University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Atmospheric Science

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 ∼ 10–40 min before the onset of high wind on the surface. Primarily, total lightning count ≥ 30 per minute was found to be associated with the Nor’westers which produced maximum surface wind speed above 50 km/h. © 2020, Indian Association for the Cultivation of Science.

World University root node

Indian Journal of Physics

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

This paper reports disturbance in the mid-latitude sub-ionospheric VLF radio signals due to the super geomagnetic storm which began on 17 March 2015. Narrow-band signals from the NAA transmitter are studied for the storm period recorded at eight mid-latitude receiving stations spread over the Europe and USA. Daytime signals amplitude at all places showed a disturbing pattern after 17 March. Fluctuation in the nighttime signals significantly increased in the succeeding nights. As a primary effect of the storm, the entire diurnal signals in the transoceanic west to east long propagation paths enhanced by 3–5 dB, which gradually decreased over the period of ~10 days following the storm recovery. A different behavior was observed in the east to west short propagation paths over the landmass, where during the peak storm the daily variations of the VLF amplitude reduced to 20–25% of a normal day and, after ~10 days the signals returned to the pre-storm condition. Modeling of the radio waves in the west to east paths shows that the D-region electron density was increased by ~8-fold and varied up to 10 days. Electron density variations in the D-region closely follows the variations of precipitated electron flux as observed by the POES satellite over the region. The elevated electron density in the D-region ionosphere caused by the extension of the auroral precipitation to the mid-latitudes along with interference among the various waveguide modes in the earth-ionosphere waveguide during the storm is suggested for the cause of observed VLF signals behaviors. © 2021 COSPAR

Advances in Space Research

Long-lasting disturbances in the mid-latitude sub-ionospheric VLF radio signals due to the super geomagnetic storm of 17 March 2015

The D-region ionospheric disturbances due to the tropical cyclone Fani over the Indian Ocean have been analysed using Very Low Frequency (VLF) radio communication signals from three transmitters (VTX, NWC and JJI) received at two low latitude stations (Kolkata-CUB and Cooch Behar-CHB). The cyclone Fani formed from a depression on 26th April, 2019 over the Bay of Bengal (Northeastern part of the Indian Ocean) and turned into an extremely severe cyclone with maximum 1-min sustained winds of 250 km/h on 2 May, 2019 which made landfall on 3 May, 2019. Out of six propagation paths, five propagation paths, except the JJI-CHB which was far away from the cyclone track, showed strong perturbations beyond 3σ level compared to unperturbed signals. Consistent good correlations of VLF signal perturbations with the wind speed and cyclone pressure have been seen for both the receiving stations. Computations of radio signal perturbations at CUB and CHB using the Long Wave Propagation Capability (LWPC) code revealed a Gaussian perturbation in the D-region ionosphere. Analysis of atmospheric temperature at different layers from the NASA's TIMED satellite revealed a cooling effect near the tropopause and warming effects near the stratopause and upper mesosphere regions on 3 May, 2019. This study shows that the cyclone Fani perturbed the whole atmosphere, from troposphere to ionosphere and the VLF waves responded to the disturbances in the conductivity profiles of the lower ionosphere. © 2020 COSPAR

Fulltext

D-region ionospheric disturbances associated with the Extremely Severe Cyclone Fani over North Indian Ocean as observed from two tropical VLF stations

The present paper reports a study on the dependence of tropospheric ozone on some of its precursors, namely NOx, PM10, and SO2 during summer monsoon over a highly polluted Kolkata region megacity of India. The intrinsic multicollinearity has been explored through Bartlett’s sphericity test. Subsequently, principal component analysis has been implemented to identify the principal components with maximum factor loadings, and accordingly, the most influential ozone precursors have been extracted. Finally, after fitting some continuous and discrete distributions to the extracted precursors, the intrinsic uncertainty has been investigated by maximizing Shannon entropy. The normal distribution has been identified to generate the maximum entropy probability distribution for surface ozone and its precursors. Future directions have been stated at the end of the study. © 2020, Shiraz University.

