Thunderstorm Events Research Articles

Mean storms: Composites of radar reflectivity images during two decades of severe thunderstorm events

AbstractThis research quantifies the spatiotemporal statistics of composite radar reflectivity in the vicinity of severe thunderstorm reports. By using over 20 years (1996–2017) of data and 500,000 severe thunderstorm reports, this study presents the most comprehensive analysis of the mesoscale presentation of radar reflectivity composites during severe weather events to date. We first present probability matched mean composites of approximately 5,000 radar images centred on tornado reports that contain one of three types of manually‐labelled convective storm modes—namely, (a) quasi‐linear convective system (QLCS); (b) cellular; or (c) tropical system. Next, we generate composites for tornado report data stratified by EF‐scale and for four temporal periods during which notable severe weather events took place. The data are then stratified by hazard, region, season, and time of day. The results show marked spatiotemporal and intra‐hazard variability in radar presentation. In general, cellular convection is favoured in the Great Plains of the United States, whereas QLCS convection is favoured in the Southeast United States. Night and cool‐season subsets showed a preference for QLCS convection, whereas day and warm‐season subsets showed a preference for cellular convection. These results agree well with the existing literature and suggest that the data extraction and organization approach is sound. Because of this, these data will be useful for future image classification studies in climate and atmospheric sciences—particularly those involving storm mode classification.

Vertical profile characteristics of thunderstorm outflows

The dynamic complexity and the limited spatiotemporal structure of thunderstorms make the collection of reliable and systematic measurements of this phenomenon, which are definitely needed to evaluate its action on structures, challenging. The Northern Tyrrhenian is a “hot-spot” for the genesis of severe potentially damaging wind phenomena, such as downbursts. In the context of the European projects “Wind and Ports” and “Wind, Ports and Sea”, a large and complex wind monitoring network has been installed just in this area. Here, three LiDAR profilers provide a vertical scanning of the atmosphere up to 250 ​m above the ground level. From their continuous recordings, a method to extract thunderstorm events is herein proposed, based on an automated procedure involving systematic quantitative controls and specific qualitative judgments. Starting from it, this paper provides a comprehensive investigation and comparison of the main parameters ruling the outflow vertical profiles of a selected subset of thunderstorms. The nose shape of the wind profiles appears mainly during the velocity ramp-up and peak stages. During the downburst, the wind direction is systematically invariant with height. The capability of LiDAR to measure the wind speed turbulence component is also discussed and its properties along the vertical profile are shown.

Pre-monsoon convective events and thermodynamic features of southwest monsoon onset over Kerala, India – A case study

The thermodynamic characteristics of the atmosphere during the pre-monsoon thunderstorm period and the transition phase from pre-monsoon to monsoon period over the gateway of Indian summer monsoon is investigated in this study. The study utilizes Stratosphere-Troposphere (ST) wind profiling Radar, Microwave Radiometer, and GPS radiosonde observations. A sharp increase in the boundary-layer and middle-layer moisture content, abrupt changes in the mixing height, lifting condensation level (LCL), convective available potential energy (CAPE), and other thermodynamic variables are noted. The pre-monsoon period is characterized by intermittent deep convection that moistens the middle layers, while stratiform clouds are observed in the monsoon period and deep moist layer is observed. The dry middle-layers observed before the monsoon onset got moistened consistently as the low-level jet strengthened. The LCL height was lower than the mixing height after the onset of monsoon. The wet-bulb zero level (WBZ) height was reduced to 2 km during the pre-monsoon and shifted gradually to higher altitudes as the onset of monsoon occurs. Variations in WBZ height may help in the predictability of monsoon onset. The study has also investigated the characteristics of a thunderstorm event before the onset and suitable indices for early indication of the storm.

