\u003ec\u003c/sub\u003e shows a significant vertical extent between 5.5 and 8.5 km, which can be converted into ice and snow particles via accretions and riming process (Korolev, 2007). The presence of hydrometeor significantly contributes to charge separation (Mansell et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Thompson and Morrison show stronger evidence of elevated q\u003csub\u003ec\u003c/sub\u003e (\u0026gt;\u0026thinsp;2.5 \u0026times; 10⁻⁵ kg kg\u003csup\u003e\u003cb\u003e-\u003c/b\u003e1\u003c/sup\u003e) and correspond well with FR variation during the active phase (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e(i)b \u0026amp; 5(i)c). The WSM6 and WDM5 also generate high q\u003csub\u003ec\u003c/sub\u003e levels\u0026thinsp;\u0026gt;\u0026thinsp;2.5 \u0026times; 10⁻⁵ kg kg\u003csup\u003e\u003cb\u003e-\u003c/b\u003e1\u003c/sup\u003e during active phase (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e(i)a, d), with scattered q\u003csub\u003ec\u003c/sub\u003e structures and coinciding with FR trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Which is indicating the favourable condition for the hydrometeor growth, charge separation, electrification and lightning.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe q\u003csub\u003er\u003c/sub\u003e simulation shows significantly high values (5 \u0026times; 10⁻⁵ kg kg-\u003csup\u003e1\u003c/sup\u003e) between active phase with a vertical extent from the surface up to 4 km in the WSM6, Thompson, and WDM5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e(ii) a, b \u0026amp; d). In contrast, the Morrison exhibits peak q\u003csub\u003er\u003c/sub\u003e during the decay phase, with maximum values exceeding 2.5 \u0026times; 10⁻⁵ kg kg-\u003csup\u003e1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e(ii)c). The presence of high q\u003csub\u003er\u003c/sub\u003e values in active phase suggesting the collision and coalescence processes to initiate the warm rain processes (WRP) in shallow MPR later at 15 UTC (Rosenfeld and Lensky \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The effect of WRP in thunderstorm has been observed and it is dissipating due to significant contribution of downdraughts generated by dominance of high gridded average rainfall up to 1.3 mm at 18 UTC as observed by the GPM observations.\u003c/p\u003e \u003cp\u003eThe q\u003csub\u003es\u003c/sub\u003e clearly indicates dominance during both the active and decay phases of FR, with significant vertical extension from 4\u0026ndash;14 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-d). The Thompson and WDM5 microphysics exhibit high q\u003csub\u003es\u003c/sub\u003e values exceeding 7.5 \u0026times; 10⁻⁵ kg kg-\u003csup\u003e1\u003c/sup\u003e within MPR and above MPR between 5.5 and 12 km well as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e(i)b, d. A high snow content zone persists for several hours (11\u0026ndash;21 UTC), suggesting active depositional growth and riming, which are crucial for graupel and snow production, ultimately leading to severe lightning. It should be noted that, in Thompson MP ice is transfer to the snow therefore Thompson shows relatively high snow but weak ice production (Ko et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, Choudhury et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlighted that snow formation is affected by strong updraughts, induced by latent heat release and regulates auto-conversion of cloud ice to snow at high altitudes. Which support the riming process, charge segregation, cloud electrification and thus contribute severe lightning. However, the WSM6 and Morrison show relatively lower snow production (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e(i)a, c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e(ii)a-d describes the q\u003csub\u003ei\u003c/sub\u003e, which is a crucial hydrometeor for understanding the microphysical characteristics of ice-phase cloud development and its role in subsequent precipitation and lightning generation. The WSM6, Morrison, and WDM5 shows a widespread and elevated q\u003csub\u003ei\u003c/sub\u003e \u0026gt; 2.5 \u0026times; 10⁻⁵ kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e between approximately 8\u0026ndash;12 km altitude. The Morrison exhibits a broader peak with highest q\u003csub\u003ei\u003c/sub\u003e upto 5\u0026times;10⁻⁵ kg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e between 9\u0026ndash;13 km, suggesting active ice production and dominance of cold cloud processes. However, this enhanced ice-phase development does not directly translate to strong surface rainfall, possibly due to weak WRP. The weakest q\u003csub\u003ei\u003c/sub\u003e is generated by the Thompson as compared to WSM6 and WDM5 MP Schemes. Weak ice production in Thompson MP schemes is due to generation of relatively smaller ice particles (Ko et al. 2020; Bao et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Understanding such critical differences is crucial for improving cloud microphysics, cloud-radiation interactions, thunderstorm dynamics. Several past studies support these observations, Hazra et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) investigated the role