Iranian Journal of Science and Technology, Transaction A: Science

Information Theoretic Study of the Ground-Level Ozone and Its Precursors Over Kolkata, India, During the Summer Monsoon

The paper reports a study on the air quality over Kolkata, India during the transition period from post-monsoon to winter. A fuzzy binary relation based approach is implemented to O3, N O2 and P M2 5 and the overall air quality index (AQI). Individual pollutants have been converted to fuzzy sets based on their concentrations as elements of the universe of discourse with appropriate fuzzy membership functions defined on them. AQI has been divided into categories based on the recommendations of the Central Pollution Control Board (CPCB), Govt of India. After defining the fuzzy relations, the fuzzy composite relations have been derived to construct fuzzy membership matrices based on the composite binary fuzzy relations. Afterwards, the membership grades of the matrices for the composite fuzzy binary relations have been studied and N O2 along with O3 have been consideblack as having highly significant influence on the overall AQI over the polluted study zone during the transition from post-monsoon to winter. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Earth Science Informatics

Fuzzy binary relation based elucidation of air quality over a highly polluted urban region of India

Aerosols can modify both short and long term weather patterns by impacting the radiation budget of Earth. Numerical simulations were performed to understand the direct effect of aerosol on radiation during an elevated dust and black carbon (BC) concentration period over south Asia. The impact of the aerosol (dust and BC separately) direct effect on meteorology and air quality (focusing on surface ozone) was assessed using a fully coupled chemical transport model (WRF-Chem). The model simulates the elevated dust and BC concentration plume well qualitatively. Our results show that elevated BC concentration can reduce surface temperature up to 2 K. Incoming short wave flux at the surface and the boundary layer height reduced up to 70% due to the radiative impact of BC. ‘This reduction in boundary layer height further increases the BC concentration at the source region. The radiative impact of dust on meteorological parameters are found to be less compared to BC at the surface level. The model simulates realistic surface ozone concentration using HTAP emission inventory. Results reveal that the presence of biomass burning can increase the surface ozone concentration by up to 40%. The radiative impact of BC can reduce the surface ozone concentration by more than 30% by altering the photolysis frequencies. © 2020 Elsevier Ltd

Atmospheric Environment

Aerosol radiative impact on surface ozone during a heavy dust and biomass burning event over South Asia

The National Capital Region (NCR) of India, Delhi, has experienced high post-monsoon pollution along with several peak pollution episodes in recent years. Diwali, the festival of lights, which is among the biggest festivals of India celebrated during the post-monsoon season, is also considered a pollution event associated as it is with the lighting of a large number of firecrackers. 2016 Diwali pollution episode continued for a week creating severe discomfort to residents of Delhi, prompting the judiciary to ban the sale and use of firecrackers in Delhi from 2017 onwards. The current study analyzes different sectoral and temporal emissions contribution to the 2016 post monsoonal pollution episode over Delhi using a fully coupled chemical transport model. The findings of the study indicate that aerosols produced from crop residue open burning at the northwestern states contributed more than 60% of the total simulated surface concentration during the period under study. Model experimental simulations show that despite emissions from within the city, what explains the severity of pollution over Delhi during the period under consideration is an additional pollution load emanating from these intense crop open burning sessions from nearby areas. Further, model simulations show that while Diwali emissions can elevate the pollution load over Delhi, the effects do not last beyond 48 h. It is found that the stagnation of the pollutants several days beyond the 2016 Diwali day was due to favorable meteorological conditions like low surface temperature, lower boundary layer height, and weak northwesterly winds. The study shows that in order to improve air quality in Delhi during the post-monsoon period, mitigation efforts should target the adjacent rural areas, especially when there is massive burning of crop residue in those areas. © 2020 WRF-Chem; Megacity pollution; Delhi; Biomass burning; PM2.5; Aerosols; Atmosphere modelling; Atmospheric physics; Air quality; Environmental analysis; Environmental assessment; Environmental pollution © 2020

Heliyon

Numerical simulations of different sectoral contributions to post monsoon pollution over Delhi

