Discovery and Use of Operational Ocean Data Products and Services

18-22 June 2018

ITCOocean, INCOIS, Hyderabad

Indian Data Buoy Program and Data Analysis

Suprit KumarODG, INCOIS

Suprit.k@incois.gov.in

With the support of the Government of Flanders,

BelgiumExcept where otherwise noted, OTGA content is licensed under a Creative Commons Attribution-Noncommercial-ShareAlike 4.0.

Overview

INCOIS Data Centre: National Oceanographic Data Centre

Data Management

In Situ observations: Indian Moored buoy Program

Different aspects of moored buoy data management @INCOIS

Details of sensors and data acquisition

Data Analysis: Observations and QC

Data utilization

Dissemination

Data Assimilation and Ocean Modeling

Remote Sensing Satellites

Oceansat-1

Ocean Colour Monitor

Oceansat-2

Ocean Colour Monitor, Scatterometer

Foreign Satellites

In-situ Observations

Argo Profiling Floats

Data Buoys

Current Meter Arrays

XBT / XCTD

Gliders

Tide gauges

BPRs

Satellite Oceanography

National Infrastructure Network

Potential Fishing Zone Advisory ServicesOcean State Forecast ServicesEarly Warning for Tsunami and Storm SurgesOcean ModellingOcean Data and Information System & Web-based ServicesCoastal Geospatial ApplicationsValue-added Services

Fishing Community

Ports and Harbours

Off-shore and Shipping

IMD, Navy, NHO

Coast Guards

Coastal States

Research Institutions

Academia

National Oceanographic Data Centre

Ocean Observing Network

Ocean Observation, Information & Advisory Services

INCOIS Data Centre: Central repository of Marine Data

In-situ and remote sensing data reception, processing, quality control and dissemination

(real-time) to in-house operational activities as well as other operational agencies in the

country

Ocean Data Management

Data assembly, standardization, meta-database generation, database,

organization, data services (discovery, visualization, transfer).

Handles all varieties (Physical, Biological, Geological, Chemical etc) of voluminous data

in real time and delayed mode. Hosts specific projects and special data centers

Serving as the National Oceanographic Data Centre, Argo National Data Centre and

Argo Regional Data Centre, Data Assembly Centre (OceanSITES)

The INCOIS Data Centre is supported by the data received from Ocean Observing

Systems in the Indian Ocean

• None of the researchers interviewed for the study had received formal training in data mgmt. practices – levels of expertise a problem as they are learning on the job

• Few researchers, especially early career, think about the long-term preservation of their data

• The demands of publication output overwhelm long-term considerations of data curation

• A great need for more effective collaboration tools, as well as digital tools that support the volume of data generated and provide appropriate privacy and access controls.

Ref: Council on Library & Information Resources: “The problem of data”. (August 2012)

Data management: Awareness, Why and What?

Good data management is fundamental:• For excellence in research• For generation of high quality data• For data sharing, replication and reuse• For long-term sustainability and

accessibility• For data security• For reputational benefit

Ref: U of Ottawa

“There is an increasing demand for timely delivery of high quality operational oceanographic services and products (Data Producers and Data Stewards)”

- Standardized data collection- The lack of standardized data collection efforts can hamper

long-term value of datasets- Data collection must be standardized to allow datasets from a

variety of sources to be integrated

- Standard vocabulary and Standard data formats- The selection and adoption of a small number of standardized

data formats is essential to ensure proper data stewardship- The use of just a few formats can enhance the ability of data

stewards to preserve information over the long term

- Quality assurance and quality control (QA/QC)

- Data archival (long-term preservation and dissemination)- Well-defined naming conventions and format descriptions

- Related descriptive metadata- Information to facilitate data dissemination

High quality data and services

Courtesy: IOC/IODE QMF and IODE QMS OTGA course

Ocean Observations

the ocean is a major driver of the world’s weather and climate

Observations are crucial to our understanding of the Earth System:Ocean is the major driver of the world’s weather and climate

