Thunderstorm Occurrence Research Articles

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.

Severe Convection at Burgas Airport: Case Study 17 September 2022

Convection monitoring and forecasting are crucial for air traffic management as they can lead to the development of intense thunderstorms and hazards such as severe turbulence and icing, lightning activity, microbursts and hail that affect aviation safety. The airport of Burgas is located in southeast Bulgaria on the Black Sea coast and occurrences of intense thunderstorms are mainly observed in the warm season between May and September. This work presents an analysis of severe convection over southeast Bulgaria on 17 September 2022. In the late afternoon, a gust front was formed that reached the Burgas airport with a wind speed exceeding 45 m/s, the record for the past 50 years, damaging the instrument landing system of the airport. To analyse the severe weather conditions, we combine state-of-the-art observations from satellite and radar with the upper-air sounding and surface. The studied period was dominated by the presence of a very unstable air mass over southeast Bulgaria ahead of the atmospheric front. As convection developed and moved east towards Burgas, it had four characteristics of severe deep convection, including gravitational waves at the overshooting cloud top, a cold U-shape, a flanking line and a cloud top temperature below −70 °C. The positive integrated water vapour (IWV) rate of change preceded the lightning activity peak by 30 min. Analysis of integrated vapour transport (IVT) gives higher values by a factor of two compared to climatology associated with the atmospheric river covering the eastern Mediterranean sea.

Thunderstorm climatology of Slovakia between 1984–2023

Thunderstorms are among the most extreme meteorological phenomena that can cause widespread destruction and loss of life. Their occurrence varies significantly across different regions and times of the year. Despite various studies on thunderstorm activity across Central Europe, direct analyses based on data from the Slovak territory still need to be made available. Given Slovakia’s diverse natural conditions, there is a need for detailed knowledge about the frequency and spatial distribution of thunderstorms in this region. To address this knowledge gap, we analysed the frequency and spatiotemporal distribution of days with thunderstorm occurrences in Slovakia between 1984 and 2023, utilising climatological data from the Slovak Hydrometeorological Institute. We limited our analysis to data of days with close thunderstorms (thunderstorms occurring within 3 km of the monitoring station). Our findings reveal a significant variation in thunderstorm occurrences across Slovakia, with peak activity in the summer, especially in June and July. However, the spatial distribution of thunderstorms differed significantly across the country, with the highest frequency observed in mountainous regions and the east-central part of Slovakia. We found significant deceasing signals of the thunderstorm activity trends during the studied period, including analyses during the colder part of the year. Furthermore, our results underscore the critical role of synoptic situations in shaping these trends, where changes in certain atmospheric patterns were closely aligned with variations in thunderstorm frequency. The interaction between these synoptic conditions and regional topography was particularly evident, reinforcing the notion that topographical and environmental complexities substantially contribute to the observed thunderstorm distribution.

Region‐dependent meteorological conditions of thunderstorm based on clustering analysis of stability and precipitable water indices

AbstractThunderstorms contribute to a large number of destructive weather events. However, due to the availability of comprehensive thunderstorm datasets, current researches on thresholds of stability indices during thunderstorms have always been limited within a specific local area during a relatively short time span. This study uses observations of thunderstorms data and ERA5 reanalysis data in summer (June–August) at 0600 and 1200 UTC from 1995 to 2019 to investigate the region‐dependent thresholds of four indices for indicating the occurrence of thunderstorm. The indices include the K index (K), the Showalter index (SI), convective available potential energy (CAPE), and precipitable water (PW). By applying the K‐means clustering method on the composite mean fields of four indices on thunderstorm days, China is objectively divided into three regions based on three stability indices (K, SI, and CAPE) and two regions based on PW index. Region‐dependent thresholds have been established for each index related to thunderstorms. When these thresholds are applied, accuracy rate of detecting thunderstorms exceeds 60% at 85% of the stations over China. The remarkable regional difference in the thresholds of the four indices are further attributed to the climatic backgrounds. Summer climatological means of surface potential temperature and surface relative humidity play a pivotal role in shaping the distinctive thresholds of K, CAPE, and PW across diverse regions, while surface potential temperature and topography are pivotal for the region‐dependent thresholds of SI. Based on the terrain characteristics and relevant summer climatology in each region, the thunderstorm thresholds can be potentially applied to regions with limited thunderstorm records but exhibiting similar climatic features beyond China. These findings would provide a reliable reference for thunderstorm monitoring by meteorological departments and related decision‐making services.

