Lightning Potential Index Research Articles

Comparison of Two-Moment and Three-Moment Bulk Microphysics Schemes in Thunderstorm Simulations over Indian Subcontinent

We simulated three different thunderstorm events over India using a regional model with two different multi-moments cloud microphysical schemes: (i) Morrison double moment (Morrison-2 M) and (ii) National Taiwan University (NTU) triple moment (NTU-3 M). The results show that the choice of microphysical scheme significantly impacts storm structures, cloud formations, lightning intensity, and rainfall patterns. The lightning flash counts from the in-situ lightning detection network (LDN) are also used to compare the simulated storms. The Lightning Potential Index (LPI) was computed for both Morrison-2 M and NTU-3 M microphysics schemes and compared it with the LDN observation. Differences in simulations are attributed to the simulation of ice crystals, snow, and graupel by the microphysical schemes. The effects on the size distributions of cloud hydrometeors between the two microphysical schemes are also found to be important. The inclusions of ice crystal shapes likely accounted for the key differences between the two microphysics schemes. The results demonstrate that the simulation of lightning event characteristics is sensitive to the choice of microphysical parameterization schemes in numerical weather prediction models. In comparison to both the microphysical schemes, some aspects are better in the 2 M microphysics scheme, and some are better in the 3 M microphysics scheme. Morrison-2 M simulates more mixing ratio for the majority of cloud hydrometeors across all cases than the NTU-3 M whereas the Morrison-2 M has less rainfall bias over the ocean for the majority of the cases. Overall, the present study suggests that the choice of microphysical scheme can greatly influence the accuracy of thunderstorm and lightning event predictions in operational weather forecasting models.

Evaluate WRF-based Lightning Potential Index (LPI) lightning parameterization over Sri Lanka during second inter-monsoon in 2018

It is important to develop accurate and reliable lightning prediction system that can be contributed towards the safety of life, both concerning forecasting for the public safety and safety of aviation and electrical power. This study aims to evaluate WRF-based Lightning Potential Index(LPI) lightning parameterization and its applicability for predicting lightning over Sri Lanka during the second inter-monsoon. The WRF-ARW model 3.9.1 was used to produce predictions for three lightning events with various physical parameterization schemes with two nested domains with a resolution of 12km and 4km respectively. The model simulated LPI values were evaluated using the Earth Networks Global Lightning (ENGLN) dataset. Results show corresponding lightning simulations were produced with spatial distribution aligned with ground-based lightning data. Results consistently show a high correlation of the LPI index with an hourly CG flash rate over the three cases. Moreover, the WRF model was able to capture the lightning using LPI in Sri Lanka, suggesting that it can be used operationally to predict lightning potential region.

Modeling Lightning Activity in the Third Pole Region: Performance of a km-Scale ICON-CLM Simulation

The Third Pole region, especially the Himalayas, is a lightning hotspot. Predicting lightning activity in this region is difficult due to the complex monsoon and westerly circulations, as well as the complex orography. Km-scale simulations can explicitly, without parameterization, tackle deep convective phenomena. A year-long (October 2019–September 2020) ICON-CLM simulation was performed with a km-scale horizontal grid spacing of 3.3 km to assess the region’s lightning activity and evaluate the model ICON-CLM. The simulated lightning potential index (LPI) was compared against the lightning proxy CAPE × PREC (CP) derived from ICON-CLM and reanalysis ERA5. In addition, the LPI was evaluated against the TRMM lightning climatology and observed flashes from the International Space Station Lightning Imaging Sensor (ISS-LIS). The LPI reproduced the major spatial, seasonal, and diurnal features of lightning activity as represented in the TRMM climatology. In contrast, the CP from both ICON-CLM and ERA5 performed less well regarding the location of events and the diurnal features. Taking the mean values of the LPI and CP of all the grids within 90 km × 90 km around the ISS-LIS detected lightning event, we found that over 80% of lightning events were recorded using the ICON-CLM simulation. Analysis of individual cases showed that the LPI predicted localization of lightning events better, but flash counts were slightly lower than the CP. Therefore, the combined use of ICON-CLM’s simulated LPI and CP can be a valuable predictor for lightning events over the Third Pole region.

