Temporal and Spatial Distribution Characteristics of Lightning Activity over the Pacific Ocean and Adjacent Land and Its Relationship with AOD and CAPE

Based on LIS/OTD gridded lightning climatology data, ERA5 reanalysis data, and MODIS atmosphere monthly global products, we examined latitudinal and daily variations of lightning activity over land, offshore areas, open sea, and all marine areas (i.e., the aggregate of open sea and offshore areas) for different seasons over the Pacific Ocean and the adjacent land areas at 65°N-50°S, 99°E-78°W, and analysed the relationships of lightning activity with CAPE (Convective Available Potential Energy) and AOD (Aerosol Optical Depth). At any given latitude, the lightning density is the highest over land, followed by offshore areas, all marine areas and the open sea in sequence. The lightning density over land is approximately an order of magnitude greater than that over all marine areas. Lightning activity over land, offshore areas, open sea, and all marine areas varies with season. The diurnal variation of lightning density over land has a single-peak pattern. Over the offshore area, open sea, and all marine areas, lightning densities have two maxima per day. The magnitude of the daily variation in mean lightning density is the largest over land and the smallest over the open sea. The lightning density over the Pacific Ocean and adjacent land areas is significantly and positively correlated with CAPE. The correlation is the strongest over land and the weakest over the open sea. Cloud Base Height (CBH) may affect the efficiency of CAPE conversion to updraft. CAPE has a positive effect on lightning activity and has a greater impact on land than on the ocean. Over the sea, both CAPE and AOD can contribute to lightning activity, but the magnitudes of the influence of CAPE and AOD on lightning activity remain to be determined. Lightning activity over land and sea is a result of the combined action of AOD and CAPE.     

A composite stability index for dichotomous forecast of thunderstorms

Thunderstorms are the perennial feature of Kolkata (22° 32???N, 88° 20???E), India during the premonsoon season (April?CMay). Precise forecast of these thunderstorms is essential to mitigate the associated catastrophe due to lightning flashes, strong wind gusts, torrential rain, and occasional hail and tornadoes. The present research provides a composite stability index for forecasting thunderstorms. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant indices with threshold ranges for the prevalence of such thunderstorms. The analyses reveal that the lifted index (LI) within the range of ?5 to ?12?°C, convective inhibition energy (CIN) within the range of 0?C150?J/kg and convective available potential energy (CAPE) within the ranges of 2,000 to 7,000?J/kg are the most pertinent indices for the prevalence thunderstorms over Kolkata during the premonsoon season. A composite stability index, thunderstorm prediction index (TPI) is formulated with LI, CIN, and CAPE. The statistical skill score analyses show that the accuracy in forecasting such thunderstorms with TPI is 99.67?% with lead time less than 12?h during training the index whereas the accuracies are 89.64?% with LI, 60?% with CIN and 49.8?% with CAPE. The performance diagram supports that TPI has better forecast skill than its individual components. The forecast with TPI is validated with the observation of the India Meteorological Department during the period from 2007 to 2009. The real-time forecast of thunderstorms with TPI is provided for the year?2010.     

Implementation of intuitionistic fuzzy logic to assess the predictability of severe thunderstorms

Thunderstorms are responsible for remarkable devastation when accompanied with lightning flashes, high wind gusts, torrential rain, hail and tornadoes. Weather hazards due to thunderstorms of such severe measure take place every year over Kolkata (22°32′N, 88°20′E), India during the pre-monsoon season (April–May). Prediction of severe thunderstorms is extremely important to cope with the devastations. However, forecasting severe thunderstorms is very difficult because the weather system is confined within a very small spatial-temporal scale. The network of observation systems is not adequate to detect such high frequency small scale weather. The purpose of the present study is to bring in the concept of Intuitionistic fuzzy logic as a decision — making technique to assess the predictability of severe thunderstorms over Kolkata in the premonsoon season. Different measures of entropies are used to extract the route of fuzziness. The intuitionistic fuzzy logic is implemented with ten years (1997–2006) observation of the occurrence/nonoccurrence of severe thunderstorms to assess the predictability. The result reveals that two consecutive severe thunderstorm days are highly probable after two consecutive non-thunderstorm days whereas the probability of severe thunderstorm is very less after three consecutive non-thunderstorm days during the pre-monsoon season over Kolkata. The result is compared with the box-and-whisker plot and validated with four years (2007–2010) observations of India Meteorological Department (IMD).     