Spatiotemporal Variability of Convective Events in Romania Based on METAR Data

Convective weather, through its heavy showers, strong winds and hail, significantly impacts human activities, having the potential to inflict serious damage on social and environmental sectors. Limited research has been conducted on this phenomenon within Romanian territory, and currently there is no referenced climatological study primarily aimed at air traffic management users in this context. This study aims to assess the climatological aspects related to convective events based on sub-hourly observation data recorded at 17 airport weather stations throughout Romania during an 11-year period (2012–2022). The spatiotemporal distribution of convective events was analyzed based on occurrences of Cumulus Congestus (TCU) clouds, Cumulonimbus (CB) clouds, thunderstorms (TSs), heavy showers (+SHs), and hail (GR). With the data being extracted from meteorological aerodrome reports (METARs) and special meteorological aerodrome reports (SPECIs). Short-term trends were determined using Sen’s slope estimator, and statistical significance was assessed through the Mann–Kendall test. The main findings indicated that the highest occurrence of convective events is located over central and western Romania, with June emerging as the extreme month in terms of convective events, while the hourly distribution emphasizes that the highest frequency of convective events occurred in the afternoon. Trend analysis in TCU, CB, and TS show tendencies toward higher frequency of convective events while the results related to +SH and GR indicate a high variability across Romanian territory. Trend analysis disclosed more substantial changes in the TS variable. The results of this study bear potential significance for a broad spectrum of human activities and the management of natural environments.

AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports

The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017–2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R.

Comprehensive study of thunderstorm indices threshold favorable for thunderstorms during monsoon season using WRF–ARW model and ERA5 over India

IntroductionThe current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures.ObjectiveFurthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms.ResultsThe results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstormsdue to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms.ConclusionsIntegrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety.Based on this work, future research can improve severe weather forecasting models’ accuracy and reliability.

Supercell Thunderstorms in Complex Topography—How Mountain Valleys with Lakes Can Increase Occurrence Frequency

Abstract This study investigates the effects of lakes in mountainous terrain on the evolution of supercell thunderstorms. With a newly developed radar-based, mesocyclone-detection algorithm, a recent study has characterized the occurrence and evolution of supercell thunderstorms in the Swiss Alpine region. That study highlights the influence of orography on both storm intensity and occurrence frequency. To disentangle the different influential factors, an idealized modeling framework is established here using the mesoscale model CM1. The modeling scenarios are based on a high-CAPE environment with unidirectional shear, where a warm bubble serves to initiate the convection. Mimicking the environment of the southern Prealps in central Europe, scenarios with a high mountain ridge, valleys, and lakes are explored. The effect on the supercells of the slopes, high-altitude terrain, and moisture sources emphasizes the highly localized nature of terrain effects, leading to a heterogeneous intensity life cycle with transitory enhancement and weakening of the supercell. The dynamic and thermodynamic impact of mountain valleys with lakes increases the range of atmospheric conditions that supports supercellular development through horizontal vorticity production, increased storm relative helicity, and higher moisture content. This influence results in a systematic location dependence of the frequency, intensity, and lifetime of supercells, as also found in observations.

