Lifted Index Research Articles

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

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

Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey

This study aims to analyze the thunderstorm (TS) events in the megacity Istanbul by using thermodynamic indices and atmospheric stability parameters for the period of 2001–2022. It was determined that TS events did not show any trend on an annual basis, mostly (%69) occurred in the warm season (May–September), and mostly (%93) lasted for a few hours (0–3 h). The thermodynamic indices and atmospheric stability parameters used in the study are Showalter Index (SI), Lifted Index (LI), Severe Weather Threat Index (SWEAT), K-Index (KI), Totals Totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), and Bulk Richardson Number (BRN). Annual and seasonal analyzes of all indices and parameters were performed for TS and non-TS events. Significant differences were found in both average, maximum, and minimum values. The Probability of Detection (POD), False Alarm Ratio (FAR), Miss Rate (MR), Critical Success Index (CIS), Hiedke Skill Score (HSS), and True Skill Score (TSS) were used to analyze the success of the threshold values presented in the literature in detecting TS events. Then, the seasonal successes of these threshold values were tested. It was observed that the performance of the selected indices varied across seasons. The highest predictive skill was generally observed during the summer season, with the POD value ranging between 0.58 and 0.97 and the TSS value varying between 0.32 and 0.57. Conversely, the lowest predictive skill was typically observed during the winter season, where the POD value ranged from 0.00 to 0.75 and the TSS value varied between 0.00 and 0.40. The ideal threshold values were determined for indices and parameters by increasing or decreasing the existing threshold values at certain rates. Success increases of up to 15% in skill scores for the proposed threshold values.

Determination of Air Stability Parameter Threshold Value for Cumulonimbus and Thunderstorm Cloud Events at Kualanamu Meteorological Station

Many studies have carried out calculations related to atmospheric lability as a reference in weather forecasts, especially cumulonimbus clouds, and thunderstorms. However, many air lability index values are found to be inappropriate in each region because conditions in each region are different from each other in the region. So it is necessary to use precise index thresholds to determine weather conditions. In the study, observational data and data from Showalter Index (SI), Lifted Index (LI), K Index (KI), Severe Weather Threat Index (SWEAT), and Convective data were used. Available Potential Energy (CAPE) for ten years (2013-2022), then statistical calculations and verification for one year (2022) are carried out. The results obtained are the atmospheric stability index with the best accuracy in predicting the presence of cumulonimbus clouds and thunderstorms at the Kualanamu Meteorological Station, Deli Serdang is the best LI index to predict TS 00 and TS 12, and the best KI index to predict CB 00 and CB 12.

CORRELATION OF ATMOSPHERIC LABILITY INDEX TO VERTICAL WIND SHEAR AT I GUSTI NGURAH RAI AIRPORT

Wind shear is a condition that is detrimental to aircraft because it can cause the aircraft to experience lift, especially during take-off or landing, where wind shear can occur due to bad and unstable weather, so research on the correlation of the atmospheric lability index to wind shear is very important to prevent a plane crash. This research aims to determine the magnitude of the correlation of the atmospheric lability index to the vertical wind shear that occurs at I Gusti Ngurah Rai Airport, Bali. In this research, the Wind Profiler Radar (WPR) brand scintec LAP-3000 was used to obtain wind shear data in the form of wind direction and wind speed data then radiosonde to obtain atmospheric lability index data in the form of Lifted Index (LI), Total-Totals Index (TT), K-Index (KI), and Convective Available Potential Energy (CAPE) index values for each month in 2019-2020. Wind shear measurement was carried out at an altitude of 100-3000 m and data were taken on the difference in wind direction ≥ 60o and the difference in wind speed ≥ 10 knots. Meanwhile, measurement of the atmospheric lability index is carried out by a flying air balloon that has been equipped with a transmitter. All LI, TT, KI, and CAPE index value data and the number of wind shear events were analyzed with Pearson Product Moment correlation. The value of the correlation coefficient (r) between the LI, TT, KI, and CAPE indices was obtained for successive wind shear events of -0.786; 0,250; 0.738, and 0.713. These results show that the LI, KI, and CAPE index values can be used as a reference to predict wind shear events because they have a strong correlation, obtaining a correlation value between 0.60 to 0.79 compared to the TT index.

