Indian Institute of Tropical Meteorology
Recent publications
The intra-seasonal variation in precipitation isotopes shows a characteristic declining trend over northeast India. As of now, no mechanism offers a consistent explanation of this trend. We have performed the isotopic analysis of precipitation (rain) and estimated net ecosystem exchange and latent heat fluxes using an eddy-covariance system in northeast India. Additionally, we have used a diagnostic model to determine the recycled rainfall in this region. We find a strong link between the enhanced ecosystem productivity and isotopic enrichment in rainwater during the premonsoon season. Subsequently, on the advent of monsoon, the Bay of Bengal generated moisture enters this region and depletes the isotopic values. Additionally, the regional-scale convective activities produce periodic lows in the precipitation isotopes. Contrary to the general understanding, our study shows that the internal factors, such as the local land-atmosphere interactions, rather than the external influences, play a significant role in governing the precipitation isotopes in northeast India.
Unlike bromine, the effect of iodine chemistry on the Arctic surface ozone budget is poorly constrained. We present ship-based measurements of halogen oxides in the high Arctic boundary layer from the sunlit period of March to October 2020 and show that iodine enhances springtime tropospheric ozone depletion. We find that chemical reactions between iodine and ozone are the second highest contributor to ozone loss over the study period, after ozone photolysis-initiated loss and ahead of bromine. Iodine chemistry plays a more important role than bromine chemistry in tropospheric ozone losses in the Arctic, according to ship-based observations of halogen oxides from March to October 2020.
Based on the ion chromatography method, the chemical characterization of rainwater (RW) samples collected over Srinagar (a location in central Himalaya) has been done during monsoon 2016 (MON-2016). The rainwater shows near acidic pH values ranging from 5.1 to 6.2 (average, 5.7 ± 0.6) during the study. The average ionic concentrations of 97 ± 10 μeq/1 were reported during MON-2016. Ca2+ has significantly high contribution of 24% as compared to NH4+ (18%), Na+ (9%), K+ (4%), and Mg2+ (3%) among cations, whereas Cl−, SO42−, and NO3− have contribution of ~ 15, 11, and 7%, respectively, among anions during chemical analysis. We have reported SO42−/NO3− ratio as 1.49, which shows contribution of 60 and 40% from SO42− and NO3− ions within the predicted limit of RW (H2SO4, 60–70%, and HNO3, 30–40%). Ca2+, Mg2+, and NH4+ have neutralization factors as 2.51, 0.37, and 2.01, respectively, due to the neutralization of acidic species in RW. The non-sea salt (NSS) contribution to total Ca2+, K+, and Mg2+ indicates the major contribution from crustal origin, whereas the NSS contribution to the total Cl− and SO42− was from the anthropogenic source. The principle component analysis (PCA) indicates that the first factor (i.e., natural sources, mainly dust and sea salts) has only ~ 9% variance. In contrast, the second factor (i.e., fossil fuel and biomass burning) has ~ 17% variance, and the third factor has 27% variance may be due to soil, agricultural, and biomass burning origin. The rest of the contributions are from mixed emission sources as well as by the transport of polluted air mass from the Indo-Gangetic Plain (IGP) and Punjab Rajasthan, Pakistan, and Afghanistan. This manuscript helps to understand the impact of crustal and anthropogenic sources in rainwater over the central Himalaya region of Uttarakhand.
X‐band radar observations are integrated with lightning location network observations to investigate the relationship between convective storm properties and lightning over the Western Ghats during a monsoon season (June–September 2014). Convective storms (cells) were identified using an objective‐tracking method and instantaneous lightning strikes within the radar domain were then linked with observed storms. This spatio‐temporal sampling of individual convective cells and lightning has facilitated process‐based study of electrified convection over the Ghats for the first time. Storm and lightning occurrences are typically high during monsoon onset and withdrawal months of June and September, respectively. A spatial correspondence between deep‐intense storms, lightning, and intense convective cores indicated presence of large hydrometeors in the mixed‐phase region of storm supported by strong updrafts and is essential for lightning. The large‐scale instability that peaked during afternoon hours was a key factor in the formation of deep‐intense storms and lightning. Results show that majority of lightning‐producing storms are located on the leeward side as opposed to the windward side. These storms have deeper top‐heights, larger areas and vertically integrated liquid, and an enhanced hail probability than those devoid of lightning. Warm season convection in the study area is accompanied by the preponderance of negative Cloud to Ground (−CG) flashes over positive Cloud to Ground (+CG) lightning. Storms with +CG features exhibited much higher (>2 times) vertical airmass flux in the mid‐troposphere (6–9 km) than storms without +CG features. Furthermore, for majority of +CG storms, intracloud flash occurrences increased significantly above the freezing level.