of cloud ice in lightning and heavy rainfall using WRF simulations and in-situ measurements from an ice nuclei spectrometer. They found that the Fletcher, 1962 or F1962 MP scheme generated hydrometeors more favourable for lightning (Fletcher, 1962). Similarly, Unnikrishnan et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported a strong spatial analogy between cloud ice water content and lightning frequency over the NE and NW Indian regions. The vertical moisture transport, combined with the WBFP and cold cloud processes initiates the growth of hydrometeors and condensational latent heat release. Which strengthens the further vertical updraughts and cloud development for severe lightning generation (Korolev, 2007; Hazra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Choudhury et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Likewise, Biswasharma et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) highlighted the dominance of high concentrations of ice particle during thunderstorms over Rampurhat compared to Nagaland. In our study, the existence of hydrometeors reinforces the critical role in shaping severe lightning episodes over WHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Temporal evaluation of lightning\u003c/h2\u003e \u003cp\u003eThe temporal performance of MP schemes has been evaluated by employing Taylor diagrams, which provide critical performance statistics such as correlation coefficient (r), standard deviation (SD), and root mean square error (RMSE) etc. (Taylor et al. 2001; Federico et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yadava et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Biswasharma et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Among MP schemes, Thompson indicates most favourable performance characteristics due to high (r\u0026thinsp;=\u0026thinsp;0.63) and balance RMSE (429 flashes hour\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) between ILLN observation and model generated FR. It means, Thompson successfully generates comparable magnitude and temporal behaviour of ILLN observed FR (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e(i)). Morrison shows highest RMSE (518 flashes hour\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) with a lower correlation (r\u0026thinsp;=\u0026thinsp;0.53), which is indicating overestimation as compared to the observation. The WSM6 shows lowest performance characteristics, displaying slightly lower RMSE (458.2 flashes hour\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and weak correlation (r\u0026thinsp;=\u0026thinsp;0.34), with performance metrics closely similar to WDM5 (r\u0026thinsp;=\u0026thinsp;0.43, RMSE: 448.8 flashes hour\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Overall, Thompson demonstrates better skill, Morrison shows a reasonable correlation despite higher RMSE. Both WSM6 and WDM5 suggest similar, but weaker lightning simulation capabilities in temporal evaluation over WHR (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e(i)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe spatial performance evaluation of the MP schemes has been carried out by preparation of spatial contingency table for each 12 km, 21 km and 51 km grid and then FSS has been computed over TD domain. The POD show increasing trend towards larger grid and MP schemes are producing stable results with low FAR and Bais. We have observed that Morrison had the highest detection skill (POD\u0026thinsp;=\u0026thinsp;0.66, CSI\u0026thinsp;=\u0026thinsp;0.39, Accuracy\u0026thinsp;=\u0026thinsp;0.55), but it slightly overpredicted (Bias\u0026thinsp;=\u0026thinsp;1.01) and suffers from high FAR (0.55) in 12 km grid (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e(ii)a). WSM6, Thompson and Morrison show slightly better performance at 21 km grid in terms of POD, CSI, Accuracy. But due to low FAR and Bais, WDM5 shows slightly better Accuracy as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e(ii)b. In the largest 51 km grid, the performance of MP schemes shows significant enhancement. For example, Thompson, Morrison, WDM5 and WSM6 show high POD (0.82, 0.84, 0.81 \u0026amp; 0.78), CSI (0.72, 0.64, 0.69, 0.64), accuracy (0.76, 0.65, 0.73, 0.68) and low FAR (0.13, 0.26, 0.17) and Bias (0.95, 1.15, 0.98, 0.94) respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e(ii)c). Therefore, Thompson can predict the FR well while maintaining the lowest FAR and Bias over TD. Although Morrison has high POD, it has significant overestimation and high FAR with low Accuracy and CSI. The spatial performance of the MP Schemes also depends upon the grid resolution. For example, Vani et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have selected sixteen lighting cases over the Maharashtra and evaluated the model performance. They have reported high FSS in 50 km grid in comparison with 10 km grid. Similarly, Kumar et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have simulated the extreme cases of lightning over the Bihar and Rajasthan and evaluated the WRF model performance. They observed POD is in the range of 0.5\u0026ndash;0.69 by using different combinations of parameterization schemes. The FSS of our analysis