NEAR SURFACE AIR TEMPERATURE ESTIMATION THROUGH PARAMETRIZATION OF MODIS PRODUCTS

Abstract. Near-surface air temperature is a key factor in many studies and its spatiotemporal patterns are highly dependent on the ground surface characteristics and vary over time and space. So Land Surface Temperature (LST) is an important parameter for air temperature estimation. In this study, it is tried to model the air temperature by deploying some of the parameters that affect it . The parameters that have been taken into account in this study include land surface temperature, Normalized Difference Vegetation Index (NDVI), Vapor Pressure (VP) and Lifted Index (LI) as a measure of atmospheric stability. To assess the impact of each of these parameters, different linear regression models, were tested. Support Vector Regression (SVR) and hybrid artificial neural network methods were also performed. To model and evaluate the time series data of Georgia State in The United States of America over 1 year have been used. The NDVI, Total Precipitable Water (TPW), LST and LI parameters are products of MODIS. VP is calculated by using a logarithmic model from the TPW. Finally, it was found out that the LST and VP have positive effects, LI has negative and NDVI had a slightly positive impact on the air temperature at 2 meters height. The achieved accuracy in the linear model when all parameters are involved was 2.29 °C with a correlation coefficient of R=0.96. Next, the SVR model was examined and applied to the linear model taking all parameters into account. It was found that it does not end up to any significant increase in accuracy but certainly increases the computation time. The accuracy of this model was about 2.25 °C with a correlation coefficient of 0.96. Finally, a hybrid artificial neural network was examined. It was found that it increases the accuracy but certainly increases the computation time. The achieved accuracy of this model was about 2.14 °C with a correlation coefficient of 0.96.

A Study of Important Atmospheric Convective Indices and Their Impacts on Aviation

Studies of the environmental impact on aircraft operations always create great interest, for both the scientific and non-scientific fraternities. It has now been fairly understood that climate changes (sea level rise, enormous increase of surface temperatures, and heat waves) have significant impacts on the aviation sector while a proper knowledge of the impacts of convective activities on the aviation sector has not yet been well known. This research presents the general mechanism of a few important atmospheric indices including, convective available potential energy (CAPE), convective inhibition (CIN), and lifted index (LI) by using radiosonde (a balloon-borne instrument). Initially, convective indices over two Indian stations (coastal and inland) are presented in terms of monthly and seasonal variations. The observed trends are discussed based on the available literature. The impacts of the atmospheric indices on the aviation industry and the feasible solutions if an airplane is caught in a severe thunderstorm are discussed.

Atmospheric thermodynamics and dynamics during convective, stratiform and nonprecipitating clouds over the metropolitan area of Rio de Janeiro – Brazil

Physical mechanisms involved in the development and forecast of clouds and precipitation are both quite complex and dependent on the local atmospheric environment, especially when severe weather conditions are imminent. Research aimed at understanding the environmental mechanisms favorable to the different atmospheric scenarios can help operational weather forecasters to issue warnings. This paper provides qualitative and quantitative contributions from radiosondes, radar, and numerical simulations to evaluate the formation of convective, stratiform, and nonprecipitating clouds over the metropolitan area of Rio de Janeiro, Brazil. The convective available potential energy (CAPE) and lifted index (LI), showed higher values on convective days (CAPE = 2600 J.Kg–1 and LI = –4 ºC), followed by nonprecipitating (CAPE = 1500 J.Kg–1 and LI = –2 ºC) and stratiform cloud days (CAPE = 1400 J.Kg–1 and LI = –1.5 ºC). High wind convergence was observed at low- (1000–850 hPa) and mid- (850–700 hPa) levels on convective days (–16.5 s–1 and –9.6 s–1, respectively). In contrast, wind divergence at the same levels was observed on stratiform (6.4 s–1 and 6.9 s–1) and nonprecipitating (9.7 s–1 and 7.3 s–1) days. Higher wind divergence (8.3 s–1) was observed on convective days at upper levels (300–200 hPa) compared with stratiform (3.2 s–1) and nonprecipitating (2.8 s–1) days. Results show a coupling of wind convergence, moisture and energy in the lower troposphere and divergence at upper levels on convective days. Despite moisture availability on stratiform days and thermodynamic energy on nonprecipitating days, the respective coupling between these conditions and dynamic triggers was not observed.

