Anirban Middey

The reciprocal relation between lightning and pollution and their impact over Kolkata, India.

Aerosol loading in the atmosphere can cause increased lightning flashes, and those lightning flashes produce NOX, which reacts in sun light to produce surface ozone. The present study deals with the effect of surface pollutants on premonsoon (April–May) lightning activity over the station Kolkata (22.65° N, 88.45° E). Seven-year (2004–2010) premonsoon thunderstorms data are taken for the study. Different parameters like aerosol optical depth and cloud top temperature from the Moderate Resolution Imaging Spectroradiometer satellite products along with lightning flash data from Tropical Rainfall Measuring Mission’s (TRMM) Lightning Imaging Sensor are analyzed. Some surface pollution parameters like suspended particulate matter, particulate matter 10, nitrogen oxides (NOX), and surface ozone (O3) data during the same period are taken account for clear understanding of their association with lightning activity. Heights of convective condensation level and lifting condensation level are collected from radiosonde observations to anticipate about cloud base. It is found that increased surface pollution in a near storm environment is related to increased lightning flash rate, which results in increased surface NOX and consequently increased surface ozone concentration over the station Kolkata.

The Applicability of Bipartite Graph Model for Thunderstorms Forecast over Kolkata

Single Spectrum Bipartite Graph (SSBG) model is developed to forecast thunderstorms over Kolkata during the premonsoon season (April-May). The statistical distribution of normal probability is observed for temperature, relative humidity, convective available potential energy (CAPE), and convective inhibition energy (CIN) to quantify the threshold values of the parameters for the prevalence of thunderstorms. Method of conditional probability is implemented to ascertain the possibilities of the occurrence of thunderstorms within the ranges of the threshold values. The single spectrum bipartite graph connectivity model developed in this study consists of two sets of vertices; one set includes two time vertices (00UTC, 12UTC) and the other includes four meteorological parameters: temperature, relative humidity, CAPE, and CIN. Three distinct ranges of maximal eigen values are obtained for the three categories of thunderstorms. Maximal eigenvalues for severe, ordinary, and no thunderstorm events are observed to be , , and , respectively. The ranges of the threshold values obtained using ten year data (1997–2006) are considered as the reference range and the result is validated with the IMD (India Meteorological Department) observation, Doppler Weather Radar (DWR) Products, and satellite images of 2007. The result reveals that the model provides 12- to 6-hour forecast (nowcasting) of thunderstorms with 96% to 98% accuracy.

Appraisal of the prevalence of severe tropical storms over Indian Ocean by screening the features of tropical depressions

Tropical cyclones are one of the nature’s most violent manifestations and potentially the deadliest of all meteorological phenomena. It is a unique combination of violent wind, heavy rainfall, and mountainous waves in sea. The maximum sustained wind speed, minimum sea level pressure, and the radius of maximum winds are important parameters for understanding a particular tropical cyclone and to differentiate it from a depression to tropical storms. The objective of this particular paper is to identify a possible range of maximum sustained wind speed, minimum sea level pressure, and radius of maximum winds which facilitates tropical depressions to lead to tropical storms over Bay of Bengal and Arabian Sea of Indian Ocean basin. The method of rough set theory which is based on condition—decision support system is implemented for the purpose. The result reveals that the threshold ranges of the maximum sustained wind speed, minimum sea level pressure and radius of maximum winds associated with tropical depression are possible that can aid in the predictability of tropical storm over Indian Ocean. The results are validated with significant tropical storms of 2009 and 2010 observations through Doppler and satellite imageries.

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

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

Nowcasting Bordoichila with a composite stability index

In operational forecast, the stability indices either individually or in combination are utilized to assess the predictability of local severe storms over a region. The objective of the present study is to identify such stability indices to assess the predictability of Bordoichila of Guwahati, India, during the pre-monsoon season (April–May) aiming to formulate a composite stability index using the most pertinent indices for nowcasting Bordoichila with considerable precision. Bordoichila, meaning the angry daughter of Assam, represents local severe storms of Guwahati during the pre-monsoon season. Precise forecast of Bordoichila is essential to mitigate the associated catastrophe over Guwahati. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant stability indices for the prevalence of Bordoichila. The method of normal probability distribution is implemented to identify the threshold ranges of the selected indices. The stability indices that are selected with appropriate ranges are lifted index, Showalter index (SI), cross total index (CTI), vertical total index, totals total, convective available potential energy, convective inhibition energy, SWEAT and bulk Richardson number. The forecast skill scores are estimated with the selected indices. The best predictor indices identified for the prevalence of Bordoichila are the cross total index (CTI) and Showalter index (SI). A composite stability index, Bordoichila prediction index, is formulated with CTI and SI with proper weightages. The forecast with BPI is validated with the observations of India Meteorological Department for the year 2007 and is implemented for real-time forecast for the years 2009 and 2011.

Nowcasting lightning flash rate and peak wind gusts associated with severe thunderstorms using remotely sensed TRMM-LIS data

The momentous weather hazards during the pre-monsoon season (April–May) over Kolkata (22° 32′ N, 88° 20′ E), India, is mostly due to lightning flashes and surface wind gusts associated with severe thunderstorms. A multi-layer perceptron (MLP) model is developed to forecast the lightning flash rate and peak wind gusts which accompany severe thunderstorms. Meteorological parameters derived from radiosonde weather observations from 1998 to 2009 are taken as input whereas lightning data from the Lightning Imaging Sensor (LIS) and wind gusts from a ground-based observatory are taken as the target output parameters. The skill of the MLP model is compared with the multiple linear regression (MLR) analysis method, and it is observed that the MLP model provides better and more accurate forecasts than the MLR analysis method. The results also reveal that the forecast accuracy is more for surface wind gusts than for the lightning flash rate, both during training and validation of the model. The MLP model forecast is validated with the India Meteorological Department (IMD) weather observations as well as Doppler weather radar and satellite imagery of 2008 and 2009 thunderstorms.

