Lightning Occurrence and Social Vulnerability

The occurrence of lightning in time and space around the world is well known. Lightning fatalities and injuries are well delineated in the United States; however, there is much less information about lightning impacts on people in the developing world. It is estimated that between 6000 and 24,000 people are killed globally per year, and 10 times as many are injured. The fatality rate per capita has become very low in the developed countries during the past century due to the availability of lightning-safe structures and vehicles, less labor-intensive agriculture, and other factors, but this reduction has not occurred where people continue to work and live in lightning-unsafe situations. Lightning safety advice often mistakenly expects that the direct strike is most common, but ground current, direct contact, side flash, and upward streamers are much more frequent mechanisms. In developed countries, the injury:death ratio is approximately 10:1, meaning that 90% survive but may have permanent disabling injuries. The proximate cause of death is cardiac arrest and anoxic brain injury at the time of the lightning strike, and, at this time, the damage from a lightning strike cannot be reversed or decreased in survivors. Lightning vulnerability in many developing countries continues to be a major issue due to widespread exposure during labor-intensive agriculture during the day when thunderstorms are the most frequent and while occupying lightning-unsafe dwellings at night

Surface wind convergence as a short-term predictor of cloud-to-ground lightning at Kennedy Space Center: A four-year summary and evaluation

Since 1986, USAF forecasters at NASA-Kennedy have had available a surface wind convergence technique for use during periods of convective development. In Florida during the summer, most of the thunderstorm development is forced by boundary layer processes. The basic premise is that the life cycle of convection is reflected in the surface wind field beneath these storms. Therefore the monitoring of the local surface divergence and/or convergence fields can be used to determine timing, location, longevity, and the lightning hazards which accompany these thunderstorms. This study evaluates four years of monitoring thunderstorm development using surface wind convergence, particularly the average over the area. Cloud-to-ground (CG) lightning is related in time and space with surface convergence for 346 days during the summers of 1987 through 1990 over the expanded wind network at KSC. The relationships are subdivided according to low level wind flow and midlevel moisture patterns. Results show a one in three chance of CG lightning when a convergence event is identified. However, when there is no convergence, the chance of CG lightning is negligible

Mitigating the Hazard of Lightning Injury and Death across Africa

Lightning injuries, deaths, and the economic consequences of lightning damage to property and infrastructure continue to be a significant public health challenge and economic development issue in many tropical and subtropical areas of the world, especially sub-Saharan Africa. This chapter will discuss the scope of the hazard, known risk factors including common cultural beliefs that inhibit public education, existing data sources, medical effects and long-term disability, lightning formation and detection, injury mechanisms, existing lightning safety programs and their challenges, and the work being done to decrease injuries, death, and property damage from lightning in Africa by the African Centres for Lightning and Electromagnetics Network (ACLENet)

Weak positive cloud-to-ground flashes in Northeastern Colorado

The frequency distributions of the peak magnetic field associated with the first detected return stroke of positive and negative cloud-to-ground (CG) flashes were studied using lightning data from northeastern Colorado. These data were obtained during 1985 with a medium-to-high gain network of three direction finders (DF's). The median signal strength of positive flashes was almost two times that of the negatives for flashes within 300 km of the DF's, which have an inherent detection-threshold bias that tends to discriminate against weak signals. This bias increases with range, and affects the detection of positive and negative flashes in different ways, because of the differing character of their distributions. Positive flashes appear to have a large percentage of signals clustered around very weak values that are lost to the medium-to-high gain Colorado Detection System very quickly with increasing range. The resulting median for positive signals could thus appear to be much larger than the median for negative signals, which are more clustered around intermediate values. When only flashes very close to the DF's are considered, however, the two distributions have almost identical medians. The large percentage of weak positive signals detected close to the DF's has not been explored previously. They have been suggested to come from intracloud discharges and thus are improperly classified as CG flashes. Evidence in hand, points to their being real positive, albeit weak CG flashes. Whether or not they are real positive ground flashes, it is important to be aware of their presence in data from magnetic DF networks

Analysis of Monthly Lightning Distribution in Nigeria

This is the first time in the literature that lightning distribution maps for Nigeria will be published, particularly on a monthly basis. A lightning dataset from the Global Lightning Dataset GLD360 network was analyzed for Nigeria from 2015 to 2021. The dataset contains over 123 million strokes, that occurred in Nigeria. Vaisala owns GLD360, which can properly detect the majority of lightning discharges throughout the world. The lightning variability on a monthly scale was first addressed for all 36 states and Federal Capital Territory in Nigeria. The monthly lightning stroke densities and occurrences for each state were an-alyzed to show the lightning-prone areas in the country. Analysis of the lightning dataset was performed with Python programming language and ArcGIS. It is expected that knowledge of lightning variability in Nigeria will help understanding of the weather conditions responsible for the lightning occurrence, the effect of lightning on power reliability, help locate severe weather, and when to safely have outdoor events in the country

Investigating the Relationship between Nigerian Rainfall Climatology and Lightning Stroke Distribution

