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New in-situ ocean subsurface time series measurements from OMNI

buoy network in the Bay of Bengal

R. Venkatesan, V. R. Shamji, G. Latha, Simi Mathew, R. R. Rao, Arul Muthiah, M. A. Atmanand

National Institute of Ocean Technology, Pallikaranai, Chennai 600100, India

Abstract

The Bay of Bengal, the north-eastern limb of the tropical Indian Ocean is a region strongly

coupled with summer and winter monsoons and tropical cyclones. The bay is also a region of

strong vertical stratification near the surface due to large inputs of fresh water through rainfall

and river runoff. It is also a region where in-situ subsurface ocean measurements are quite

sparse both in space and in time. The National Institute of Ocean Technology (NIOT) deployed

instrumented moored buoys in the Bay since 1997 to provide continuous time series

measurements of surface meteorological and oceanographic parameters at selected locations. In

the recent years several research studies have shown the important role of variability of heat

storage in the near-surface layers on the intraseasonal and interannual evolution of monsoons

and cyclones. Hence a strong need was felt to augment some of these buoys with subsurface

temperature, salinity and current sensors to continuously record the temporal evolution of their

vertical structures. Under a new initiative, the NIOT has deployed six moored buoys attached

with sensors to collect subsurface oceanographic parameters on real time basis in the bay. These

are coded as OMNI (Ocean Moored buoy Network for Northern Indian Ocean) buoy system. The

time series of vertical profiles of temperature and salinity in 500 m water column from the

surface, currents in the topmost 100 m water column are monitored at discrete depths in the bay.

The OMNI buoy programme addresses a long-standing need to understand the observed

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variability of upper ocean thermohaline and current structures on several time scales that has

important bearing on the evolution of seasonal monsoons and cyclones. This paper presents an

account on the evolution, status and usefulness of the new Indian OMNI buoy programme.

Keywords: OMNI buoy, subsurface time series measurements, Bay of Bengal

*For correspondence. (E-mail: venkat@niot.res.in)

1. Introduction

The recent advances made in observational technologies in the fields of sensors, platforms and

real time communication now provide unprecedented capability to monitor changes in our

oceans and atmosphere that present new opportunities to understand the role of the oceans on

climate change. Understanding our oceans is critical to protect people from natural disasters and

the impending challenges of changing climate. Monitoring marine environment is essential to

provide information for research and services. Influence of oceanic processes on weather and

climate has been known for decades. Our ability to forecast weather and global climate changes,

thus minimising disastrous effects of events such as cyclones, storm surges and tsunamis, remain

contingent on our capability to observe the changes in ocean in real time at spatial and temporal

scales with the required resolution and accuracy. Traditionally routine ocean measurements are

carried out by ships of opportunity - cargo, fishery and naval vessels in a random manner.

Special campaign measurements are made by research vessels to study a particular phenomenon

or process. Moored buoy observations provide continuous time series measurements at fixed

locations for forecasting and warning operations and to a variety of basic and applied research

studies. In addition certain marine meteorological parameters such as sea level pressure and

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surface air temperature that cannot be measured directly by satellites can be monitored by these

data buoys. A number of data buoys were deployed by the NIOT at selected locations in the

eastern Arabian Sea and the Bay of Bengal which are operational since 1997 primarily to

monitor surface met-ocean parameters on real time basis.

The NIOT buoy data have been widely used both by operational and scientific communities to

understand and predict the observed variability of monsoons, cyclones and short-term climate

change through an improved approach of air-sea interaction processes. Premkumar et al.1 have

carried out preliminary analysis of the NIOT met-ocean buoy data in which they have used data

collected from one buoy in the eastern Arabian Sea and three buoys in the bay. It was observed

that the sea surface temperature (SST) dropped abruptly by almost 3oC in the eastern Arabian

Sea during the passage of a cyclone in June 1998 and the buoys in the bay showed prominent

intraseasonal oscillations. Bhat et al.2

have studied the intraseasonal variation over the bay in

SST and surface wind speed using the NIOT buoy data. Bhat et al.3 have assessed the accuracy

of the SST derived from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager

(TMI) over the bay using in situ data collected from the NIOT moored buoys and research ships.