Seasonal Variability of Rainfall and Thunderstorm Patterns in Kenya

This paper presents an analysis of spatial and temporal variation of rainfall and thunderstorm occurrence over Kenya from January 1987 to December 2017. The meteorological data used were obtained from the Kenya Meteorological Department (KMD) for the same period. This included the monthly thunderstorm occurrences and rainfall amounts of 26 synoptic stations across the country. The characteristics of monthly, seasonal and annual frequency results were presented on spatial maps while Time series graphs were used to display the pattern for annual cycle, seasonal variations and the inter-annual variability of rainfall amounts and thunderstorm occurrences. A well-known non-parametric statistical method Mann Kendall (MK) trend test was used to determine and compare the statistical significance of the trends. Thunderstorm frequencies over the Eastern, Central and Coast regions of the country showed a bimodal pattern with high frequencies coinciding with March-April-May (MAM) and October-November-December (OND) rainy seasons. Very few thunderstorm days were detected over June-July-August (JJA) season. The areas to the western part of the country, near Lake Victoria, had the highest thunderstorm frequencies in the country over the three seasons: MAM, JJAS and OND. The annual frequency showed a quasi-unimodal pattern. These places near Lake Victoria showed significantly increasing thunderstorm trends during the MAM and OND seasons irrespective of the rainfall trends. This shows the effects of Lake Victoria over these areas, and it acts as a continuous source of moisture for thunderstorm formation. However, most stations across the country showed a reducing trend of thunderstorm frequency during MAM and JJA seasons. The importance of these findings is that they could support various policy makers, and users of climate information, especially in the agriculture and aviation industries.

Convective Properties and Lightning Activity in Different Categories of Thunderstorms over the Beijing Area during Five Warm Seasons

Based on comprehensive observations, including total lightning, Doppler radar, precipitation, and other meteorological data, the variations in thunderstorm properties and lightning activity of different categories for thunderstorms over the Beijing area during five warm seasons were investigated. According to the morphology of radar echo, thunderstorms were classified into five categories, including single convective cells, multi-cells, linear mesoscale convective system (MCS), nonlinear MCS, and weak convective precipitation system (WCPS). The diurnal variability of lightning, thunderstorm occurrence, and precipitation showed late-afternoon maxima, with the peak time of lightning frequency occurring before that of precipitation. Despite WCPS having the lowest lightning frequency, the percentage of +CG/CG was the highest with large peak currents. The convective available potential energy (CAPE) of linear MCS, multi-cells, nonlinear MCS, single cells, and WCPS categories followed a pattern from largest to smallest. Meanwhile, warm cloud depth (WCD) exhibited a smaller value in the well-organized thunderstorm categories and a larger value in the WCPS. The topographic forcing mechanism and large wind gradient along mountain slopes facilitated convection occurrence and enhancement, further promoting lightning production. Meanwhile, the nocturnal convection mechanism significantly impacted the activity of nonlinear MCS and WCPS.

Spatio-temporal changes in the pre-monsoon thunderstorm activities of northeast India over the past four decades

Thunderstorms inflict significant danger to both human and environment. Here, we present a thorough examination of recent changes in thunderstorm occurrences in the northeast and eastern regions of India.

Assessment of the Impacts of Thunderstorm on Flight Operations at Murtala Mohammed International Airport, Ikeja, Lagos State, Nigeria

The inefficiency of the aviation industry and the persistent rise in aviation hazards have been linked to weather phenomena. As a result, researchers are looking for better solutions to the problem. The study examined the impact of thunderstorms on flight operations at Murtala Mohammed International Airport, Lagos. The data on thunderstorms and flight operations were sourced from Nigerian Meteorological Agency (NiMet) and Nigerian Airspace Management Agency (NAMA) respectively. In order to meet the research target, descriptive statistics (mean, standard deviation, and charts) and inferential statistics (Pearson’s Product Moment Correlation (PPMC) and Regression) were used. The significance level for all inferential analyses was set at 5% (0.05). The study revealed that 77.4% of thunderstorms occurred during the rainy season (April-October) while 22.6% occurred during the dry season (November-March). It also revealed some fluctuating movements of a thunderstorm in the study area. According to the findings, thunderstorms occur most frequently at the airport in June and less frequently in January and December. The study also discovered that thunderstorms at the airport are positively and significantly related to flight delays and cancellations, while the association between flight diversions and thunderstorm occurrence is positive but statistically insignificant. Furthermore, flight delays, flight diversions, and flight cancellations interact positively among themselves. The regression result of the study revealed that a 1% increase in thunderstorm occurrence leads to a 19.4% increase in flight delay, a 7.1% increase in flight cancellation, and a 4.3% increase in flight diversion. As a result, the study presented various regression models that may be utilized to make predictions. The study proposes consistent thunderstorm observation at the airport and steady forecasts using the regression models, based on the findings. However, it further recommends that pilots, air traffic controllers, and meteorologists be trained and retrained so that they can provide better and more efficient services.