Comparison of tropical and midlatitude thunderstorm characteristics anchored in thermodynamic and dynamic aspects

Thunderstorms prevailing over tropics and midlatitudes depict dissimilar features relating to the thermodynamic and dynamic aspects. The identification of the physical characteristics of the tropical and midlatitude thunderstorms is the main objective of the present study. The stations Kolkata (22.6°N, 88.4°E) and Denver (39.47°N, 104.32°W) are selected from the tropics and midlatitudes for the comparative analyses. The study reveals that the average storm relative helicity (SRH) and the lapse rate between 700 and 500 hPa level is much higher over Denver compared to Kolkata during thunderstorm days. The study further reveals that the surface to mid troposphere (upto 500 hPa) become drier (～2 times) over Denver than Kolkata prior to the occurrence of thunderstorms while the upper tropospheric (300–100 hPa) humidity remains comparable for both the locations.     

The relationship between lightning activity and surface wet bulb temperature and its variation with latitude in Australia

Summary Convective Available Potential Energy (CAPE) is the driving force for thunderstorm development. CAPE is closely controlled by wet bulb temperature. The lightning activity measured by a network of ten lightning flash counters widely distributed across continental Australia was studied as a function of wet bulb temperature. At each of the stations, the monthly total of lightning ground flashes, N, increased sharply with the increase of the monthly mean daily maximum wet bulb temperature, T_w, max. The dependence was strongest in the tropics and became less pronounced at temperate latitudes. In Darwin (latitude 12° S), the lightning ground flash activity increased by over three orders of magnitude over a 7 °C range of T_w, max. The corresponding increases for Coffs Harbour (latitude 30° S) and for Melbourne (latitude 38° S) were about one and a half orders of magnitude and about half an order of magnitude, respectively, each over a 10 °C range of T_w, max. Power law approximations were derived for each of the ten stations and showed that the logarithm of N was directly proportional to the power, P, of T_w, max. The value of P showed a sharp exponential decrease with increasing latitude away from the equator.     

Convective energies in forecasting severe thunderstorms with one hidden layer neural net and variable learning rate back propagation algorithm

Thunderstorms are perennial features of India. However, the severe thunderstorms of pre — monsoon season (April–May) over Kolkata (22°32′N, 88°20′E) are of great concern for imparting devastating effect on life and property on the ground and aviation aloft. The study is thus, focused on developing one hidden layer neural network model with variable learning rate back propagation algorithm to forecast such thunderstorms. Convective available potential energy (CAPE) and convective inhibition energy (CIN) are selected as the input parameters of the model after the estimation of various skill scores like, Probability of Detection (POD), False Alarm Ratio (FAR), Heidke Skill Score (HSS) and Odds Ratio Skill Score (Yule’s Q) on different stability indices. During training the model, the squared error for forecasting severe thunderstorms is observed to be 0.0022 when the values of CIN within the range of 0 to 140 J kg^?1 is taken as the input whereas the error is observed to be 0.0114 while the values of CAPE within the range of 2000 to 7000 J kg^?1 is considered as the input. The values of CIN and CAPE at twelve to six hours prior to the occurrence of severe thunderstorms are considered in this study. During validation of the model, the percentage of prediction error with the values of CIN as input is observed to be 0.042% and that with CAPE as input is 0.162%. The values of CIN within the range of 0–140 J kg^?1 are observed to be more persistent in forecasting severe thunderstorms over Kolkata than the values of CAPE within the range of 2000–7000 J kg^?1.     

一种新的雷电日及雷电参数统计方法

为更好地应用雷电定位系统的自动监测数据统计雷电日及雷电参数,利用滇中地区1987～2006年49个气象站点雷电观测资料和2005～2006年闪电定位探测资料,在对比分析气象观测雷电资料与闪电定位探测雷电资料的基础上,提出了一种新的适用于自动监测雷电日的统计方法--网格法.网格法划分统计区域详细、合理,其雷电日统计值与传统气象雷电日有可比性.长期气象雷电日资料是选定网格大小的参考标尺,滇中地区取0.175°×0.175°网格统计值为其年平均雷电日,取0.3°×0.3°网格统计值为其最大雷电日.用网格法对雷电参数进行统计结果显示:滇中地区雷电日和雷电密度有显著的局地变化特征,地闪密度与闪电强度成反比关系.此结论为防雷工程设计、雷电灾害评估和雷电成因分析提供了较好的理论基础.     