Investigation of the Transient Nature of Thunderstorm Winds from Europe, the United States, and Australia Using a New Method for Detection of Changepoints in Wind Speed Records

Abstract This paper investigates the transient characteristics in 41 velocity records of 19 thunderstorm events from around the world—9 from Europe, 9 from the United States, and 1 from Australia. The transient features of thunderstorm winds were examined by introducing an objective method for the detection of changepoints in the time series. The methodology divides velocity records into segments characterized by different statistical properties. The segmentation is based on the following properties of the isolated segments: mean (M) and the standard deviation (SD). This study demonstrated that the maximum velocity during the thunderstorm peak in the events from Europe is typically 2–4 times larger than the mean wind speed before the thunderstorm. The duration of the thunderstorm velocity peak was 2–5 min in approximately 60% of the analyzed records using the M statistic and 5–10 min when analyzed using the SD statistic. Therefore, the velocity fluctuations caused by thunderstorm winds last longer than the abrupt changes in the mean wind field. Similarly, the ramp-up time was longer when the records were analyzed using the SD statistic. The segmentation methodology was tested for different duration of velocity records and using data with different sampling frequencies. The performances of the introduced method were compared against the results of two other segmentation procedures proposed in the literature. One of the practical applications of this method is the physical separation between the thunderstorm and nonthunderstorm components of a wind event. Significance Statement Thunderstorm outflow winds are short-lived phenomena produced by cold downdrafts that originate in thunderstorm clouds. This study analyzes the transient nature of thunderstorm winds from Europe, the United States, and Australia using a segmentation method applied to anemometer velocity records. This segmentation method identifies abrupt changes of mean wind speed and wind fluctuations in the velocity data. This research provides the means of isolating different segments within the thunderstorm wind records in an objective way that is based on rigorous mathematical principles. The proposed method can automatically distinguish thunderstorm from nonthunderstorm winds. The peak velocities in thunderstorm outflows are 2–4 times the mean wind speed before the thunderstorm. The most intense episodes of thunderstorm winds usually last 2–5 min.

Multi‐resolution incremental 4D‐Var for WRF: Implementation and application at convective scale

AbstractThe current four‐dimensional variational (4D‐Var) data assimilation (DA) algorithm, available in the community Weather Research and Forecasting (WRF) model's DA (WRFDA) system, can run only at the same resolution as that of the WRF model forecast, which makes it computationally prohibitive for operational applications at convective scale. The Multi‐Resolution Incremental 4D‐Var (MRI‐4DVar) has been developed in this study in order to speed up WRF 4D‐Var through a three‐stage procedure in each outer loop. One key aspect of WRF MRI‐4DVar is the introduction of the inverse control variable transform within WRFDA that allows the proper resolution change between different outer loops for the control variables projected in the vertical empirical orthogonal function (EOF) space. MRI‐4DVar's computational efficiency and forecast performance are demonstrated by applying it to an afternoon thunderstorm event over northern Taiwan with a 2 km model resolution setting. Comparing to the full‐resolution 4D‐Var experiment, two MRI‐4DVar configurations with a speed‐up of 4.5 and 7.5 times performed similarly well in terms of Fractions Skill Score (FSS) of 6‐hr accumulated rainfall forecasts and hourly variation of total rainfall amount, indicating MRI‐4DVar's potential for operational applications at convective scale.

A rare winter thunderstorm event near Hong Kong

A rare winter thunderstorm event near Hong Kong is documented in this paper. The thunderstorm occurrence was confirmed by the observation of one of the authors and lightning location information systems (both regional and global). It developed over the frontal cloud band associated with the winter monsoon. Observing the upper air measurements, however, the thermodynamic conditions did not seem favorable to the occurrence of the thunderstorm. The convective available potential energy could not be determined in the cool and dry lower troposphere. There was only a moister layer in the middle troposphere. The weather forecaster did not anticipate the occurrence of thunder and lightning. However, some features could have changed dynamically, which suggested the possibility that a thunderstorm would take place. For instance, waves could be identified in the middle troposphere and a broad troughing flow could be analyzed in the lower troposphere. The integrated wave vapor was also of a rather high level. It is hoped that this paper can serve as a useful reference for forecasters in assessing the possibility of thunderstorms occurring during winter over subtropical latitudes in association with frontal cloud bands.