Ref: Infographic released on WOD 2017, GCOS, WMO

In-Situ Observations

Argo Floats Moored BuoysDrifting

Buoys

Tide

Gauges

XBTCurrent

Meter

Arrays

Research

Vessels

NIO/INCOISNIOTINCOI

S

ARGONCOIS

NIO/INCOISNIOT/NCAOR/CMLRE

SOI/NIO

T

Tsunami Buoy

SATELLITE

NIOT/INCOIS

BPR

Acoustic

Transducers

Antenna

Tsunami Buoy

SATELLITE

NIOT/INCOIS

BPR

Acoustic

Transducers

Antenna

Deep Ocean

Tsunami

Buoys

Coastal

HF

Radars

NIOT NIOT

Gliders

INCOI

S

In Situ: in the natural or original position or place

Discovery and Use of Operational Ocean Data Products and Services, ITCOocean, June 2018

ADCP

WRB

Moored buoy

Floating or Submerged platforms equipped with measurement sensors moored to anchors on the seafloor through cables

1. Structural design : Surface, hull, and underwater

2. Mooring structure

3. Suit of sensors: Met, Ocean and Others

4. Power system

5. Onboard processing

6. Communication

7. Shore-based facility

Tsunami Buoy Configuration

Images: NIOT

Complex engineering marvel

Sensors

AP

Data Logger & Processing

Unit

SatelliteTransceiver

Battery

LES - TATA Communications

Pune - India

INMARSATSATELLITE

AT

WS&D

WT

ADCP

NIOT Chennai

Storage

OOSReception System

INCOISFTP/Rec SERVER

AntennaAntenna

FTP

SatelliteTransceiver

Data Reception @ Land

NIOT

StorageNIOTMAIL Server

Wave

INCOIS

Buoy System

INCOISMAIL Server

Slide courtesy: Dr S Ramasundaram, OOS, NIOT :

ADDRESS State-of-Art Advanced Data Reception & Analysis

System

Advantages of Moored buoy observations

Stable and proven reliability: (N*24*365*x~106) of data messages per year

-High quality, real time data in scheduled time

-Unlimited life span, can be recovered and refurbished

-From weather to climate scales: Long time series observations

- Reserve buoyancy and power

Disadvantages:

-High operating costs, expensive to build a network

-No contingency and rapid deployment

History of Moored Buoy Observations

National Data Buoy Programme started incollaboration with NORAD Norway

1996

First buoy deployed 1997

Buoy network established with sensors for surfacemet-ocean parameters

1998

NORAD support till 2000

New facility established for NDBP at NIOT Campus 2004

After the Tsunami in 2004, first Tsunami buoydeployed in

2005

Tsunami buoy network established in 2007

CAL-VAL buoy for SAC ISRO 2008

OMNI buoy network, with surface and subsurfacesensors , established in deep waters of NorthernIndian ocean

2012

Moored system in Arctic 2014

Courtesy: NIOT

History of Moored Buoy Observations

National Data Buoy Programme (NDBP) of India started in 1996 for in situ met-ocean measurements in Arabian Sea and Bay of Bengal (DOD/NIOT)

3 Hrly Pressure, Temperature, Wind, Water Temp, Salinity,

Currents and Wave from August 1997.

12 Buoys initially (2002), it grew up to 47 buoys.

Present Buoy Network

MET OCEAN BUOY OMNI BUOY TSUNAMI BUOY

Images: NIOT, NCAOR

Types of buoy Systems

ARCTIC BUOY

OMNI (Ocean Moored buoy Network for northern Indian ocean)

Courtesy: NIOT

12 ActiveDeep sea Met-Ocean buoys

Spatial coverage:

-5 in Arabian Sea

-7 in Bay of Bengal

Temporal coverage:

-from October 2010 to present

OMNI BuoysOcean Moored buoy Network for northern Indian ocean

OMNI buoy structure

Courtesy: NIOT (Venkatesan et al. 2013)

