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The city of Mumbai, India, frequently receives extreme rainfall (>204.5 mm day−1) during the summer monsoonal period (June–September), causing flash floods and other hazards. An assessment of the meteorological conditions that lead to these rain events is carried out for 15 previous cases from 1980 to 2020. The moisture source for such rain events over Mumbai is generally an offshore trough, a midtropospheric cyclone, or a Bay of Bengal depression. The analysis shows that almost all of the extreme rain events are associated with at least two of these conditions co-occurring. The presence of a narrow zone of high sea surface temperature approximately along the latitude of Mumbai over the Arabian Sea can favor mesoscale convergence and is observed at least 3 days before the event. Anomalous wind remotely supplying copious moisture from the Bay of Bengal adds to the intensity of the rain event. The presence of midtropospheric circulation and offshore trough, along with the orographic lifting of the moisture, give a unique meteorological setup to bring about highly localized catastrophic extreme rainfall events over Mumbai. The approach adopted in this study can be utilized for other such locales to develop location-specific guidance that can aid the local forecasting and emergency response communities. Further, it also provides promise for using data-driven/machine learning–based pattern analysis for developing warning triggers. Significance Statement We have identified the meteorological conditions that lead to extreme heavy rains over Mumbai, India. They are that 1) at least two of these rain-bearing systems, offshore trough, midtropospheric circulation, and Bay of Bengal depression moving north-northwestward are concurrently present, 2) an anomalous high SST gradient is present along the same latitude as Mumbai, and 3) the Western Ghats orography favors the rainfall extreme to be highly localized over Mumbai.

Global urban population is projected to double by 2050. This rapid urbanization is the driver of economic growth but has environmental challenges. To that end, there is an urgent need to understand, simulate, and disseminate information about extreme events, routine city operations, and long-term planning decisions. This paper describes an effort underway in India involving an interdisciplinary community of meteorology, hydrology, air quality, and computer science from national and international institutes. The urban collaboratory is a system of systems for simulating weather, hydrology, air quality, health, energy, transport, and economy and its interactions. Study and prediction of urban events involve multiscale observations and cross-sector models, heterogeneous data management, and enormous computing power. The consortia program (NSM_Urban) is part of “weather ready cities,” under the aegis of India’s National Supercomputing Mission. The ecosystem “Urban Environment Science to Society” (UES2S) builds on the integrated cyberinfrastructure with a science gateway for community research and end-user service with modeling and interoperable data. The collaboratory has urban computing, stakeholder participation, and a coordinated means to scaffold projects and ideas into operational tools. It discusses the design and the utilization of high-performance computing (HPC) as a science cloud platform for bridging urban environment and data science, participatory stakeholder applications, and decision-making. The system currently integrates models for high-impact urban weather, flooding, air quality, and simulating street- and building-scale wind flow and dispersion. The program with the work underway is ripe for interfacing with regional and international partners, and this paper provides an avenue toward that end.

Understanding Rapid Intensification (RI) is crucial for improving the Tropical Cyclone (TC) intensity forecast skill and TC induced disaster preparedness. The physical processes that lead to RI are not well studied over the North Indian Ocean (NIO) region. Two TC cases, i.e., Titli (exhibited RI) and Phethai (not exhibited RI) from the 2018 post-monsoon season are considered to understand the environmental conditions responsible for making RI happen. The Hurricane Weather Research and Forecasting (HWRF) model produced simulations for Titli and Phethai are analyzed to understand the intensification changes. The purpose of this study is to investigate the environmental and storm structure characteristics that led to the RI of TC Titli when compared to TC Phethai. The Phethai and Titli simulations are initially validated against the best estimations by India Meteorological Department (IMD) and compared in terms of the environmental vertical wind shear as this parameter is often negatively correlated with TC intensification. Area averages of the deep-layer and mid-layer vertical wind shear over the TC environment suggest that shear did not affect the intensification of the Titli simulation. Initially, Titli contained higher magnitudes of relative humidity throughout the troposphere within the storm’s circulation. This steered to a higher upward mass flux in the troposphere and rapid intensification of relative vorticity (circulation) in the middle troposphere prior to any significant change ensued at the surface. After a deep vortex was established, the mass flux (and lower-tropospheric convergence) continued to rise, leading to RI. These results offer a basis for future research to understand better and forecast the development of RI.