Using Extreme Value Theory to Assess the Mortality Risk of Tornado Outbreaks

The majority of tornado fatalities occur during severe thunderstorm occurrences that produce a large number of tornadoes, termed tornado outbreaks. This study used extreme value theory to estimate the impact of tornado outbreaks on fatalities while accounting for climate and demographic factors. The findings indicate that the number of fatalities increases with the increase of tornado outbreaks. Additionally, this study undertook a counterfactual analysis to determine what would have been the probability of a tornado outbreak under various climatic and demographic scenarios. The results of the counterfactual study indicate that the likelihood of increased mortality increases as the population forecast grows. Intensified El Niño events, on the other hand, reduce the likelihood of further fatalities. La Niña events are expected to increase probability of fatalities.

Severe Meteorological Factors Affecting Civil Aviation Flights at Iraqi Airports

By studying the impact of severe weather conditions on civil aviation flights at Iraqi international airports, data were obtained from the General Authority for Meteorology and Seismic Monitoring, the Iraqi Civil Aviation Authority, and Iraqi Airways, as daily data for Baghdad International Airport, Erbil Airport, Sulaymaniyah Airport, Najaf Airport, and Basra Airport. The frequency of occurrence of thunderstorms, dust storms, fog, and snow was calculated and analyzed depending on the extent of horizontal visibility to determine the hours of air closure and aircraft movement at all airports of the study. This paper also discussed the temperature and its effect on the take-off and landing processes, the results showed that Baghdad International Airport was greatly affected by the occurrence of fog, followed by the airports of Erbil and Sulaymaniyah and their impact by the occurrence of thunderstorms and snow, while both Najaf Airport and Basra International Airport were greatly affected by dust storms.

Can We Integrate Spatial Verification Methods into Neural Network Loss Functions for Atmospheric Science?

Abstract In the last decade, much work in atmospheric science has focused on spatial verification (SV) methods for gridded prediction, which overcome serious disadvantages of pixelwise verification. However, neural networks (NN) in atmospheric science are almost always trained to optimize pixelwise loss functions, even when ultimately assessed with SV methods. This establishes a disconnect between model verification during versus after training. To address this issue, we develop spatially enhanced loss functions (SELF) and demonstrate their use for a real-world problem: predicting the occurrence of thunderstorms (henceforth, “convection”) with NNs. In each SELF we use either a neighborhood filter, which highlights convection at scales larger than a threshold, or a spectral filter (employing Fourier or wavelet decomposition), which is more flexible and highlights convection at scales between two thresholds. We use these filters to spatially enhance common verification scores, such as the Brier score. We train each NN with a different SELF and compare their performance at many scales of convection, from discrete storm cells to tropical cyclones. Among our many findings are that (i) for a low or high risk threshold, the ideal SELF focuses on small or large scales, respectively; (ii) models trained with a pixelwise loss function perform surprisingly well; and (iii) nevertheless, models trained with a spectral filter produce much better-calibrated probabilities than a pixelwise model. We provide a general guide to using SELFs, including technical challenges and the final Python code, as well as demonstrating their use for the convection problem. To our knowledge this is the most in-depth guide to SELFs in the geosciences. Significance Statement Gridded predictions, in which a quantity is predicted at every pixel in space, should be verified with spatially aware methods rather than pixel by pixel. Neural networks (NN), which are often used for gridded prediction, are trained to minimize an error value called the loss function. NN loss functions in atmospheric science are almost always pixelwise, which causes the predictions to miss rare events and contain unrealistic spatial patterns. We use spatial filters to enhance NN loss functions, and we test our novel spatially enhanced loss functions (SELF) on thunderstorm prediction. We find that different SELFs work better for different scales (i.e., different-sized thunderstorm complexes) and that spectral filters, one of the two filter types, produce unexpectedly well calibrated thunderstorm probabilities.

