Heat waves are often termed as the silent killer and have become even more important as recent studies suggest that the heat wave have become second most devastating extreme weather events in terms ...of human deaths and losses. It is also been largely realised by scientific community that it is not just the high temperatures which are responsible for the gruesome effect of heat waves but several other meteorological parameters play a vital role in aggravating the impact and causing much more damages. In view of the above the attention of scientific community, weather forecasters as well as disaster managers has shifted to also take into account the different meteorological parameters like maximum and minimum temperatures, relative humidity, wind speed, duration/spell of heat waves and its intensity which are aggravating the impact of heat stress. In this background, this study is undertaken as an attempt to quantify the effect of different meteorological parameters on heat wave on different regions of India for different summer months (March, April, May and June). In this study the impact of individual meteorological parameter as well their cumulative effect is studied based on data of 30 years (1981-2010) for 300 stations. The effect of different meteorological parameters is identified for different months for different regions of the country. Also the cumulative scores are calculated for different regions considering different meteorological parameters, as a first initiative to perform heat hazard analysis and zonation over the entire country. This could serve as initial step for planning mitigation and adaptation strategies throughout the country. These scores as thresholds for different regions may be also useful for operational forecaster's for early impact based warning services as well as for the disaster managers, for taking effective and timely actions.
The tropical cyclones (TCs) formed over the North Indian Ocean (NIO) have paramount socio-economic impacts over India and neighbouring countries due to heavy rainfall, strong wind and high storm ...surge. In this study, mean rainfall characteristics of three different intensity stages of TCs over the NIO have been examined using a merged satellite-gauge daily rainfall product to better TC rainfall prediction over the region. A total of 32 TCs with 160 days of rainfall over the NIO between October 2015 and December 2021 have been considered. The mean positions of TCs formed over the AS are more than 450 km west during the post-monsoon season than the pre-monsoon season. During the post-monsoon TCs, mean translational speed increases with increase in TC intensity over the Bay of Bengal (BoB), while TCs in the Arabian Sea (AS) move rather slower with increase in intensity. Heavy TC rainfall areas have seen to be larger during the pre-monsoon season than the post-monsoon season over both BoB and AS basins. It is due to smaller mean sea level pressure, and stronger lower and middle level winds over both basins of the NIO during the pre-monsoon season as compared to the post-monsoon season. However, intensity of mean rainfall is higher for the TCs over the AS than the BoB during the pre-monsoon season. Heavy rainfall radius is maximum for depression and deep depression stages of TCs during the pre-monsoon season, while it is maximum for cyclonic storm and severe cyclonic storm stages of TCs during the post-monsoon season over both basins of the NIO. The largest heavy rainfall radius of about 650 km in the northeast geographical quadrant is observed for the pre-monsoon depression and deep depression stages of TCs over the BoB basin. The most intense daily rainfall occurs during the pre-monsoon TCs and the least intense daily rainfall occurs during the post-monsoon TCs over the AS basin. A consistent increase in daily maximum rainfall and decrease in its distance from the TC centre with the increase in TC intensity are observed over the AS for both seasons. The distance of maximum daily rainfall grid from the TC centre is the largest of about 355 km for depression and deep depression stages of TCs formed over the AS during both pre-monsoon and post-monsoon seasons. Furthermore, results indicate that translational speed of TCs has no impact on daily maximum rainfall over the BoB, whereas daily maximum rainfall shows statistically significant negative correlation with the TC translational speed over the AS. This study will be very useful for better TC rainfall forecasting over the NIO region.
Hazards associated with tropical cyclones (TCs) are long-duration rotatory high velocity winds, very heavy rain, and storm tide. India has a coastline of about 7516 km of which 5400 km is along the ...mainland. The entire coast is affected by cyclones with varying frequency and intensity. Thus classification of TC hazard proneness of the coastal districts is very essential for planning and preparedness aspects of management of TCs. So, an attempt has been made to classify TC hazard proneness of districts by adopting a hazard criteria based on frequency and intensity of cyclone, wind strength, probable maximum precipitation, and probable maximum storm surge. Ninety-six districts including 72 districts touching the coast and 24 districts not touching the coast, but lying within 100 km from the coast have been classified based on their proneness. Out of 96 districts, 12 are very highly prone, 41 are highly prone, 30 are moderately prone, and the remaining 13 districts are less prone. This classification of coastal districts based on hazard may be considered for all the required purposes including coastal zone management and planning. However, the vulnerability of the place has not been taken into consideration. Therefore, composite cyclone risk of a district, which is the product of hazard and vulnerability, needs to be assessed separately through a detailed study.