Exploring hail and lightning diagnostics over the Alpine-Adriatic region in a km-scale climate model

Abstract. The north and south of the Alps, as well as the eastern shores of the Adriatic Sea, are hot spots of severe convective storms, including hail and lightning associated with deep convection. With advancements in computing power, it has become feasible to simulate deep convection explicitly in climate models by decreasing the horizontal grid spacing to less than 4 km. These kilometer-scale models improve the representation of orography and reduce uncertainties associated with the use of deep convection parameterizations. In this study, we perform km-scale simulations for eight observed cases of severe convective storms (seven with and one without observed hail) over the Alpine-Adriatic region. The simulations are performed with the climate version of the regional model Consortium for Small-scale Modeling (COSMO) that runs on graphics processing units (GPUs) at a horizontal grid spacing of 2.2 km. To analyze hail and lightning we have explored the hail growth model (HAILCAST) and lightning potential index (LPI) diagnostics integrated with the COSMO-crCLIM model. Comparison with available high-resolution observations reveals good performance of the model in simulating total precipitation, hail, and lightning. By performing a detailed analysis of three of the case studies, we identified the importance of significant meteorological factors for heavy thunderstorms that were reproduced by the model. Among these are the moist unstable boundary layer and dry mid-level air, the topographic barrier, as well as an approaching upper-level trough and cold front. Although COSMO HAILCAST tends to underestimate the hail size on the ground, the results indicate that both HAILCAST and LPI are promising candidates for future climate research.

Estimation of Lightning Activity of Squall Lines by Different Lightning Parameterization Schemes in the Weather Research and Forecasting Model

Based on three-dimensional lightning data and an S-band Doppler radar, a strong relationship was identified between lightning activity and the radar volume of squall lines. A detailed analysis of the squall line investigates the relationship following an exponential relationship. According to the correlation between lightning and the radar volume, three radar-volume-based lightning parameterization schemes, named the V30dBZ, V35dBZ, and V40dBZ lightning schemes, have been established and introduced into the weather research and forecasting (WRF) model. The performance of lightning precondition by different lightning parameterization schemes was evaluated, including the radar-volume-based schemes (V30dBZ, V35dBZ, and V40dBZ), as well as existing lightning schemes (PR92_1, PR92_2, and the Lightning Potential Index (LPI)). The evaluation shows that the simulated spatial lightning density and temporal lightning frequency by the radar-volume-based lightning schemes are more consistent with the observations. While the two PR_92 lightning schemes significantly underestimated the magnitude of lightning density. The radar-volume-based lightning parameterization schemes are proven to be more reliable in estimating lightning activity than other lightning schemes.

Performance evaluation of lightning potential index and flash count using WRF microphysical parameters over Rajasthan and West Bengal, India

Accurate lightning prediction stands as a pressing global challenge, demanding robust solutions for safeguarding lives and valuable assets. In this study, we employ the mesoscale numerical model, Weather Research and Forecasting (WRF-ARW, version 4.0.3), to conduct numerical simulations of lightning occurrences during the 2021 monsoon season in Hooghly (on 07 June) and Jaipur (on 11 July). These events resulted in 31 and 11 casualties, respectively. The WRF model is integrated at horizontal resolutions of 9 km and 3 km for both regions, utilizing six-hourly NCEP-FNL datasets at a 0.25º resolution. The primary objective of this inquiry is to identify the most suitable microphysics scheme among WSM-6, NSSL-2, and MORRISON for a comprehensive assessment of lightning activity. Model performance is meticulously evaluated through skill scores, including Equitable Threat Score (ETS), False Alarm Rate (FAR), Accuracy (ACC), and Probability of Detection (POD), focusing specifically on hourly rainfall. Furthermore, a comprehensive spatial evaluation assesses the Lightning Potential Index and lightning flash count using the McCaul Method. The model-simulated results effectively depict lightning conditions in both regions, showing slight spatial and temporal discrepancies compared to observational datasets. Validation of the simulated lightning flash count is accomplished using data from the Lightning Detection Network (LDN) operated by the Indian Institute of Tropical Meteorology (IITM). The NSSL-2 microphysical scheme demonstrates noteworthy efficacy in identifying lightning occurrences in both regions. Rainfall representations correspond remarkably well with Indian Monsoon Data Assimilation and Analysis (IMDAA) data, indicating precipitation levels of 20–40 mm and 70–80 mm in Jaipur and Hooghly, respectively. The NSSL-2 microphysics scheme exhibits commendable proficiency, with consistently high model skill scores (ACC and POD ∼0.9) for lightning events. This study signifies a significant step toward the development of an operational lightning warning system, offering the potential to substantially reduce the risks associated with lightning occurrences. Consequently, such a system has the capacity to enhance safety and preparedness measures for regions affected by lightning phenomena.

