GODAE High Resolution Sea Surface Temperature Pilot Project

GODAE High ResolutionGODAE High ResolutionSea Surface Temperature Sea Surface Temperature

Pilot ProjectPilot Project

GHRSST-PP and the NOAA-GHRSST-PP and the NOAA-NASA Collaboration for Data NASA Collaboration for Data Management and ReanalysisManagement and Reanalysis

Kenneth CaseyNOAA/NESDIS/NODC

NOAA-NASA GHRSST-PP OverviewOctober 20, 2004 (2)

GHRSST-PP ObjectivesGHRSST-PP Objectives

• To deliver a new generation of operational To deliver a new generation of operational SST productsSST products

• To ensure that duplication of SST activities To ensure that duplication of SST activities are minimizedare minimized

• To implement an operationally efficient To implement an operationally efficient methodology for real time fusion of SST methodology for real time fusion of SST datadata

• To develop and foster considerable To develop and foster considerable scientific and operational knowledge scientific and operational knowledge during the lifecycle of the GHRSST-PPduring the lifecycle of the GHRSST-PP


International FrameworkInternational Framework

http://www.ghrsst -pp.org

Applications and User Services (AUS)

User Information Services (UIS)

GTSGlobal coverage satellite and in

situ data streams

Regional coverage

satellite and in situ data streams

Specialist satellite and in situ data

servers

Global Data Analysis Centre(s) (GDAC)

Global coverage L2P and Analysed products

Data provisionlayer

Regional dataassembly layer

Global dataanalysis layer

Applications anduser layer

Regional coverage L2P and Analysed products

Regional Data Assembly Centre(s) (RDAC)


International FrameworkInternational Framework

Long Term Stewardship and Reanalysis Facility(delayed mode users)

RDACand

GDAC(near real time

users)

RDACs


GHRSST-PP Science TeamGHRSST-PP Science Team• Craig Donlon (Chair), Hadley Craig Donlon (Chair), Hadley

Centre for Climate Prediction and Centre for Climate Prediction and Research, Met Office UKResearch, Met Office UK

• Bill Emery, University of Colorado, Bill Emery, University of Colorado, USAUSA

• Chelle Gentemann, Remote Chelle Gentemann, Remote Sensing Systems, USASensing Systems, USA

• Chris Mutlow, Rutherford Appleton Chris Mutlow, Rutherford Appleton Laboratory, UKLaboratory, UK

• Doug May, Naval Oceanographic Doug May, Naval Oceanographic Office, USAOffice, USA

• Gary Wick, NOAA/OAR ETL, USAGary Wick, NOAA/OAR ETL, USA• Ian Barton, CSIRO Marine Ian Barton, CSIRO Marine

Research, AustraliaResearch, Australia• Ian Robinson, Southampton Ian Robinson, Southampton

Oceanography Centre, UKOceanography Centre, UK• Jim Cummings, Naval Research Jim Cummings, Naval Research

Laboratory/US-GODAE, USALaboratory/US-GODAE, USA• Richard Reynolds, NOAA/NESDIS Richard Reynolds, NOAA/NESDIS

NCDC, USA NCDC, USA • Neville Smith, BMRC, AustraliaNeville Smith, BMRC, Australia

• Hiroshi Kawamura NASDA/University Hiroshi Kawamura NASDA/University of Tohoku, Japan of Tohoku, Japan

• Nick Rayner, Hadley Centre for Nick Rayner, Hadley Centre for Climate Prediction and Research, Met Climate Prediction and Research, Met Office UKOffice UK

• Peter Minnett, RSMAS, University of Peter Minnett, RSMAS, University of Miami, USAMiami, USA

• Bob Evans, RSMAS, University of Bob Evans, RSMAS, University of Miami, USAMiami, USA

• Pierre LeBorgne, Meteo France O&SI Pierre LeBorgne, Meteo France O&SI SAF, FranceSAF, France

• Andy Harris, NOAA/NESDIS ORA, USAAndy Harris, NOAA/NESDIS ORA, USA• Ed Armstrong, JPL PO.DAAC, USAEd Armstrong, JPL PO.DAAC, USA• Ken Casey, NOAA/NESDIS NODC, USAKen Casey, NOAA/NESDIS NODC, USA• Jorge Vasquez, JPL, PO.DAAC, USAJorge Vasquez, JPL, PO.DAAC, USA• David Llewellyn-Jones, Univ. of David Llewellyn-Jones, Univ. of