Climatology of lightning activity in South China and its relationships to precipitation and convective available potential energy

This study examined lightning activity and its relationship to precipitation and convective available potential energy(CAPE) in South China during 2001–12, based on data from the Guangdong Lightning Location System, the Tropical Rainfall Measuring Mission satellite, and the ERA-Interim dataset. Two areas of high lightning density are identified: one over the Pearl River Delta, and the other to the north of Leizhou Peninsula. Large peak-current cloud-to-ground(LPCCG) lightning(75 kA) shows weaker land–offshore contrasts than total CG lightning, in which negative cloud-to-ground(NCG) lightning occurs more prominently than positive cloud-to-ground(PCG) lightning on land. While the frequency of total CG lightning shows a main peak in June and a second peak in August, the LPCCG lightning over land shows only a single peak in June.The ratio of positive LPCCG to total lightning is significantly greater during February–April than during other times of the year. Diurnally, CG lightning over land shows only one peak in the afternoon, whereas CG lightning offshore shows morning and afternoon peaks. The rain yield per flash is on the order of 10~7–10~8kg per flash across the analysis region, and its spatial distribution is opposite to that of lightning density. Our data show that lightning activity over land is more sensitive than that over offshore waters to CAPE. The relationships between lightning activity and both precipitation and CAPE are associated with convection activity in the analysis region.     

Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model

Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5–35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.     

Analysis of lightning-induced forest fires in Austria

Besides human-caused fires, lightning is the major reason for forest fire ignition in Austria. In order to analyse the causes of ignition and to characterise lightning-induced forest fires, fire records were compared with the real appearance of lightning events by using the Austrian Lightning Detection and Information System for the period from 1993 to 2010. A probability was estimated for each forest fire being caused by lightning by using a decision tree and decision matrices based on flash characteristics (e.g. amplitude, time, location). It could be shown that 15 % of documented forest fires were lightning-caused. Nearly all lightning-caused fires were found during the summer months, whereas almost 40 % of all fires occurring from June to August were naturally caused. Most lightning-caused fires took place in the south and east of Austria. Lightning fires were more frequent at higher altitudes and primarily affected conifer forests. The median burned area was lower than that for anthropogenic forest fires.     

安徽闪电分布特征和不稳定条件分析

利用安徽2008—2011年LD-Ⅱ型闪电定位系统资料,以统计方法分析安徽闪电分布特征,发现安徽闪电的发生较为集中在7—8月,闪电极性以负闪为主(约占总闪的93%以上),同时存在南北差异。南部的闪电数和平均电流强度比北部大,但北部正闪比例略高于南部。进一步结合2008—2011年阜阳、安庆两站探空资料,选取了抬升指数、对流有效位能、700~400 hPa平均相对湿度、700 hPa假相当位温、850~500 hPa温差、沙氏指数与K指数7个大气不稳定参数,以两站为中心的0.5个纬距范围内,分析了探空放球时间后6 h内,大气不稳定参数与闪电活动的关系,确定了闪电活动的不稳定参数阀值,为闪电活动预报奠定基础。     

星地多源闪电资料在新疆地区的应用分析

根据2019年7月20日新疆大部分区域出现局部强对流天气过程,利用ADTD、LLS、WWLLN、风云四号卫星闪电成像仪组产品LMIG、大气电场多源闪电观测资料,对比分析这5种资料在新疆地区的应用情况。结果表明:LLS地闪探测效率最高,LMIG和WWLLN地闪探测效率较低;ADTD、LLS、WWLLN三者定位结果在时间和空间上对应基本一致,仅探测效率有所差别。新疆地区部分LMIG数据存在时间和空间上的偏差,使用前需要利用相关资料进行数据检验。卫星全天空、实时监测,可以弥补新疆南部(简称南疆)地面观测站覆盖不到的区域,两者可互为补充。     