The present study evaluates the skill of seasonal forecasts of temperatures over India during April to June using the Monsoon Mission Coupled Forecasting System (MMCFS) model hindcasts, which are initialized with February initial conditions. Model hindcast data of 1981‐2017 period have been used for the analysis. The India Meteorological Department (IMD) gridded temperature data set has been used for model verifications. The MMCFS model captures the annual cycle of temperatures reasonably well, but with a higher mean and smaller variability compared to observations. The model hindcasts show a significant skill for seasonal forecasts of temperatures over most of northwest and central India. Empirical Orthogonal Function (EOF) analysis suggests that the model captures temporal and spatial characteristics of different modes of maximum temperatures but with less accuracy. The model teleconnections of maximum temperatures with Indian Ocean sea surface temperatures (SSTs) and El Niño–Southern Oscillation (ENSO) are weakly represented. The model is also found capable of predicting the spatial distribution of heat wave characteristics such as heat wave frequency (HWF) and heat wave duration (HWD) reasonably well. The present study suggests that the MMCFS Model can be used to generate a useful outlook of hot weather seasonal temperatures and heat waves over India. This article is protected by copyright. All rights reserved.
Cloud microphysical processes and rainfall over the Indian summer monsoon (ISM) region are unique because of the strong interaction among clouds, thermodynamics, and dynamics. The heating and presence of water vapor during ISM help for the formation of cloud particles in stratiform and convective clouds. In this study, we have analyzed the role of cloud microphysical processes behind the ISM rainfall (ISMR) and their inter-annual and sub-seasonal variability from the Goddard Earth Observing System (GEOS) model data (i.e., MERRA reanalysis). The spatial distribution of microphysical process rates (e.g., auto-conversion, freezing, accretion of rain and snow) over the Indian subcontinent are in line with the rainfall distribution. Besides, the interannual variability of these cloud microphysical process rates is coupled to that of the ISMR. It is revealed that these microphysical processes are in line with the spatial distribution of more (less) rainfall over the Indian subcontinent during active (break) spells. They also have significant sub-seasonal variability like ISMR over the ISM region. The variance is more in the synoptic scale than in quasi-biweekly mode (QBM) and monsoon intraseasonal oscillation (MISO) scales. Further, the sub-seasonal variances of microphysical process rates are well correlated with the mean ISMR. Results also reveal the teleconnection of cloud microphysical processes over the ISM region with the ENSO phenomena. We hope that the understanding of detailed microphysical processes during ISM will help the development of a climate model for depicting the mean monsoon. It may then enhance the skill of seasonal prediction.