The impact of soil moisture (SM) in the regional climate model RegCM4.6 is investigated for the simulation of Indian summer monsoon seasonal rainfall and its spatiotemporal variability. For this purpose, the model is initialized with soil moisture from five different sources such as the (1) European Space Agency Climate Change Initiative (ESACCI), (2) Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), (3) Climate Prediction Center (CPC), (4) ECMWF's reanalysis (ERA) of the twentieth century (ERA-20C), and (5) corrected ERA (CERA). There is a notable discrepancy in the magnitude and distribution of SM among the different sources. In the first stage of the experiment, the model is integrated for two deficit (1982 and 1987), two excess (1983 and 1988), and two normal (1984 and 1989) monsoon seasons to identify the best suitable SM source. Soil moisture tends to have a positive relationship with precipitation and evapotranspiration and a negative relation with sensible heat flux. The CPC SM having a correlation coefficient of 0.6 performs better than the other sources, and it is hence used for long-term simulations of the recent 18 monsoon seasons. We suggest that the initialization of RegCM using CPC data is suitable since the model skill is higher than the other sources of SM. Comprehensive statistical analyses also confirm that the CPC is a better choice over the other sources, and the skill of the model, while using CPC improves over the control experiment. The performance index is higher with the CPC data than the control experiment for a longer-period simulations. The number of phase-synchronizing events based on the standardized anomaly index suggest that the CPC anomaly is aligned in the same direction to the observed rainfall anomaly in 13 of the 18 seasons, whereas the control experiment is aligned for nine of the 18 monsoon seasons. Also, the skill of the model increases from 0.48 with the control experiment to 0.56 with the CPC-initialized experiment.

This study presents the reversal nature in rainfall over heavy rainfall zone (HRZ; more than 80% of the long period average (LPA) of the Indian summer monsoon rainfall (ISMR)) and low rainfall zone (LRZ; less than 40% of ISMR-LPA) in India. The India Meteorological Department (IMD) high-resolution (0.25° × 0.25°) dataset is used from 1901 to 2016. The single and multiple change-point detection techniques are used to find the change in rainfall patterns over both regions. Further, the study period is divided into two halves P1 (1901–1958) and P2 (1959–2016) to examine the change in rainfall patterns in the recent and past periods. In P2, the rainfall pattern gets reversed, and ISMR has shown a significant increasing (decreasing) trend over the LRZ (HRZ). The increasing/decreasing number of moderate- and high-intensity rainfall events is one of the causes for this reversal pattern. Additionally, the number of dry days is increased over the HRZ and decreased over the LRZ. This study further confirms that the “dry becomes drier and wet becomes wetter” paradigm is not solely acceptable for India. The present study provides information about changes in dry days and ISMR variability in the context of climate change, which will be useful to agricultural risk management, water resources, drought monitoring, model developers, and policy planner on the adaptation strategies for climate change.

This study assessed two different vortex initialization (VI) and relocation methods for improved prediction of tropical cyclones (TCs) over the Bay of Bengal (BoB) using the triply nested (27/9/3 km) state-of-the-art Hurricane Weather Research and Forecasting (HWRF) model. The first VI method, “cold-start,” obtained the initial TC vortex from the global analysis. The second one, “cyclic-start,” received the initial vortex from the 6-h forecast of the previous forecast cycle of the same model. In both the strategies, the vortex was corrected to the position, strength, and structure defined by the India Meteorological Department. A total of 32 forecast cases (from five cyclones) over the BoB were considered. The cyclic-start experiments yielded better initial structure and asymmetry as compared to the cold-start experiments. The average statistics indicated that the cyclic initialization improved the 24-h track prediction (by 29%), while the cold initialization was better for the 72-h prediction (by ~ 28%). The intensity was consistently improved in the cyclic-start experiment by up to 68%. The number of cyclic initializations depended on the TC duration. On average, the cyclic initialization improved the representation (strength and size) of the initial vortex up to nine cycles after the first cold start and exhibited an improved skill of 25%; beyond nine cycles, the skill improvement was only 12%. Diagnostic analyses of very severe cyclonic storm (VSCS) Phailin (rapidly intensified) and VSCS Lehar (rapidly weakening) revealed that the cyclic initialization realistically represented equivalent potential temperature, upper-level cloud condensate, and moisture intrusion, which improved the model performance. This study brought out the benefit of the (cyclic) VI for improved TC prediction capabilities in the BoB basin.