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

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

Simulating Hail and Lightning Over the Alpine Adriatic Region—A Model Intercomparison Study

AbstractHail is a significant convective weather hazard, often causing considerable crop and property damage across the world. Although extremely damaging, hail still remains a challenging phenomenon to model and forecast, given the limited computational resolution and the gaps in understanding the processes involved in hail formation. Here, eight hailstorms occurring over the Alpine‐Adriatic region are analyzed using simulations with the Weather Research and Forecasting (WRF) and the Consortium for Small Scale Modeling (COSMO) models, with embedded HAILCAST and Lightning Potential Index (LPI) diagnostics at kilometer‐scale grid spacing (∼2.2 km). In addition, a systematic model intercomparison study is performed to investigate the ability of the different modeling systems in reproducing such convective extremes, and to further assess the uncertainties associated with simulations of such localized phenomena. The results are verified by hailpad observations over Croatia, radar estimates of hail over Switzerland, and lightning measurements from the LINET network. The analysis reveals that both HAILCAST and LPI are able to reproduce the affected area and intensities of hail and lightning. Moreover, hail and lightning fields produced by both models are similar, although a slight tendency of WRF to produce smaller hail swaths with larger hailstones and higher LPI compared to COSMO is visible. It is found that these differences can be explained by systematic differences in vertical profiles of microphysical properties and updraft strength between the models. Overall, results are promising and indicate that both HAILCAST and LPI could be valuable tools for real‐time forecasting and climatological assessment of hail and lightning in current and changing climate.

Numerical simulation of a widespread lightning event over north India using an ensemble of WRF modeling configurations

Widespread event of lightning occurred in two states of eastern India on June 25, 2020, killing 83 people in Bihar and 24 people in Uttar Pradesh (UP). Lightning event is studied using ground-based lightning observational data and Weather Research and Forecasting (WRF) model. A Specialized forecast system for lightning and thunderstorm is needed to minimize the loss of lives. The updraft and ice particle concentration play essential roles in the initialization of lightning inside the thundercloud. The charge generation and separation inside the clouds play an essential role in the severity of lightning. The Lightning Potential Index (LPI) measures the potential of charge generation and separation inside the clouds. It plays a significant role in determining lightning-prone zones.As the monsoon intensified over the Indo Gangetic Plains, lightning simulation is carried out using an ensemble of different WRF model configurations. The model has been integrated from 24th June 0000 UTC to 26th June 0600 UTC with an ensemble of 9 members. 8 members are simulated with a single domain (3 km resolution), and only the second member is simulated with a triple nested domain (9, 3, and 1 km resolution) over Bihar. The ensemble consists of 1 control +8 members with different configurations of the WRF model. LPI is calculated using the model-derived parameters. Furthermore, the model explicitly simulated lightning flash count using a lightning parameterization scheme. The results are analyzed using the Indian Institute of Tropical Meteorology (IITM) ground-based observation data. Every ensemble member's results are distinct from each other. This shows the sensitivity of every configuration. Member 2 (M2) is the triple nested member; it uses the same physics option as member 1 (M1). Moreover, M2 shows comparatively good results towards the observation. Simulated radar reflectivity is compared and analyzed using Indian National Satellite System (INSAT-3D) 3D Brightness Temperature (BT). To check the results of all ensemble members model skill score has been calculated. Ensemble probability is calculated to measure the spatial pattern and threat of lightning based on LPI, and it recorded the high lightning threat (90–100) % probability over northwest Bihar (Gopalganj), and it also recorded the heavy rainfall (300 mm). The skill scores of simulated lightning flash count in M2, M8, and M9 showed reasonably good POD values (0.6, 0.5, and 0.69, respectively).