The summer (June through September) monsoon 2020 has been very erratic with episodes of heavy and devastating rains, landslides and catastrophic winds over South Asia (India, Pakistan, Nepal, Bangladesh), East Asia (China, Korea, and Japan), and Southeast Asia (Singapore, Thailand, Vietnam, Laos, Cambodia, Philippines, Indonesia). The withdrawal of the summer monsoon over India was delayed by 2 weeks. The monsoon season over East Asia has been the longest. China recorded a Dam burst in the twentieth century. Furthermore, the Korean Peninsula has experienced back-to-back severe tropical cyclones. Could the lockdown activities initiate to control the COVID-19 spread a possible cause for these major episodes? The strict enforcement of the lockdown regulations has led to a considerable reduction of air pollutants—dust and aerosols throughout the world. A recent study based on satellites and merged products has documented a statistically significant mean reduction of about 20, 8, and 50% in nitrogen dioxide, Aerosol Optical Depth (AOD) and PM2.5 concentrations, respectively over the megacities across the globe. Our analysis reveals a considerable reduction of about 20% in AOD over South as well as over East Asia, more-over East Asia than over South Asia. The reduced aerosols have impacted the strength of the incoming solar radiation as evidenced by enhanced warming, more-over the land than the oceans. The differential warming over the land and the ocean has resulted in the amplification of the meridional ocean-land thermal contrast and strengthening of the monsoon flow. These intense features have supported the surplus transport of moisture from the oceans towards the main lands. Some similarity between the anomalous rainfall pattern and the anomalous AOD pattern is discernable. In particular, the enhancement of rainfall, the reduction in AOD and the surface temperature warming match very well over two regions one over West-Central India and the other over the Yangzte River Valley. Results further reveal that the heavy rains over the Yangzte River Valley could be associated with the preceding reduced aerosols, while the heavy rains over West-Central India could be associated with reduced aerosols and also due to the surface temperature warming.
The current study is designed to simultaneously assess for the first time similarities and differences in pollutant escalation (especially fireworks) in four mega and metro cities in India, i.e., Delhi, Ahmedabad, Mumbai and Pune, during the most important Indian festival, Diwali. The four cities are networked in the System of Air Quality and Weather Forecasting And Research (SAFAR). The data was collected through online and cumulative sampling. Particulates were analyzed for concentration trends, chemical speciation, and trace gas variations. The attitude and culture of the inhabitants in each city decided the amplitude and duration of the event. On Diwali day, PM2.5 and PM10 (maximum) in Delhi increased by 353% and 213%, respectively, compared to pre-Diwali day. The increment in PM2.5 in Pune and Ahmedabad is 50% of that in Delhi, whereas, in Mumbai, it is 1/7th of Pune. NO2 in Delhi surpassed the permissible concentration during Diwali night. Metal content (K, Mg, Na, Mn and Pb) in PM2.5 nearly doubled in all cities due to firecrackers. Prevailing meteorological conditions controlled the dispersal of pollutants. 'Ventilation Coefficient' appears to be deterministic as a pollutant sink except for wet removal. The health concern is assessed through inhalation dose (6–12 pm peak period), Delhi faced quadruple dose on Diwali day over pre-Diwali day, and it reduced close to triple on post-Diwali day. The study elucidates the need for city-specific multi-mode information to design effective control measures to curb festivity-related air pollution. Article Highlights • The networked cities showed specific peak times and concentrations on the event day • Online and gravimetric samplings of PM2.5 agree well • Weather and peak pollution magnitude determine dispersion efficiency at each station • Post-Diwali inhalation dose remains considerably high in Delhi and Ahmedabad Graphical abstract
Data from a high-resolution sediment core off Goa in the eastern Arabian Sea (EAS) show that the Holocene surface-salinity variation off Goa contains four alternating high- and low-salinity events. These events are in contrast with the Bay-of-Bengal (BoB) surface-salinity variation after 5 ka BP, suggesting an asymmetry in the rainfall associated with the Indian summer monsoon over the eastern and western parts of the Indian subcontinent and its surrounding seas. This zonal asymmetry in rainfall is also seen in modern rainfall data. The historical rainfall over the Indian subcontinent indicates that the Northwest India and West Peninsular India and their rainfall subdivisions, which feed freshwater to the EAS, are mutually strongly correlated, but they are not correlated with Northeast India and North Central India and their subdivisions, which feed freshwater to the BoB. This mid-Holocene zonal asymmetry in rainfall over the eastern and western parts of the subcontinent appears to have sustained through to modern times. The Holocene salinity events off Goa are closely comparable to the evolution of Harappan Civilization in the Indus Valley, suggesting that the Holocene salinity variation in the EAS is connected to, and is a reliable indicator of, rainfall over the Harappan Civilization Region. High-resolution core data off Goa show four alternating high- and low-salinity events during the Holocene.These events are coherent with the Bay of Bengal (BoB) surface-salinity variation till ~5 ka BP, but diverge thereafter.This zonal contrast between the eastern and western parts of the Indian subcontinent is also seen in modern rainfall data.This zonal asymmetry in rainfall may be associated with the northward propagation of rain bands and northwestward movement of low-pressure systems.The analysis favours a flood-forced decline of the Harappan Civilisation. High-resolution core data off Goa show four alternating high- and low-salinity events during the Holocene. These events are coherent with the Bay of Bengal (BoB) surface-salinity variation till ~5 ka BP, but diverge thereafter. This zonal contrast between the eastern and western parts of the Indian subcontinent is also seen in modern rainfall data. This zonal asymmetry in rainfall may be associated with the northward propagation of rain bands and northwestward movement of low-pressure systems. The analysis favours a flood-forced decline of the Harappan Civilisation.