Seasonal prediction of Indian summer monsoon rainfall has been considered as one of the important factors to decide the social and economic aspect of India because of its multi-sectorial dependencies. This study evaluates the performance of seven state-of-the-art GCMs in simulating the summer monsoon rainfall on a seasonal scale over the period of 1982–2008 using the GCM reforecasts. The rainfall simulated by the models is compared with the IMD observed rainfall dataset at 0.25° × 0.25°. Preliminary analysis of spatial pattern and statistics shows that the models IITM-CFSv2, NCEP-CFSv2 and ECMWF are some of the prominent models that capture the seasonal rainfall pattern and possess good skill. ECMWF performs very well in simulating the rainfall pattern as well as the rainfall intensities. Comprehensive statistical analysis such as standard deviation ratio and skill scores concludes that the IITM-CFSv2 produces the rainfall pattern as well as the variability of the summer monsoon better than its counterparts. The multi-model simple mean also tends to improve with the addition of IITM-CFSv2. Though the rainfall trend and variance simulated by IITM-CFSv2 is quite in agreement with the observed, there lies a significant dry bias over the north-west India. The mean simulated rainfall is quite less with the CFSv2 models. Though the IITM-CFSv2 simulates lesser rainfall at all the four-lead times, it is quite capable in capturing the rainfall variability. The models ECMWF and GFDLA04 are well performers in terms of mean rainfall estimates whereas the models CFSv2 is better in terms of reproducing the rainfall variability.

In the present study, local insitu observations are utilized in the Land Data Assimilation System (LDAS) to generate high-resolution (4 km and hourly) soil moisture (SM) and soil temperature (ST) data over India. Further, the impact of the LDAS-derived SM and ST initialization on simulation of pre-monsoon (March–May) thunderstorms over the Gangetic West Bengal region are assessed. The high-resolution (4 km and hourly) land surface conditions such as SM and ST data are prepared for the period from 2010 to 2013 using the LDAS forced with various insitu observations from Automatic Weather Stations (AWS), Meteorological Aviation Reports (METAR), and micro-meteorological tower observations over India. Four thunderstorm (TS) events during the pre-monsoon season of 2010 are considered for the numerical experiments using climatological SM and ST at coarser resolution (CNTR) and LDAS-generated high-resolution SM and ST (WLDAS) for initializing the Weather Research and Forecasting (WRF) Model. The efficacy of the LDAS-generated SM and ST data are verified against micro-meteorological tower observations at Kharagpur, West Bengal, and the results indicate the magnitude and variation in the data product are close to observations. The initialization of high-resolution LDAS-generated SM and ST in the WRF improves the simulation of near-surface air temperature, humidity, and pressure at Kharagapur. Most importantly, the location and timing of the storm are relatively better simulated in the WLDAS than CNTR over the Kolkata region. The study encourages the use of more local observations in the generation of high-resolution SM and ST data for application in the simulation of pre-monsoon TSs.

This is the first study concerning the assimilation of the INSAT-3D/3DR radiance in the Hurricane Weather Research and Forecasting (HWRF) model and assesses its credibility to improve track, intensity, and precipitation forecasts of tropical cyclone (TC) Titli that occurred over the Bay of Bengal (BoB), which showed rapid intensification (RI) and weakening through its lifetime. The inbuilt Gridpoint Statistical Interpolation (GSI) method is used with a 3-D variational (3DVAR) configuration. Three sets of numerical experiments such as control (CNTL) (no assimilation), Global Telecommunication System (GTS) (observations from GTS network), and INSAT-3D/3DR (INSAT-3D/3DR sounder radiance data and GTS observations) were carried out with seven different initializations. The radiance analysis reproduced the initial vortex and the prominent synoptic scale features associated with TC Titli. The average root-mean-square errors (RMSE) of the analysis were relatively lower in the INSAT-3D/3DR compared to the CNTL and GTS. The HWRF performance is enhanced for track simulation, with improvements in mean landfall position errors by 40%–70% and 26%–52% for the INSAT-3D/3DR and GTS runs, respectively. The assimilation of radiance data has a positive impact on the simulation of warm core and thermodynamic structures, which has led to a more accurate intensity prediction (by 30–47%) over the CNTL. The assimilation run could realistically simulate the RI and weakening phases of the TC. A cold dry air intrusion is also observed when associated with the weakening. The study highlights the need to incorporate INSAT-3D/3DR radiances for improved TC predictions over the BoB basin.