in case of all MP schemes is comparable with these previous studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Spatial and temporal evaluation of rainfall\u003c/h2\u003e \u003cp\u003eSeveral studies have reported strong spatial and temporal associations between rainfall and lightning over different regions (Lal and Pawar, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Petersen and Rutledge, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Williams et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). In this context, we have also evaluated the model performance to examine whether microphysics schemes that perform well for the lightning prediction can exhibit similar skill for rainfall, or whether different model configurations and tuning are required to represent both the processes. The spatial and temporal performance of the model for predicting rainfall in comparison with GPM derived grid average rainfall shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e(i). In temporal performance, GPM data shows a high SD (0.36 mm) as compared to the model simulated rainfall (0.02\u0026ndash;0.3 mm). WSM6 shows the highest correlation (r\u0026thinsp;=\u0026thinsp;0.74) with GPM data, while Thompson (r\u0026thinsp;=\u0026thinsp;0.52) and Morrison (r\u0026thinsp;=\u0026thinsp;0.45) show moderate correlations. WDM5 shows the lowest correlation (r\u0026thinsp;=\u0026thinsp;0.26) with lowest SD (0.02 mm). It is to be noted that Thomson and Morrison schemes show the best performance for lightning simulation but not for rainfall over TD.\u003c/p\u003e \u003cp\u003eThe evaluation of spatial performance using FSS for four different thresholds (0.1 mm, 0.5 mm, 1 mm, and 2 mm) of rainfall, has been carried out at grid resolution of 12 km as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e(ii). The WSM6, Thompson, Morrison and WDM5 MP schemes were able to predict (0.1 mm threshold) rainfall with moderate to low POD (0.64, 0.37, 0.29, 0.25) and low FAR (0.02\u0026ndash;0.04) as shown Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e(ii)a. The Morrison shows the highest Accuracy (0.63) but with high bias (0.68) at 0.1 mm threshold. Furthermore, we observed that the POD and CSI are decreasing while FAR is increasing from 0.1 to 2 mm threshold. It means that model could not capture rainfall at a higher threshold over the TD domain (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e(ii)). In both lightning and rainfall, Morrison shows high POD but suffers from the FAR and Bais issues (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e(ii), 8(ii)). Navale and Singh (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have explored the influence of topography on the performance of the WRF model for simulation of rainfall compared with IMD GPM over the NW Himalayan region, particularly over Uttarakhand and Himachal Pradesh. They found that WRF model simulates the rainfall well with considerable FSS such as Hit Rate (0.78\u0026ndash;0.85), FAR (0.20\u0026ndash;0.42), accuracy (0.70\u0026thinsp;\u0026minus;\u0026thinsp;0.60), CSI (0.70\u0026thinsp;\u0026minus;\u0026thinsp;0.52) for 0.1 mm threshold. Overall, WSM6 showed relatively better performance in capturing temporal variations while Morrison showed higher FSS for spatial variations. We noted that for lightning simulation, Morrison and Thompson consistently performed better than the other schemes. Consequently, no single microphysics scheme emerges as uniquely superior for simulating both rainfall and lightning, indicating scheme-dependent performance across processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe vertical profiles of domain-averaged q\u003csub\u003ec\u003c/sub\u003e, q\u003csub\u003er,\u003c/sub\u003e q\u003csub\u003esa\u003c/sub\u003e and q\u003csub\u003ei\u003c/sub\u003e simulated using four MP schemes during the active phase of the thunderstorm shown in Fig.\u0026nbsp;9(i)a-d. All MP schemes show high q\u003csub\u003ec\u003c/sub\u003e between 3\u0026ndash;5 km; however, Thompson and Morrison schemes maintain relatively higher q\u003csub\u003ec\u003c/sub\u003e and a deeper vertical extent reaching to mid-troposphere (5\u0026ndash;8 km). This indicates that more supercooled liquid water stays higher in the cloud, which is essential for non-inductive charge separation through graupel-ice interactions (Takahashi, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Tsenova and Mitzeva \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The WSM6 and WDM5 schemes produce shallow cloud water profiles, which favor faster conversion to precipitation. The q\u003csub\u003er\u003c/sub\u003e peaks in the lower troposphere (2\u0026ndash;4 km) across all schemes, but Morrison shows comparatively stronger rainwater production and a more coherent vertical structure near the surface. This suggests efficient warm-rain processes and rapid conversion of cloud water into rain causing high surface rainfall (King et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In contrast, Thompson scheme shows decreasing rainwater, consistent with delayed precipitation formation due to prolonged liquid water retention aloft. q\u003csub\u003es\u003c/sub\u003e dominate above ~\u0026thinsp;6 