Occurrence Of Lightning Research Articles

The Efficacy of Red Flag Warnings in Mitigating Human-Caused Wildfires across the Western United States

Abstract Red flag warnings (RFWs) are issued by the US National Weather Service to alert fire and emergency response agencies of weather conditions that are conducive to extreme wildfire growth. Distinct from most weather warnings that aim to reduce exposure to anticipated hazards, RFWs may also mitigate hazards by reducing the occurrence of new ignitions. We examined the efficacy of RFWs as a means of limiting human-caused wildfire ignitions. From 2006-2020, approximately 8% of wildfires across the western United States, and 19% of large wildfires (≥40 ha), occurred on days with RFWs. Although the occurrence of both lightning- and human-caused wildfires was elevated on RFW days compared to adjacent days without RFWs, we found evidence that modification of short-term behavioral choices on RFW days may reduce the number of certain human-caused ignitions (e.g., debris burning). By contrast, there is limited historical evidence that RFWs reduce the number of ignitions caused by habitual behaviors (e.g., smoking) or infrastructure (e.g., powerlines). Furthermore, the conditional probability of a human-caused wildfire becoming a large wildfire was 33% greater on days with RFWs, underscoring the value of wildfire prevention on these days. While RFWs are helpful in certain cases, our results suggest that their efficacy as a wildfire prevention measure has been somewhat limited in the western United States. As biophysical wildfire potential and the density of people living in wildfire-prone areas increase, so do the benefits of improved wildfire early warning systems that complement other wildfire mitigation and adaptation efforts.

A Comparison of Thunderstorm Identification Methods

Abstract Climatological analysis of thunderstorm occurrence is important since thunderstorms have far-reaching societal impacts. Historically, the majority of thunderstorm-related climatologies have focused on Eulerian frames of reference resulting in thunderstorm day or lightning density summaries. However, a Lagrangian frame of reference is required to answer more in-depth questions such as spatial distribution and variability in thunderstorm initiation. Lagrangian approaches generally utilize object identification and tracking algorithms with tracks then classified as being a thunderstorm or not. Prior methods for thunderstorm identification have been based on classification criteria such as radar reflectivity thresholds, a combination of radar reflectivity thresholds and temporal longevity thresholds, or a combination of radar reflectivity and cloud-to-ground lightning occurrence. However, a comparison of different methods and an evaluation of their accuracy have not been documented in the peer-reviewed literature. This study examines the impact of thunderstorm identification methodology choice. Differences in three thunderstorm identification methodologies are examined over the central plains of the United States during an 11-yr study period. Results show that there is a sensitivity to methodological choice with differences in the number of candidate thunderstorms classified as thunderstorms, as well as the resulting spatial pattern of thunderstorms. Additionally, this study provides insight into the accuracy of each method via a multiyear comparison of each method to regional lightning mapping array total lightning detections where significant differences were observed between methods with the method incorporating cloud-to-ground lightning data providing the best performance metrics. Significance Statement Previous research has utilized a variety of criteria to identify thunderstorms in order to generate datasets of thunderstorm occurrence. This study directly compares commonly used criteria, demonstrating that the choice of thunderstorm identification criteria impacts the composition and accuracy of resulting datasets of thunderstorm occurrence.

Meteorological factors associated with dry thunderstorms and simultaneous lightning-ignited wildfires: The 15 June 2022 outbreak in Catalonia

Summer heatwaves and extended dry spells create optimal meteorological conditions for occasional dry thunderstorms to produce simultaneous lightning-ignited wildfires (LIW). Concurrent ignitions put a significant burden on the firefighting's initial attack, potentially allowing incipient LIWs to escape and grow into large fires. While we can reasonably forecast lightning activity, predicting dry thunderstorm conditions and potential LIW outbreaks remains challenging. In the present study, we analyze the meteorological factors associated with a LIW outbreak that took place in Catalonia on 15 June 2022, with 22 LIW reported in three consecutive days. The fire hazard was high, but not different from past LIW episodes. ERA5-derived indices related to low-level moisture showed extreme values compared to previous studies. Atmospheric conditions with an elevated lifting condensation level coupled with the synoptic framework were set for the formation of dry thunderstorms. Radar reflectivity profiles revealed sub-cloud evaporation, and rain-gauge records corroborated the occurrence of dry lightning. In the context of global warming, we expect an increase in the frequency of LIW outbreaks in the European Mediterranean region due to an increase in lightning-ignition efficiency, which refers to the average chance of fire per lightning stroke.

