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Geostationary Coastal Waters Imaging as a Component of IOOS
Geostationary Coastal Waters Imaging as a Component of IOOS
Curtiss O. DavisCollege of Oceanic and Atmospheric Sciences
Oregon State University, Corvallis, Oregon 97331
W. Paul BissettFlorida Environmental Research Institute
Tampa, Florida
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Presentation OutlinePresentation Outline
The coastal ocean is a complex and dynamic system. It will require continuous in situ sampling, frequent high resolution remote
sensing data and high resolution coupled physical, bio-optical models to adequately describe coastal ecosystem dynamics.
• Current and planned ocean color sensors in low earth orbit will not provide the required coverage.
• Ocean color measurements from geostationary orbit can provide frequent imaging of coastal waters:– Sample frequently enough to resolve tidal dynamics, Track blooms, oil
spills, etc.• Coastal Ocean Applications and Science Team (COAST)• Monterey Bay September 2006 experiment
– Demonstration data set for algorithm development– Tracking a Harmful Algal Bloom (HAB)
• Using Geostationary Ocean color data with IOOS data and coastal ocean models
• Summary
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What we expect to have in 2010What we expect to have in 2010
Visible Infrared Imaging Radiometer Suite (VIIRS)
• Being built by Raytheon SBRS
– SeaWiFS, MODIS heritage
• First flight on NPOESS Preparatory Project (NPP) in 2008 then NPOESS satellites starting in 2011
• Seven ocean color channels and 2 SST channels
ChannelName
channel Center
Channel Width
Ltypical ocean
Required SNR/NET
VIIRS SNR/NET
M1 412 nm 20 nm 44.9 352 670 M2 445 nm 18 nm 40 380 506 M3 488 nm 20 nm 32 415 515
M4 555 nm 20 nm 21 361 446
M5 672 nm 20 nm 10 242 ~ 400
M6 751 nm 15 nm 9.6 199 ~ 400
M7 865 nm 39 nm 6.4 215 314
M15 10.8 m 1.0 m 300K .070 .041
M16 12.0 m 1.0 m 300K .072 .041 M
•Approximately 1 km GSD ocean color–742 m GSD and Nadir, 1092 m at +/- 850 km, 1597m at End of Scan (+/- 1500 km)–Designed to meet global ocean imaging requirements at 1 km GSD –Maximum revisit frequency of twice a day at 1030 and 1530
•Approximately 1 km GSD ocean color–742 m GSD and Nadir, 1092 m at +/- 850 km, 1597m at End of Scan (+/- 1500 km)–Designed to meet global ocean imaging requirements at 1 km GSD –Maximum revisit frequency of twice a day at 1030 and 1530
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Why we need a Geostationary imager for coastal ocean dynamics
Why we need a Geostationary imager for coastal ocean dynamics
• Tides, diel winds (such as the land/sea breeze), river runoff, upwelling and storm winds drive coastal currents that can reach several knots. Furthermore, currents driven by diurnal and semi-diurnal tides reverse approximately every 6 hours.
• Daily sampling at the same time (e.g. MODIS and in the future VIIRS) cannot resolve tides, diurnal winds, etc.
• Frequent sampling to resolve tides from a geostationary platform and will provide the management and science community with a unique capability to observe the dynamic coastal ocean environment.
• Higher spatial resolution (300 m vs. 1000 m)• Additional channels to measure solar stimulated
fluorescence, suspended sediments, CDOM and HABs. Example tidal cycle from
Charleston, OR. Black arrows VIIRS sampling, red arrows HES-CW sampling.
Example tidal cycle from Charleston, OR. Black arrows VIIRS sampling, red arrows HES-CW sampling.
These improvements are critical for coastal waters.
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1 km MODIS Sept 9, 2006 - Chlorophyll 250m
Enhanced spatial resolution required to resolve coastal features
Enhanced spatial resolution required to resolve coastal features
Monterey Bay, CA images from Bob Arnone, NRLSSC Monterey Bay, CA images from Bob Arnone, NRLSSC
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COAST and Risk Reduction ActivitiesCOAST and Risk Reduction Activities
• The Coastal Ocean Applications and Science Team (COAST) was created in August 2004 to support NOAA to develop coastal ocean applications using geostationary ocean color measurements:– Mark Abbott, Dean of the College of Oceanic and Atmospheric Sciences
(COAS) at Oregon State University is the COAST team leader,– COAST activities are managed through the Cooperative Institute for
Oceanographic Satellite Studies (CIOSS) a part of COAS, Ted Strub, Director
– Curtiss Davis, Senior Research Professor at COAS, is the Executive Director of COAST.
• Initial activity to evaluate geostationary ocean color requirements and suggest improvements
• Beginning in 2006 conduct field experiments to collect example data that can be used for evaluating requirements and developing algorithms for the geostationary ocean color measurements for the coastal ocean.
• COAST will support NOAA through GOES-R Risk Reduction activities and Algorithm Working Groups to develop requirements, algorithms and models for using geostationary ocean color data.
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Monterey Bay September 3-16, 2006Experiment Plan
Monterey Bay September 3-16, 2006Experiment Plan
• Monterey Bay has long-term physical, biological and optical monitoring– Links to data at http://www.cencoos.org
• Intensive effort for 2 weeks to assure that all essential parameters are measured:– Supplement standard measurements at the site with shipboard and
glider measurements of water-leaving radiance, optical properties and products expected from HES-CW algorithms,
– Additional atmospheric measurements as needed to validate atmospheric correction parameters,
– As needed, enhance modeling efforts to include bio-optical models that will utilize HES-CW data (NRL).