Variables measured on OMNI buoys

• Surface meteorological

– Wind speed and direction

– Air temperature

– Air pressure

– Humidity

– Short wave radiation

– Incoming long wave radiation

– Precipitation

• Surface Ocean parameters

– Sea surface temperature

– Sea surface conductivity (salinity)

– Wave

– Current speed and direction

• Sub surface parameters

– Temperature and salinity at depths

starting from 5m, 10m, 15m, 20m

30m, 50 m, 75 m, 100 m, 200m and

500m

– Currents at depth levels 15m to 110m

at every 5mCourtesy: NIOT

Sensors onboard OMNI buoy

Courtesy: NIOT (Venkatesan et al. 2013)

Sensors onboard OMNI buoy

Sensors sampling

Data Processing

ASCII Binary

Other FormatsLike excel sheet,

NetCDF etc.

Data Storage

Database

Triggered basedReal Time QC

Data Acquisition

E-Mail

VSAT

FTP

INSAT

Offline

Delayed Mode QC

Data Conversion

ASCII Binary

Other FormatsLike excel sheet,

NetCDF etc.

Data Dissemination

E-Mail

VSAT

FTP

Web

Offline

Data Backup

DatabaseFTP Server

Data Flow

End-to-end System: Reception Processing QC Archival Dissemination

OMNI buoy data availability: Overview

1. Real-time monitoring with 12 active buoys2. High resolution (1hr HD and 3hr in RT) data3. Both meteorological and oceanographic variables

– Surface winds, air temperature, humidity,pressure, radiation, rainfall

– Upper ocean (1–500 m) temperature, conductivity– Surface layer (1–105 m) currents– Wave parameters (selected)

Deployment and maintenance by NIOT

Data processing, and dissemination by INCOIS

Two-tier OMNI data processing at INCOIS:-

1) Real Time data (excel/ASCII), after real time QC (Quality Control) goes

into Database for archival, distribution and visualization. Data

visualization and metadata information available online from.

www.odis.incois.gov.in/index.php/in-situ-data/moored-buoy

2) Delayed mode data (obtained from NIOT Hard Disk Data) converted into

NetCDF format.

Buoy/Deployment wise separate files for: 1. Temperature, Conductivity, Salinity

(derived) 2. Currents (Surface and subsurface) 3. Surface meteorological variables 4.

Wave parameters

3) Data goes through both objective (based on standard practices as impossible

value, range, position and time, stuck value, spike) and subjective quality

checks

No data are thrown out, they are just flagged

OMNI buoy data processing

1. Trigger based quality controlled is automatically done on the incoming data and QC flags are assigned. The tests are:

Impossible time and position

Spike test

Range test (Hard and soft instrumental range)

Stuck value test (current value equal to previous values).

Association test

2. Data is re-checked indelayed mode using background climatology (COADS). Visual Quality control tool developed.

QC checks on real time data

(Detailed information in INCOIS-DMG-TR-01-2009)

QC Process-Delayed mode

Comparison with NIOA Temp (degC)

Comparison with NIOA Sal (psu)

AD0619° 00’ N 66° 58’ E

ARGO

RAMA

ARGO

RAMA

(15°N, 90°E)

(15°N, 90°E)

OMNI-BD08

1(8.18°N,89.68°E)

OMNI-BD08

1((8.18°N,89.68°E)

Standard RT QC checks along with statistics

Independent comparisons with available datasets (RAMA,ARGO,WHOI…).

Temperature (°C)

Salinity (psu)

Delayed mode quality control

QC Process-Delayed mode

BD11

Easy case

QC Process - Delayed mode

BD10

What about this?