Relative Importance of Large‐Scale Environmental Variables to the World‐Wide Variability of Thunderstorms

AbstractThis study uses a 16‐yr Tropical Rainfall Measuring Mission (TRMM) Convective Features (CFs) and ERA‐Interim reanalysis data to investigate the relative importance of four large‐scale environmental variables to thunderstorms with random forest models. These four variables include Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), low‐level wind shear, and warm cloud depth (WCD). First, these selected four environmental variables show a distinguished difference between CFs with and without lightning flashes. Specifically, CFs with at least one flash have higher CAPE, CIN, and lower WCD than those without lightning. Then, using these four variables, the geographical distribution of thunderstorms, especially the land‐ocean contrast in the occurrence of thunderstorms, is closely reproduced with a global random forest model. Such results suggest that a random forest model with key large‐scale environmental variables can be a useful tool to estimate the occurrence of global lightning thunderstorms. The study also investigates the relative importance of the selected variables to the occurrence of thunderstorms regionally. Relatively higher skill scores in the regional random forest model than the global one indicate the variation of roles of large scale environment variables over different regions. Though the data‐driven models can be utilized to estimate the occurrence of global thunderstorms, how to link the regional relative importance of these variable to the physical processes of thunderstorms needs further investigation.

Cosmic ray shower rate variations detected by the ARGO-YBJ experiment during thunderstorms

The ARGO-YBJ detector, located at the Yangbajing Cosmic Ray Laboratory (4300 m a. s. l., Tibet, China), was a “full coverage” air shower array. The high altitude location and the frequent occurrence of thunderstorms, made ARGO-YBJ suitable to study the effects of atmospheric electric fields (AEF) on secondary cosmic rays. By analyzing the data of the ARGO-YBJ detector recorded during thunderstorms, significant variations of the rate of detected showers have been observed. During 20 thunderstorm episodes in 2012, the variations of the shower rates (both increases and decreases of amplitudes up to a few percent) are found to be correlated to the intensity and polarity of the AEF, and strongly dependent on the primary zenith angle. To understand the observed behavior, Monte Carlo simulations have been performed with corsika and g4argo (a code based on geant4). We found that the data are well consistent with simulations, assuming the presence of a uniform electric field in a layer of thickness of 500 m in the atmosphere above the observation level. Due to the AEF accelerates/decelerates and deflects the secondary charged particles (mainly electrons and positrons) according to their charge, modifying the number and position of particles with energy exceeding the detector threshold. For the differences in electron and positron flux, spectrum, and lateral distribution, the AEF has an asymmetric effect on the shower particles, producing significant variations of the particle pattern on the ground, and, consequently, on the rate of detected showers, consistent with observations.2 MoreReceived 8 February 2022Accepted 5 July 2022DOI:https://doi.org/10.1103/PhysRevD.106.022008© 2022 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCosmic ray & astroparticle detectorsCosmic rays & astroparticlesGravitation, Cosmology & Astrophysics

Climate-Induced Fire Hazard in Forests in the Volga Federal District of European Russia during 1992–2020