Agarwal et al.4 have validated scatterometer wind speed against the NIOT buoy wind data at

three locations in the eastern Arabian Sea and the Bay of Bengal and also compared simulated

SST with north Indian Ocean buoy data. Bhat and Narasimha5 used the NIOT moored buoy data

for studies of Indian summer monsoon experiments. They used SST from the NIOT moored

buoys to compare with optimally interpolated SST downloaded from National Oceanic and

Atmospheric Administration (NOAA) and wind speed for their summer monsoon study.

Lakshmi et al.6

have studied bimodal distribution of SST over the north Indian Ocean using the

NIOT buoy data. Vaid et al.7 have used daily SST products of NOAA to understand the

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variability in the tropical Indian Ocean and compared with SST observed from the NIOT

moored buoy network in the north Indian Ocean.

Parekh and Sarkar8 have studied the dependence of the difference in ocean sub-skin and bulk

temperature (Tsubskin-T2.5m) on environmental parameters over the north Indian Ocean using buoy

data. Parekh et al.9 have compared surface wind speed and SST from satellite measurements

(TMI and Muiltifrequency Scanning Microwave Radiometer (MSMR)) and the numerical

weather prediction model (ERA-40) outputs with the NIOT buoy observations from both the

eastern Arabian Sea and the Bay of Bengal. Parekh et al.10

have developed empirical retrieval

algorithm for wind speed using the NIOT buoy and MSMR brightness temperature data.

Satheesan et al. 11

have studied the performance of QuikSCAT derived wind vectors using in-situ

data obtained from moored buoys over the north Indian Ocean. Ruchi Kalra et al.12

have used the

NIOT buoy data in an approach based on the radial basis function type of artificial neural

network to map remotely sensed waves in the deep and coastal oceans. Jignesh Kshatriya et al.13

have estimated wave periods using the NIOT buoy data and TOPEX/Poseidon altimeter in the

Indian Ocean. Raj Kumar et al.14

have attempted assimilation of altimeter data in the open ocean

wave model (SWAN) with the aim of enhancing the quality of prediction of significant wave

height using the NIOT buoy data. Sengupta et al.15

used the subsurface measurements from

moored the NIOT buoys during spring of 1998–2000 and suggested that the warming of the

mixed layer (~20 m deep) in the eastern Arabian Sea warm pool is a direct response to net

surface heat flux Qnet (~100 W m-2

) minus penetrative solar radiation Qpen (~45 W m-2

).

Chinthalu et al.16

have studied the response of the bay to two tropical cyclones, during 15–31

October 1999, using data collected by the NIOT moored buoy network. Jossia Joseph et al.17

have studied inertial oscillations forced by a cyclone in the bay during September 1997 using the

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NIOT moored buoy data. Sengupta and Ravichandran18

studied strong monsoon intraseasonal

oscillations (ISO) during the summer of 1998 in the bay using the NIOT moored buoy data along

with satellite cloud data. Hareesh Kumar et al.19

have reported intraseasonal oscillations in the

meteorological and oceanographic fields of the bay during the summer monsoon season of 1999

using the NIOT buoy data. Anant Parekh et al.20

and Agarwal et al. 21

have reported sub-monthly

(10-20 days) intraseasonal variability in BoB using the NIOT buoy and TMI data. Sengupta et

al.22

have studied the premonsoon intraseasonal variability in the Arabian Sea using the NIOT

buoy and satellite data. Swain et al.23

compared the National Centre for Environment Prediction

(NCEP) based latent heat flux and sensible heat flux derived from bulk aerodynamic formulae

with that of ship and the NIOT buoy derived fluxes. All the above listed studies are based on first

of its kind surface met-ocean parameters recorded by the NIOT moored buoy network in the Bay

of Bengal and the eastern Arabian Sea. All the above listed studies clearly reveal the utility of the

surface time series data sets collected from the NIOT buoy network.