Issues

Harsh environment

Vandalism

Bio-fouling

Ship availability

Piracy

Images: NIOT

Observations

Wind Speed

Airtemperature

Humidity

Pressure

Meteorological data from BD08

(18.18°N,89.68°E-BD08)

BD08 (Surf. U and V) 18° 10’ N 89° 40’ E

BD08 (U and V) 18° 10’ N 89° 40’ EBD08 (U and V profiles) 18° 10’ N 89° 40’ E

BD08 (Temperature and Salinity) 18° 10’ N & 89° 40’ E

Temperature (°C)

Salinity (psu)

Excellent subsurface data

Applications: food for thought

Making sense of the available data

1. Each variable tells its own story: Derived products and understanding

2. Comparison with Observations, Model and Reanalysis datasets

4. Study of temporal variability: High frequency to frequency (Diurnal tointraseasonal to interannual)

5. Processes studies

6. ..

Most importantly…

Monitoring extreme events such as Cyclones and improve our understanding for better prediction and preparedness

cumulative Rate

BD10 16° 30’ N 88° 00’ E

2013BD08 18° 18’ N 89° 68’ E

Latent heat (W/m2)

Sensible heat (W/m2)

Daily Latent and sensibleheat fluxes calculatedusing COARE 3.0:-Using humidity, wind, air temp., pressure and SST (1m, 5m) from

Buoys along with satellite SST

Rainfall data: Good quality hourly and daily rainfall derived from very high resolution (2-min) records

Comparison withTRMM 3B42V7 daily data

(mm)

Comparison with RAMA fluxes data

Daily to sub-daily time scales

Diurnal rain-rate(mm/h) for Jun-Sep 2014

Derived products

Comparison with TRMM 3B42V7 daily data

How much accurate TRMM?Spatio temporal

variability?Fluxes?

Buoy and Satellite data comparisons

MSSSTB MBSST1 EX#3 EX#4 SST1RMS SST1COREI / *: 28.65 28.42 0.8635 1.173 0.9010 0.6726

BD08 (18.18°N,89.68°E-BD08) 1m (black), 5m (blue), 10m (green), Satellite SST (red)

BD10 16° 30’ 01” N 88° 00’ 00” E

1m buoy 5m buoy

Mean 28.42 28.17

Bias 0.23 (0.49)

-0.24

STD 1.17 (0.86)

1.44 (1.41)

RMSE 0.9 (0.58) 0.48

Correl 0.67 (0.9) 0.96

Rainfall data

SST Statistics

``

Comparison of radiation (W/m2): RAMA (15°N, 90°E) vs. OMNI buoy in the Bay of Bengal

Net shortwave (Wm-2)

Net longwave (Wm-2)

Monitoring extreme events: tropical cyclones

Tropical Cyclones Jal and Phailin

TC Jal4–7 October 2010SCSBD13

TC Phailin9–12 Oct. 2013VSCSBD10

TC Jal

TC Phailin

Wind Observations from BD13 during TC JAL

m/s

deg

rees

m/s

BD13

Surface met. Observations during TC JAL

oCoC

hP

a

BD13

Surface met. Observations during TC JAL

w/m

2w

/m2

mm

/h

BD13

Temperature Salinity

Surface

Upper ocean structure during TC JAL

Surface

Cooling and increase of salinity: Typical cyclonic response

BD13

Current structure during TC JAL

BD13 Cm

2.s-2.c

pd

-

1

Difference of clockwise and anticlockwise rotary spectra: Excitation of inertial currents

PVD diagram of surface current

Temperature Salinity

Research Highlights:

Summary:An abrupt increase of 1 psu (decrease of 1 °C) in salinity (temperature) in thenear-surface layers was observed from buoy measurements. Analysis shows thatvertical processes play major role (70% contribution) on this observedvariability.

Upward movement of thermocline in near-inertial frequencies playedsignificant role in mixed layer temperature and salinity variability, by much freerturbulent exchange between the mixed layer and thermocline.