km, with Thompson scheme showing high q\u003csub\u003es\u003c/sub\u003e within the MPR. This increase in snow mass indicates active depositional growth and riming processes, which are closely linked to graupel formation and lightning electrification. The WSM6 scheme shows relatively lower snow aloft, reflecting weaker cold-cloud microphysical processes. The q\u003csub\u003ei\u003c/sub\u003e reveals marked scheme-dependent differences. The Thompson scheme produces slightly less but more uniform ice distribution due to a smaller ice particle spectrum within the MPR (Bao et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The WSM6 scheme shows comparatively extensive ice profiles, consistent with its single-moment formulation and emphasis on precipitation efficiency rather than electrification. Overall, the vertical hydrometeor profiles indicate that Thompson, Morrison scheme increases mixed-phase processes which are critical for lightning generation, whereas WSM6 scheme favors efficient warm-rain production and surface rainfall.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;9(ii)a-d presents Contoured Frequency by Altitude Diagrams (CFADs) of simulated reflectivity, highlighting pronounced differences in storm vertical structure across microphysics schemes. WSM6 scheme shows high-frequency reflectivity exceeding 40 dBZ primarily below ~\u0026thinsp;4 km, indicating efficient warm-rain processes and strong rainfall production, but weaker mixed-phase development aloft. In contrast, Thompson scheme shows moderate-to-high reflectivity extending coherently into the mixed-phase region (5.5\u0026ndash;8.5 km), indicating active riming and graupel growth, which are favorable for charge separation and lightning generation. Morrison scheme shows large spread in reflectivity, supporting precipitation growth but with less organized mixed-phase structure, while WDM5 shows intermediate behavior. These differences demonstrate that the choice of microphysics strongly controls storm structure, with Thompson scheme favoring lightning-related processes while WSM6 scheme emphasizing rainfall production.\u003c/p\u003e \u003cp\u003eOverall, the evaluation indicates that the WRF-simulated lightning and rainfall are substantially sensitive to the choice of MP scheme. The Thompson scheme shows better temporal skill for lightning, while Morrison performs better in spatial detection, and WSM6 captures rainfall variability more effectively. However, no single scheme consistently outperforms best for both lightning and rainfall, highlighting that WRF simulations are not uniformly reliable across processes. These contrasting performances underscore the need for further targeted tuning and region-specific optimization of model physics for improved simultaneous simulation of lightning and precipitation over complex terrain. The contrasting performance of microphysics schemes for lightning and rainfall arises from fundamental differences in how they represent mixed-phase cloud processes, hydrometeor growth, and precipitation conversion. Lightning production is strongly controlled by the coexistence of supercooled liquid water, graupel, and ice within the mixed-phase region, which promotes non-inductive charge separation through riming and collisions (Takahashi, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Saunders, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Williams et al. 2002). Schemes such as Thompson, which incorporate more detailed ice-phase physics and riming processes, tend to sustain hydrometeors within the charging zone for longer durations, thereby enhancing electrification and lightning activity (Thompson et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Morrison et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Choudhury et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, rainfall simulation is more sensitive to warm-rain processes, auto-conversion rates, and melting efficiency, which are emphasized in simpler bulk schemes such as WSM6, leading to more efficient precipitation production near the surface (Hong and Lim, 2006; Halder and Mukhopadhyay, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a result, schemes optimized for electrification processes do not necessarily maximize rainfall skill, and vice versa. Similar scheme-dependent behavior has been reported in earlier WRF-based lightning and precipitation studies over India and other convective regions (Mohan et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vani et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Haldar and Mukhopadhyay 2016), highlighting that no single microphysics formulation can simultaneously optimize both lightning and rainfall simulations, particularly over complex terrain.\u003c/p\u003e \u003cp\u003eIt is to be noted that the analysis is based on a single severe pre-monsoon lightning event, and therefore, the results may not be fully representative of the wide range of convective environments and storm types that occur over the western Himalaya. Model performance and microphysics sensitivity may vary under different synoptic conditions, seasons, and storm intensities. In addition, uncertainties associated with lightning parameterization, observational detection efficiency, and model resolution over complex terrain can influence the results. Future studies using a larger sample of events across multiple seasons and regions are needed to generalize the findings and further refine model physics for improved lightning and rainfall simulations.