2. Deep Sea Instrumented Buoy System - OMNI

In the recent years several research studies have shown the important role of SST and heat

storage in the near-surface layer on the intraseasonal evolution of monsoons and cyclones. In the

global scenario the research studies carried out with long time series data sets collected by

moored buoys fitted with subsurface temperature, salinity, current sensors under Tropical Ocean

and Global Atmosphere (TOGA), World Ocean Circulation Experiment (WOCE) and Prediction

and Research Moored Array in the Atlantic (PIRATA) programmes in the tropical Pacific and

the Atlantic Oceans have clearly revealed the importance of upper ocean data on the evolution of

near-surface stratification, heat storage and SST, which act as an important surface boundary

condition for weather and climate prediction models. The WOCE experiment has collected data

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globally which has helped to study the long term behaviour of the oceans for better prediction by

climate models (Gould24

).The subsurface measurements of temperature, salinity, currents made

in the equatorial Pacific Ocean under TOGA and WOCE programmes have significantly

contributed to successful prediction of El Niño/La Niña events that influence major part of the

global weather and climate patterns (McPhaden et al.25

).The PIRATA program has helped in

understanding the relative contributions of the different components of the surface heat flux and

ocean dynamics to the seasonal and interannual variations of SST in the Atlantic Ocean

(Bourlès,et al.26

).These programmes have helped to improve our knowledge and understanding

of ocean–atmosphere variability in the tropical ocean basins.

In the Indian scenario, some important field experiments were carried out in the Arabian Sea and

the Bay of Bengal during summer monsoon season. The rich data sets collected from these

experiments viz., the Summer Monsoon Experiments (ISMEX-73, MONSOON-77, MONEX-79

and MONEX-90), Arabian Sea Monsoon Experiment (ARMEX), Monsoon Trough Boundary

Layer Experiment (MONTBLEX), Bay of Bengal Monsoon Experiment (BOBMEX) and Joint

Air-Sea Monsoon Interaction Experiment (JASMINE) have provided valuable insights on the

coupling between the ocean and summer monsoon. During the Monsoon Experiment programme

the short time series measurements made from stationary ships have shown changes in the

surface meteorological forcing and the response characteristics of the ocean near-surface

thermohaline structure in the east central Arabian Sea and the northern Bay of Bengal under the

onset and progress regimes of summer monsoon (Rao et al.27

). The oceanic component of the

MONTBLEX programme mainly focused on the observed short-term variability of the near-

surface thermohaline structure in the head bay during active and break regimes of summer

monsoon and revealed that the small-scale vertical structure in the thermohaline fields was more

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prominent during September 1990 compared to August 1990. A relationship between the genesis

of meteorological systems and increase in heat content in the near-surface layer was also

reported (Sanilkumar et al.28

). During BOBMEX the thermohaline structure as well as its time

of evolution was found to be different in the northern bay compared to that in the southern bay.

Over both the regions, the SST decreased during rain events and increased during cloud-free

conditions. Over the season as a whole, the upper-layer salinity decreased in the northern bay

and increased in the southern bay. The intraseasonal variation in SST during 1999 was found to

be of smaller amplitude than in 1998 (Bhat et al.2). The JASMINE programme mainly focused

on the coupled ocean-atmospheric system in the eastern equatorial Indian Ocean and the

southern Bay of Bengal during active and break periods of the summer monsoon (Webster et

al.29

). These studies although based on short data records have pointed out the importance of the

temporal variation of subsurface ocean conditions towards a better understanding of the summer

monsoon. The necessity to deploy buoys with subsurface measurements has been felt by the

international scientific community and accordingly a few buoys were deployed in the tropical

Indian Ocean under the aegis of Research Moored Array for African-Asian-Australian Monsoon

Analysis and Prediction (RAMA) (McPhaden et al.30, 31

).These RAMA buoys have been

providing the first of its kind data that add great value for our understanding on the coupling

between ocean and monsoons and cyclones on different time scales. From the Indian side a need

was also felt to evolve a new moored data buoy system to continuously record vertical profiles of

temperature, salinity and currents at selected locations in the bay to supplement the RAMA

programme. This new moored buoy array in the historically data-sparse north Indian Ocean

provides measurements to advance monsoon research and forecasting. It is designed specifically

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for studying large-scale ocean–atmosphere interactions, mixed-layer dynamics, and ocean

circulation related to the monsoons on intraseasonal to interannual time scales.