Supervised Learning-Based Prediction of Lightning Probability in the Warm Season

The accurate prediction of lightning is crucial for forecasters to respond effectively to its related hazards. The rapid development and confined spatial extent of convective storms, in which lightning frequently occurs, pose considerable challenges for accurately predicting their locations using numerical weather prediction (NWP) models. Lightning occurrence is often prognosed using thermodynamic parameters, convective available potential energy (CAPE), the severe weather threat index (SWEAT), the lifted index (LI), etc. A high-resolution NWP model provides a prediction of these thermodynamic parameters at high spatiotemporal resolution with high accuracy for a few hours. However, a complicated algorithm is required to handle all the useful high-resolution variables from the NWP model. The recently emerging machine learning technique can solve this issue by properly handling these “big data” without any model distributional assumption. In this study, we developed a random forest algorithm for nowcasting and very short-range forecasting (useful for ~6 h), named LightningRF. LightningRF was trained by using lightning occurrence as a response variable and characteristic parameters from the NWP as predictors. It was also applied to analysis and forecast fields, showing a high probability of lightning within the observed lightning regions. This highlights the potential of helping forecasters improve their lightning forecasting skills using real-time probabilistic forecasts from a trained model.

A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates

Abstract. Lightning is an important atmospheric process for generating reactive nitrogen, resulting in the production of tropospheric ozone, as well as igniting wildland fires, which result in potentially large emissions of many pollutants and short-lived climate forcers. Lightning is also expected to change in frequency and location with the changing climate. As such, lightning is an important component of Earth system models. Until now, the Canadian Earth System Model (CanESM) did not contain an interactive-lightning parameterization. The fire parameterization in CanESM5.1 was designed to use prescribed monthly climatological lightning. In this study, we have added a logistical regression lightning model that predicts lightning occurrence interactively based on three environmental variables and their interactions in CanESM5.1's atmospheric model, CanAM5.1 (Canadian Atmospheric Model), creating the capacity to interactively model lightning, allowing for future projections under different climate scenarios. The modelled lightning and resulting burned area were evaluated against satellite measurements over the historical period, and model biases were found to be acceptable. Modelled lightning had a small negative bias and excellent land–ocean ratio compared to satellite measurements. The modified version of CanESM5.1 was used to simulate two future climate scenarios (SSP2-4.5 and SSP5-8.5; Shared Socioeconomic Pathway) to assess how lightning and burned area change in the future. Under the higher-emissions scenario (SSP5-8.5), CanESM5.1 predicts almost no change to the global mean lightning flash rate by the end of the century (2081–2100 vs. 2015–2035 average). However, there are substantial regional changes to lightning – particularly over land – such as a mean increase of 6 % in the northern mid-latitudes and decrease of −8 % in the tropics. By the century's end, the change in global total burned area with prescribed climatological lightning was about 2 times greater than that with interactive lightning (42 % vs. 26 % increase, respectively). Conversely, in the northern mid-latitudes the use of interactive lightning resulted in 3 times more burned area compared to that with unchanging lightning (48 % vs. 16 % increase, respectively). These results show that the future changes to burned area are greatly dependent on a model's lightning scheme, both spatially and overall.

The kinematic and thermodynamic environment during cloud-to-ground lightning occurrence in Poland

The kinematic and thermodynamic environment during cloud-to-ground lightning occurrence in Poland

Lightning‐Induced Energetic Electron Precipitation Observed in Long‐Term DEMETER Spacecraft Measurements

AbstractWe analyze low‐altitude DEMETER spacecraft measurements obtained between 2006 and 2010, complemented by WWLLN lightning location data, to investigate the importance of lightning‐generated whistlers for the energetic electron precipitation from the Van Allen radiation belts. We focus, in particular, on the United States region, where a significant seasonal variation in the occurrence of lightning has been observed. We show that both the precipitating electron fluxes and very low frequency wave intensities correlate well with the total lightning occurrence in the region. We further demonstrate that lightning‐induced electron precipitation is more significant during periods of low geomagnetic activity compared to periods of high geomagnetic activity and during the nighttime than during the daytime. The energies of precipitating energetic electrons extend up to about 700 keV, roughly in agreement with the cyclotron resonance theory.