This paper shows the relevance of the problem of fire hazard in the forests of the Volga Federal District (VFD) of European Russia. The Nesterov index and the Selyaninov hydrothermal coefficient (HTC) are considered as indicators of fire hazard. The changes in climatic conditions in the VFD during 1955–2018 are shown; a trend towards warming and an increase in aridity in the study region were revealed. The repeatability of various fire hazard classes from May to September was calculated using the Nesterov method. It is shown that in July, the most dangerous situation was in the south of the VFD, where the repeatability of class IV fire hazard reached 27%. Using the HTC index, the degree of aridity of the district in the summer period was estimated. The frequency of the most arid conditions (HTC < 0.5) increases from the north to the south of the district, from 6% (Kirov Region) to 47% (Orenburg Region). Using the TT index, the potential thunderstorm danger in the VFD was assessed. With the help of the constructed maps, the hotspots of the most probable occurrence of thunderstorms were detected. The use of Rosstat data on the number of forest fires from 1992 to 2020 made it possible to consider the spatiotemporal distribution of forest fires in 14 administrative regions of the VFD. The distribution of the number of fires by the regions is shown depending on their forest cover and season. The peak of the number of fires was revealed in 2010, when the entire territory of the study region was covered by a severe drought, as a result of which the area of forests covered by fire increased many times over. In recent years (since 2017), there has been an increase in the area of burned forest due to the active phase of climate warming.

Sensitivity analysis of weather research and forecasting (WRF) model output variables to the thunderstorm lifecycle and its application

Accurate and timely forecasts of thunderstorms at lead times of more than 6 h can greatly improve the efficiency of air traffic flow management. To achieve this goal, thunderstorms occurring at Urumqi International Airport in 2020 were investigated using Weather Research and Forecasting (WRF) model output variables. An attempt was made to study the sensitivity of WRF output variables to the different stages (cumulus, mature, and dissipating) of the thunderstorm lifecycle. The variables considered in this paper include the wind speed (WSPD), composite radar reflectivity, echo top height, convective available potential energy (CAPE), convective inhibition (CIN), and lift index (LI). It was found that CIN is the most sensitive to an approaching thunderstorm. WSPD is extremely sensitive to the thunderstorm occurrence, closely followed by CIN. CAPE, CIN, and LI are all sensitive to dissipating thunderstorms. To improve thunderstorm forecasts, a simple and practical objective thunderstorm forecasting method, i.e., the thunderstorm probability (TSP) forecasting method, based on the combination of the above-mentioned variables, was proposed. Comparison of objective TSP forecasts and manual subjective forecasts indicated that TSP forecasts performed much better than did manual forecasts over the summer period of 2020.

Pre-monsoon Thunderstorm Season Climatology of Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) over Eastern India

The present work analyses climatological variations of Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) over the eastern India (Odisha, West Bengal and Jharkhand) during the pre-monsoon season, where thunderstorms are frequent and disastrous. The work utilises European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data: ERA-5 for 1987-2016, supplemented with information about thunderstorm occurrences over the region from India Meteorological Department (IMD). The CAPE and CIN values can differentiate thunderstorm days (TD) from non-thunderstorm days (NTD), with favourable conditions of convective weather on days of thunderstorms over the region, evident by CAPE and CIN. The coastal areas of Odisha and West Bengal, along with the Jharkhand bordering the northern West Bengal region, have higher CAPE and lower CIN values. The trend analysis of CAPE and CIN has been performed using the non-parametric Mann-Kendall test, which shows an apparent transformation of indices over time for different regions of states for TD and NTD. CAPE shows an increasing trend over the coastal districts of Odisha and West Bengal during TD and for the whole of West Bengal during 12 UTC NTD. The NTD cases show a decreasing trend over Odisha (both 00 and 12 UTC) and 00 UTC over West Bengal. CIN shows an increasing trend for TD and decreasing trend for NTD over whole Odisha, whereas, for West Bengal, trends are positive for coastal regions during TD and negative on the entire state during NTD. For Jharkhand, both the CAPE and CIN values show an increasing trend over the state during NTD, whereas for TD, both increasing/decreasing trends are visible. The analysis complements the observations of thunderstorm occurrence over the region to understand areas with higher potential of thunderstorm occurrence during pre-monsoon season.