Diagnostic Study of the Lightning Potential Index and Electric Field in Two Thunderstorm Cases over Bangladesh

Diagnostic Study of the Lightning Potential Index and Electric Field in Two Thunderstorm Cases over Bangladesh

The diurnal cycle of the lightning potential index over the Mexican tropical continental region during tropical cyclone Bud

AbstractAtmospheric processes over the Mexican continental territory can be influenced by the occurrence of tropical cyclones (TCs) over the adjacent oceans. Furthermore, the Mexican territory is characterized by the presence of diurnal cycles of lightning. The Lightning Potential Index (LPI), that is a measure of the potential for charge generation and separation that leads to lightning production in convective storms, was assessed for the diurnal variability of lightning that exhibited a strong diurnal cycle over the Mexican continental territory when TC Bud was over the adjacent eastern Pacific Ocean. The assessment, from 0000 UTC 10 June to 2000 UTC 15 June 2018, used the Weather Research and Forecasting (WRF) model with a new hybrid terrain‐following sigma‐pressure vertical coordinate. Two ensembles with various cumulus and microphysical parameterizations were performed with a grid spacing of 2 km. In one ensemble, sea surface temperature (SST) was prescribed from the Real‐time global (RTG) SST analysis product and allowed to evolve interactively with the modeled atmosphere. Then, all the ensemble members were compared against available observations from the World Wide Lightning Location Network (WWLLN) to evaluate which model configurations perform best. It is not known if the LPI is capable of reproducing diurnal cycles of lightning over tropical regions; and the results allow gaining an understanding of the LPI when it reproduces the observed diurnal variability of lightning over land. The ensemble members that had better performances were those that included the prescribed SST.

Predictability analysis and skillful scale verification of the Lightning Potential Index (LPI) in the COSMO-D2 high resolution ensemble system

Abstract. During the last decade, the constant improvement in computational capacity led to the development of the first limited-area, kilometer-scale ensemble prediction systems (L-EPS). The COSMO-D2 EPS (now ICON-D2) was the operational L-EPS at the German weather service (DWD) and has a spatial resolution of around 2.2 km. This grid resolution allows large scale, deep convective processes such as thunderstorms or heavy showers to be handled explicitly, without any physical parametrization. Special parameters involving both clouds microphysics and large scale lifting – such as the Lightning Potential Index, or LPI – have also been developed in order to try to bring the forecasting of deep convection and therefore also of lightning activity to a new level of spatial accuracy. With such high resolution forecasts comes however also a higher error potential, at least for gridpoint-verification. The use of this high resolution setup in an ensemble prediction system might however bring huge benefits in terms of skill and predictability. This work is a preliminary attempt to apply innovative verification approaches such as the dispersion Fractions Skill Score (dFSS) and the ensemble-SAL (eSAL) to the LPI in the COSMO-D2 EPS. Aim of this work is to assess the relationship between the ensemble error and the ensemble dispersion at different spatial scales. For the summer months 2019, the COSMO-D2 EPS shows a general tendency to overestimate the predictability (underestimate the ensemble spread) of the lightning events, though the spread-error relationship varies greatly for different forecast lead times. With the help of the dFSS, one can also express this relationship in terms of skillful scales. On average, the system produces a useful forecast during the afternoon hours for horizontal scales of around 200 km. However, the ensemble members show an average horizontal dispersion that amounts to around half of that value, at more or less 100 km.

Performance of HAILCAST and the Lightning Potential Index in simulating hailstorms in Croatia in a mesoscale model – Sensitivity to the PBL and microphysics parameterization schemes

Hailstorms, although extremely damaging severe weather hazards, remain a very challenging phenomenon to predict. To better understand dynamic processes and model performance, which can be helpful in forecasting hailstorms, three selected hailstorms in Croatia are simulated with the WRF model at convection-permitting (1 km) grid spacing using the HAILCAST module. In addition, the performance of the Lightning Potential Index (LPI) algorithm in representing the observed lightning activity during the selected hailstorms is analyzed. A multiphysics ensemble of 12 sensitivity simulations with the combinations of four different microphysics and three different planetary boundary layer parameterization (PBL) schemes is adopted to assess the forecasting ability of HAILCAST and LPI and their sensitivity to the choice of microphysics and PBL parameterization schemes. First, the model's ability to reproduce surface measurements of 2-m temperature, 2-m relative humidity, 2-m equivalent potential temperature and 10-m wind are examined using root mean square error (RMSE) decomposition. Then, the LPI is assessed against lightning observations via the object-based Structure-Amplitude-Location (SAL) method. Finally, an upscaled neighborhood verification method is proposed to assess HAILCAST against hail observations from the Croatian hailpad network. The results show that the observed hail and lightning activity is represented well by the model. There is a greater sensitivity to the choice of microphysics scheme than the PBL scheme, with National Severe Storms Laboratory double-moment scheme (NSSL2) microphysics scheme differing the most among the entire sensitivity ensemble. Nonetheless, both HAILCAST and LPI show promising performance in simulating observed hail and lightning activity, although HAILCAST tends to overestimate the area affected by hail. Nonetheless, the discrepancies between model configurations highlight the importance of simulating convection correctly to obtain a meaningful forecast of hail and lightning.