Data Fusion of GEO FY-4A GIIRS and LEO Hyperspectral Infrared Sounders with Surface Observations: A Hong Kong Case Study

Abstract Hyperspectral infrared satellite observations from geostationary platforms allow for the retrieval of temperature and water vapor measurements with higher temporal and vertical resolution than was previously available. The Chinese satellite Fengyun-4A (FY-4A) includes the Geostationary Interferometric Infrared Sounder (GIIRS), which has the ability to measure vertical profiles of temperature and water vapor from space at times when ground-based upper-air soundings are not available and can fill an important need in short-range weather prediction. In this study, convective available potential energy (CAPE) and lifted index (LI), which are used for forecasting atmospheric instability, were computed using the SHARPpy algorithm used by the NWS Storm Prediction Center. However, remote infrared and microwave sensing is lacking detailed information in the boundary layer, so the addition of the NOAA Meteorological Assimilation and Data Ingest System (MADIS) surface data may be necessary in order to get accurate temperature and moisture measurement near the surface. This study uses 10–16 May 2019 in the coastal region near Hong Kong for evaluating the use of hourly surface observations combined with satellite soundings from FY-4A GIIRS at 2-h intervals. The GIIRS plus MADIS surface-based CAPE and LI estimates are compared to estimates derived from low-Earth-orbiting (LEO) SNPP and NOAA-20 from NOAA, MetOp from EUMETSAT, NWP reanalysis, and local radiosondes. In the case study, the 2-h sampling interval of the GIIRS geostationary sounder was able to capture the rapid transition (16 h) from a stable to an unstable atmosphere in both CAPE and LI. The use of surface observations with satellite soundings gave mixed results, possibly due to the complex terrain near Hong Kong.

Analysis of the rainfall event in 2018-2019 using the air stability index method at the Meteorological Station of Sultan Iskandar Muda Banda Aceh

The purpose of the present research is to determine the forecast of rain events with good results and can provide information on rain events to be right on target. In this study, an analysis of rainfall events in 2018 and 2019 was carried out using rainfall and radiosonde data at the Meteorological Station of Sultan Iskandar Muda Banda Aceh. The analysis used with the atmospheric stability index method, which was taken by Radiosonde data, obtained information on atmospheric parameters such as temperature, dew point, pressure, and wind. This parameter can be used to predict rain events using the air stability index, where the indices used are Showalter Index (SI), Lifted Index (LI), CAPE, Total-Total Index (TT), K-Index, Severe Weather Threat Index (SWEAT). From the results of the stability indices verified by rainfall data observed by the Meteorological Station of Sultan Iskandar Muda. The results showed that the LI, SI, TT, and KI indices were good enough to predict rainfall, while the CAPE and SWEAT indices were still not good at predicting rainfall events. This information can then be used as a basic reference which is quite good to use through the upper air stability index approach in develop the rainfall forecast value.

Monthly analyses of convection-related irregular flights and their linear projections for the future climate in China

Convective weather such as thunderstorms and rain is one of the main causes of irregular flights including delays, cancelations, turnbacks and diversions. In China, summer (April–September) flights accounted for 94% of irregular flights due to convective weather in 2016–2019. The impact of summer convective weather conditions on irregular flights is however not well understood. In this research, we find that thunderstorms, as indicated by the lifted index (LI), are greatly related to these irregular flights over Southeast China. The global climate model ensemble indicates there will be robust increases in the occurrence of convective weather environments in response to further global warming. We also find that as the LI is decreasing over time, the likelihood of thunderstorm-related irregular flights is increasing. Such an increase indicates there will be a 17% increase in irregular flights by the end of the century.

Synoptic patterns of severe hailstorm events in Spain

A study of severe hailstorm (with size ≥3 cm) over Spain has been conducted using 8 years (2012–2019) of data. The 73 events of heavy hail selected were classified using the moisture flux at the 850 hPa pressure level (qv)850 and the lifted index (LI) as variables and a principal component analysis, coupled with a K-means clustering, as statistical technique. The application of this procedure provided four groups with their respective final cluster centre. We studied four cases, selected for being close to each final cluster centre. It was found that high values of low levels moisture flux (~ 89 g m/kg s on the average) are accompanied by static instability (SBCAPE ~600 j/kg and average LI ~ −4). The temperature values exceeded 20 °C at the 850 hPa pressure level and range between −10 °C and − 20 °C at the 500 hPa pressure level. The most typical configuration corresponded to geopotential and thermal trough at mean levels of the troposphere and low pressure at low levels. Likewise, high values of precipitable water (> 25 kg/m2) have been detected and the presence of storm relative helicity and shear was moderate or strong in all cases.