Accurate renditions of country-scale methane (CH4) emissions are critical in understanding the regional CH4 budget and essential for adapting national climate mitigation policies to curtail the atmospheric build-up of this greenhouse gas with high warming potential. India housing 30% of the Asian population is currently appraised as a region of CH4 source based on the inventories. To date, there have not been many reported efforts to estimate the regional CH4 emissions using direct measurements of boundary layer CH4 concentrations at multiple locations over India. Here, 2 years (2017–2018) of in situ CH4 observations from three distantly placed stations over the peninsular India is combined with state-of-the-art inversion using a Lagrangian particle dispersion model for the estimation of CH4 emission. This study updates CH4 emission over the peninsular India (land area south of 21.5°N) as ~ 10.63 Terra gram (Tg) CH4 year⁻¹, which is 0.13 Tg CH4 year⁻¹ higher than the existing inventory-based emission. On seasonal scale, the changes from the existing CH4 emission inventories are 0.12, 0.05, 0.055 and 0.28 Tg CH4 year⁻¹ during winter, pre-monsoon, monsoon and post-monsoon seasons respectively. Spatial distributions of seasonal variability of posterior emissions suggest an enhancement over the eastern region of peninsular India compared to the western part. The study with observations from three stations over the peninsular India provides an update on the inventory-based estimation of CH4 emissions and urges the importance of more observations over the Indian region for the accurate estimation of fluxes.
The variability of Indian Ocean shallow meridional overturning circulation (SMOC) is studied using the century long ocean reanalysis simple ocean data assimilation (SODA) data. Though SMOC exhibits stronger southward transport during boreal summer, it displays stronger variability during boreal winter. The spectrum analysis of winter SMOC index reveals presence of highest amplitude between 5 to 7 years at 95% confidence level, suggesting the dominance of intra-decadal SMOC variability. The robustness of intra-decadal SMOC variability is also confirmed in different ocean reanalysis data sets. Composite analysis of filtered upper Ocean Heat Content, sea level, thermocline depth and Sea Surface Temperature anomalies for strong (weak) SMOC years show negative (positive) anomaly over north and East of Madagascar. Correlation analysis, of filtered SMOC index and sea level pressure (zonal winds) over the India Ocean, found significant negative (positive) correlation coefficient north of 40 °S (around 10 °S) and significant positive (negative) correlation coefficient over the 45 °S to 70 °S (20 °S to 50 °S and north of 5 °S). This meridional pattern of correlation coefficient for sea level pressure, manifesting the out of phase relationship between sub-tropics and high latitude mean sea level pressure, resembles with Southern Annular Mode (SAM). We conclude that the intra-decadal variability of mean sea level pressure leads to zonal wind variation around 10 °S modulating SMOC, which in turn affects the upper ocean thermal properties in the east and north of Madagascar. This study for the first time brought out coherent intra-decadal evolution of SAM and SMOC during boreal winter.