Towards performance-based design under thunderstorm winds: a new method for wind speed evaluation using historical records and Monte Carlo simulations

Accurate load evaluation is essential in any performance-based design. Design wind speeds and associated wind loads are well defined for synoptic boundary layer winds but not for thunderstorms. The method presented in the current study represents a new approach to obtain design wind speeds associated with thunderstorms and their gust fronts using historical data and Monte Carlo simulations. The method consists of the following steps (1) developing a numerical model for thunderstorm downdrafts (i.e., downbursts) to account for storm translation and outflow dissipation, (2) utilizing the model to characterize previous events and (3) extrapolating the limited wind speed data to cover life-span of structures. The numerical model relies on a previously generated CFD wind field, which is validated using six documented thunderstorm events. The model suggests that 10 parameters are required to describe the characteristics of an event. The model is then utilized to analyze wind records obtained at Lubbock Preston Smith International Airport (KLBB) meteorological station to identify the thunderstorm parameters for this location, obtain their probability distributions, and utilized in the Monte Carlo simulation of thunderstorm gust front events for many thousands of years for the purpose of estimating design wind speeds. The analysis suggests a potential underestimation of design wind speeds when neglecting thunderstorm gust fronts, which is common practice in analyzing historical wind records. When compared to the design wind speed for a 700-year MRI in ASCE 7-10 and ASCE 7-16, the estimated wind speeds from the simulation were 10% and 11.5% higher, respectively.

Study on Seasonal Variations of Plasma Bubble Occurrence over Hong Kong Area Using GNSS Observations

In this study, the characteristics and causes of the seasonal variations in plasma bubble occurrence over the Hong Kong area were investigated using the local Global Navigation Satellite System (GNSS) network. Generally, the occurrences of plasma bubbles were larger in the two equinoxes than in the two solstices. Furthermore, two seasonal asymmetries in plasma bubble occurrence were observed: plasma bubble activity was more frequent in the spring equinox than in the autumn equinox (equinoctial asymmetry), and more frequent in the summer solstice than in the winter solstice (solstitial asymmetry). The equinoctial asymmetry could be explained using the Rayleigh–Taylor (R–T) instability mechanism, due to larger R–T growth rates in the spring equinox than in the autumn equinox. However, the R–T growth rate was smaller in the summer solstice than in the winter solstice, suggesting the R–T instability mechanism was inapplicable to the solstitial asymmetry. Our results showed there were more zonally propagating atmospheric gravity waves (GWs) induced by thunderstorm events over the Hong Kong area in the summer solstice than the winter solstice. So, the solstitial asymmetry could be attributed to the seeding mechanism of thunderstorm-driven atmospheric GWs.

Brief Communication: An electrifying atmospheric river – understanding the thunderstorm event in Santa Barbara County during March 2019

Abstract. On 5 March 2019 12:00 UTC, an atmospheric river (AR) made landfall in Santa Barbara, CA, and lasted approximately 30 h. While ARs are typical winter storms in the area, the extraordinary number of lightning strikes observed near coastal Santa Barbara made this event unique. The Earth Networks Global Lightning Network (ENGLN) detected 8811 lightning flashes around southern California (30 to 37∘ N and 130 to 115∘ W) in 24 h, which is roughly 2500 times the climatological flash rate in this region. The AR-related thunderstorm resulted in approximately 23.18 mm accumulated precipitation in 30 h in Santa Barbara. This article examines synoptic and mesoscale features conducive to this electrifying AR event, characterizing its uniqueness in the context of previous March events that made landfall in the region. We show that this AR was characterized by an unusual deep moist layer extending from the low to mid-troposphere in an environment with potential instability and low-elevation freezing level. Despite the negligible convective available potential energy (CAPE) during the peak of the thunderstorm near Santa Barbara, the lifting of layers with high water vapor content in the AR via warm conveyor belt and orographic forcing in a convectively unstable atmosphere resulted in the formation of hail and enhanced electrification.