Leicester, UKLeicester, UK• Andrew Bingham. JPL PO.DAAC, USAAndrew Bingham. JPL PO.DAAC, USA• Jean-Francois Piolle, IFREMER, FranceJean-Francois Piolle, IFREMER, France• Chris Merchant, Southampton Chris Merchant, Southampton

Oceanography Centre, UKOceanography Centre, UK


GHRSST-PP OutputsGHRSST-PP Outputs• An operational data An operational data

stewardship, exchange and stewardship, exchange and delivery systemdelivery system

• Standardized, real-time Standardized, real-time L2P SST productsL2P SST products

• Real time L4 SST (gap free, Real time L4 SST (gap free, gridded) gridded)

• Reprocessed L4 SST for Reprocessed L4 SST for Climate Data RecordsClimate Data Records

• High Resolution Diagnostic High Resolution Diagnostic Data Sets (HR-DDS)Data Sets (HR-DDS)

• Matchup Data Base (MDB)Matchup Data Base (MDB)• Master Metadata Master Metadata

Repository (MMR)Repository (MMR)


The GHRSST-PP ConceptIn principle, the merging and analysis of complementary satellite and in situ measurements can deliver SST products with enhanced accuracy, spatial and temporal coverage.

Emphasis on synergy benefits

Datamerging

Quality control and uncertainty estimation

AnalysisProducts

ObservationProducts

L4

L2P


Users and ApplicationsUsers and Applications


GODAE and the GHRSST -PP

FOAM 1/ 9deg 72hr DAILY forecast made on 11/ 02/ 2003M ERCATOR bi - weekly forecast made on 5/ 02/ 2002

Better SST Datafor assimilation

Init. cond ./ forcing/boundary Con.

Better Weather forecastsBetter Ocean forecastsBetter Climate data record Better OUTCOMES

TailoredOUTPUTS

Applications• Alleviation of Poverty• Safety/security of life

and property• Monitoring of natural

hazards/disasters• Detection and

predicting climatevariability

• Management of naturalresources

• Public health • Safety of marine

operations• Preserving and restoring

the health of ecosystems• Water quality water

quality indicators.• Tourism and leisure

FEEDBACK


Users and ApplicationsUsers and ApplicationsSampling from one RDACs User Requirement’s Document:Sampling from one RDACs User Requirement’s Document:

• MERCATORMERCATOR• MET OFFICE, UK, FORECASTING OCEAN ASSIMILATION MODEL (FOAM)MET OFFICE, UK, FORECASTING OCEAN ASSIMILATION MODEL (FOAM)• MEDITERRANEAN OCEAN FORECASTING SYSTEM (MFS)MEDITERRANEAN OCEAN FORECASTING SYSTEM (MFS)• EUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTINGEUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTING• TOWARDS AN OPERATIONAL PREDICTION SYSTEM FOR THE NORTH TOWARDS AN OPERATIONAL PREDICTION SYSTEM FOR THE NORTH

ATLANTIC EUROPEAN COASTAL ZONES (TOPAZ)ATLANTIC EUROPEAN COASTAL ZONES (TOPAZ)• BALTIC OPERATIONAL OCEANOGRAPHIC SYSTEM (BOOS)BALTIC OPERATIONAL OCEANOGRAPHIC SYSTEM (BOOS)• NORWEGIAN METEOROLOGICAL INSTITUTE (MET.NO)NORWEGIAN METEOROLOGICAL INSTITUTE (MET.NO)• DANISH METEOROLOGICAL INSTITUTE DANISH METEOROLOGICAL INSTITUTE • ROYAL DANISH ADMINISTRATION OF NAVIGATION AND HYDROGRAPHYROYAL DANISH ADMINISTRATION OF NAVIGATION AND HYDROGRAPHY• NORWEGIAN POLAR INSTITUTENORWEGIAN POLAR INSTITUTE• NORWEGIAN MET. INSTITUTE’S MARINE FORECASTING CENTRENORWEGIAN MET. INSTITUTE’S MARINE FORECASTING CENTRE• BLACK SEA GOOSBLACK SEA GOOS• WMO AFFILIATED INTERNATIONAL PROJECTSWMO AFFILIATED INTERNATIONAL PROJECTS• PROUDMAN OCEANOGRAPHIC LABORATORYPROUDMAN OCEANOGRAPHIC LABORATORY