A multiclass short-term lightning predictor for the Amazon region using ground-based meteorological data and artificial neural network

Lightning prediction is a complex task due to the intricate nature of this phenomenon. Various sectors of society, including aviation and civil defense, can benefit from accurate lightning occurrence information. Methodologies that can predict lightning occurrence are crucial to minimize the issues related to this phenomenon. In this work, we propose a novel approach using the machine learning model called Artificial Neural Network (ANN) to predict lightning occurrence one hour ahead. We use ground-based meteorological data from the National Institute of Meteorology (INMET) and the number of lightning per hour to feed the ANN model. We used a lightning dataset of six years of observation (2015–2020) from GLD360 to train, validate, and test machine learning algorithm models. A model was developed for each weather station. We use data from 24 stations distributed throughout the Brazilian Legal Amazon region. The multiclass predictor was based on three categories: no lightning, low-intensity, and high-intensity of lightning. We also developed a solution comprising two cascade ANN models, each performing binary prediction. The first predicts the occurrence or no occurrence of lightning, and the second predicts the intensity: low or high. The cascade approach showed superior performance compared to the regular approach.

Inter-comparison of World Wide Lightning Location Network (WWLLN) and Lightning Detection Sensor Network (LDSN) data over India

Atmospheric lightning is a dominant weather related disaster which requires continuous monitoring efforts. While the world wide lightning location network (WWLLN) measures the very low frequency (VLF) signals emanating from the lightning, other regional networks, such as Lightning Detection Sensor Network (LDSN) use the broad band radio frequency signals. Present investigation compares the cloud-to-ground (CG) lightning occurrences recorded by the WWLLN and LDSN during 02 October – October 09, 2022 over India. Results show that though the WWLLN monitored the CG lightning occurrences about 20% lesser than the LDSN, spatial distribution of the occurrences and trends were in very good agreement.

Possible Influence of Solar Activity on Lightning Occurrence: A Preliminary Study Over Nepal

One of the fundamental mysteries of lightning is its initiation mechanism. Many aspects of lightning phenomenon are well known; however, the exact processes that initiate lightning discharge are not fully understood. This study delves into a relatively underexplored domain: the relationship between solar activity specifically solar sunspots and F10.7 indices, as well as solar wind speed, on the occurrence of lightning events. The study utilizes a rich dataset spanning from 1998 to 2008, capturing an extensive period of solar activity during Solar Cycle 23. Multiple correlation analyses were conducted, comparing solar activity parameters with lightning occurrence. Lightning data was sourced from the Lightning Imaging Sensor (LIS) whereas Sunspot data was obtained from the Solar Influences Data Analysis Center (SIDC) and F10.7 as well as Solar wind data was obtained from the OMNI dataset. From this preliminary study, we observed that the correlation between solar parameters and the lightning occurrence over Nepal are found to vary at different time period from insignificant correlation to moderate correlation. Hence, further exploration is needed to shed more light to interpret the relationship between solar activity and the lightning occurrence in Nepal.

Lightning under Different Land Use and Cover, and the Influence of Topography in the Carajás Mineral Province, Eastern Amazon

Knowledge about regions where lightning occurs is important both for understanding storm development and direction. This can assist in very short-term weather forecasts and in developing lightning warning systems, aiming to minimize exposure of people and equipment in the open sky. A survey on the occurrence of lightning in different types of land use and coverage and different elevation strata in the region of the Itacaiúnas River watershed (IRW), located in the Carajás Mineral Province, in the Eastern Amazon, from 2012 to 2021 was conducted. The results showed significant differences in the occurrence of lightning in mining areas and deforested areas. When comparing the large proportion of deforested areas with the mining area, the results suggested that in IRW mining areas, the lightning incidence is expressively higher. The assessment of electrical activity at different elevations in the region suggested that the slope of the terrain and its thermodynamic effects on the formation of storms have more influence than altitude on lightning activity. The results showed the importance of adopting initiatives aimed at protecting both the local population and mining workers, as well as equipment exposed to the open sky in this region.