Satellite Based Interpretation of Stability Parameters on Convective Systems over India and Srilanka

India is frequently exposed to convective systems (CS) during the hot period of pre-monsoon season. Huge thunderstorms are more often over India in the recent years. To analyze these systems, the stability parameters considered as good precursors for understanding the physics of the convective systems in prior to their occurrence. The present study is concerned about the analysis of stability parameters such as K Index (KI), Lifted Index (LI), Total Totals Index (TTI), Humidity Index (HI), Convective available potential energy (CAPE), Convective Inhibition (CIN), Lifting Condensation Level (LCL) and Level of free Convection (LFC) associated with severe convective systems over India and Srilanka during April, 2015. First, INSAT-3D satellite based brightness temperature (BT) product was used for analyzing the initial, mature and dissipation stages of the convective system at every 30 min interval. The data products from MODIS Terra satellite, MODIS Aqua satellite, Era-Interim ECMWF reanalysis satellite data and IASI satellite were used for understanding the thermodynamics related to the occurrence of these convective systems. The results of stability parameters that were derived from MODIS Terra and Aqua satellite’s data products indicated the favorable threshold values for the occurrence of convective systems. Results from Era-Interim ECMWF reanalysis satellite data also helps us to understand the dynamics involved behind the convective system. The results from IASI satellite also helped us to understand the severity of convective systems during cloudy situations. The results of this study impulse the need of satellites for analyzing the convective systems prior to their occurrence.

Squall Line and Its Vertical Motion Under Different Moisture Profiles in Eastern China

The impacts of different moisture profiles on the structure and vertical motion of squall lines were investigated by conducting a set of numerical simulations. The base state was determined by an observational sounding, with high precipitable water representing moist environmental conditions in the East Asian monsoon region. To reveal the impact of moisture at different levels, the moisture content at the middle and low levels were changed in the numerical simulations. The numerical results showed that more convective cells developed and covered a larger area in the high moisture experiments, which was characteristic of the convection during the Meiyu season in China. In addition, high moisture content at low levels favored the development of updrafts and triggered convection of greater intensity. This was demonstrated by the thermodynamic parameters, including Convective Available Potential Energy (CAPE), Lifted Index (LI), Lift Condensation Level (LCL), and Level of Free Convection (LFC). Dry air at middle levels led to strong downdrafts in the environment and updrafts in clouds. This could be because dry air at middle levels favors the release of latent heat, thereby promoting updrafts in clouds and downdrafts in the environment. Therefore, high relative humidity (RH) at low levels and low RH at middle levels favors updrafts in the cloud cores. Additionally, moist air at low levels and dry air at middle levels promotes the development of convective cells and the intensification of cold pool. The squall line can be organized by the outflow boundary induced by cold pool. The balance of cold pool and environmental wind shear is favorable for the maintenance and strengthening of squall lines.

Impact of Atmospheric Stability Indices on Convective Systems Over India and Srilanka

Thunderstorms are real-time global phenomena, as their occurrence can take place at anytime at any place. Though their duration is less when compared to large scale processes, their damage is devastating to human life. Thunderstorms are linked with damage factors such as lightning, damaging wind, hails and rain. Real-time satellite data provide atmospheric data which is useful for prediction of thunderstorms. In this paper, an attempt is made to analyze the statistical based stability indices from INSAT-3D, MODIS and ECMWF satellites for the now casting of thunderstorms. The occurrences of severe thunderstorms over India and Srilanka during the month of October 2013, 2014 and 2015 have been analyzed. In these three years, five severe thunderstorm cases were identified using Insat-3D cloud images and thunderstorm reports. Atmospheric stability indices such as K Index (KI), Lifted Index (LI), Total Totals Index (TTI), Total Precipitable water (TPW), Humidity Index (HI) related with severe convection system over India and Srilanka during October month were identified to provide guidance for the study of convection and thunderstorm activity. These indices give us a clear indication of development of convective system before 3 to 4 hours. Results of this study indicate the importance of satellite data for studying the development and decay of convective systems.