GHRSST Overview TalkGHRSST Overview TalkA Collaborative Effort Between A Collaborative Effort Between

NOAA/NODC and NASA/PO.DAACNOAA/NODC and NASA/PO.DAACOctober 20October 20thth, 2004, 2004PO.DAAC Team

Manager: Pat Liggett

Deputy Manager: Robert Benada

Sue Heinz: Raytheon Manager

Jorge VazquezEdward ArmstrongAndrew BinghamChris FinchRosanna Sumagaysay


OutlineOutline

• GHRSST Processing GHRSST Processing SystemSystem– RDACs, GDAC, RDACs, GDAC,

ArchiveArchive– Data ProductsData Products– Data FlowData Flow– OceanidsOceanids– Data VolumesData Volumes

• GHRSST Data GHRSST Data ManagementManagement– MMRMMR– MDBMDB– User ServicesUser Services

• NASA/NOAA NASA/NOAA SignificanceSignificance


GHRSST Processing SystemGHRSST Processing SystemRDACs, GDAC & ArchiveRDACs, GDAC & Archive

GHRSST Data Flow

Remote Sensing SystemsLead Chelle Gentemann

1gigabyte/dayincludes L2P, L4, and L2 AMSR-E

MedspirationLead Jean Francois Piolle

7 gigabtyes/dayincludes includes L2P DDS and L4

NODC/NOAALong Term Stewardship and Reanalysis

PO.DAACGlobal Data Assembly Center

GDAC, RDACOCEANIDS, User Services

NAVOCEANOLead Bruce Mackenzie

8 gigabytes/dayincludes Global AVHRR and LAC data

BluelinkLead Anthony Rea

7 gigabytes/dayL2P AVHRR off of Australia

MODIS RDACLead Ed Armstrong3-46 gigabytes/day

Includes global MODIS L2P data

RDACSRegional Data Assembly Centres


GHRSST Processing System GHRSST Processing System

Data ProductsData Products

http://www.ghrsst-pp.org

SST definitions and data products within the GHRSST-PP

Infrared SST measurements

Microwave SST measurements

Analysed SST product

Diurnal warmingmodel

Skin-subskinmodel


• Level 2 Level 2 Preprocessed data Preprocessed data (L2P)(L2P)– Level 2 data from Level 2 data from

each sensor in a each sensor in a standard netcdf standard netcdf format that has format that has complete error complete error statistics associated statistics associated with each sensor.with each sensor.



• High Resolution High Resolution Diagnostic Data Diagnostic Data Sets (HR-DDS) Sets (HR-DDS) – L2P data extracted L2P data extracted

in given predefined in given predefined boxes to be used for boxes to be used for validation studies. validation studies. All data from All data from different sensors co-different sensors co-located on common located on common grid.grid.




• Recommended data Recommended data products for Near products for Near Real-time SST fieldsReal-time SST fields– L2 SST, Cloud Fraction, L2 SST, Cloud Fraction,

Internal SST Error Estimate Internal SST Error Estimate

(rms uncertainty)(rms uncertainty)

Using AIRS Data Within the GHRSST-PPUsing AIRS Data Within the GHRSST-PP• Recommended data Recommended data

products for GHRSST-PP products for GHRSST-PP Diagnostic Data StudiesDiagnostic Data Studies– IR Spectral Radiance and Cloud-IR Spectral Radiance and Cloud-

Cleared RadianceCleared RadianceL2, L3 SST L2, L3 SST

– Boundary layer Boundary layer moisture/temperature (total column moisture/temperature (total column and layered)and layered)

– Surface emissivitySurface emissivity– Cloud fraction and cloud-top Cloud fraction and cloud-top

pressurepressure– Cloud flags (including aerosol)Cloud flags (including aerosol)

Source: Denise Hagan, JPL




Map of Global Sites for GHRSST Diagnostic Data Sets

•Example of Single Sensor Error Characterization for AIRS based on comparison with shipboard in situ MAERI data (Caribbean):

Mean Bias

(N=120)

RMS (N=120)

Source: Denise Hagan, JPL – AIRS Valuable Reference Sensor for GHRSST Diagnostic Data Sets and SST Error Assessment