Geological evidence of extensive N-fixation by volcanic lightning during very large explosive eruptions

Most of the nitrogen (N) accessible for life is trapped in dinitrogen (N2), the most stable atmospheric molecule. In order to be metabolized by living organisms, N2 has to be converted into biologically assimilable forms, so-called fixed N. Nowadays, nearly all the N-fixation is achieved through biological and anthropogenic processes. However, in early prebiotic environments of the Earth, N-fixation must have occurred via natural abiotic processes. One of the most invoked processes is electrical discharges, including from thunderstorms and lightning associated with volcanic eruptions. Despite the frequent occurrence of volcanic lightning during explosive eruptions and convincing laboratory experimentation, no evidence of substantial N-fixation has been found in any geological archive. Here, we report on the discovery of a significant amount of nitrate in volcanic deposits from Neogene caldera-forming eruptions, which are well correlated with the concentrations of species directly emitted by volcanoes (sulfur, chlorine). The multi-isotopic composition (δ18O, Δ17O) of the nitrates reveals that they originate from the atmospheric oxidation of nitrogen oxides formed by volcanic lightning. According to these first geological volcanic nitrate archive, we estimate that, on average, about 60 Tg of N can be fixed during a large explosive event. Our findings hint at a unique role potentially played by subaerial explosive eruptions in supplying essential ingredients for the emergence of life on Earth.

Techniques for lightning prediction: A review

Lightning is a natural occurrence which is created through the mixture of hot and cold air in the cloud. Sudden occurrence of lightning has caused damages to many lives and properties, for this reason; there is a need to develop a system that can predict lighting occurrence for people to take necessary precaution. However, accurately predicting lightning has been a challenge among researchers, as they find it difficult to select the right approach and algorithms to use when predicting lightning. Thus, this paper presents a systematic literature review on the best techniques for lightning prediction by reviewing relevant papers that are systematically collected based on the inclusion and the exclusion criteria from four different academic databases which includes Scopus, IEEE Xplore, Science direct, and SpringerLink. The findings from the review shows that the Random Forest algorithm is mostly used for lightning prediction and has generally out performed all other algorithms that have been used in lightning prediction in remote region. Also the review finds out that there is an inverse relationship between predicting system accuracy and lead time. Another observation in the research is that numerical weather prediction predicts more accurately compare to geo satellite prediction.

Five-Year Analysis of Lightning Activities in Different Climatic Regions of Sichuan Province, China

Sichuan is a high-incidence area of thunderstorm activity in China. Based on the data of the total lightning location system from 2018 to 2022, the total lightning, cloud-to-ground (CG) lightning, and intracloud (IC) lightning activity regularity for the Sichuan province (SC) and its three climate subregions: Sichuan Basin (SB), Panxi district (PD), and West Sichuan Plateau (WSP) are analyzed, and the influences of different climate and topography conditions on lightning activities are also discussed. The results show that (1) for the whole province, the annual mean value of total lightning is about 850 thousand. The SB has the most lightning occurrences, and the WSP has the largest IC and +CG proportion. The southwest of PD, the north-center of PD, and the southeast of SB are the three high-value centers of lightning density. (2) For SB, the better thermodynamic and moisture conditions account for the most lightning occurrences. For PD, the lightning distribution is attributed to the joint effect of specific meteorological conditions and mountainous topography. For WSP, the convections are weak and shallow, which lead to high IC and +CG proportion. (3) The IC lightning mainly occurs below 12 km, and the IC height is much lower on WSP. The spatial and seasonal variation of IC height corresponds well to the cloud base height (CBH) and cloud top height (CTH). (4) The seasonal lightning frequency distribution in the three regions is similar, but the diurnal variation is quite different. The lightning activity mainly occurs between 1400 and 2200 LT on WSP but shows obvious nocturnal in SB. (5) Most CG intensity concentrates in the range below 50 kA, and IC concentrates in the range below 75 kA.

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Abstract. Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE (CAPE) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

Lightning Nowcasting Using Solely Lightning Data

Lightning is directly or indirectly responsible for significant human casualties and property damage worldwide. A timely prediction of its occurrence can enable authorities and the public to take necessary precautionary actions resulting in diminishing the potential hazards caused by lightning. In this paper, based on the assumption that atmospheric phenomena behave in a continuous manner, we present a model based on residual U-nets where the network architecture leverages this inductive bias by combining information passing directly from the input to the output with the necessary required changes to the former, predicted by a neural network. Our model is trained solely on lightning data from geostationary weather satellites and can be used to predict the occurrence of future lightning. Our model has the advantage of not relying on numerical weather models, which are inherently slow due to their sequential nature, enabling it to be used for near-future prediction (nowcasting). Moreover, our model has similar performance compared to other machine learning based lightning predictors in the literature while using significantly less amount of data for training, limited to lightning data. Our model, which is trained for four different lead times of 15, 30, 45, and 60 min, outperforms the traditional persistence baseline by 4%, 12%, and 22% for lead times of 30, 45, and 60 min, respectively, and has comparable accuracy for 15 min lead time.