Tropical cyclone (TC) “Fani” (April 26–May 04, 2019) over the Bay of Bengal (BoB) is the first extreme severe cyclonic storm (ESCS) of the season that struck Puri coast with a maximum sustained wind speed of 110 knots. In the past 130 years, TC Fani is the first of its kind with the peculiarity of being persistent with the intensity of severe cyclonic storm (SCS) over land for more than 24 h, thus causing substantial casualties in the major cities of Odisha. Two high-resolution mesoscale modeling systems, (1) state-of-the-art advanced research version of weather research and forecasting (WRF-ARW) model with a single fixed domain (9 km horizontal resolution) and (2) TC specific hurricane WRF (HWRF) with three domains (27/9/3 km horizontal resolution) are used for real-time prediction of Fani in quasi-operational setup. Storms surge prediction is also carried out using IIT Delhi dynamical storm surge model. Both the models, WRF and HWRF, were able to predict the system from its genesis stage (5 days in advance of landfall) with reasonable accuracy in position and strength. The tracks from both models are in good agreement with the observed track. The average track errors for WRF and HWRF are 101 km and 85 km, respectively. The landfall time and position are well predicted, with 60 h of lead time. HWRF model performance in predicting landfall time, position, and intensity is significant. Along with the statistical analysis, structural and vertical analysis of wind, enthalpy flux, and thermal distribution are carried out by considering two represented initial conditions from both WRF and HWRF models. Maximum reflectivity and precipitable water diagnostic studies depict that both the models show asymmetricity at the peak intensity of ESCS. The rainfall during the landfall day from both the models is comparable with the gauge-satellite merged observed rainfall datasets. Along with TC track, intensity, and structural forecasts associated with storm surge at different vulnerable coastal cities are attempted to predict with reasonable accuracy.

The variability in Indian summer monsoon rainfall (ISMR) has an immense socioeconomic impact on India, especially over the monsoon core region. In this study, a thorough analysis of the ISMR (June to September—JJAS) has been performed for East India (EI), a part of the monsoon core zone, and for its four subdivisions (Odisha, Chhattisgarh, Jharkhand, and Bihar). The long-term climatology, variability, and trends of various categorical rainfall events as defined by India Meteorological Department (IMD) have been analyzed by using IMD high-resolution (0.25° × 0.25°) gridded dataset for 116 years (1901–2016). Also, the percentage contribution of various rainfall events to the total seasonal rainfall and wet days is estimated. To get the changes in the frequency of various dry spells, rainfall events, and associated seasonal rainfall, study period is divided into two halves, earlier period (P1: 1901–1958) and recent period (P2: 1959–2016). The results suggest that the percentage contribution of medium-intensity rainfall events (7.6–64.4 mm/day) to the total ISMR is higher (~ 72%) followed by high-intensity rainfall events (~ 15%) and low-intensity rainfall events (12%), while most percentage contribution to the total number of wet days is from low-intensity rainfall events (~ 54%) followed by medium-intensity rainfall events (~ 43%) and high-intensity rainfall events (~ 3%). In the recent period (P2), the ISMR significantly decreased over the central part of EI because of the significant decrease in the frequency of low and medium-intensity rainfall events and wet days. The remarkable decrease of wet days leads to an increase in the short (1 week) and long-duration (> 2 week) dry spells in P2. And at the same time, high-intensity rainfall events significantly increased over EI in the recent period (P2) and led to an increase in the flood situation over this region. The profound relation of deficit rainfall of EI with ENSO is getting weak in the P2. This study is certainly useful in determining the effects on various sectors because of the variability and changes in various categorical extreme rainfall events, short and long-duration dry spells during summer monsoon time over EI. Also, this study may assist the risk management sectors in acclimating advanced technologies for a sustainable future in the changing climate of the present global warming era.

The northeast monsoon (October–December; NEM) rainfall is a very important entity to about 250 million people residing in the South Peninsular India (SPI) region as it is their principle rainy season and contributes mostly to their annual rainfall. The aim of the study is to obtain the future projections of NEM rainfall over this region representing three different greenhouse gas emission scenarios (i.e., RCP 2.6, RCP 4.5 and RCP 8.5) for two future time slices, i.e., near (2020–2049) and far future (2070–2099) estimated from a set of high-resolution regional climate simulations performed under CORDEX-SA experiments. To achieve that, first, an assessment of ten CORDEX-SA regional climate model (RCM) simulations is done for NEM rainfall over SPI for present climate (1976–2005) against India Meteorological Department high resolution (0.25o × 0.25o) gridded rainfall analysis dataset. Then, the change in NEM rainfall over SPI in the near and far future is computed after applying standardized reconstruction technique to adjust the bias present in the models. The results suggest that most of the CORDEX-SA experiments are able to simulate the spatial distribution of NEM seasonal rainfall and its variability over SPI but there is an inability in capturing realistic magnitudes and the errors are more over the east and west coast where most of the rainfall occurs. The experiment with RCA4 driven by EC-EARTH global model and REMO2009 driven by MPI-ESM has a fairly lesser bias than the other models, whereas the bias is more in LMDZ-IITM-RegCM4. The experiments by CCAM models have very similar characteristics in representing the rainfall pattern. The standardized reconstruction bias correction technique was found to significantly improve the performance of the climate models in representing the climatological mean and inter-annual variability of (IAV) of NEM rainfall over SPI, but in case of categorical rainfall years, improvement is seen only in the normal rainfall years. In the future, for RCP 2.6, there is a rise in NEM rainfall in the first half of the twenty-first century, which is projected to decline after that; however, in the other two scenarios, the rainfall is projected to increase. It is also found that there may be lesser excess rainfall years and more deficit rainfall years in the RCP 4.5 and 8.5 scenarios in the near future, and no deficit rainfall years are projected in the far future in both these scenarios based on the present climate. It is also noticed that the variability in the NEM rainfall over this region may remarkably increase in all the three future scenarios, which will highly impact various water resources management sectors. Thus, this study is very useful in determining the effects on various sectors due to the variability in NEM rainfall over this region and for adapting to climate change using advanced technologies for a sustainable future.