• Level 4 Merged Analyzed SST Level 4 Merged Analyzed SST productsproducts– 10km global product (Remote Sensing 10km global product (Remote Sensing

Systems)Systems)– 2 km very high resolution regional 2 km very high resolution regional

products, including areas such as the products, including areas such as the MediterraneanMediterranean


GHRSST Processing System GHRSST Processing System - - OCEANIDSOCEANIDS

• Autonomous data stream ingest, management and distribution

• Built-in redundancy

• GUI for monitoring data flows

• Provides instant anomaly messaging to DAAC operators

• Manage over 50 data streams (50,000 files/month – 1 TB/month)

• OCEANIDS is enabling the DAAC to work with near real time data streams and serve customers such as COOS and IOOS.


GHRSST Processing System GHRSST Processing System – – OCEANIDS – OCEANIDS – Data Stream Data Stream InterfacesInterfaces

User InterfaceGDAC (JPL U.S.)

RDACs

OCEANIDS(Data Management)

MMR/MDB

L4(MISST – U.S.)

Archive(NODC – U.S.)

Near Real TimeUsers

Delayed mode Users

Medspiration (Europe)

MISST(U.S.)

BlueLink(Australia)

PO.DAAC(U.S.)

NGSST(Japan)

NAVOCEANO(U.S.)

Data Tools


GHRSST Processing System GHRSST Processing System – – Data VolumesData Volumes

RDAC Contact DatasetUncompressed

Data Rate (GB/day)

Estimated Compressed

Rate (GB/day)Notes

Medspiration Jean-Francois Piolle L2P 13.800 Includes Global AATSR

HR-DDS 1.000 0.200 50% for AATSR, 95% for AMSR-EL4 0.035

MISST RDAC Chelle Gentemann L2P TMI 0.155 104 cells x 3200 scan lines x 15orbits/day x 31bytes=155 MB/dayL2 AMSRE 0.500 243 cells x 4400 scan lines x 15orbits/day x 31bytes= 500 MB/dayL4 0.032 global daily ~9km = 4096x2048x4bytes*= 32 MB/day

Bruce McKenzie GAC 16 0.588 42 MB/orbit x 14 orbits/day [based on 30 byte L2P record]GAC 17 0.599 42 MB/orbit x 14 orbits/dayLAC 17 1.540 55 MB/orbit x 28 orbits/dayLAC 16 2.090 55 MB/orbit x 38 orbits/day, not currently distributedGOES12 NHEM 0.897 23 MB/sector x 39 sectors/dayGOES12 SHEM 0.324 9 MB/sector x 36 sectors/dayGOES12 FULL 0.384 48 MB/sector x 8 sectors/dayGOES10 NHEM 0.576 16 MB/sector x 36 sectors/dayGOES10 SHEM 0.396 11 MB/sector x 36 sectors/dayGOES10 FULL 0.384 48 MB/sector x 8 sectors/day

BlueLINK:Australian BoM RDAC

Anthony Rea L2P AVHRR 7.000 1.800

a) AVHRR SSTs at full resolution and in ‘swath’ formatb) Data from 3 Australian sites to be provided;c) Data from two operational satellites only to be provided;d) L2P ‘multiplier’ is 34 (compared to SST data w. no metadata).e) Assumes currently achieved 75% compression ratio

JPL RDAC Ed Armstrong L2P MODIS Terra (4 km) 1.480 0.490288 files/day * (2030*1354) pixels/file * 30 bytes/pixel ) / 10^9 = 23.74 GB/day for Global 1 km. Divide by 16 for Global 4 km = 1.48 GB/day, getting about 3:1 compression ratio with gzip

L2P MODIS Aqua (4 km) 1.480 0.490Same as above

SeasnetAnotonio Ramos and Michel Petit

L2P 3.000 Seasnet

JMA Tsurane Kuragano MGDSST L4 0.013Global, 0.25 deg resolution SST analysis, analysis error, and difference from climatology. 380 MB/month.

