The difference in raindrop size distribution (DSD) characteristics between convective and stratiform rain and in monsoon and pre-monsoon season have been investigated over a tropical location, Kolkata, using seven years data collected from a Joss-Waldvogel disdrometer. During the Indian Summer Monsoon (ISM) and the pre-monsoon season, Kolkata gets substantial rain. Interannual variability of DSD has been evident. The normalized drop size distributions of DSD show double peak at 20-50 mm/h rain rate due to drop break up and coalescence process. For convective rain, concentration of higher drops (>3 mm) are more significant compared to stratiform rain. In the pre-monsoon period, significant contribution of normalized DSD in the afternoon to late evening has been evident due to nor’westers. Contribution of normalized DSD of higher drops (>3 mm) are more in the pre-monsoon convective rain compared to monsoon convective rain. The larger slope of μ-Λ relationship has been observed in pre-monsoon convective period which corresponds to higher D _m values. The relationship between radar reflectivity ( Z ) and rain rate ( R ) distinguishes the monsoon and pre-monsoon seasons. Radiometric measurements have been used to examine the effects of the atmospheric instability parameter CAPE and cloud base height (CBH) on the formation of larger drop sizes. This study will be useful for understanding precipitation microphysics and improving the quantitative precipitation estimation algorithm.

The paper investigates the long-term spatiotemporal characteristics of various satellite-derived vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), as well as Gross Primary Productivity (GPP) and Sun-induced Chlorophyll Fluorescence (SIF) over the Kolkata conurbation and its surrounding areas from 2003 to 2016. Additionally, it analyzes the correlation between these vegetation indices and atmospheric parameters like rainfall, soil moisture (SM), evapotranspiration (ET), and land surface temperature (LST). Monthly variations of these parameters are observed, and inter-annual variability is examined using linear regression techniques. The study also observes the time average spatial correlation between vegetation indices and weather parameters. Moreover, it investigates the time-lag effect (0, 1, 2, and 3 months) using Pearson correlation coefficient analysis between VI and other meteorological parameters. NDVI and EVI exhibit maximum correlation with rainfall, SM, ET, and LST within specific lag periods. NDVI and EVI show a slow response rate to rainfall, and their sensitivity depends on SM and ET. A positive correlation is observed between NDVI and ET, indicating that NDVI increases with vaporized water in the atmosphere. A negative correlation is noted between NDVI and LST in the region studied. The study’s insights are valuable for predicting future vegetation index characteristics based on meteorological parameters in tropical urban areas like Kolkata and its surroundings. This predictive capability can aid in mitigating adverse weather effects on vegetation.

The Sundarban is the world’s biggest mangrove forest, located on Bangladesh’s and India’s southern coasts. The forest protects biodiversity and the local population from various natural disasters. This paper has primarily focused on the change in land use land cover (LULC) of a location in Sundarban delta (21°56’ N, 89°10’ E) using two LANDSAT satellite images for the year 2015 and the lockdown period of 2020, with the help of Geographical Information System (GIS) and supervised machine learning technique. With the reduction of upstream water and sediment supplies, the water body in the Bay of Bengal has increased. The extent of vegetation and agricultural land has decreased due to urbanization and erosion by the Bay of Bengal. The difference between the LULC before and after the Amphan cyclone indicates that land area has been reduced over this area.