Lightning Potential Index and its spatial and temporal characteristics in COSMO NWP model

Among severe meteorological hazards, lightning is considered one of the most dangerous; however, its forecast is difficult because the formation of lightning is the result of processes in the clouds that are difficult to model accurately. In this study, we predict lightning activity using the Lightning Potential Index (LPI), which is quite often used to determine areas with expected lightning activity, and analyse its spatial and temporal characteristics. Specifically, we performed simulations of LPI (15 min values) using the COSMO NWP model for 10 selected thunderstorm events of 2018 in Central Europe. We used the model runs with 1- and 2-moment cloud microphysics and with a lead time of 12 h presented in our previous study, while in this study we performed deeper analyses and verified the 15 min LPI values in space and time using ground-based observations of lightning. Our results showed that 2-moment cloud microphysics provide better LPI forecasts which confirms the suggestion of our previous study. The distribution of predicted lightning activity related to the model orography was examined and found consistent with the occurrence of recorded lightning discharges. The Fraction Skill Score analysis revealed that for 2-moment cloud microphysics a skilful forecast was reached at smaller scales than for 1-moment microphysics, namely at scales around 90 km for LPI thresholds 30, 40 and 50 Jkg−1. We also evaluated the forecasts using a performance diagram, which in contrast to other results did not confirm that forecasts using 2-moment cloud microphysical scheme were more accurate than forecasts using 1-moment cloud microphysical scheme. Spatial verification of LPI showed that depending on the distance limit (15–90 km) and the LPI threshold (from LPI > 0 Jkg−1 to LPI > 50 Jkg−1), the probability of lightning discharge occurrence was ca 30–90% and the proportion of successfully predicted lightning discharges reached up to 77%. We consider this result satisfying, though the spatial verification remains challenging. Contrary to spatial verification of LPI, the temporal verification of LPI turned out to be even more efficient (in 70% of cases the time difference between the defined beginnings of forecasted and detected lightning activity was maximum 45 min). In future, we plan to perform lightning prediction in another NWP model, namely the ICON NWP model. We also plan to analyse more thunderstorm events.

Lightning forecasting in Bangladesh based on the lightning potential index and the electric potential

This work documents the performances of diagnostic and explicit lightning forecasts for selected high impact weather events over Bangladesh. The lightning flashes are calculated by three methods: (1) a diagnostic lightning parameterization that bases its algorithm on the level of neutral buoyancy from a convective parameterization scheme, (2) lightning potential index (LPI) using local graupel and ice contents, and (3) explicit prediction of electric fields and lightning initiation from WRF-ELEC. Five lightning events that occurred on 02 April 2019, 17 May 2019, 20 April 2020, 26 May 2020, and 20 May 2021 were examined to evaluate the forecasting capability of each scheme for the lightning over a 24-h forecast period. Prior to diagnosing lightning activity, the composite radar reflectivity and the neighborhood score metrics (ETS, FSS) for hourly rainfall were analyzed to determine if the simulated precipitation structure is consistent with the observations. Analyses based on performance diagrams were also included for a better illustration of how simulations perform on predicting precipitation. The spatial distribution of LPI and lightning flashes is compared against the NASA Lightning Imaging Sensor (LIS) dataset. The qualitative results show that in most of the studied cases, there is a good agreement between the observations and the WRF-ELEC-based simulations in terms of detecting the primary regions of lightning activity. The LPI based predictions perform also considerably well. The results from this endeavor constitute an essential initiative towards the implementation of an effective operational lightning warning system to mitigate lightning-related hazards in Bangladesh and northeast India.