Observed and model‐simulated thermodynamic processes associated with urban heavy rainfall events over Bangalore, India

AbstractA total of 32 rainfall events spanning the period from 2012 to 2014 over the urban Indian city of Bangalore were simulated using the Weather Research and Forecast (WRF) model. Model simulations were carried out with a four‐nested domain initialized with Global Forecast System (GFS) data and the forecast was generated on an hourly basis. The forecasted rainfall at hobli level (Bangalore has 34 hobli divisions with an area of each hobli of the order of about 10 km2) was evaluated in terms of its intensity and pattern of spatial distribution by comparing it with corresponding rain‐gauge observations. Also, the rainfall forecast skill of the model was evaluated statistically by computing root mean square error, bias and mean absolute error. Thermodynamic variables such as equivalent potential temperature, convective available potential energy (CAPE), convective inhibition (CIN), K index (KI), lifted index (LI) and total totals index (TTI) were also derived from the simulated model parameters for all the events and then verified against corresponding observations. The results showed that the WRF model could simulate the rainfall events and associated thermodynamic features qualitatively; however, there were few hoblis where the relative errors in the forecast were > 100%. The forecast errors were relatively lower for cases during the southwest monsoon season compared with other seasons. It was found that the model underestimated thermodynamic indices such as CAPE, dew point depression and the simulated LI were positive; these were indicative of the model's limitation in simulating intense convection and a possible reason for underpredicted rainfall simulations.

Utilization of surface meteorological data, Himawari-8 satellite data, and radar data to analyze landspout in Sumenep, East Java, Indonesia (case study of 20 November 2017)

One of the extreme weather that happened in Indonesia is landspout. Landspout is virtually a tornado that forms from a thunderstorm that is not a supercell. On November 20, 2017 at 08.00 UTC there was a landspout in the village of Kertasada, Kalianget District, Sumenep Regency of Madura, East Java, Indonesia which caused damage to dozens of houses and many shops and warehouses. The study was done to determine the differences condition of the atmosphere before, during, and after the phenomenon based on synoptic analysis, atmospheric instability analysis, satellite image analysis, and radar image analysis. The results of the synoptic analysis of two weather parameters, pressure and temperature, also supported the occurrence of landspout phenomenon. Pressure and air temperature for two days before the occurrence of the landspout had a relatively similar pattern, but the average pressure appeared higher on the day of the incident. Temperature patterns showed a decrease from one hour before the incident, which was 3.2°C. The surface wind analysis showed wind speed during the day of the incident was higher than the previous two days, with a difference of 6 m s-1. The analysis of atmospheric instability when the incidence of the Lifted-Index (LI) values indicates the ‘unstable atmosphere and possible thunderstorm criteria. K-Index (KI) shows the possible to become moderate convetive’ The total index (TT) showed a stronger convective, local lightning potential and a value were higher than the previous two days. Severe Weather Threat Index (SWEAT) showed the potential of lightweight thunderstorm with greater instability value than the previous day while the Convective Avalilable Potential Energy (CAPE) index showed a large and potentially stormy energy. Then, in this research used Himawari-8 satellite and Radar C-band to detected convective clouds. According to Satellite Animation and Interactive Diagnosis (SATAID) satellite data, there is a Cumulonimbus (cb) cloud clump in the Sumenep region of Madura, which is meant to be potentially a landspout.

A study comparison of two system model performance in estimated lifted index over Indonesia.

Lifted index (LI) is one of atmospheric stability indices that used for thunderstorm forecasting. Numerical weather Prediction Models are essential for accurate weather forecast these day. This study has completed the attempt to compare the two NWP models these are Weather Research Forecasting (WRF) model and Global Forecasting System (GFS) model in estimates LI at 20 locations over Indonesia and verified the result with observation. Taylor diagram was used to comparing the models skill with shown the value of standard deviation, coefficient correlation and Root mean square error (RMSE). This study using the dataset on 00.00 UTC and 12.00 UTC during mid-March to Mid-April 2017. From the sample of LI distributions, both models have a tendency to overestimated LI value in almost all region in Indonesia while the WRF models has the better ability to catch the LI pattern distribution with observation than GFS model has. The verification result shows how both WRF and GFS model have such a weak relationship with observation except Eltari meteorologi station that its coefficient correlation reach almost 0.6 with the low RMSE value. Mean while WRF model have a better performance than GFS model. This study suggest that estimated LI of WRF model can provide the good performance for Thunderstorm forecasting over Indonesia in the future. However unsufficient relation between output models and observation in the certain location need a further investigation.