The present study investigates the spatiotemporal characteristics of rainfall events during the summer monsoon season over Odisha (one of the vulnerable zone for heavy rainfall) with the main aim for heavy-to-extreme rainfall events. India Meteorological Department (IMD) station observations and gridded rainfall analysis datasets for a period of 34 years (1980–2013) are used and four frequency indices (heavy-to-extreme, light-to-moderate, dry days, and wet spells) and four intensity indices (daily maximum rainfall, 5-day maximum rainfall, seasonal rainfall total, and daily intensity index) from both the datasets are evaluated. Furthermore, the above-stated indices are analysed over the four meteorological zones of Odisha, as classified by IMD. The analysis reveals that both the heavy-to-extreme rainfall days and dry days are increasing, while the light-to-moderate rainfall days and wet days are decreasing. It is also found that the rate of increase in rainfall amount and number of wet-day are higher in the southern than northern Odisha. This implies that the climate is becoming drier as one move from south to north and the gradient is also increasing with time. The climatological analysis suggests not only the heavy-to-extreme rainfall days and intensity are more in urban (Khordha) and highly elevated (Eastern Ghat; height ~ 1.6 km) areas but also the trend is increasing over those regions. The Murphy skill score of daily rainfall between two datasets is 0.94; however, the number of the extreme rainfall events is more in station data (297) than the gridded data (150) during the study period.

The increased temperature and humidity in the atmosphere under global warming is the primary cause of the upsurge of heat waves in the tropical belt. The central east coast of India (CECI; Odisha, Andhra Pradesh, and Telangana) is one of the most heavily affected areas in terms of casualties due to heat waves during pre-monsoon (March–May; MAM). Thus, there is a need to analyze the characteristics of pre-monsoon weekly maximum temperature (Tmax) and associated heat waves over the CECI. In the present study, characteristics of weekly Tmax from 23 March to 31 May over the CECI associated with heat waves have been analyzed using the India Meteorological Department gridded (1o × 1o) analysis data set of daily maximum temperature for the period 1980–2015. The recent changes in the weekly Tmax and frequency of various heat-wave spells (1-, 2-, 3-, and 5-day) were also evaluated. The results suggest that the climatological weekly Tmax along the coastal region is less than that in the interior parts for all 10 weeks, and the inter-annual variability and coefficient of variation exhibit similar patterns. The continuous increase in Tmax and its variability is observed as the season progresses, leading to increased intensity and frequency of heat waves in most parts of the CECI. In the recent period, a notable increase in the weekly Tmax and its variability has been observed over most parts of the CECI that has resulted in more heat waves. This study is very beneficial for determining the effects on various sectors for the planning of adaptation methodologies through appropriate strategies for a tolerable future over the CECI in the context of global warming.