Under this new initiative on the advice of a National Expert Committee for the OMNI buoy

program under Ministry of Earth Sciences the NIOT has deployed a network of six moored buoy

systems equipped with subsurface sensors to measure oceanographic parameters on real time

basis in the bay (Fig. 1). These buoys were deployed initially at two locations BD13 [86oE,

11oN] and BD14 [85

oE, 8

oN] during October 2010 and later at four locations BD08 [89

oE, 18

oN],

BD10 [88oE, 16

oN], BD11 [83

oE, 14

oN] and BD12 [94

oE, 10

oN] during May 2011 in the bay.

The OMNI buoy is designed in such a way that it measures both surface met-ocean and

subsurface oceanographic parameters with sensors fitted at specific levels. The subsurface

current sensors are also attached to the buoy to measure vertical profiles of horizontal currents in

the topmost 100 m water column. All the met-ocean parameters, their sensors, ranges,

resolutions, accuracies, sampling and transmission intervals measured at the surface and at

different subsurface levels by these buoys are listed in Table. 1.

All the meteorological and oceanographic measurements are transmitted via satellite every 3

hours in real time and are available to all interested users for operational and research

applications through Indian National Centre for Ocean Information Services (INCOIS,

Hyderabad). The choice of total number of buoy moorings is a compromise between the need to

put out a large enough arrays of buoys for a long period of time to gain fundamentally new

insights into coupled ocean–atmosphere interaction in the region, and the practical constraints of

resources in terms of funding, ship time, deployment and maintenance activities. The buoy

maintenance every six months is regularly accomplished by the NIOT team along with an inter-

comparison of the buoy subsurface conductivity and temperature data with the ship borne

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conductivity, temperature and depth (CTD) sampling depending on the availability of ship (Fig.

2). Some of the potential applications of these buoy data sets are discussed in the following

sections.

2.1 Operational Applications

The buoy data are found to be immensely useful to monitor and predict the evolution of

monsoons and cyclonic storms over the bay. In India, the India Meteorological Department

(IMD) is the primary organization to issue weather forecasts for the Indian region and its

neighbourhood. These forecasts are made by taking into account both atmospheric measurements

and surface marine meteorological parameters from the neighbouring seas. All the marine

meteorological and ocean subsurface data also have immense applications in the areas of

exploitation of fishery resources, ship routing, offshore industry and naval operations. The data

available on surface waves and currents can be effectively used for optimum ship routing to save

time and fuel costs. Generally during rough weather conditions caused by cyclonic storms and

active monsoon conditions, the ships try to keep off from disturbed zones. This limits the

availability of ship data on sea level pressure, surface wind speed, waves, currents and SST

during crucial times. During these disturbed weather conditions these moored buoys will

continue to provide very valuable data thus greatly helping IMD for more accurate predictions of

cyclonic storms in the bay. These buoy data are available for assimilation into models in real

time by several operational forecasting agencies all over the world to issue forecasts with

significant improvements (Vilard A. et al.32

). In addition to IMD, the INCOIS, the Indian Navy

and the Coast Guard also make use of these buoy data sets to forecast ocean state conditions

routinely to meet their operational needs. During 3-7, November, 2010 a cyclone named Jal

traversed between two OMNI buoys BD13 and BD14 and hit the southeast coast of India. The

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peak surface winds touched 16m/s. The response of the near-surface thermal structure at these

two buoy locations clearly show rapid cooling and deepening of the surface mixed layer (Fig. 3).

2.2 Research and Development Applications

The analysis of OMNI buoy data will provide valuable insights for our understanding leading to

better predictions of monsoons and cyclones in the bay. The availability of long time series of

data from these OMNI buoys at selected locations complementing both the RAMA buoys and

the ARGO profiling floats would be very valuable for our understanding of the evolution of

active-break cycles of summer and winter monsoons, and the life cycles of monsoon lows,

depressions, deep depressions, cyclones and severe cyclones, which mostly originate over the

bay. Hence advance knowledge about the heat stored in the upper layers of the bay becomes a

vital input for the understanding and predictions of weather and short-term climate. It is in this

context the information generated on the subsurface thermohaline structure helps the

meteorological and oceanographic communities to do improved predictions.