A detailed lightning spatio-temporal study employing lightning detection networks has never been made for Nigeria. This type of research is critical for developing national plans because Nigeria has a land area of 923,769 square kilometers, a population of 206 million (2020), a population density of 223 persons per square km, and is one of the countries with the most lightning strikes in the world, with a lightning strike density of up to 147 strokes/square km/year as determined in this paper. This study gives a comprehensive national assessment of the spatio-temporal distribution of lightning across Nigeria annually from 2015 to 2021. The study also identifies the lightning hotspot in the country's 36 states and Federal Capital Territory, which are divided into six regions. A 7 -year lightning dataset was compiled from the Global Lightning Dataset GLD360 network for Nigeria from 2015 to 2021. Vaisala owns the GLD360, which can detect the majority of lightning discharges worldwide. The lightning dataset was analyzed using the Python programming language and ArcG Is. The dataset contains over 123 million lightning strokes that occurred in Nigeria. Between 9 and 25 million lightning strokes occur each year, with the lowest (9.1 million) and highest (24.9 million) strokes in 2015 and 2018, respectively. The South-South region has the highest average lightning stroke density of 38 strokes/km 2/ year. Cross River State in the South-South region is identified as the lightning hotspot with the highest lightning strokes density of 79 strokes/km 2 /year in 2021 and average stroke density of 53 strokes/km 2 /year, Further, we investigate the relationship between rainfall and lightning distribution in Nigeria with yearly spatio-temporal plots from 2015 to 2021. The result

New WMO certified megaflash lightning extremes for flash distance (768 km) and duration (17.01 seconds) recorded from space

Initial global extremes in lightning duration and horizontal distance were established in 2017 (Lang et al. 2017) by an international panel of atmospheric lightning scientists and engineers assembled by the WMO. The subsequent launch of NOAA’s latest GOES-16/17 satellites with their Geostationary Lightning Mappers (GLMs) enabled extreme lightning to be monitored continuously over the western hemisphere up to 55° latitude for the first time. As a result, the former lightning extremes were more than doubled in 2019 to 709 km for distance and 16.730 s for duration (Peterson et al. 2020). Continued detection and analysis of lightning “megaflashes” (Sequin, 2021) has now revealed two flashes that even exceed those 2019 records. As part of the ongoing work of the WMO in detection and documentation of global weather extremes (e.g., El Fadli et al. 2013; Merlone et al. 2010), an international WMO evaluation committee was created to critically adjudicate these two GLM megaflash cases as new records for extreme lightning.We thank S. A. Rutledge and two other reviewers for their valuable comments. M. J. Peterson was supported by the U.S. Department of Energy through the Los Alamos National Laboratory (LANL) Laboratory Directed Research and Development (LDRD) program under project number 20200529ECR. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract 89233218CNA000001). T. Logan supported by a NOAA Grant NA16OAR4320115 “Lightning Mapper Array Operation in Oklahoma and the Texas Gulf Coast Region to Aid Preparation for the GOES-R GLM.” I. Kolmasova was supported by GACR Grant 20-09671. S. D. Zhang was supported by a NOAA Grant NNH19ZDA001N-ESROGSS. The participation of J. Montanya in this work is supported by research Grant ESP2017-86263-C4-2-R funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe,” by the “European Union”; and Grants PID2019-109269RB-C42 funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

WMO assessment of weather and climate mortality extremes : lightning, tropical cyclones, tornadoes, and hail

A World Meteorological Organization (WMO) Commission for Climatology international panel was convened to examine and assess the available evidence associated with five weather-related mortality extremes: 1) lightning (indirect), 2) lightning (direct), 3) tropical cyclones, 4) tornadoes, and 5) hail. After recommending for acceptance of only events after 1873 (the formation of the predecessor of the WMO), the committee evaluated and accepted the following mortality extremes: 1) ''highest mortality (indirect strike) associated with lightning'' as the 469 people killed in a lightning-caused oil tank fire in Dronka, Egypt, on 2 November 1994; 2) ''highest mortality directly associated with a single lightning flash'' as the lightning flash that killed 21 people in a hut in Manica Tribal Trust Lands, Zimbabwe (at time of incident, eastern Rhodesia), on 23 December 1975; 3) ''highest mortality associated with a tropical cyclone'' as the Bangladesh (at time of incident, East Pakistan) cyclone of 12-13 November 1970 with an estimated death toll of 300 000 people| 4) ''highest mortality associated with a tornado'' as the 26 April 1989 tornado that destroyed the Manikganj district, Bangladesh, with an estimated death toll of 1300 individuals| and 5) ''highest mortality associated with a hailstorm'' as the storm occurring near Moradabad, India, on 30 April 1888 that killed 246 people. These mortality extremes serve to further atmospheric science by giving baseline mortality values for comparison to future weather-related catastrophes and also allow for adjudication of new meteorological information as it becomes available.https://www.ametsoc.org/ams/index.cfm/publications/journals/weather-climate-and-society2018-01-30hj2017Geography, Geoinformatics and Meteorolog