The exorable climate changes leads to changes in the frequency and intensity of extreme rainfall events worldwide; however, the amendment in extreme rainfall events is not uniform over space, precisely it is more localized and a great threat to the society. Thus, study of rainfall extremes at a finer spatial scale is essential and identifying the large-scale parameters that are responsible factors is highly needed. Odisha state in India is one of the most vulnerable to weather extremes and considered as a study region. The present study is bi-fold, firstly examining the changes in the extreme rainfall (≥ 204.5 mm/day) over Odisha and exploring the foremost large-scale meteorological parameters responsible for heterogeneous characteristics of extreme rainfall within Odisha during 1980–2017 summer monsoon period. India Meteorological Department gridded high-resolution (0.25° × 0.25°) rainfall analysis and ERA-Interim (0.25° × 0.25°) reanalysis data at daily scale are used for the analysis. The study region has an increasing trend in extreme rainfall events and it is evident that the Indian Ocean is warmer during extreme rainfall events compared to the dry events, particularly near the seashore of Odisha. The stronger (weak) and cyclonic (anti-cyclonic) flows at 850-hPa exhibit during extreme rainfall (dry) events. The moisture flux is convergent during extreme rainfall events, while it is reverse during dry events. The monsoon trough has been shifted to south (north) from its normal position during extreme rainfall (dry) events. A detailed investigation is carried out for extreme rainfall events over five different regions in Odisha. It reveals that the wind at 850 hPa, omega at 500 hPa, and SST play the important role for Region I, while OLR and omega at 500 hPa are dominating for the Region IV in the occurrence of extreme rainfall. Moreover, the role of the dominant climatic parameters for the extreme rainfall occurrence varies for the other three regions. Analysis confirms that the role of main meteorological parameters is statistically significant for the extreme rainfall events over the respective region. Although the Odisha is a small state in India, not only the long-term trend in extreme rainfall varies region-to-region but also responsible factors associated with the climatic conditions differ for the occurrence of the extreme rainfall.

This study focuses on improving the vortex of Tropical Cyclone (TC) using different initialization techniques and its subsequent impact on the performance of Advanced Research Weather Research and Forecasting (ARW) mesoscale model. The Initialization of TC vortex from global analyses (CNTL) is found to be poor due to coarse horizontal and vertical structure, and is improved by assimilating available observations using 3DVAR technique (3DV). In another experiment (VAR_VI), vortex is corrected using vortex initialization and relocation procedures to correspond with India Meteorological Department (IMD) ‘observed’ position and intensity estimates, and then assimilation of additional observations is undertaken. The initial vortex improvement in terms of horizontal and vertical structure is noted in both experiments with the VAR_VI results being better than for the 3DV experiment. Simulations following the VAR_VI experiment showed remarkable improvement in simulating track, intensity and structure of the two TCs it was evaluated for (TC Giri and TC Jal). The large scale dynamical and thermo-dynamical fields such as steering flow, vertical wind shear and warm core structure were also improved. For TC Giri, the VAR_VI could reproduce the observed intensification rate of 10 ms−1 within 12 h, while for TC Jal, the VAR_VI run could simulate the inland rainfall due to sheared convective clouds. Results suggested that the improvements would likely to be more for stronger TCs. This study highlights the continued need and value of improvement of TC vortex using initialization and in-situ and remote sensing observations over the Bay of Bengal region.

The present study investigates the climatological rainfall vulnerability districts associated with monsoon depressions (MDs) over Odisha using both observational and modeling perspectives for a period of 34 years (1980–2013). Four heavy rainfall cases associated with MDs crossed Odisha are simulated with the help of high-resolution advanced research weather research forecasting system, and the rainfall vulnerability mapping for more than 1-mm rainfall values at district level are evaluated. The observational analyses on the frequency of MDs for the study period exhibit a decreasing trend. It is noticed that the rainfall vulnerability districts are present to the left side of the track of the MDs. The model simulation result depicts that the location of formation of MDs is always behind the actual position of the system and also do the landfall after the actual time. The model has a positive bias in the prediction of the intensity of rainfall and a spatial shift in the distribution of rainfall. The model is showing higher Heidke skill for less intensity rainfall threshold values. The analysis also suggests that the maximum rainfall vulnerable districts are present 150–200 km away from the original position, still these districts are present to the left side of the MDs. Analysis on the horizontal and vertical structure of MD suggests that the presence of relative humidity is less in WRF and the distribution of relative vorticity is not same as the observation, which may be the major causes of having such error in predicting heavy rainfall.

Sea surface temperature (SST) varies significantly in the presence of tropical cyclones (TCs). Using fixed SST throughout the integration of high resolution TC models is general practice in research and operational endeavor over the North Indian Ocean. The present study is to assess the impact of updating realistic SST in TC lifetime on track, intensity and rainfall of TCs. The Hurricane Weather Research Forecast (HWRF) model of single domain with 9km resolution is used. A total of 31 forecast cases are considered from 6 TCs during 2007- 16 with unique track and intensity characteristics. A set of two numerical experiments are done without (CNTL) and with 6-hourly SST update (SST) in TC lifetime.Mean track and intensity errors show that there is an improvement of 3–41% in track during 12–120h forecast length for SST run. The SST runs improve landfall position and time prediction by 20% and 33% respectively. The cross track error of SST run is comparable (44km) with average errors available for this basin (34km); and along track errors are improved by 60% as compared to CNTL as well as average errors of the basin. The model is biased to overestimate a weaker TC and underestimate a stronger TC, however, the bias is reduced in SST run by 5–51%. The analyses of wind, enthalpy flux and warm core structures provide insight for realistic intensity prediction of SST run unlike CNTL. Rainfall intensity and radial distribution is also improved in SST run. Thus, this study highlights the significance of ocean coupling with TC models to advance forecast guidance. Keywords: tropical cyclone, HWRF, SST, Bay of Bengal