The signature of intraseasonal oscillations in the observed parameters can be better characterised

with the help of these buoy data. In addition, studies related to warm and cold pools, fresh water

pools, mixed and barrier layers can be carried out to explain their structure and variability in the

bay. The evolution of mixed layer depth, isothermal layer depth as well as barrier layer thickness

as observed by BD13 in the southern Bay of Bengal is shown in Fig. 4. These data sets collected

by buoys also provide valuable clues for understanding the genesis, intensification and

movement of meteorological disturbances in the bay. In addition, the signatures of regional

climate events such as Indian Ocean Dipole, El Niño and La Niña can be examined in the buoy

time series data sets in greater detail. In addition the subsurface data can also serve to

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characterise the signature of propagating planetary scale waves in the ocean that have important

bearing on ocean dynamics. The current measurements at subsurface levels provide unique

information on the prevailing circulation patterns that can be used for heat and salt transport

estimates and characterising the propagating waves in the ocean. The switching over between the

active and break regimes of the monsoon is an important issue that remains unresolved for short-

term weather prediction during the summer monsoon season and these data sets would become

very useful for improved predictions. The time evolution of some standard surface

meteorological parameters such as atmospheric pressure, air temperature, wind speed, significant

wave height, relative humidity, irradiance, downwelling long wave radiation and rainfall during

June, 2011 to March, 2012 is shown in Fig. 5a. The time evolution of horizontal currents (1.2 –

100 m) and near surface thermohaline structure (5-500 m) during September, 2011 to February,

2012 period is shown in the Fig. 5b. The detailed analysis and interpretation of these data sets is

beyond the scope of the present study and shall be reported elsewhere.

2.3 Validation of Remote Sensing Data

In recent years remote sensing satellites have opened up new vistas to monitor several

parameters related to air-sea interface such as sea surface winds, waves, currents, temperature,

salinity, sea surface height anomaly, colour and sea ice at repeat intervals over the entire global

ocean. However sea truth validation assumes special importance in qualifying these remotely

sensed data. Satellite measurements have the advantage of being global in coverage when

compared to in-situ data. The increased use of satellite data did not diminish the need for in-situ

oceanographic measurements as in-situ techniques are required for measurements of variability

below the surface of the ocean. Also, satellite systems rely on complicated algorithms to convert

measurements of electromagnetic radiation into geophysically meaningful variables. To be

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useful, satellite data must be calibrated and validated against in-situ observations in order to

detect and remove potential biases induced by orbital errors (Jeremy and Kenneth33

),

instrumental errors and atmospheric effects (e.g., water vapour, clouds, rainfall and aerosols). It

is in this context buoy network provides very valuable sea truth data to validate some of these

satellite measurements. Several research studies have been carried out to validate the remote

sensing data using the NIOT buoy data. Validation of the retrieved and corrected SST (K-SST)

from Kalpana satellite has been carried out by Shahi et al.34

using near-simultaneous

observations of the NIOT moored data buoys in the eastern Arabian Sea and the Bay of Bengal.

An inter-comparison of the OMNI buoy surface seawater temperature with the TMI SST data at

three specific locations in the Bay of Bengal is shown in Fig. 6.

2.4 Validation of Numerical Models

The performance of numerical models can be evaluated through validation process. The NIOT

buoy data are useful to validate the performance of ocean, atmospheric and coupled models.

Thompson et al.35

studied the variability in the long-term temperature and sea level over the

north Indian Ocean during the period 1958-2000 with Ocean General Circulation Model and

validated model results with field observations from tide gauges, Topex/Poseidon (T/P) satellite,

the NIOT and the WHOI moored buoys. Rao and Joshi36

used three-dimensional Princeton

Ocean Model to investigate the dynamics of the shelf flow response to the spatial and temporal

variability of the wind stress forcing along the southwest coast of India and validated their model

outputs with the NIOT buoy observations. Wave hindcast experiments in the Indian Ocean using

Mike-21 surface wave model were validated by comparing with the NIOT buoy data (Remya et

al.37

). Vethamony et al.38

conducted a numerical model study to find out improvement in wave

prediction when NCMRWF winds blended with MSMR winds in numerical model with the

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NIOT buoy data. Parekh et al.39

carried out sensitivity experiments for momentum transfer

coefficient and provided new relationship for the north Indian Ocean using the NIOT buoy data

for the OGCM studies. The time series of subsurface data sets of temperature, salinity and

current would be extremely useful for validation of Ocean General Circulation and Coupled

Ocean-Atmosphere models and also help to improve model parameterization schemes for better

simulation of physical processes.