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study examined a severe pre-monsoon lightning event over the complex terrain of WHR on 23 May 2023 and evaluated the ability of the WRF model to simulate lightning and associated convective processes using four MP schemes (WSM6, Thompson, Morrison, and WDM5). Lightning simulations were evaluated against ILLN observations, while rainfall was assessed using GPM observations. The analysis focused on temporal evolution, spatial distribution, thermodynamical and microphysical characteristics. All MP schemes captured the broad temporal evolution of lightning activity, including the initial, active, and decay phases, but differed significantly in magnitude, timing, and variability. Morrison reproduced the timing of peak FR but with overestimated magnitude, whereas the Thompson scheme showed the closest agreement with observed lightning variability with reduced false detections. WSM6 and WDM5 showed larger timing offsets and higher variability. A pronounced increase in CAPE occurred from the initial phase (\u0026lt;\u0026thinsp;250 J kg⁻\u0026sup1;) to the active phase (250\u0026ndash;750 J kg⁻\u0026sup1;) of thunderstorm, with some zones exceeding 2000 J kg⁻\u0026sup1;. Morrison effectively captured this transition. During the active phase, Thompson and Morrison simulated enhanced mid-level cloud water (q\u003csub\u003ec\u003c/sub\u003e ≳ 2 \u0026times; 10⁻⁵ kg kg⁻\u0026sup1;) between 4\u0026ndash;8 km, supporting sustained updrafts and latent heat release. Elevated snow mixing ratios (q\u003csub\u003es\u003c/sub\u003e ≳ 7 \u0026times; 10⁻⁵ kg kg⁻\u0026sup1;) extending into the mixed-phase region indicated active depositional and riming growth favorable for lightning. In contrast, WSM6 produced stronger low-level rainwater peaks (q\u003csub\u003er\u003c/sub\u003e ≳ 5 \u0026times; 10⁻⁵ kg kg⁻\u0026sup1; below ~\u0026thinsp;4 km), reflecting efficient WRP and enhanced surface precipitation during the decay phase of thunderstorm. These contrasting hydrometeor profiles explain the divergent performance of MP schemes for simulating lightning and rainfall. Schemes that retain supercooled liquid water and ice in the MPR favour charge separation and lightning, whereas schemes emphasizing rapid WRP enhances surface rainfall but limits electrification. Consequently, no single MP scheme consistently outperforms others for both lightning and rainfall over the WHR.\u003c/p\u003e \u003cp\u003eOverall, WRF performance over the complex Himalayan terrain is strongly process- and scheme-dependent, indicating that microphysics configurations optimized for one atmospheric process may not be transferable to others. The contrasting behavior of MP schemes for lightning and rainfall highlights the need to treat electrification and precipitation as distinct yet interacting outcomes of storm microphysics, rather than assuming a single optimal setup. These findings have important implications for high-impact weather prediction over mountainous regions, where lightning, cloudbursts, and intense rainfall frequently co-occur. Improving model reliability therefore requires region-specific and process-aware tuning of microphysics schemes, guided by detailed observational evaluation. More broadly, the results emphasize that robust lightning prediction must be grounded in physically consistent representations of mixed-phase cloud processes, which are also relevant to other orographically influenced convective environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;Competing of interest:\u003c/strong\u003e The authors declare that they have no financial or personal relationships that could have influenced the work reported in this manuscript.\u003c/p\u003e\n\u003ch2\u003eUse of AI Tools\u003c/h2\u003e\n\u003cp\u003eThe authors used an AI-based language editing tool to improve the grammar, clarity, and readability of the manuscript. The AI tool was not used for scientific interpretation, data analysis, figure preparation, or the generation of research content. All scientific conclusions, analyses, and interpretations were developed and verified by the authors.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSanjeev Kumar: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing-original draft, Writing-review \u0026amp; editing. N. Umakanth: Data curation, Formal analysis, Methodology, Software. Alok Sagar Gautam: Conceptualization, Investigation, Project administration, Validation. Rupraj Biswasharma: Conceptualization, Investigation, Methodology, Validation, Visualization, Writing-original draft, Writing-review \u0026amp; editing. Swapnil S. Potdar: Data curation, Methodology, Software, Writing-review \u0026amp; editing. Karan Singh: Data curation, Software. Devendraa Siingh: Investigation, Project administration, Resources, Supervision, Validation. R. P. Singh: Visualization, Writing - original draft.