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The pressing need for accurate and reliable precipitation measurements and forecasting poses theoretical and technological problems. Remote-sensing instruments with increased coverage and sensitivity (such as space-borne and ground-based radar) are available; however, their full exploitation requires physical calibration and validation based on a deep knowledge of precipitation microphysics. This study reports a detailed analysis of the evidence of non-terminal velocities in a mid-latitude (Bologna, Italy) and a tropical location (Kolkata, India). The data from two identical disdrometers OTT-Parsivel2 were analyzed to shed light on the nature of the raindrops that fall at a velocity that is significantly higher (i.e., super-terminal drops) or lower (sub-terminal drops) than the terminal velocity expected for the raindrop sizes. The results show a significant fraction of super- and sub-terminal drops in both locations. The percentages of both super- and sub-terminal drops were higher in Kolkata. However, the difference was more notable for convective rain. The percentages of both super- and sub-terminal drops were found to be high within a drop diameter of 1 mm. The number of sub-terminal drops seemed to increase with an increase in diameter for drops larger than ~2.5 mm. The natural rain in Bologna showed stronger evidence of drop break-up in correspondence with the evolution of non-terminal velocities. Moreover, this study once again pointed toward the fact that the process of break-up cannot be neglected in natural rain of tropical or mid-latitude locations. We found that 7% and 10% of rain samples in Bologna and Kolkata seemed to be subjected to drop break-up. The results indicate that radar measurements of rain in the tropics or mid-latitude regions, relying on the Gunn–Kinzer relationship between velocity and diameter, should be verified by observations of disdrometers for a high precision QPE.

Lightning is one of the most severe weather events causing significant loss of human lives and resources. Increasing number of lightning fatalities due to recent climatic changes is emerging out to be a serious concern for India during last few years. Proper characterization and parameterization of the same, therefore, is extremely crucial. However, lightning is an extremely dynamic phenomenon having enormous spatio‐temporal inhomogeneity especially over such a vast country like India with varied topographic and climatological features. Therefore, proper parameterization of lightning activity over India needs consideration of different lightning climatologies. This study has attempted to resolve the issue by regionalizing Indian subcontinent in different lightning climatologies based on lightning density and associated atmospheric variables that is, CAPE, specific humidity at different pressure levels, temperature, k index and cloud particle size and identified seven distinct lightning climatologies over India. A regression model is proposed for estimating the annual and seasonal (monsoon and pre‐monsoon) lightning activities over the seven resulting lightning zones based on the said atmospheric variables using machine learning techniques. Four machine learning models have been tested among which Random forest has shown the best accuracy. The regression model has shown an R‐squared score of 0.81 during monsoon season and 0.71 during the pre‐monsoon. The atmospheric features based on their influences on the lightning activity in these seven climatologies has been ranked which presented the evidences of largely varied interplay between different atmospheric variables and lightning over different parts of the country and during different seasons.

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