We have studied the upper air RSRW data of 00 UTC during the pre-monsoon season, i.e. March–May of 2016–2018 for 6 capital cities viz. Kolkata, Bhubaneswar, Guwahati, Patna, Ranchi in the Eastern ...part of India and one island station, Port Blair. We have analyzed thermodynamic stability indices to identify the indices which have been most suitable for the prediction of thunderstorm events for each location. Based on the consensus of suitable indices and considering the spatial variation, we have proposed a scheme for predicting whether there would be any thunderstorm at a particular location within 24 hours. Verification has been carried out for 2019–2020 depending on availability of data and the performance of the proposed scheme has been compared with existing latest operational methods in India. We find that the proposed scheme can predict thunderstorms with reasonable accuracy and has better performance, mostly than that of existing operational methods.
We have studied the thermodynamic stability indices and their effectiveness in predicting premonsoon thunderstorms for different cities all over India. For that purpose, we have studied 10 ...thermodynamic stability indices for 24 cities all over India. We have analyzed the upper air radiosonde data at 00 UTC during the premonsoon season of 2016-2018. The mean, standard deviation, and range of variation of the indices have been obtained separately for thundery and nonthundery days. For each of the indices, we have determined the optimum threshold values that give the best prediction skill for each location. Further, we have identified a list of suitable indices that are more effective in predicting thunderstorms for each location. Finally, we have proposed a scheme for premonsoon binary thunderstorm prediction, i.e., whether thunderstorms will occur or not at a particular place, on the basis of optimum consensus among the suitable indices.
The c group of Gram-negative gliding bacteria, has a long history of cosmopolitan occurrence. It has great biodiversity despite the absence of sexual reproduction. This wide biodiversity may be ...reflected in the wide spectrum of its secondary metabolites. These cyanobacterial secondary metabolites are biosynthesized by a variety of routes, notably by non-ribosomal peptide synthetase or polyketide synthetase systems, and show a wide range of biological activities including anticancer, antibacterial, antiviral and protease inhibition activities. This high degree of chemical diversity in cyanobacterial secondary metabolites may thus constitute a prolific source of new entities leading to the development of new pharmaceuticals.
Spaceborne precipitation radars provide an unprecedented opportunity to study three-dimensional structure of precipitation, particularly over the open ocean where in-situ observations are rather ...meagre. In this study, instantaneous surface precipitation characteristics and their vertical structures during the tropical cyclones (TCs) over the North Indian Ocean (NIO) between 2014 and 2022 have been analysed using the dual-frequency precipitation radar onboard the Global Precipitation Measurement Core Observatory. Although stratiform precipitation accounts for more than 70% of total TC surface precipitation area, convective precipitation contributes about half of the total TC surface precipitation amount over the NIO. About 90% of stratiform TC precipitation area yields surface precipitation of less than 10 mm/hour. The vertical structures of stratiform and convective TC precipitation vary with surface precipitation intensity and have nearly similar characteristics over both basins of the NIO. This preliminary quantitative TC precipitation analysis would be useful for better understanding of precipitation processes during TCs over the NIO, and for further advancement in numerical models through improved parameterization schemes for TC precipitation forecasting.