The Hurricane Weather Research and Forecasting (HWRF) is increasingly becoming known for its better performance in simulating tropical cyclones (TCs) over different basins globally, and the Advanced Research version of the Weather Research and Forecasting (WRF) model is widely used for the same in both research and operational settings. These two models are now operational at India Meteorological Department (IMD) for TC predictions over the North Indian Ocean. The near real-time forecast of HWRF and WRF models is evaluated in a quasi-operational setup based on 62 forecast cases from 10 recent TCs during 2013–2017 over the Bay of Bengal. Multi-satellite estimated winds are used to compare initial and simulated vortex structures, and HWRF has been found to be better. The track prediction is comparable in both the models for shorter forecast lengths (up to 30 h), while the HWRF is skillful (by 27%) for longer forecast, thus leading to a better estimation of landfall position and time. HWRF is significantly better (error

Northeast monsoon (October–December, NEM) is of a huge societal and economic importance over South Peninsular India (SPI) as the region does not experience noteworthy rainfall during the southwest monsoon (June–September; ISM) over India. The aim of the present study is to unveil the behavior of various NEM rainfall events over SPI in a long-term period to unravel the effects of global warming on regional rainfall. This study involves the analysis of long-term climatology, variability, trends, and global teleconnections of various categorical rainfall events as defined by India Meteorological Department (IMD) and its associated rainfall over SPI and its six subdivisions during NEM using a high-resolution (0.25° × 0.25°) data from IMD for the period 1901–2016. The results suggest that the variability of NEM rainfall has increased in the recent period (1959–2016). The seasonal rainfall has increased over Tamil Nadu, Rayalaseema, as well as SPI because of an increase in the number of high-intensity rainfall events in the recent period with respect to the earlier period (1901–1958), while it has decreased over the other subdivisions. The percentage contribution of moderate rainfall events to the seasonal rainfall is more compared to the other events. It is also found that the promising relationship of various NEM rainfall events and its associated rainfall with ENSO, IOD, and ISMR have weakened in the recent decades after 1988. This study is very useful in determining the effects on various sectors due to the variability of heavy rainfall events over SPI during this season and assists the risk management sectors in adapting advanced technologies for a sustainable future in the present global warming era.

The present study evaluates the performances of mixed-convection schemes (MCSs) in which different cumulus schemes are activated over the land and ocean separately to simulating the Indian Summer Monsoon (ISM). The regional climate model RegCM4.4 is used and the initial and boundary conditions are derived from the National Centers for Environmental Prediction (NCEP) and Department of Energy (DOE) reanalysis version 2 (NCEP-R2). The NCEP-R2 and the Indian Meteorological Department (IMD) precipitation analysis at 0.25° × 0.25° are used to evaluate the model simulated results. The four cumulus schemes Grell, MIT-Emanuel, Tiedtke, and Kain and Fritsch have been used to setting ten different combinations of MCSs. The MCSs having Tiedtke/Emanuel scheme over the land and the Grell scheme over the ocean (L:Ti_O:Gr; L:Em_O:Gr) are found qualitatively and quantitatively better to simulating the semi-permanent characteristics of the ISM. These MCSs are effectual in predicting seasonal mean precipitation intensity and distribution during the ISM and closer to IMD precipitation analysis than any other MCSs. It is found that the MCS having Grell scheme over the land and ocean, spatial gradient of precipitation is reasonably well when compared to IMD precipitation analysis. The representation of precipitation over the central India is poor in all the MCSs; this is probably due to increased cloud cover in RegCM4 which in turn, leads to reducing surface air temperature over land and thus reduce convective instability in the model simulations. The present study proposes a statistically based screening method to identify the useful MCSs that have reasonable skills. The best performing MCSs examined for normal and deficit monsoon years are used to simulate 15 consecutive monsoon seasons (1982–1996). Simulations of several monsoon years confirm that the two MCSs namely L:Ti_O:Gr and L:Em_O:Gr are suitable to simulate the ISM.