An evolutionary power spectral density model of thunderstorm outflows consistent with real-scale time-history records

This paper presents a new approach for modelling the evolutionary power spectral density (EPSD) of thunderstorm outflows based on a database of 129 real-scale thunderstorm time-histories. Two equivalent methods of decomposition of the wind velocity are investigated, outlining the derivation of the EPSD. The analyses are based on the hypothesis that the residual turbulent fluctuations can be treated as a uniformly modulated process, whose reliability is verified studying the steadiness of its up-crossing rate. The assumption of considering a constant turbulence intensity is checked through the evaluation of the dynamic response of a set of SDOF systems in time-domain and through the response spectrum technique. Two models for the time-modulating function of the EPSD are proposed that include the parameters characteristic of both the thunderstorm event and the background wind. Finally, the steps for the derivation of the EPSD are outlined pointing out the limits of the representation of the PSD of the reduced turbulent fluctuations through spectral models commonly adopted in wind engineering. The final aim of this research is finalising a triad of complementary methods for evaluating the dynamic response of structures to thunderstorm outflows - EPSD, time-domain and response spectrum - consistent with real-scale time-history records.

Time–height variation of winds and turbulence during typical tropical pre-monsoon thunderstorm events observed from high-resolution Doppler wind lidar measurements

High spatiotemporal resolution measurements of horizontal and vertical winds made using a Doppler wind lidar during development and mature stages of few typical tropical thunderstorm events in 2014 over a continental Indian station are used to study the dynamical changes that take place in the lower atmospheric layers close to surface. Results show significant time–height variability in horizontal wind especially 2–3 h before rainfall on ground. The recorded vertical motions on the thunderstorm days showed that during its developing stage, alternating updrafts and downdrafts are prevalent through the entire vertical column of the lower atmosphere with the magnitude of fluctuations being higher compared to those on non-thunderstorm days. Turbulence intensity is high in magnitude during the evolution of intense convection. Alternating layers of convergence and divergence with horizontally time-stratified layers of strong positive and negative wind shear are observed which seem to contribute to strengthening and vertical growth of local convection. Thus, high-resolution time–height variations in horizontal and vertical winds in the lower atmosphere during the life cycle of a tropical thunderstorm provide a better understanding of the atmospheric dynamics associated with it and short-duration heavy precipitation it produces.

Long-term analysis of thunderstorm-related parameters over Visakhapatnam and Machilipatnam, India

In this present work, an attempt has been made to analyze various thunderstorm-related parameters and their influence over the two stations Visakhapatnam (VSK) and Machilipatnam (MTM). The thunderstorm-related parameters used in the present study are convective available potential energy (CAPE), lifted index, K-index, total totals index (TTI), humidity index, convective inhibition, thunderstorm prediction index (TPI), deep convective index (DCI) and updraft vertical velocity. This analysis was carried out using NCEP NCAR reanalysis monthly data for the time period from 1948 to 2012. These parameters have given good guidance for studying the thunderstorm event. We also analyzed IMD thunderstorm occurrence days reported at two stations, i.e., VSK and MTM with NCEP NCAR (daily data) calculated CAPE, TTI, TPI and DCI parameter threshold days in pre-monsoon season for every year during the time period 2010 to 2019. Out of those four parameters, TTI has shown good correlation with the IMD recorded days. So we have attempted the prediction of thunderstorms using artificial neural network (ANN) and auto-regressive moving average (ARMA) techniques for TTI parameter. While using these techniques, we have experimented in three training sets, i.e., 90%, 80% and 70%. Another attempt has been made to assess the skill of ARMA and ANN techniques in forecasting the occurrence of thunderstorm activity at VSK and MTM stations. The present study suggests that ANN has high skill than ARMA. From this study, we can understand that VSK has more chances for thunderstorms than MTM.

Complementing Optical Remote Sensing with Synthetic Aperture Radar Observations of Hail Damage Swaths to Agricultural Crops in the Central United States

AbstractThe normalized difference vegetation index (NDVI) has been frequently used to map hail damage to vegetation, especially in agricultural areas, but observations can be blocked by cloud cover during the growing season. Here, the European Space Agency’s Sentinel-1A/1B C-band synthetic aperture radar (SAR) imagery in co- and cross polarization is used to identify changes in backscatter of corn and soybeans damaged by hail during intense thunderstorm events in the early and late growing season. Following a June event, hail-damaged areas produced a lower mean backscatter when compared with surrounding, unaffected pixels [vertical–vertical (VV): −1.1 dB; vertical–horizontal (VH): −1.5 dB]. Later, another event in August produced an increase in co- and cross-polarized backscatter (VV: 0.7 dB; VH: 1.7 dB) that is hypothesized to result from the combined effects of crop growth, change in structure of damaged crops, and soil moisture conditions. Hail damage regions inferred from changes in backscatter were further assessed through coherence change detections to support changes in the structure of crops damaged within the hail swath. While studies using NDVI have routinely concluded a decrease in NDVI is associated with damage, the cause of change with respect to the damaged areas in SAR backscatter values is more complex. Influences of environmental variables, such as vegetation structure, vegetation maturity, and soil moisture conditions, need to be considered when interpreting SAR backscatter and will vary throughout the growing season.