0709号超强台风圣帕(Sepat)的闪电活动特征

利用全球闪电定位网 (WWLLN) 获取的闪电定位资料和中国气象局 (CMA) 提供的台风定位资料, 分析了2007年第9号超强台风圣帕的闪电时空演变特征。分析结果表明: 在热带低压至强热带风暴时期, 台风中心闪电活动频繁, 外围闪电少; 台风成熟时期, 呈现明显的三圈结构; 减弱消散时期, 中心闪电骤减, 几乎为零, 外围闪电密度远远超过中心闪电密度。眼壁闪电和台风总闪电存在阶段性变化。在台风中心最大风速急剧增大的阶段, 眼壁上的闪电两次爆发, 而在第二次眼壁闪电爆发后的两个小时, 中心风速达到最大值, 表明闪电活动有可能对台风增强有指示意义。台风眼壁置换是台风强度发生变化的一个转折点, 也是台风闪电活动发生变化的一个转折点, 从台风眼壁置换开始, 眼壁上闪电数接近于零。闪电次数跟云顶亮温存在显著性相关。结合热带测雨计划任务卫星 (TRMM) 上装载的闪电成像仪 (LIS) 和微波辐射计 (TMI) 资料, 进一步对比分析了台风闪电与强对流区域的关系, 发现闪电易发生在修正极化亮温低于225 K的深对流系统中, 但并不是所有的深对流中都能探测到闪电的发生。WWLLN和LIS探测到闪电发生区域基本一致。     

基于TRMM/LIS资料的浙江省及周边地区闪电特征和气象要素分析

利用TRMM/LIS 0.1°超高分辨率闪电定位产品, 分析了浙江省及周边地区(117.5~123.0°E, 26~32°N)卫星闪电资料的时空分布特点; 并结合中国区域地面气象要素驱动数据集、亚洲大陆气溶胶光学厚度数据集, 分析了该区域闪电与气象要素的关系。结果表明: 研究区域内闪电平均密度为5.97 f1/(km2·a), 其中陆地闪电平均密度为7.94 f1/(km2·a), 海洋闪电平均密度为2.09 f1/(km2·a), 陆地闪电平均密度为海洋闪电平均密度的3.80倍; 平均闪电密度值逐月变化特征在陆地和海洋区域有很好的一致性, 夏季闪电密度最大, 冬季闪电密度值最小; 陆地闪电密度日变化呈现单峰结构, 海洋闪电密度日变化呈现双峰双谷波形。该区域陆地气温、地面辐射、比湿及降水率均与闪电密度的月变化成正相关, 其中地面降水率和闪电密度月变化相关系数最高, 为0.858 0;气溶胶光学厚度与闪电密度月变化呈现弱的负相关, 相关系数为-0.397 8。      

镇江地区雷暴日的时空分布及雷暴日异常多年的特征分析

对1959—2000年镇江地区4个县市年雷暴活动日数时间序列进行EOF分析，并在此基础上，分析了它们的时空分布特征，并用合成分析研究了雷暴活动日数异常多年同期及前期的500hPa高空环流背景场先兆特征和赤道东太平洋海温异常情况，为雷电预报和防雷减灾工作提供有参考意义的结果。     

Seasonal variation of lightning activity over the Indian subcontinent

The seasonal variation of lightning flash activity over the Indian subcontinent (0°N–35°N and 60°E–100°E) is studied using the quality checked monthly lightning flash data obtained from lightning imaging sensor on board the Tropical Rainfall Measuring Mission satellite. This paper presents results of spatio-temporal variability of lightning activity over the Indian subcontinent. The study of seasonal total lightning flashes indicates that the lightning flash density values are in qualitative agreement with the convective activity observed over this region. Maximum seasonal total flash counts are observed during the monsoon season. The propagation of the inter-tropical convergence zone over this region is also confirmed. Synoptic conditions responsible for variation of lightning activity are also investigated with the help of an observed dataset. The mean monthly flash counts show a peak in the month of May, which is the month of maximum temperatures over this region. Maximum flash density (40.2 km^?2 season^?1) is observed during the pre-monsoon season at 25.2°N/91.6°E and the annual maximum flash density of 28.2 km^?2 year^?1 is observed at 33.2°N/74.6°E. The study of the inter-annual variability of flash counts exhibits bimodal nature with the first maximum in April/May and second maximum in August/September.     