The study targets long‐term analysis of rapid intensification (RI) magnitudes and destructiveness of tropical cyclones (TCs), as well as factors that are responsible for those magnitudes over the ...North Indian Ocean (NIO). Out of 131 TCs during 1990–2021, 50 TCs (38%) exhibited RI in their lifetime. Results indicate that the lifetime maximum intensity (LMI) and landfall intensity (LFI), along with the potential destructive index (PDI), are directly proportional (correlation coefficient > 0.8) to the lifetime maximum intensification rate. Most RI TCs (~80%) made landfall with an average LMI, LFI and PDI of 95 knots, 85 knots and 4 × 107 knot3, respectively. And the destructive indices are more than double compared to landfalling non‐RI TCs. Recent years have witnessed an increasing trend in RI magnitude and the frequent occurrence of very RI cases (intensity change ≥50 knots in 24 h). It infers that recent TCs have achieved RI and higher magnitudes in a short duration (~20 h). The higher intensification rates are promoted by lower wind shear as well as strong surface latent and sensible heat fluxes. The higher sea surface temperature by ~0.2°C, oceanic heat content by ~60 × 107 J·m−2, lower tropospheric humidity by ~0.2 g·kg−1 and moist static energy by ~6 × 106 J·m−2 in the recent period (2007–2021) supports higher intensification rates as compared to the earlier period (1990–2006). The deep‐layer wind shear has decreased by 1.0 m·s−1 in recent years, which supports higher RI magnitudes. This study highlights the risk associated with RI magnitudes and the efforts to be made for improved predictions of higher intensification rates.
The figure illustrates the changes in the cyclone rapid intensification (RI) rates (cyclone symbols) and ocean and atmospheric parameters (such as sea surface temperature, ocean heat content, atmospheric specific humidity and moist static energy), schematically indicated with shading. TP1 and TP2 indicate the time periods 1990–2006 and 2007–2021, respectively. Green curved arrows indicate the surface fluxes. The strong colour shading in the TP2 indicates the increases in sea surface temperature and oceanic heat content, which supports the enhancement of atmospheric moisture through the surface fluxes (density of green curved arrows is more). That enriched moisture helps to boost the moist static energy (MSE) and, in turn, encourages achieving higher RI magnitude in TP2 as compared to TP1 (weak colour shading and less dense curved arrows). Results infer that normal RI (30 knots in 24 h) is common in TP1, whereas the very RI (>30 knots in <24 h) events dominate in TP2. It is presumed that very RI magnitudes may reach to higher potential intensity in their lifetime and be likely to have landfall with higher intensities, posing more destruction.
The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km ...resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007–11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%–51%) for a 12–72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%–10% and 8%–24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ∼13%–28% and 5%–15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%–40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.
A tropical cyclone (TC) Vayu developed over the Arabian Sea during June, 2019. It followed a northward track from southeast Arabian Sea to northeast Arabian Sea close to Gujarat coast during 10–12 ...June 2019 as a very severe cyclonic storm. It skirted south Gujarat coast by recurving west-northwestwards during 13th–14th June and again made a northeastward recurvature on 16th June towards Gujarat coast. However, it weakened over Sea on 17th. There was large divergence among various models in predicting the track of TC Vayu leading to over warning for Gujarat state and also delay in dewarning leading to evacuation of people from coastal region. Hence, a study has thus been taken up to analyze the performance of various numerical weather prediction (NWP) models in forecasting the track of TC Vayu so as to find out the reason for above limitation of NWP models. Results suggest that there is a need to relook into the existing multi-model ensemble (MME) technique which outperforms individual models in track forecasting. There is also a need to improve the individual deterministic model guidance so as to suitably represent the interaction between mid-latitude westerlies with the TC and steering anticyclone by improving the initial and boundary conditions through augmented direct and remotely sensed observations over the Arabian Sea and their assimilation in NWP models.
Research highlights
The multiple interactions among the wind fields of TC Vayu, middle latitude westerlies and anticyclones over central India & Arabian Peninsula led to the unique track of Vayu with two recurvatures in its life cycle.
The prediction of time and point of recurvature in the track of TCs is still a challenge for the NWP models and hence the operational forecast, as models could not represent the interaction of mid-latitude westerlies with the TC and steering anticyclone over either side of the TC.
Comparing the average track forecast errors of different models and multi-model ensemble (MME) for the recurving TCs during 2009–2019, the MME shows minimum average track forecast error. However, the consistency in MME based track forecast decreases with increase in lead period.
There is a need to look into the existing MME and improve it by re-defining the best constituent members and improving the performance of individual models through augmentation of direct & remotely sensed observations, data assimilation and the physical processes in the model.
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