TOTAL (GB/day) 36.273

TOTAL (TB/year) 13.240

JPL RDAC OPTION: Do global MODIS 1 km instead of 4 km

Ed Armstrong L2P MODIS Terra (1 km) 23.740 7.900288 files/day * (2030*1354) pixels/file * 30 bytes/pixel ) / 10^9 = 23.74 GB/day for Global 1 km, getting about 3:1 compression ratio with gzip

L2P MODIS Aqua (1 km) 23.740 7.900Same as above

TOTAL OPTION (GB/day): 80.793TOTAL OPTION (TB/yr): 29.489


GHRSST Data Management GHRSST Data Management Master Metadata RepositoryMaster Metadata Repository

Two types of metadata Two types of metadata records based on records based on NASA DIF format:NASA DIF format:

• Data Set Record (DSD):Data Set Record (DSD):– Information common to Information common to

all files (i.e., same all files (i.e., same sensor, data center etc.)sensor, data center etc.)

– Static:Static: Generally Generally prepared once for each prepared once for each data set data set

• File Record (FR):File Record (FR):– File specific information File specific information

(e.g., parameter, time (e.g., parameter, time and space etc.)and space etc.)

– Dynamic:Dynamic: Prepared on a Prepared on a file-by-file basis by each file-by-file basis by each Regional Data Assembly Regional Data Assembly Center (RDAC) Center (RDAC)

• Both types integrated Both types integrated into a relational into a relational database: the Master database: the Master Metadata Repository Metadata Repository (MMR).(MMR).


GHRSST Data Management GHRSST Data Management Master Metadata RepositoryMaster Metadata Repository

Parse meta[QA check]

RDAC submission

GDAC submission

Create/update/modify mySQL tables [SQL]

[Perl scripts]

Pass QA

Fail QA – return to source

meta

Metadata queries by external user


GHRSST Data Management GHRSST Data Management Matchup Database (MDB)Matchup Database (MDB)

• Record of in situ/satellite matchups from Record of in situ/satellite matchups from buoys and other in situ platformsbuoys and other in situ platforms– Source for satellite sensor error statisticsSource for satellite sensor error statistics

• Not an explicit metadata record but Not an explicit metadata record but captures actual data (5x5 grids of captures actual data (5x5 grids of matchup information)matchup information)

• These databases will facilitate the data These databases will facilitate the data discovery of multiple satellite platforms discovery of multiple satellite platforms and products for the customerand products for the customer


GHRSST Data Management GHRSST Data Management Matchup Database (MDB)Matchup Database (MDB)

Matchups from Pathfinder SSTMatchups from Pathfinder SST

• 1985-19881985-1988• 1996-19991996-1999


GHRSST Data ManagementGHRSST Data ManagementUser ServicesUser Services

• GDAC Customer GDAC Customer Relationship Relationship Management (CRM) Management (CRM) PlanPlan– GoalsGoals– Target AudiencesTarget Audiences– Strategies & Strategies &

ActivitiesActivities– Evaluation Evaluation

MechanismsMechanisms



• GoalsGoals– Leverage existing PO.DAAC CRM infrastructure to ensure Leverage existing PO.DAAC CRM infrastructure to ensure

a high level of customer satisfactiona high level of customer satisfaction– Fully integrate RDACs, GDAC and NODC into the Fully integrate RDACs, GDAC and NODC into the

Customer Knowledge Base for 24/7 supportCustomer Knowledge Base for 24/7 support– Implement and distribute metrics developed by the Implement and distribute metrics developed by the

GHRSST Project Office into the GDAC reporting systemGHRSST Project Office into the GDAC reporting system– Develop and maintain web and information access portal Develop and maintain web and information access portal

unique to GHRSST-PPunique to GHRSST-PP



• Target Audiences – Target Audiences – Customer BaseCustomer Base– GHRSST ProjectGHRSST Project– RDACsRDACs– Management Stakeholders Management Stakeholders

• JPL, PO.DAAC, NODC, JPL, PO.DAAC, NODC, NOAA, NASA,NOAA, NASA,

– L2P Data Operational L2P Data Operational UsersUsers

– L4 Public Data CustomersL4 Public Data Customers– NODC Long Term ArchiveNODC Long Term Archive



• Strategies and Strategies and ActivitiesActivities– GHRSST-PP Web GHRSST-PP Web

Portal – data and Portal – data and information accessinformation access

– 24/7 Knowledge 24/7 Knowledge Base Customer Base Customer SupportSupport

– Customer access to Customer access to all communication all communication channels: Email, channels: Email, telephone, fax, web.telephone, fax, web.