MCGLN: A multimodal ConvLSTM-GAN framework for lightning nowcasting utilizing multi-source spatiotemporal data

Lightning phenomena can instigate a cascade of calamities, encompassing fires, electrical infrastructure damage, and risks to human safety. Deep-learning-based lightning nowcasting models have demonstrated significant effectiveness in disaster prevention and mitigation. However, existing studies often neglect the impacts of surface features on lightning activities, and conventional lightning prediction techniques based on convolutional and recurrent networks face challenges such as the loss of feature information. Addressing these issues, this paper presents a novel model for lightning nowcasting, the Multimodal ConvLSTM-GAN for Lightning Nowcasting (MCGLN). This model integrates a Generative Adversarial Network (GAN) with a Convolutional Long Short-Term Memory network (ConvLSTM), utilizing multi-source data as inputs. It incorporates a spatiotemporal encoder-forecaster framework within the Generator to improve the capture of multidimensional spatiotemporal feature information, thus boosting predictive accuracy. MCGLN offers probabilistic prediction results, allowing users to customize warning thresholds following their specific tolerance for false and missed alarms. The performance of the MCGLN model is evaluated through empirical analysis, utilizing real lightning datasets sourced from Zhejiang and surrounding areas. Experimental results demonstrate that: (a) The MCGLN model outperforms existing methods in terms of detection capability and overall performance, showing significant improvements in the modeling process. (b) Increasing the number of data sources improves detection capabilities, reduces the probability of false alarms, and boosts the model performance. (c) The use of radar data enhances the recognition of high-probability lightning occurrences, and the inclusion of surface feature data increases the capture of terrestrial lightning genesis.

Modeled Multidecadal Trends of Lightning and (Very) Large Hail in Europe and North America (1950–2021)

Abstract We have developed additive logistic models for the occurrence of lightning, large hail (≥2 cm), and very large hail (≥5 cm) to investigate the evolution of these hazards in the past, in the future, and for forecasting applications. The models, trained with lightning observations, hail reports, and predictors from atmospheric reanalysis, assign an hourly probability to any location and time on a 0.25° × 0.25° × 1-hourly grid as a function of reanalysis-derived predictor parameters, selected following an ingredients-based approach. The resulting hail models outperform the significant hail parameter, and the simulated climatological spatial distributions and annual cycles of lightning and hail are consistent with observations from storm report databases, radar, and lightning detection data. As a corollary result, CAPE released above the −10°C isotherm was found to be a more universally skillful predictor for large hail than CAPE. In the period 1950–2021, the models applied to the ERA5 reanalysis indicate significant increases of lightning and hail across most of Europe, primarily due to rising low-level moisture. The strongest modeled hail increases occur in northern Italy with increasing rapidity after 2010. Here, very large hail has become 3 times more likely than it was in the 1950s. Across North America trends are comparatively small, apart from isolated significant increases in the direct lee of the Rocky Mountains and across the Canadian plains. In the southern plains, a period of enhanced storm activity occurred in the 1980s and 1990s.

An application of deep learning for lightning prediction in East Coast Malaysia

This paper presents the application of deep learning (DL) approach namely Feed-Forward Neural Networks (FFNN) in predicting the location of lightning occurrences within 100 km radius from Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pekan, Pahang Malaysia. The recorded data were obtained from Malaysia Meteorology Department (MET Malaysia), where the inputs of the DL are the intensity of the lightning in kilo Ampere, direction in degrees, distance and major axis that measures in km, while the output is the latitude and longitude of the lightning occurrences. The data are divided into training, validation and testing to measure the performance of the developed DL model. The findings of the study demonstrated the promising results of FFNN in terms of obtaining the minimum error which significantly increasing the accuracy of the predictions. To show the effectiveness of FFNN, the comparison study has been conducted with Long Short-Term Memory (LSTM) networks. From the simulation, it can be seen that FFNN can be used as an effective tool for predicting the location of lightning occurred better than the LSTM.

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.