3 Technical Aspects of OMNI Buoy System

The moored data buoys are floating platforms which consists of buoys which are 3 m diameter,

plastic-over-foam, discuss shape, with an aluminium instrument vessel with four moon pool to

attach sensors to measure meteorological parameters. These buoys are equipped with global

positioning system, beacon light and satellite transceiver. They are powered by lithium batteries

and the optimum performance of the specific mooring design is based on the type of buoy,

location and water depth. The data buoy is equipped with a data acquisition and processing unit.

The sensors are programmed to acquire data samples for a specified duration of time and

frequency. The data buoy starts the sampling process for approximately 30 minutes before the

transmission of data in synoptic mode. The data acquisition and processing of various sensors are

carried out in parallel. The averaging of the data is done at the end of the sampling process and

the data is transmitted to the data reception facility at the NIOT every three hours via

INMARSAT satellite telemetry. These data are placed on the GTS for operational weather,

climate and ocean forecasting applications. Best of practice manual is being followed and the

data is disseminated to INCOIS after quality control.

3.1 Sensor Systems:

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The sensors used in the buoy system were selected considering past experience on similar buoy

system for long term performance at sea. The reliability of each sensor used in the OMNI buoy

system is proven based on reliability of similar systems used in RAMA/TAO/TRITON/ ATLAS

and RAMA buoys. They have to withstand hostile weather conditions at sea and the data stability

is another important factor which is given priority while choosing the sensor. These sensors are

recommended by a National Expert Committee which comprises of experienced scientists from

the fields of oceanography and meteorology. The meteorological sensors are assembled and

integrated on the top of the buoy assembly. The meteorological sensors fixed on the sensor arm

include air temperature, relative humidity, wind speed and direction which are approximately 3

m above the MSL. New sensors like radiation and rainfall are included in the OMNI buoy

network which is also placed on the sensor arm at 2 m and 1 m respectively above the MSL. The

surface temperature and conductivity (to yield salinity) are measured from the buoy at a nominal

depth of 1 m. Subsurface CT sensors are assembled and mounted on the inductive cable of the

mooring system to measure temperature and conductivity at 10 different levels and Acoustic

Doppler Current Profiler to measure subsurface currents as shown in Fig. 7. The details of the

sensors used are given in the Table-1 with the accuracies and resolutions listed are based on the

manufactures supplied specifications.

3.2 Mooring systems

Specially designed mooring system with induction mooring cable, acoustic release and anchors

were used to position systems in deep sea bed. This mooring has a design lifetime of one year

and has to be serviced annually. These inverse catenary surface moorings are anchored to the

ocean floor in depths of typically 2000-4000 m. The mooring analysis was done using

ORCAFLEX software to select mooring components and designed to withstand extreme weather

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conditions. The most important specification required for the buoy design decision is the

telemetry rate that the buoy system will support for communication to shore. The buoy systems

rely on lithium battery using compact, low-power satellite communication system. These buoy

systems have captured features during Jal cyclone in November 2010 and Thane cyclone in

December 2011 and withstood rough weather conditions and very high sea state to collect data

and transmit in real time. Maintaining these buoy systems is quite challenging due to both natural

and manmade reasons. Vandalism and piracy are on the increase in certain regions of the Bay of

Bengal and the Arabian Sea. The availability of ship time as per the planned schedule is another

challenge to maintain the buoy network for its optimum performance. The rough weather during

storms will affect the schedule of the maintenance operations for servicing the buoys.

Uninterrupted data transmission through satellite is another important issue. The challenges are

also associated with the development of new sensors, reliable power sources and technologies

that perform better in the harsh marine environment.