A diagnostic study of cloud physics and lightning flash rates in a severe pre‐monsoon thunderstorm over northeast India

AbstractNumerical simulations of a thunderstorm event that occurred in the pre‐monsoon season over the North‐Eastern region of India (NEI) and Bangladesh are performed using Weather Research and Forecasting (WRF) Advanced Research Weather Research and Forecasting Model (ARW, version 3.8.1). Doppler weather radar indicates severe convective activity lasted for more than 10 hr. These extremely deep convective clouds with minimum cloud‐top temperature −70 °C at 19 km were triggered by the mixing of a moist air mass transported from the Bay of Bengal in the south and dry air transported from the northwest. A cyclonic circulation observed over the Tibetan plateau is likely to be associated with the strong southerly low‐level wind over NEI, as the plateau acts as a source of heat‐lows during the pre‐monsoon season. The coexistence of ice particles and supercooled water in the storms resulted in a large number of lightning flashes during the storm as observed from the Tropical Rainfall Measuring Mission Lightning Imaging Sensor (TRMM‐LIS). Co‐location of supercooled cloud water droplets helps in forming graupel through riming that plays a vital role in these convective systems. Lightning flashes calculated from WRF simulation using the Morrison microphysical and cloud top height based dynamical lightning parametrization scheme was found comparable with the observed flashes from TRMM‐LIS. Since the WRF model could simulate the thunderstorm, we recommend using this state‐of‐the‐art regional model in thunderstorm and lightning predictions for northeast India which would be useful in preparedness for such extreme events.

Estimation of vertical structure of latent heat generated in thunderstorms using CloudSat radar

AbstractThe Earth's atmosphere is highly coupled between the vertical layers and the surface. An understanding of circulations in the atmosphere is important for developing models and improving weather forecasting. The latent heat produced in the atmosphere is one of the key driving forces of these circulations. It is therefore very important to estimate the latent heat in the atmosphere accurately, especially in thunderstorm clouds, which have proved to be one of the major sources of gravity waves in tropical regions. The current space‐based latent heat retrievals are limited to precipitation‐based estimation which cannot define the complete structure of a thunderstorm where precipitation is not the main indicator of the severity. A novel method is proposed in this study which retrieves the latent heat profiles of thunderstorm clouds using CloudSat W‐band radar profiles. A realistic database of simulated thunderstorm events developed using the Regional Atmospheric Modelling System – Cloud Resolving Model (RAMS‐CRM) is compared with observations using the Bayesian Monte Carlo method to derive an estimate with an uncertainty analysis for each estimate. The method is validated in the southeast Asian region with European Centre for Medium‐Range Weather Forecasts driven RAMS‐CRM profiles. The algorithm performance on observation data using linear fit, regression and bias analysis is discussed. A case study retrieval is also performed to demonstrate the retrieval on real CloudSat data.

Spatio-temporal characteristics of agro-climatic indices and extreme weather events during the growing season for summer maize (Zea mays L.) in Huanghuaihai region, China.