– Measures of Measures of Effectiveness-Effectiveness-provide statistics provide statistics report to report to stakeholdersstakeholders

– Privacy policiesPrivacy policies– Operational Operational

communicationscommunications– Solicit customer Solicit customer

feedback and feedback and implement process implement process improvementimprovement



• Provide monthly analytic Provide monthly analytic reports to stakeholders on reports to stakeholders on customer statisticscustomer statistics– Number of Customers Number of Customers

accessing data via electronic accessing data via electronic transfer, subsetting toolstransfer, subsetting tools

– Characterization of Characterization of customerscustomers

– Number of customer Number of customer interactions interactions

– Results of closed end Results of closed end surveys to customers surveys to customers accessing dataaccessing data

• Provide monthly statistics on Provide monthly statistics on data access, availability and data access, availability and transfer:transfer:– Number of successful data Number of successful data

transferstransfers– Number of data files served Number of data files served

(volume?)(volume?)– FTP statistics FTP statistics

• Number of Users,Number of Users,• Number of Files downloadedNumber of Files downloaded• Data volume transferredData volume transferred

• Provide data quality metricsProvide data quality metrics– Quantify number of customers Quantify number of customers

providing feedback for providing feedback for validation studies against validation studies against climatologies, in-situ data, etc. climatologies, in-situ data, etc.

Evaluation MechanismsEvaluation Mechanisms – monthly and quarterly analytic – monthly and quarterly analytic reports to Stakeholdersreports to Stakeholders



Regional Data Assembly Centers/

Value Added Providers/LTA

MedspirationRSSNAVOCEANOBluelinkJMAMODIS MISSTNaval Research MontereyNODC

Data User Communitie

s

DiscoveryData AccessBuilding KB

QueriesApplications

NOAANASA

GHRSST-PPJPLESA

GDAC Customer

Relationship

GDAC CRM Overview


NOAA-NASA SignificanceNOAA-NASA SignificanceWhy is all of this important?Why is all of this important?

• SST fundamental parameter for understanding climate SST fundamental parameter for understanding climate change. New generation of SSTs used in data change. New generation of SSTs used in data assimilation must come with full error assimilation must come with full error characterizations.characterizations.

• Directly related to NASA’s Earth Science Enterprise Directly related to NASA’s Earth Science Enterprise Focus themes. Includes:Focus themes. Includes:

““How can predictions of climate variability and change be How can predictions of climate variability and change be improved”improved”??

““How can weather forecast duration and reliability be improved by How can weather forecast duration and reliability be improved by new space-based observations, data assimilation, and modeling?”new space-based observations, data assimilation, and modeling?”

““The NASA Earth Science Applications Theme was created to The NASA Earth Science Applications Theme was created to accelerate and expand the delivery of Earth system science accelerate and expand the delivery of Earth system science research results to serve decision support for American citizens research results to serve decision support for American citizens and the world.” and the world.”


NOAA-NASA SignificanceNOAA-NASA SignificanceWhy is all of this important?Why is all of this important?

• Will also support themes dealing with Carbon Will also support themes dealing with Carbon Cycle, Coastal Management, Water and Energy Cycle, Coastal Management, Water and Energy CyclesCycles

• Will support themes dealing with improved data Will support themes dealing with improved data management, including standardization of data management, including standardization of data formats, metadata, user support, and integration formats, metadata, user support, and integration of new technologies for faster data transfer.of new technologies for faster data transfer.

• Also addresses all four of NOAA’s Goals: Climate, Also addresses all four of NOAA’s Goals: Climate, Ecosystems, Weather and Water, Commerce and Ecosystems, Weather and Water, Commerce and TransportationTransportation

• And…GHRSST is IOOS-compliant!!And…GHRSST is IOOS-compliant!!


New TechnologiesNew Technologies

• Aspera: Enables large data set Aspera: Enables large data set transfers over any network transfers over any network regardless of network conditions and regardless of network conditions and distance.distance.

• RightNow: Enables user and RDAC RightNow: Enables user and RDAC access to knowledge base system for access to knowledge base system for maximizing customer communication maximizing customer communication and satisfactionand satisfaction


Conclusion…Conclusion…

This is a great example of NOAA and This is a great example of NOAA and NASA working together to better NASA working together to better

meet the needs of our shared meet the needs of our shared community of users!community of users!

Documents

GODAE High Resolution Sea Surface Temperature Pilot Project