4. Summary and Conclusions

The last few decades have witnessed remarkable progress in our ability to observe and

understand the north Indian Ocean–monsoon coupled system. The advances are mainly due to

sustained observations (XBTs, moored buoys, current meter moorings, tide gauges and

satellites), special field campaigns (MONEX, MONTBLEX, BOBMEX, ARMEX and

JASMINE) and model studies (INDOMOD). The new knowledge acquired from the above is

leading to improve our understanding of different phenomenon and processes of the north Indian

Ocean-monsoon coupled system. In the north Bay of Bengal it is well known that freshwater

from rivers and rainfall inhibit vertical mixing near the surface. But the influence of freshwater

on surface fluxes, SST and material exchange across the surface involves several unknowns and

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open questions. Hence the long-standing need for comprehensive and continuous measurements

on surface met-ocean parameters along with the subsurface ocean measurements is being

fulfilled with the deployment of OMNI buoy network in the bay. Even in the early stages of this

programme, the OMNI buoy network has provided valuable data for describing and

understanding variability at selected locations in the bay. These data are useful to study the

observed diurnal, intraseasonal and interannual variability in the near-surface theromohaline and

current structures in the bay. The data would also supplement RAMA buoy and ARGO profile

data in the bay. The measurements of met-ocean and near-surface ocean parameters have

immense applications in several fields. These buoy data are available for real time assimilation

into models by several operational forecasting agencies all over the world to issue forecasts with

significant improvements. The moored buoy data are useful to validate the performance of

ocean, atmospheric and coupled models and provide unlimited opportunities for research

scientists to describe and explain the variability on different time scales at selected locations in

the bay. The buoy network also provides very valuable sea truth data to validate satellite

measurements and numerical model outputs. The OMNI buoy programme forms an important

component of the Indian Ocean Observing System to address the long-standing need for all these

applications. The archived OMNI buoy data are available to research community on request

made to INCOIS.

5. Acknowledgements

We thank Ministry of Earth Sciences MoES, Govt. of India, for funding the OMNI buoy

programme and the members of the National Expert Committee for evolving this programme.

We are grateful to the Directors of NCAOR, Goa and INCOIS, Hyderabad for providing all the

facilities and logistic support. We also thank the staff of Ocean Observation Systems (OOS)

17

group, Vessel Management Cell of the NIOT and ship staff for their excellent help and support

onboard. We thank Ms Fugro Oceanor, Norway for providing the buoy systems.

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23

Fig.1. The track of Jal cyclone and location map of OMNI buoy systems in the Bay of

Bengal.

24

Fig. 2.Inter-comparison between the ship CTD and the BD08, BD11 and BD13 buoy subsurface CTs taken during

September, 2011.

0

100

200

300

400

500

5 10 15 20 25 30 35

Dep

th (m

)Temperature (oC)

BD08_TEMPERATURE vs CTD

CTD

BD 08

0

100

200

300

400

500

30 31 32 33 34 35 36

Dep

th (m

)

Salinity (PSU)

BD08 SALINITY vs CTD

CTD

BD 08

0

100

200

300

400

500

600

5 10 15 20 25 30 35

Dep

th (m

)

Temperature (oC)

BD11_TEMPERATURE vs CTD

BD11

CTD

0

100

200

300

400

500

600

30 31 32 33 34 35 36D

epth

(m)

Salinity (PSU)

BD11_SALINITY vs CTD

CTD

BD11

0

100

200

300

400

500

600

5 15 25 35

Dep

th (m

)

Temperature (oC)

BD13_TEMPERATURE vs CTD

CTD

BD13

0

100

200

300

400

500

600

32 33 34 35 36

Dep

th (m

)

Salinity (PSU)

BD13_SALINITY vs CTD

CTD

BD13

25

Fig. 3. Observed surface wind speed (m/s) and subsurface temperature (oC) during 1-15 Nov,

2010 at BD13 and BD14 buoy locations

26

Fig. 4. Observed variability of near-surface density structure (a), thermal structure (b) and barrier layer

thickness (c) at BD13 buoy location. Thick line in (a) represents mixed layer depth and in (b) represents

isothermal layer depth.

27

Fig. 5a. The daily time series of the standard surface meteorological parameters at the BD08 buoy location.

28

Fig. 5b. Evolution of subsurface current vectors, salinity and temperature at BD08 location.

29

Fig.6. Comparison of satellite TMI SST data with the daily averaged OMNI buoy surface

water temperature at three locations in the Bay of Bengal.