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eIndian Institute of Tropical Meteorology, Pune, is funded by the Ministry of Earth Sciences, Government of India. We are sincerely thankful to Head of the Department of Physics; and the Vice-Chancellor of Hemvati Nandan Bahuguna Garhwal University, Srinagar, for providing the support and infrastructure necessary for this research. We gratefully acknowledge the data providers and modelling platforms that supported this study, including the Global Hydrometeorology Resource Center (GHRC), the European Centre for Medium-Range Weather Forecasts (ECMWF), NASA's Giovanni portal, and the National Centers for Environmental Prediction (NCEP) team, whose resources were crucial to the analysis and outcomes of this work.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe Weather Research and Forecasting (WRF) Model version 4.5 used in this study is publicly available from NCAR at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mmm.ucar.edu/models/wrf\u003c/span\u003e\u003c/span\u003e and via the official GitHub repository at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wrf-model/WRF/tree/release-v4.5\u003c/span\u003e\u003c/span\u003e. Lightning observations from the Indian Lightning Location Network (ILLN) are available upon reasonable request, subject to institutional data policies. The dataset supporting this study is publicly available at Zenodo (Kumar et al. \u003cspan class=\"CitationRef\"\u003e2026\u003c/span\u003e). The archived dataset includes lightning flash grids, rainfall time series, vertical hydrometeor profiles, and spatial rainfall fields used for model evaluation. The data can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.18747188\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamo C, Solomon R, Medaglia CM, Dietrich S, Mugnai A (2007) Cloud Microphysical Properties from Remote Sensing of Lightning within the Mediterranean. Measuring Precipitation From Space: EURAINSAT and the Future. 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Atmos Chem Phys 24:11637\u0026ndash;11651. ttps://doi.org/10.5194/acp-24-11637-2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSupplementary Fig and Tables\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lightning simulation, Cloud microphysics, WRF model, Western Himalaya, Mixed-phase processes, Extreme convection","lastPublishedDoi":"10.21203/rs.3.rs-9085936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9085936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLightning causes substantial loss of life and infrastructure damage each year. This study presents the first numerical simulation of a severe pre-monsoon thunderstorm that occurred on 23 May 2023 over the complex western Himalayan region (WHR). We evaluate the performance of four WRF microphysics (MP) schemes in simulating lightning and rainfall using ground-based lightning observations and satellite-observed rainfall data. All MP schemes capture the observed temporal variability of lightning, but with notable differences in magnitude, timing, and variability. The Morrison scheme reproduces the timing of peak lightning activity at 15:00 UTC but substantially overestimates flash rates, whereas the Thompson, WDM5, and WSM6 schemes simulate the peak 1\u0026ndash;2 hours earlier. Spatial analysis reveals dominant lightning activity along the Himalayan foothills, which is reasonably represented by all MP schemes. Morrisonve phase, Morrison and Thompson simulate enhanced cloud water mixing ratios, supporting latent heat release and sustained convection. Elevated mixing ratios of rainwater, snow, and ice further indicate active mixed-phase processes favorable for cloud electrification. Temporal and spatial evaluations show that Thompson provides the closest agreement with observed lightning variability, while Morrison exhibits higher variability and false detections. Rainfall evaluation indicates improved detection skill for WSM6 and Morrison. Overall, the results demonstrate that no single microphysics scheme consistently outperforms others for both lightning and rainfall, highlighting non-uniform model skill across processes. These findings emphasize the need for region-specific and process-oriented tuning of model physics to improve lightning and rainfall simulations over complex mountainous terrain.\u003c/p\u003e","manuscriptTitle":"Evaluation of WRF Microphysics Schemes for Simulating Lightning and Rainfall over the Complex Terrain of the Western Himalayan Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 11:31:49","doi":"10.21203/rs.3.rs-9085936/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f657fbc-e202-4cf3-9df9-1f16ce9c8684","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"50967482430553238588129259352891341346","date":"2026-05-07T05:21:29+00:00","index":12,"fulltext":""},{"type":"reviewersInvited","content":"5","date":"2026-05-06T03:01:13+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T11:31:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 11:31:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9085936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9085936","identity":"rs-9085936","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}