Monsoon convection characteristics over land and sea within 150 km of the west coast of India are studied using coastal Mumbai S-band radar. The intraseasonal and interannual monsoon variabilities in cloud characteristics are investigated for the contrasting monsoon seasons of 2013 and 2014. The cloud characteristics studied are frequency of occurrences, cloud top height (CTH), longitudinal distribution, diurnal variation, and scale-wise distribution of cloud cells. The number of cloud cells is about four times higher over land than over sea. The maximum frequency of CTH is found in the cumulus category (3–4 km). The mean CTH varies from 4.49–5.44 km. No significant difference between the CTH over the land and sea regions is found. The contribution of congestus to total cloud cells is found maximum over both land and sea. The longitudinal variation of cloud frequency shows maximum frequency at a distance of 50–60 km from the location of radar over both sea and land. The maximum over the land region is the new feature revealed in the analysis. The diurnal variation of clouds shows a broad structure with maximum in the local noon and minimum in the morning hours. The mean duration of the clouds is 40–44 min both over land and sea. The contribution by the mesoscale convective system (MCS) is dominant (57–63%). The study of cloud distribution over land and sea over the west coast of India using radar data is the first of its kind and has brought out detailed structure of cloud distribution with time and space.
The campaign mode observational program 'Winter Fog Experiment' (WiFEX) was set up at the Indira Gandhi International Airport (IGIA), New Delhi, during the winter months of 2016–17 and 2017–18. Using the WiFEX data, in this study, we examine the microphysical structure of fog formed in a polluted environment and attempt to predict visibility (Vis) using the fog index approach. The examination of eleven fog events demonstrates that the mean droplet concentration (up to 674.94 #/cm−3) and liquid water content (LWC, up to 0.29 g m−3) are high in dense fog cases (Vis < 200 m). The droplet spectrum shows bi-modal distribution and dominance of smaller droplets in the 3–7 µm range. For most fog cases, the droplet spectrum extends up to 50 µm. The mature phase of the fog depicts a relatively increased population of droplets in the higher-sized bins, highlighting the formation of larger droplets. Moreover, we found that Vis is inversely related to the liquid water content and the fog droplet number concentration. Fog index-based visibility parameterization has been developed to diagnostically compute visibility for the different categories of fog events, namely category-IIIB (CAT-IIIB) and category-IIIC (CAT-IIIC), using the meteorological variables. Out of 14 CAT-IIIB and 19 CAT-IIIC fog events, the 'WiFEX-in' could predict seven CAT-IIIB and 12 CAT-IIIC fog events, respectively. However, significant under-prediction was evident for the total CAT-IIIB fog hours and over-prediction for the total CAT-IIIC fog hours. It is found that the observed and predicted fog hour differences were related to the errors in the fog onset, dissipation, and magnitude of predicted liquid water content during CAT-IIIB and CAT-IIIC events and the same are discussed.
Plain Language Summary Early prediction of sub‐seasonal to inter‐annual variations in Indian summer monsoon rainfall has multifaceted benefits (e.g., agriculture, economy, etc.). Hence any significant improvement in the prediction skill could be highly appreciated. The Ocean and Atmosphere observations are increased tremendously during the recent decades due to satellites and improved observational networks. Since monsoon prediction partly depends on the state of the Ocean and Atmosphere (i.e., model starting point or “analysis”), these observations can be used to improve the analysis, thereby the predictions. Even though sophisticated data assimilation techniques have been demonstrated to strengthen the analysis quality, their operational utilization in the context of monsoon prediction is still far from the reality due to the difficulty in adaptation and computational limitations. The recent improvements in high‐performance computing and data assimilation research under Monsoon Mission have aided us in implementing an advanced data assimilation method to the operational monsoon prediction model. Using this new analysis, the seasonal monsoon predictions improved. The present study reports the enhancements and attempts to explore probable mechanisms responsible for the improvement. The study is vital to operational agencies in adopting advanced data assimilation methods, particularly to boost monsoon predictions and elsewhere.