Contrasting lightning projection using the lightning potential index adapted in a convection-permitting regional climate model

Lightning climate change projections show large uncertainties caused by limited empirical knowledge and strong assumptions inherent to coarse-grid climate modeling. This study addresses the latter issue by implementing and applying the lightning potential index parameterization (LPI) into a fine-grid convection-permitting regional climate model (CPM). This setup takes advantage of the explicit representation of deep convection in CPMs and allows for process-oriented LPI inputs such as vertical velocity within convective cells and coexistence of microphysical hydrometeor types, which are known to contribute to charge separation mechanisms. The LPI output is compared to output from a simpler flash rate parameterization, namely the CAPE times PREC parameterization, applied in a non-CPM on a coarser grid. The LPI’s implementation into the regional climate model COSMO-CLM successfully reproduces the observed lightning climatology, including its latitudinal gradient, its daily and hourly probability distributions, and its diurnal and annual cycles. Besides, the simulated temperature dependence of lightning reflects the observed dependency. The LPI outperforms the CAPE times PREC parameterization in all applied diagnostics. Based on this satisfactory evaluation, we used the LPI to a climate change projection under the RCP8.5 scenario. For the domain under investigation centered over Germany, the LPI projects a decrease of 4.8% in flash rate by the end of the century, in opposition to a projected increase of 17.4% as projected using the CAPE times PREC parameterization. The future decrease of LPI occurs mostly during the summer afternoons and is related to (i) a change in convection occurrence and (ii) changes in the microphysical mixing. The two parameterizations differ because of different convection occurrences in the CPM and non-CPM and because of changes in the microphysical mixing, which is only represented in the LPI lightning parameterization.

A Study of Enhancing the Prediction Techniques of Lightning Over Bangladesh Based on the Lightning Potential Index and Electric Field

The thunderstorm is a typical extreme weather event that has become one of the most alarming disasters in Bangladesh because of its more frequent occurrences and devastations in recent years. The purpose of this study is to investigate the physical and dynamical characteristics of thunderstorms using Weather Research and Forecasting (WRF) model. Three domains of 9-km, 3-km, and 1-km grid size have been used for the simulation of two events over Dhaka (25-April-2018) and Khulna (11-May-2018). The simulated results are then compared with the Bangladesh Meteorological Department (BMD) and University of Wyoming observed data. Lightning Potential Index (LPI) has been calculated by using different hydrometeors and vertical wind. For the calculation of electric field of thunder-cloud, we have considered an algorithm where ice and graupel are the main charge carriers. Convective Available Potential Energy (CAPE) and Total Total Index (TTI) are found over 1500 J/kg and 48°C respectively ten hours prior to the event. Vertical wind has been found around 4 m/s before the event. Ice and graupel mixing ratios are found at very high concentrations at cloud center. Maximum value of LPI is found 0.163 J/kg in Dhaka and 0.8 J/kg in Khulna. The electric field is found to be 70 kV/m at a vertical height of 8 km in Dhaka and -125 kV/m at a vertical height of 7 km in Khulna. All the parameters are recognized to be favorable for the occurrence of thunderstorms and lightning which indicates that the techniques used in this study may be useful to predict future thunderstorm events in Bangladesh.

Evaluating different lightning parameterization schemes to simulate lightning flash counts over Maharashtra, India

Thunderstorms source of lightning discharge is a major hazard to humans. In India, the loss of human life due to thunderstorms is high because of frequent lightning during pre-monsoon season (March-May). Therefore, simulation of lightning flash counts based on various lightning parameterization schemes and Lightning Potential Index (LPI) in Weather Research and Forecasting (WRF) model are evaluated over Maharashtra, India. The offline diagnostic methods for calculating lightning flashes are also evaluated from the model generated storm parameters. The observation from Maharashtra Lightning Detection Network (LDN) is used to validate the simulated total lightning flash for four events. The lightning flashes calculations by following Price and Rind (1992) based on cloud top height (PR92CTH) and vertically integrated ice water path (IWP) simulated the spatial pattern comparably well. The lightning parameterization based on cloud top height defined by the radar reflectivity factor threshold of 20 dBZ (DLP2) has performed better as compared to observation. The results show (i) better spatial pattern and frequency distribution of lightning flashes, (ii) accumulated rainfall, maximum reflectivity and time evolutions are in good agreement with flashes, (iii) correlation between simulated flash and hydrometeors are higher, (iv) the number of matching grid boxes due to randomness is also higher, 74.9%, 56.5%, 68.1% and 82.7% of matching grid boxes for the four cases and (v) the results based on LPI are also in consistent with the results of DLP2. The study highlights the robustness of DLP2 and indicates a promising future for the operational forecast of lightning prediction.

A Study of Enhancing the Prediction Techniques of Lightning Over Bangladesh Based on the Lightning Potential Index and Electric Field

A Study of Enhancing the Prediction Techniques of Lightning Over Bangladesh Based on the Lightning Potential Index and Electric Field

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

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