云南省雷电灾害易损性分析及区划

利用云南省124个气象台站1971—2005年年平均雷暴日数资料及2001—2005年雷电灾害资料,从灾害易损性的角度出发,对云南省雷电灾害进行系统的分析研究,计算了各州市的雷击密度、雷击灾害频数、经济易损性指标、生命易损性指标,最后进行了综合评估,初步形成了云南省雷灾易损性区划。结果表明,某一地区雷击灾害的发生及其造成的损失情况既与该地所处的地理位置、大气环境状况有关,也与该地人口密度、经济发展状况有关。所形成的区划对各地防御和减轻雷电灾害,采取有效管理措施提供了比较客观的科学依据。     

闪电的光辐射能分布特征

对TRMM卫星上装载的闪电成像仪(LIS)所获取的闪电光辐射能进行了分析,发现在不同地区和不同的空间、时间尺度上闪电光辐射能都很好地遵循对数正态分布。文中利用了一个国际上公认的一维雷暴云起电和放电模式,对闪电放电中和的电荷量进行了数值模拟。蒙特卡罗算法表明,闪电放电所中和的电荷量也遵循对数正态分布,云中的软雹浓度、破碎系数、温度递减率、放电的电场阈值等参量的综合作用可以对其进行较好的解释,而其中任意一种参量的单独作用则不然。     

Analysis of the Winter Cloud-to-Ground Lightning Activity and Its Synoptic Background in China during 2010-20

Cloud-to-ground(CG)lightning data and the ECMWF ERA-Interim reanalysis dataset are analyzed to gain insight into the spatiotemporal distribution and synoptic background of winter-season CG flashes between December 2010 and February 2020 in China.We identify three Winter Lightning Frequent Areas(WLAs):the southwest side of the Yunnan-Guizhou Plateau(WLA1),the east side of the Yunnan-Guizhou Plateau(WLA2),and the Poyang Lake Plain(WLA3).The CG lightning flashes most frequently occur at local midnight and have a monthly peak in February.The CG lightning in WLA1 is mostly generated in non-frontal weather;however,the lightning in WLA2 and WLA3 mostly occurs in frontal systems.The frontal circulation situation is divided into four typical types:transversal trough after high pressure,low vortex,confrontational convergence,and asymptotic convergence.In all typical weather patterns,the lightning occurs downstream of a 500 hPa trough and is accompanied by a southwesterly low-level jet.The convective parameters of winter thunderstorms differ greatly from those of summer thunderstorms.The maximum convective available potential energy(MCAPE)and K-index(KI)are more useful metrics than convective available potential energy(CAPE)and Showalter index(SI)during winter.This study further deepens the understanding of the distribution characteristics of winter CG lightning in China,which motivates further research to improve the ability of winter thunderstorm prediction.     

Atmospheric factors governing winter thunderstorms in the coastal region of the eastern Mediterranean

The factors controlling lightning activity over central Israel and the adjacent Mediterranean Sea were studied. Potential predictors were correlated at 12-h intervals with total number of flashes. Since during the winter season lightning is generated in this region by Cyprus Lows, the data includes 283 observations on days in which this system prevailed for December to February, which covered four winters. The average lightning rate was 26.8 h^-1, with a high standard deviation of 55.2 h^-1. The total number of flashes at night exceeded the daytime number by 35%, in agreement with previous studies. The CAPE values were on the order of hundreds of J kg^-1. A statistical linear multi-regression model was developed for the number of lightning flashes based on 35 atmospheric variables. The correlation between the modeled and the observed number of lightning flashes was 0.67, and 0.81 for the logarithm of the number of lightning flashes (log-lightning). This suggests that the lightning intensity responds exponentially to its governing factors. A linear multi-regression stepwise model for the log-lightning selected seven predictors as significant and yielded a correlation of 0.74. This model was validated by three holdout and three leave-one-out validation experiments. The composition and hierarchy of the significant predictors reflect the dominance of the thermodynamic factors, in particular instability, in determining lightning activity. Though thunderstorms are local or meso-scale phenomena, the synoptic-scale atmospheric variables were found to be powerful predictors for their intensity.