In the present work, monsoon low-level jet (MLLJ) characteristics and its relationship with rainfall activity have been studied using three years (2016–2018) of radiosonde observations taken from a high altitude site (Mahabaleshwar, 1348 m AMSL) in Western Ghats, India. Initial analysis of zonal and meridional winds showed an apparent monthly variation with respect to altitude with robust features in zonal winds compared to meridional winds. Analyzed zonal wind and vertical shear in zonal wind during south west monsoon showed clear intra seasonal variation with respect to altitude especially below 5 km. Derived MLLJ characteristics, such as core speed, core height, and westerly wind depth, also exhibited apparent intra-seasonal variation where core height was comparatively invariant. Strong zonal wind and vertical shear in the zonal wind including higher core speed and westerly wind depth was noticed during July and August compared to June and September. Further, MLLJ association with rainfall activity has been analyzed. An increase in core speed and westerly wind depth was noted during the active period of monsoon compared to the break period. Later, the evolution of MLLJ characteristics before, during, and after heavy and high rainfall has been analyzed which showed strengthening of zonal wind, vertical shear in zonal wind, core speed, and westerly wind depth before and during the rainfall events categorized. Abundant moisture transport from the Arabian Sea to the Indian land mass prior to the event was noticed in the analysis of zonal water vapor flux. Intensification of few MLLJ characteristics mostly brings copious moisture, possibly leading to heavy or high rainfall over the study region.
Understanding and quantifying the influence of volatile organic compounds (VOCs) on ozone and secondary organic aerosol formation is essential for better prediction/estimation of these products. A total of 9 VOCs along with surface ozone were measured during the year 2019 at Pune (India) location. The ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) estimations are compared for 2 methods-using measured VOC concentrations and using their photochemical initial concentrations (PIC). The OFP and SOAFP estimated based on the measured VOC concentrations provide an incomplete understanding of these 2 formation processes. This is mainly because measured VOCs don't account for the photochemical losses that compounds undergo from the source to the receptor. The PIC values of VOCs have been estimated in this study to highlight the importance of considering the photochemical losses. For example, the PIC value of highly reactive compound, isoprene, was found to be 152% higher (1.48 ppbv) than its measured value (0.59 ppbv). The resultant total OFP estimate based on PIC values of all the VOCs was found to be 53.30 ± 35.02 ppbv as compared to 45.99 ± 29.35 ppbv obtained from measured VOCs. Based on k-means clustering analysis, it was found that the highest ozone formation was favored under transition regime chemistry when PIC values were considered. The average total SOAFP based on PIC values was found to be 1.32 ± 1.40 ppbv, while it was 1.17 ± 1.18 ppbv for measured VOCs. The aromatics contributed to over 90% of total SOAFP estimated for the region.
Autonomic computing investigates how systems can achieve (user) specified “control” outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.
A giant clam shell (Tridacna maxima) collected from the lagoon of Minicoy Island in the southern Lakshadweep Archipelago, India, was used for a high-resolution stable isotope (δ¹⁸O, δ¹³C) analysis. The results reveal a cyclic pattern in δ¹⁸Oshell values, interpreted as combined signatures of seasonal temperature and δ¹⁸Osw fluctuations over the period from 2004 to 2014. These δ¹⁸Oshell cycles are characterized by a slight background scatter governed by rainfall events leading to short-term and limited freshening of the water in the partly restricted lagoon. The most striking features of the isotope data are exceptionally negative outliers in δ¹⁸Oshell values beginning in mid-2010. This first anomalous isotope excursion is followed by a phase of lowered growth rates lasting until the beginning of 2011. It is observed that this sudden change in oxygen isotope composition and shell precipitation was caused by anomalous sea surface warming, which was previously documented for the region in 2010 and caused widespread coral bleaching throughout the Lakshadweep Archipelago. Even though several other negative excursions in δ¹⁸Oshell values follow, the cyclicity in the isotope signal and the growth rates become again more regular in the distal part of the shell, indicating a gradual recovery of the bivalve after the initial thermal stress event. The results reveal that even short high-temperature events can significantly perturb the biology of giant clams and require long recovery phases. This information is particularly significant for conservation efforts for this endangered bivalve group in a world with ongoing global warming.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
322 members
Bs Murthy
  • Department of Air Pollution, Transport Modeling and Middle Atmospheric Climate
Supriyo Chakraborty
  • Centre for Climate Change Research
Naveen Gandhi
  • Centre for Climate Change Research
Dr. Homi Bhabha Road, 411008, Pune, Maharastra, India
Head of institution