The spatial analysis of the wet spell probability over the Indian region has been carried out using daily gridded (0.5º × 0.5º) rainfall data of 1971-2005 during the summer monsoon period, i.e. June-September. A threshold was applied to the weekly cumulative rainfall to convert the rainfall data into wet spell information. A Markov chain model was employed to estimate the initial and conditional probabilities of the wet spell for each grid and the spatio-temporal distribution of the wet spells probabilities was analyzed. The probability maps were able to capture the summer monsoon scenario over the Indian region, representing the onset, progression and withdrawal of monsoon rainfall. Higher wet spell probability was observed over the west coast and northeastern parts of India, i.e., the initial probability was maximum over these regions. However, lower probability values were observed in West Rajasthan, Gujarat and southern India. A threshold of 80% of the maximum initial probability was used to standardize the spatially-variable probability information, and a week with more than the threshold values was considered as a probable wet week. The duration of the longest probable wet spell was highest along the west coast and in northeastern India, whereas it was lowest in western and southern India. The start and duration of the longest spell of the probable wet week can be used for rainfed-agricultural planning, i.e., the start of sowing/planting, selection of crops and varieties based on their length of growing period, optimum harvesting period to avoid wet spell, etc. © 2020 Universidad Nacional Autónoma de México, Centro de Ciencias de la Atmósfera. All Rights Reserved.

Atmosfera

Spatial analysis of wet spell probability over India (1971-2005) towards agricultural planning

The objective, accurate and rapid quantification of agricultural drought is the key component of effective drought planning and management mechanism. The present study proposed a new index, i.e. multivariate phenology-based agricultural drought index (MADI), for quantification of the agricultural drought using long-term (1982–2015) crop phenological parameters. The 15-day global inventory modelling and mapping studies time-series normalized difference vegetation index (NDVI) data (~ 8 km) were interpolated at daily scale and smoothened using Savitzky and Golay filtering technique. Different crop phenological parameters, i.e. start of season, end of season, length of the growing period (lgp), integrated NDVI (iNDVI), etc., were estimated using a combination of threshold and derivative approaches for individual pixels during kharif season. Based on the time of occurrence, the agricultural droughts may lead to delay in crop sowing, reduction in cropped area and/or decreased production. Hence, the lgp and iNDVI were selected among all phenological parameters for their capability to represent alterations in crop duration and crop production, respectively. The long-term lgp and iNDVI of individual pixel were detrended and transformed into standardized lgp (Slgp) and standardized iNDVI (SiNDVI) to eliminate the existing trends developed due to technological improvements during study period and existing heterogeneity of Indian agricultural system, respectively. The MADI was calculated by fitting Slgp and SiNDVI into joint probability distribution, where the best joint distribution family along with associated parameters was selected based on the goodness-of-fit for individual pixel. The values of MADI vary between − 4 and + 4, where the negative and positive values represent drought and non-drought conditions, respectively. The efficacy of the proposed index was tested over the Indian region by comparing with the multivariate standardized drought index, which considers the impacts of both meteorological and soil moisture drought using copula approach. © 2020, Springer Nature B.V.

Natural Hazards

Quantification of agricultural drought over Indian region: a multivariate phenology-based approach

Fore-warning plays an essential role for effective drought mitigation planning. A new approach, i.e. fractional wetness (fw), has been proposed towards fore-warning of early season agricultural drought over Indian region. The optimum soil moisture triggers the subsequent crop sowing activities that has been conceptualized in this approach. Shortwave Angle Slope Index (SASI) images derived from time-series (2001–2012) MODIS data was used for fractional-wetness (fw) computation. fw is the surrogate indicator for the percentage moisture available for a given time, based on its long-term range. The zone-wise fwthresholds was developed to estimate the area-favourable-for-crop-sowing (AFCS), which was validated with subsequent months’ crop-planted-area derived from fractional vegetation cover (fc). The mean absolute error (MAE) was found to be ∼5.73% of the agricultural area. The present methodology is capable of fore-warning the agricultural-drought one month prior to the traditional method. Moreover, the comparison of predicted AFCS with all India food-grains area corroborates the feasibility of proposed approach in quantifying the agricultural-drought, in terms of its intensity, extent and progression. © 2018, © 2018 National Remote Sensing Centre, ISRO.