Diurnal and long-term variation of instability indices over a tropical region in India

Climatology of atmospheric instability is studied over Gadanki using high-resolution radiosonde launched daily during April 2006 to April 2017. The diurnal and seasonal variation of instability parameters is discussed in relation with surface meteorological parameters. Seasonal variations depict strong variability in instability which is masked by stronger diurnal variation with descending Lifting Condensation Level (LCL) and Level of Free Convection (LFC) between 11 and 18 IST resulting in high Convective Available Potential Energy (CAPE) values and heavy rainfall. On a seasonal basis, parcel parameters are high during the late monsoon and post-monsoon while the instability parameters like Total Totals index (TT) and Vertical Totals index (VT) show highest values in the pre-monsoon associated with strong convection. LFC and LCL start descending with ascent in Equilibrium Level (EL) before the monsoon onset. However after the onset, atmospheric instability falls sharply as supported by decreasing TT, VT and CAPE with increasing LI. The 11-year long-term variation depicts slightly elevated LFC and LCL and declining EL values indicating a decrease in the instability with a decrease in CAPE and K Index (KI) and increase in Lifted Index (LI) and Convective Inhibition (CIN).

Numerical Model and Forecasting Potential of Aircraft Weather Hazards over Kenya

The study investigated the forecasting potential of aircraft weather hazards over Kenya using numerical model. The various causes of accidents/incidents were identified using analyzed investigated weather-related aviation hazards data obtained from Kenya Civil Aviation Authority database system from 2008 through 2014. The parameters analyzed included the Winds, Temperature, Relative humidity, Convective Available Potential Energy (CAPE), Lifted Index (LI) and Dew Point Temperature. Their combined effect was able to provide the means of identifying the areas of strong convection. The model was run on a grid point distance resolution of 10 km and runs finite differencing scheme, based on Taylor series. Grid Analysis and Display System (GrADS) which is embedded on the model upon installation, was used as the default post processing tool. Key findings revealed that Wind, Fog, Turbulence, Heavy rain showers, and low ceiling influenced aircraft operations. It was shown that the majority of the hazards were linked with cumulonimbus clouds. Landing phase of flight affected aviation most.

Association of atmospheric pollution and instability indices: A detailed investigation over an Indian urban metropolis

Convection has a significant role in maintaining the atmospheric dynamics and thermodynamics, particularly in the tropical regions during pre-monsoon season, which may be due to the changing patterns in atmospheric instability and pollution. A critical analysis is done on the variability of instability indices and their significant signature to meteorological parameters and atmospheric pollution over Indian region in the warming atmosphere during 2005–2015. The present study represents that the solid as well as gaseous pollutants, in combination, produce a damping force in suppressing convective activities over the eastern coastal regions of India. A significant anti-correlation (r~−0.6 to −0.8) between instability parameters [Convective Available Potential Energy (CAPE) and Lifted Index (LI)] and atmospheric pollutants [gaseous (NO2 and SO2) and solid (BC and PM2.5)] has been obtained in the eastern coastal regions on a long-term basis. To improve the level of agreement between pollution and instability, a unitless and dimensionless index called KLURT index has been introduced, which provides correlation (r) value as high as ~0.6. On a real time basis, KLURT index is found to be useful as an effective precursor of thunderstorm events. The final part of this study indicates a prediction technique using KLURT index which gives a high prediction efficacy of 75%, low FAR value, extremely good BS 0.06, an excellent bias (~0.96) and a good lead time of 1h for a threshold value of 12.5 in terms of predicting intense convections at the urban location, Kolkata. Thus, the present study provides an appropriate means to manifest convection as a function of changing anthropogenic factors both in long and short-term basis with high correlation values and provides significant efficacy in predicting severe weather, thereby demonstrating the usefulness of this hypothesis in various socio-economic aspects especially at the current tropical urban location.