Prediction of remotely sensed cloud related parameters over an inland urban city of India: a neuro-computing approach

ABSTRACT Artificial neural network (ANN) is a mathematical model useful for forecasting on the any type of available data. This tool is not only useful in environment but also covers wide ranges of applicability. Utilizing this model, a study was carried out in an inland area of Nagpur for forecasting satellite-derived cloud parameters. Nine ANN architects are developed based on five pollutant parameter (aerosol optical depth, RSPM, SPM, SO2, NOx), meteorological and some cloud parameter. The models are used to simulate concentration of pollutants as well as the forecast and validation of cloud top temperature, cloud ice water path and cloud liquid water path during different seasons (winter, pre-monsoon and post-monsoon). Models based on back-propagation neural network were tested using the collected data of study area. The ANN models were trained using gradient descent algorithms to check the robustness and adaptability of the models. ANN models based on both satellite and ground-based data variables demonstrate the best performance and are skilled at resolving patterns of pollutant dispersion to the atmosphere during 2006–2013 for Nagpur city.

Study of near surface boundary layer characteristics during pre-monsoon seasons using micrometeorological tower observations

Studying the boundary layer is imperative because severe weather in this portion of the atmosphere impacts on environment and various facets of national activities and affects the socioeconomic scenario of a region. Near surface boundary layer characteristics are investigated through the vertical variation of fluxes of heat, moisture, momentum, kinetic energy and Richardson number during the pre-monsoon season (April-May) at Kharagpur (22o 30’ N, 87o 20’ E) and Ranchi (23o 32’ N, 85o 32’ E) with 50 and 32 m tower data, respectively, on thunderstorm and non-thunderstorm days. The temporal variation of fluxes within the boundary layer and the kinetic energy at different logarithmic heights are observed to vary significantly between thunderstorm and non-thunderstorm days. The heat and momentum fluxes show a maximum peak while the moisture flux shows a sudden attenuation just before the occurrence of thunderstorms. The wind field depicts to play a crucial role at the inland station Kharagpur, which is in the proximity of the Bay of Bengal, compared to the station Ranchi, situated over hilly terrain on Chotanagpur. The micrometeorological study of the boundary layer reveals a significant finding pertaining to observe the passage of thunderstorms. It is observed that the ratio of the potential temperature (θ) and equivalent potential temperature (θe) remains confined within a critical range between 0.85 and 0.90 during the passage of thunderstorms.

The Coupled Influence of Instability Indices and DWR Data in Estimating the Squall Speed of Thunderstorms

The specific forecast of occurrences and the associated consequences of thunderstorms is still a difficult task for both NWP models and professional weather forecasters due to the small spatial and temporal scales involved. In operational forecast, many indices are being used to assess the stability of the atmosphere and predict the possibility of thunderstorm development. It is also well established that the Doppler weather radar (DWR) has the capability of capturing the fast developing convective systems such as thunderstorms. The instability indices as well as the DWR data are utilized in the present study to estimate the speed of squall associated with thunderstorms during the pre monsoon season over Kolkata (22° 32′N, 88° 20′E), India. The ranges of the selected indices and the DWR data are estimated using the normal probability distribution function. The statistical skill score analysis is implemented to select the instability indices relevant for estimating the squall speed of thunderstorms over Kolkata. The threshold ranges of the selected indices and the DWR data are used as the inputs while the target output being the squall speed associated with thunderstorms. The method of rough set theory is adopted in this study to identify the best combination of the instability indices and DWR data for estimating the squall speed. The method of rough set theory is capable of dealing with inconsistency in the data set, if any, while simulates the condition — decision support system. The certainty factor of the rough set theory is computed in this study for the condition which is the coupled influence of the instability indices and DWR data on the decision that is, the squall speed associated with thunderstorms. The results are validated with the observations of 2010.

An investigation on the predictability of thunderstorms over Kolkata, India using fuzzy inference system and graph connectivity

Abstract The purpose of this study was to develop a computing system (CS) with fuzzy membership and graph connectivity approach to estimate the predictability of thunderstorms during the pre-monsoon season (April–May) over Kolkata (22°32′N, 88°20′E), India. The stability indices are taken to form the inputs of the CS. Ten important stability indices are selected to prepare the input of the fuzzy set. The data analysis during the period from 1997 to 2006 led to identify the ranges of the stability indices through membership function for preparing the fuzzy inputs. The possibility of thunderstorms with the given ranges of the stability indices is validated with the bipartite graph connectivity method. The bipartite graphs are prepared with two sets of vertices, one set for three membership functions (strong, moderate and weak) with the stability indices and the other set includes the three membership functions for the probability of thunderstorms (high, medium and low). The percentages of degree of vertex (ΔG) are computed from a sample set of bipartite graph on thunderstorm days and are assigned as the measure of the likelihood of thunderstorms. The results obtained from graph connectivity analysis are found to be in conformity with the output of fuzzy interface system (FIS). The result reveals that the skill of graph connectivity is better and supports the FIS in estimating the predictability of thunderstorms over Kolkata during the pre-monsoon season. The result further reveals from the minimum degree of vertex connectivity that among the ten selected stability indices, only four indices: lifted index, bulk Richardson number, Boyden index and convective available potential energy, are most relevant for estimating the predictability of thunderstorms over Kolkata, India.