While destruction associated with floods during the monsoon season and cyclones receives wide attention, the extreme weather in the form of hail, lightning and high winds have also caused widespread devastation over India on a small spatial scale in recent years, especially during the period of March to June. India Meteorological Department (IMD) organized a special forecast improvement campaign during the period March to June of 2017–2019 when the weather forecasts at all offices of IMD were targeted towards an accurate forecast of the extreme form of thunderstorms and their associated impact in short range to nowcasting timescale and their dissemination. The purpose of this study is to quantify the improvement in operational thunderstorm forecast accuracy, in short range (24 h Severe Weather Guidance at subdivisional level) and nowcast scale (nowcasts for individual stations valid for 3 h and issued every three hours) during March to June of 2017 to 2019 and compare the same with the accuracy of previous years. As a result of these efforts, there has been a significant jump in forecast accuracy in the 24-h thunderstorm forecast as well as 3-h nowcast guidance for thunderstorms across the country. Probability of Detection (POD) scores for India as a whole for the 24-h thunderstorm forecast has doubled, while the false alarms (FAR) have remained at the same level as before the start of the forecast campaign. The results indicate that since a thunderstorm is a disastrous weather event, the forecasters generally tend towards spatial over-forecasting. However, this is not uniform across the months. There is systematic lower accuracy in the season transition months of March (winter to summer) and June (dry summer to wet summer). While POD decreases in both March and June, FAR decrease throughout the season. The significant evolution of atmospheric parameters (moisture in particular) as the season changes, favours the maturation of thunderstorms to cumulonimbus stage as the season progresses, and the problem of over forecasting in March becomes a problem of under forecasting of thunderstorms in June. Another reason for false alarms is the unconscious linkage of the thunderstorm with the pattern of rainfall occurrence. However, since all rain-giving clouds over India do not necessarily mature to the cumulonimbus stage, and vice versa, the two are not always related. This is particularly true for the more arid regions of the country, especially in March, where false alarms are higher. The poor density of reporting observatories compared to the mesoscale nature of the events may also increase false alarms, especially over the small maritime islands and the arid regions of the mainland. The accuracy of the All India 3 hourly station level nowcast also improved systematically since 2017. Despite these constraints, the improvements at all scales were possible due to (a) augmentation of observation network by the rapid expansion of Doppler radars network throughout the Indian mainland as well as the installation of a ground-based lightning detection network, (b) numerical modeling products introduced in 2019 to provide short-range forecasts for all aspects of convection; both of which are incorporated into the forecast framework through Standard Operating Procedures (SOP) to standardize the forecast procedure throughout the Indian region. A more objective forecast strategy, using data generated from a denser network of DWRs and crowdsourcing methods as well as more accurate mesoscale models will go a long way to further improve the thunderstorm forecasts.

Heavy rainfall (HRF) forecasting in hilly region is always a challenge to the operational forecasters. Synoptic Analogue Model (SAM) is considered as one of the useful tools for HRF forecasting in topographically influenced hilly regions. In every monsoon season, the Teesta river catchment and its adjoining areas in Sub-Himalayan West Bengal and Sikkim (SHWB-S) generally receive several events of HRF. With the primary objective to find the method to issue HRF warning over Teesta river catchment and adjoining areas in SHWB-S, a SAM has been developed by analyzing 18 years (1998-2015) data comprising prevailing synoptic situations affecting the area and daily rainfall data of subsequent day of HRF. In addition, impact of different synoptic systems on the distribution of HRF has also been studied. The results revealed that there exists a good agreement between daily HRF warnings issued with the corresponding HRF event observed over this region on the next day.

This article presents the study of recent trends of surface temperature and rainfall over some selected stations of North Bengal based on 30 years' data (1979-2008). The study revealed that the degree of warming are more pronounced over two most rapidly developing towns of Jalpaiguri and Malda which may be attributed to rapid urbanization and infrastructure development taking place in the districts; the degree of warming observed over Malda being more pronounced than that over Jalpaiguri and it is the least over Balurghat. Also, the intensity of seasonal as well as annual cumulative rainfall over Balurghat is increasing more significantly; the increase in intensity being most significant during the pre-monsoon and monsoon seasons; the geographical location and orography of Balurghat, the least developed town, may be attributed to such an increase.

1. The most important results of convective activity and thunderstorms are at its peak during premonsoon period from March to May which results in heavy rainfall as well as play a major role in the movement of aircrafts belongs to civil and defence air station at Mohanbari. As such the study of thunderstorms activity over Mohanbari for Six years (2000-2005) during pre-monsoon period has been taken by analyzing surface and upper air observations.

The Primary aim in this paper is to find an alternative approach that consists of modeling the pattern of dry and wet spell over some districts of Assam. The Markov Chain Model is used to predict the length of dry and wet spells during the Indian summer monsoon season (June to September). This information may help the agronomists and agricultural scientists in crop planning. Five districts viz., Dibrugarh, Kamrup, Sonitpur, Dhemaji and North­ Lakhimpur are considered here for this study. Markov Chain Model is fitted for each of the district and the results of the five districts are pooled. This pooled result reveals that during the period 1987-1992, the probability for the day being wet when the immediately preceding day is dry for different years varies from 0.44 to 0.54 while the probability of the day being wet when the immediately preceding day is wet for different years varies from 0.74 to 0.86. It is also found that in the Indian summer monsoon season after about every consecutive 4 - 7 wet days a dry day is expected to occur whereas alter about consecutive 2 dry days, a wet day is expected to occur. The number of days required for the process to reach the state of equilibrium varies from 4 - 7 days.

This article presents the method to issue Quantitative Precipitation Forecast (QPF) for Teesta catchment. A synoptic analog model has been developed analyzing 10 years (1998-2007) data for Teesta catchment. The outcomes are then validated with the realized Average Areal Precipitation (AAP) for the corresponding synoptic situations during south-west monsoon season 2008 (1st June to 30th September) over Teesta basin and results revealed that there exists a good agreement between day-to-day QPF with corresponding realized AAP calculated over this basin next day. In addition, occurrence of heavy rainfall has also been studied in this paper.