The stability of maize production is essential to global food security. Climate factors, such as temperature, precipitation, and solar radiation, directly affect the development of maize plants and hence the final grain yield. In this study, we investigated the spatial distributions and temporal trends of agro-climatic indices and severe weather indicators during the actual growing season for summer maize in Huanghuaihai region of China. The results showed that during the growing season, accumulated effective thermal time had significantly increased. From R3 to R6, accumulated effective thermal time, effective precipitation, and photosynthesis active radiation all showed an increasing trend, with the rate of 20.3°Cday, 1.1mm, and 7.3MJm-2 per decade, respectively. From VE to R3, most of the study years showed a > 50% ratio of high-temperature days to subtotal days and > 7 consecutive days without available precipitation. During most of the study years, there were at least 0.5 thunderstorm events from V6 to VT stage in the locations of study; days with strong winds accounted for more than half the subtotal days during the V6-VT stage. And potential risk of lodging may be reduced by the decrease in days with strong wind. The results of this study could be used in optimizing agricultural management in summer maize production in order to take advantage of beneficial climatic elements while combating adverse climatic elements.

A preliminary study on pre-monsoon summer thunderstorms using ground-based total lightning data over Gangetic West Bengal

This paper presents the preliminary results of observation of total lightning characteristics of pre-monsoon thunderstorms of 2018 over Gangetic West Bengal around Kolkata. Mainly, the analysis results of total lightning associated with pre-monsoon moderate and severe thunderstorm events, locally known as Kalbaishakhi or Nor’wester, are presented. The total lightning data include both in-cloud and cloud-to-ground lightning flashes and are obtained from the Earth Networks Total Lightning Network. Most of the events were multi-cellular in nature as inferred from lightning data. The total lightning count over an area of 10 km radius around the wind sensors, increased rapidly about $$\sim $$ 10–40 min before the onset of high wind on the surface. Primarily, total lightning count $$\ge 30$$ per minute was found to be associated with the Nor’westers which produced maximum surface wind speed above 50 km/h.

Predicting Lightning-Related Outages in Power Distribution Systems: A Statistical Approach

This paper presents a novel data-driven approach for predicting lightning-related outages that occur in power distribution systems on a daily basis. In order to develop an approach that is able to successfully fulfill this objective, there are two main challenges that ought to be addressed. The first challenge is to define the extent of the target area. An unsupervised machine learning approach is proposed to overcome this difficulty. The second challenge is to adequately identify characteristics of lightning-related outages and to explore the relationship between these outages and weather-related variables (thunderstorm events). In this paper, these outages are clustered into a few manageable groups. Then, a probabilistic model is presented to estimate the likelihood of each group of outages. Finally, a machine learning classification algorithm that can handle the imbalanced problem is developed to predict what group will the outage belong to on a specific day in a specific area of the system under study. Actual outage data, obtained from a major utility in the U.S., in addition to radar weather forecast data are utilized to build the proposed approach. Also, three case studies are provided to show several issues associated with predicting lightning-related outages, and to demonstrate how the proposed approach can address those problems adequately.

Assigning Lightning Seasons to Different Regions in the United States

Climate change is predicted to increase the frequency of severe thunderstorm events in coming years (1). Severe thunderstorms are often accompanied by lightning, which causes an average of 243 injuries and 27 deaths per year in the United States (US) alone (2). With these predicted increases in lightning events, it is important to understand and practice safety during severe thunderstorms. We examined severe thunderstorm data from the National Oceanic and Atmospheric (NOAA) database in seven states in the US (Colorado, Florida, Maine, Minnesota, New Jersey, Washington and West Virginia) to characterize lightning seasons over three years (2000, 2008, and 2017). We hypothesized that (i) the majority of severe thunderstorm events will occur in the summer months in all states examined for all years analyzed, (ii) climate change will cause an unusual number of severe thunderstorm events in winter months in all states, (iii) thundersnow would be observed in Colorado, and (iv.) there would be no difference in the number of severe thunderstorm events between states in any given year examined. We were able to classify lightning seasons in all states observed, with the most severe thunderstorm events occurring in May, June, July, and August. Colorado, New Jersey, Washington, and West Virginia were found to have severe thunderstorm events in the winter, which could be explained by increased winter storms due to climate change (1). Overall, it is important to quantify when lightning seasons occur to avoid lightning-related injuries or death.

A Modeling Study of the Severe Afternoon Thunderstorm Event at Taipei on 14 June 2015: The Roles of Sea Breeze, Microphysics, and Terrain

A Modeling Study of the Severe Afternoon Thunderstorm Event at Taipei on 14 June 2015: The Roles of Sea Breeze, Microphysics, and Terrain