Girish et. al; Ocean Dynamics(2014)Mixed layer heat and salt budget terms estimated from buoy during cyclone Jal

TC Jal track and Buoy location

Observed oceanic response to tropical cyclone Jal

Real time observations during TC Phailin

Temperature (oC)

Salinity (psu)

SWH (cm)

~ 1 oC Cooling and ~2.5 psu increase

BD10

U (cm/s)

(m/s)

(hPa)

Wm-2

Real time monitoring of tropical cyclones: TC Hudhud

Air P

ressu

re (h

Pa)

Win

d S

peed

(m/s

)cu

rren

t sp

(cm

/s)

Sub Surface current (cm/s)

BD10

Documenting complete lifecycle of cyclones usingavailable in situ data

BD12 BD13 BD10 BD11

subsurface current data from BD14?

What happens when observations are not reliable?

2-D slab model simulated and buoy U and V

A two dimensional

model

of wind forced

inertial oscillations

Inter-comparison and validation

BD08 Subsurface temperature observations compared with MOM, ROMS, HYCOM and INCOIS-GODAS

OMNI

Validation of model simulations

Comparison of salinity

OMNI vs. ROMS

(18.18°N,89.68°E-BD08)

Comparison of

Subsurface currents

(18.18°N,89.68°E-BD08)

Model simulations : Further improvements

MLD BLT QSW

QSWml

QLW

QL

QS

Qpen

Q

Monsoonal Cooling

SST (⁰C) (⁰C

)

W/m

2

degC

degC

Thangaprakash et. al; Oceanography(2016)

What controls seasonal evolution of SST?

From seasonal to diurnal variability

18.18°N,89.68°E-BD08

SST Polar plot

Air pressure

Real-Time Upon request

IMD and associated RMC Centers Universities : IIT’s –

Mumbai, Delhi, Bhubaneswar, Kharagpur; IISc;

Anna; Andhra; Bharthidhasan; BITS; VIT; AMET;

PDPU; Vels; Mangalore; NIT-Suratkal; CUSAT;

SKU; SGGSIE&T; IMU; Behrampur; Annamalai

Global Telecommunication System Govt. Organizations : SAC; CMLRE; NPOL; NRSC;

RRSC; IITM; ICMAM; NIO; NCSCM; NGRI;

Central Agricultural Research Institute, Port Blair Ports : Krishnapatnam Port Co. Ltd.; Marmagao

Port Trust; JNPT;

Naval Operations Data Processing and Analysis

Centre (NODPAC), Kochi

Commercially : COWI India Pvt. Ltd.; Noble

Denton Marine Assurance and Advisory; L & T

Rambell Consulting Engineers Limited;

Naval Command Met Offices Foreign : National Oceanography Centre, UK;

Iranian National Institute of Oceanography and

Atmospheric Sciences, Tehran; Russian State

Hydro meteorological University; University of

Manchester;

Data Dissemination

Metadata Editor

Search Interface Metadata View

ISO – 19115-2 compliant

GCMD Science Keywords

Metadata submission by

MoES Institutions

Spatial, Temporal

Keywords & Free Text

Search.

Metadata discovery: A portal for ease of discovery

Followed by real-time QC, data to Global Telecommunication System

Data conversion to FM-18 format as per WMO Manual 306

Pushed to RTH, New Delhi

RTH, New Delhi have responsibility to float same in world wide GTS network

Also OceanSITES network: a worldwide system of long-term high resolution multi-parameters data buoys as climate reference stations, GOOS initiative

International impact

Ocean Data Information System

(www.odis.incois.gov.in)

Data reception system for receiving LCMB data through INSAT – 2008

Software for INSAT data reception for 5G buoys; forwarding raw data to NIOT

Visual Quality Control Tools:

Offline quality control based on human intervention

In-house Software’s using Open Sources

Out of all our data users, more than 60% ask for buoy data

Based on overall feedback, rating of data quality is “Very Good” and data delivery is

tends to “Excellent” (calculation based on 21 feedbacks received between 2012-2016)

To promote the data further, INCOIS data centre conducts annual data awareness and

utilization workshops for students as well as researchers

Data support for student dissertations and thesis

User awareness and feedbacks

THANK YOU