30

MOORING FOR OMNI BUOY

19MM ALLOY LONG LINK CHAIN,

MAX. 5M LONG

CT SENSOR, APPROX. 6.0M

FRAME – ADCP

16MM INDUCTIVE CABLE, 500M

10’’ TRAWL FLOATS IN NET (x20)

18MM POLYPROPYLENE ROPE. 4000M

GLASS SPHERES, NET BUOYANCY 25.4Kg

(x3)(FLOATS ARE MOUNTED ON CHAIN

WITH SHACKLES)

CONNECTING RING (DROP LINK)

CT SENSOR, APPROX. 1.0M

ADCP-HEAD, APPROX. 7.5M

CT SENSOR, APPOX. 10M

(10m, 15m, 20m, 30m, 50m,

75m, 100m, 200m, 500m)

18MM NYLON ROPE

ACOUSTIC RELEASE

16MM CHAIN-LONG LINK, 1M

BOTTOM WEIGHT APPOX. 1900Kg

SEA SURFACE

SEA BED

SENSOR ARM

RADIATION SENSOR

RAINFALL SENSOR

INSTRUMENTATION CYLINDER

KEEL WEIGHT

UPPER MAST

BUOY

Fig.7. Mooring configuration of OMNI buoy system

31

Table.1 Sensors used in OMNI buoy system

Sl.

No. Parameter Sensor type

Make/

Model Resolution Accuracy Range

Sample

rate

Sampli

-ng

period

Data

recor-

ded

1 Wind speed Cup

Anemometer

Lambrecht/

1453 0.1 m/s ± 2% 0-35 m/s 1 Hz 10 min. 1 h

2 Wind

direction

Vane +

fluxgate

compass

Lambrecht/

1453 0.1° 1.5° - 4° 0-359° 1 Hz 10 min. 1 h

3 Air

temperature Pt/100 RTD

Rotronic/

MP 102 A

0.0015° C ± 0.3° C -30-70° C 1 Hz 1 min. 1 h

4 Relative

humidity Capacitance 0.47 ± 1% 0-100 % RH 1 Hz 1 min. 1 h

5 Air Pressure Pressure

transducer

Vaisala/

PTB 330 0.01 hPa ±0.15hPa

500-1100

hPa

1sample

per hr.

Instanta

neous 1 h

6 Rainfall Capacitance

R. M.

Young:

/50202

0.058 ± 1 mm 0-50 mm 1 Hz 1 min. 2 min.

7

Down

welling long

wave

radiation

Pyrgeometer Eppley/PIR 1.27 W/m2

5 % 0-700 W/m2 1 Hz 1 min. 1 h

8

Down

welling

Shortwave

radiation

Pyranometer Eppley/

PSP 0.488 W/m2 3%

0-2800

W/m2 1 Hz 1 min. 1 h

9 Water

temperature Thermistor

Seabird /

MicroCAT

SBE37

T: 0.0001° C 0.002° C

-5 - 35° C

1

sample

per h.

Instanta

neous.

1 h

10 Conductivity Conductivity

cell

Seabird /

MicroCAT

SBE37

C: 0.0001

mS/cm

0.003

mS/cm

0-70 mS/cm

1

sample

per h.

Instanta

neous.

1 h

32

11 Water

pressure Strain gauge

Seabird /

MicroCAT

SBE37

P: 0.002% 0.1 %

0-100 bar

1

sample

per h.

Instanta

neous.

1 h

12

Directional

wave

spectra

Accelerometer

Angular rate

sensor

Magnetometer

Kongsberg

/ Seatex

MRU 4

Pitch and roll:

<0.001°

Heave: 5cm

Pitch and

Roll: 0.05°

Heading:

1.2°

Heave: ± 50

m

Heading: ±

180°

1 Hz 17.5

min. 1 h

13

Ocean

current

profile

Accoustic

Doppler

Current

Profiler

Teledyne

RD

Instrument

150 KHz

Velocity: 0.1 cm/s dir: 0.01°

Velocity: ±

5 mm/s

dir: ± 2°

0-256 cm/s

1

sample

per h

Instanta

neous

1 h

12 Single point

Doppler

Volume

Sampler

Teledyne

RD

Instrument

150 KHz

velocity: 0.1cm/s dir: 0.01°

Velocity: 1%

dir: ± 2° 0-600 cm/s

1

sample

per h

Instanta

neous I h