The complexity of the Indian summer monsoon precipitation makes it’s prediction a challenging task as it is not only influenced by the large-scale flows but also by the micro-scale features. In a dynamical model, precipitation is resulted from the formation of clouds. The cloud formation and its processes occur at a micro scale. Current state-of-the-art dynamical models lack proper representation of the cloud processes, particularly at high resolutions for which the cloud processes are parameterized, thereby poorly resolving the precipitation. This study aims at examining the impact of the cloud parameters on the simulation of Indian summer monsoon precipitation in Regional Climate Model version 4 (RegCM4). The autoconversion coefficient which determines the conversion of cloud water into precipitation in the Explicit Moisture Convergence scheme is adjusted in the RegCM4. The impact of autoconversion is experimented with ten different values and it is found that it has a significant effect on the simulation of precipitation during summer monsoon season. The experiments are conducted by changing autoconversion from 1.5 × 10−4 to 7.5 × 10−4/s along with the default value of 2.5 × 10−4/s. On changing the autoconversion values from 60 to 300% of the default value, the precipitation pattern improves over most parts of India. The model simulates the rainfall better when the autoconversion coefficient is changed to 7.5 × 10−4/s. With the best outcome with the adjusted autoconversion and control configuration, the model is simulated for seventeen monsoon seasons and the analyses of RegCM4 simulated vertically integrated moisture transport, convective available potential energy and atmospheric moisture budget suggest that the model efficacy is enhanced in higher autoconversion value than the control one. Statistical evaluations using bias, correlation coefficients, comprehensive rating matrices and skill score confirm the suitability of higher autoconversion rate for summer monsoon simulations. The model with adjusted autoconversion coefficient (at 7.5 × 10−4/s) has improved the representation of seasonal precipitation distribution and its year-to-year variation including other derived features. The rainfall pattern is improved over North West India and North East India especially, the monsoon core regions. The mean seasonal rainfall is in phase 94% of the time with the modeler-adjusted moisture as compared to 82% in the control in the long term simulation.

There was an error in footnote no. 1 in the initial online publication. The original article has been corrected.

North Indian Ocean (NIO), which comprises of Bay of Bengal (BoB) and Arabian Sea (AS) basins, is one of the highly potential regions for Tropical Cyclones (TCs) in the world. Significant improvements have been achieved in the prediction of the movement of TCs, since the last decade. However, the prediction of sudden intensity changes becomes a challenging task for the research and operational meteorologists. Hence, the present study focuses on finding the climatological characteristics of such intensity changes over NIO regions. Rapid Intensification (RI) is defined as the 24-h maximum sustained surface wind speed rate equal to 30 knots (15.4 ms−1). The results suggest that the TCs formed over the NIO basin are both seasonal and basin sensitive. Since 2000, a significant trend is observed in RI TCs over the basin. At least one among three cyclones getting intensified is of RI category. More number of RI cases have been identified in the BoB basin than the AS. The post-monsoon season holds more RI and rapid decay cases, with 63% and 90% contribution. Most of the TCs are attaining RI onset in their initial stage. Further, India is receiving more landfalling RI TCs, followed by Bangladesh and Oman. The east coast of India, Tamil Nadu, and Andhra Pradesh are the most vulnerable states to these RI TCs. The cyclogenesis locations associated with RI cases hold higher moisture, and sea surface temperature as compared to the Non-RI cases.

We present a numerical investigation of the processes that influenced the contrasting rapid intensity changes in Tropical Cyclones (TC) Phailin and Lehar (2013) over the Bay of Bengal. Our emphasis is on the significant differences in the environments experienced by the TCs within a few weeks and the consequent differences in their organization of vortex-scale convection that resulted in their different rapid intensity changes. The storm-relative proximity, intensity, and depth of the subtropical ridge resulted in the establishment of a low-sheared environment for Phailin and a high-sheared environment for Lehar. Our primary finding here is that in Lehar's sheared vortex, the juxtaposition in the azimuthal phasing of the asymmetrically distributed downward eddy flux of moist-entropy through the top of the boundary layer, and the radial eddy flux of moist-entropy within the boundary layer in the upshear left-quadrant of Lehar (40-80 km radius) establishes a pathway for the low moist-entropy air to intrude into the vortex from the environment. Conversely, when the azimuthal variations in boundary layer moist-entropy, inflow, and convection are weak in Phailin's low-sheared environment, the inflow magnitude and radial location of boundary layer convergence relative to the radius of maximum wind dictated the rapid intensification.