• Aircraft overflights for at least three clear days and one partially cloudy day (to evaluate cloud clearing) during the two week period. – High altitude to include 90% or more of the atmosphere– 30 min repeat flight lines for up to 6 hours to provide a time series for
models and to evaluate changes with time of day (illumination, phytoplankton physiology, etc.)
• All data to be processed and then distributed over the Web for all users to test and evaluate algorithms and models.
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SAMSONSpectroscopic Aerial Mapping System with On-board
Navigation
SAMSONSpectroscopic Aerial Mapping System with On-board
Navigation
• The Florida Environmental Research Institute (FERI) has developed a low-cost, robust HyperSpectral Imager, the Spectroscopic Aerial Mapper with On-board Navigation (SAMSON).
• SAMSON provides for a full HSI dataset 256 bands in the VNIR (3.5 nm resolution over 380 to 970 nm range) at 75 frames per second, with a SNR, stability, dynamic range, and calibration sufficient for dark target spectroscopy.
• Data sampled at 5 m GSD and binned to 100, 300, 375 and 500 m to evaluate need for higher GSD.
– Binned data will have SNR in excess of 1000:1 – noise will be added to simulate lower SNR data.
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A red tide incubator in Monterey Bay?A red tide incubator in Monterey Bay?
2002 red tide
Image from P. Bissett
2004 red tideImage from R. Kudela 2005 Red Tide
MERIS satellite imagery, 9/17
J. Gower, IOS, Sidney BC
Physical, chemical and biological influences in this region:• In the upwelling shadow (stratification)• Downstream of Elkhorn Slough plume (stratification, nutrients, dinoflagellate seed populations)
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709 nm is ideal channel for Monterey Bay HABs
709 nm is ideal channel for Monterey Bay HABs
Harmful Algal Bloom in Monterey Bay threatens
beach areas.
• Bloom near coast and on the order of 2 x 5 km would not be
resolved in 1 km VIIRS data.
• Additional channels on HES-CW aid bloom identification.
PHILLS-2 airborne hyperspectral data from Paul Bissett, Florida Environmental Research Institute. (October 2002 Ceratium spp. bloom)
Harmful Algal Bloom in Monterey Bay threatens
beach areas.
• Bloom near coast and on the order of 2 x 5 km would not be
resolved in 1 km VIIRS data.
• Additional channels on HES-CW aid bloom identification.
PHILLS-2 airborne hyperspectral data from Paul Bissett, Florida Environmental Research Institute. (October 2002 Ceratium spp. bloom)
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September 12, 2006Grid 03: 10:07 - 10:32 September 12, 2006
Grid 03: 10:07 - 10:32
709 nm channel used to identify
HAB
Primary species Akashiwo sanguinea
FERI SAMSON data
709 nm channel used to identify
HAB
Primary species Akashiwo sanguinea
FERI SAMSON data
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HES-CW Data flow and Risk Reduction Activities
HES-CW Data flow and Risk Reduction Activities
Raw sensor data
Calibrated radiances
at the sensor
Water Leaving
Radiances
In-Water Optical
Properties
Applications and products
Users
CalibrationCalibration Atmospheric Correction
Atmospheric Correction
Optical properties Algorithms
Optical properties Algorithms
Product models and algorithms
Product models and algorithms
now-cast and forecast models Data
assimilation into models
Data assimilation into models
Education and outreach
Connection to IOOSConnection to IOOS
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COAST Risk Reduction Plans: Now-cast and forecast models
COAST Risk Reduction Plans: Now-cast and forecast models
• Now-cast and forecast models are currently under development for the coastal ocean;– Model development will be closely coupled with IOOS,– Current emphasis is on getting the physics right and on assimilating
surface currents, wind data and other physical parameters,– Some bio-optical models that could make excellent use of HES-CW data
have been demonstrated,– Work in this area will require the HES-CW demonstration data set to be
collected in 2007-2008,– Plan to initiate COAST modeling efforts in 2009.
• A second class of prognostic models for HABs are being developed for several coastal regions– Begin limited effort in 2006 to support those models specifically
emphasizing the utility of HES-CW data to improve skill of those models– Utilize the HES-CW demonstration data set beginning in 2009.
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Geostationary ocean color needed to support the higher temporal and spatial resolution required for
coastal models
Geostationary ocean color needed to support the higher temporal and spatial resolution required for
coastal models
July 31 SeaWiFS Chlor-a (mg/m3)
.5
2
3
4
5
39:30N
39:00N
Node A
UCSB
Small diatoms
Large diatoms
Satellite Measured
ECOSIM run for July 31, 2001 with ROMS Physical model 15 minute time step and
300 m spatial resolution(Paul Bissett, Florida Environmental Research
Institute)
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Harmful Algal Blooms in the Gulf of Mexico Harmful Algal Blooms in the Gulf of Mexico
• In the Gulf of Mexico, blooms of the toxic algae Karenia brevis result in shellfish bed closures and lost tourism that cost the state of Florida millions of dollars each year.
• Similar problems in other parts of the country with other toxic species.
• Ship based monitoring very expensive and time consuming
• Inadequate data frequently leads to unnecessary closings.
• HABSOS system being developed to provide early warnings using SeaWiFS data and models
• Geostationary ocean color data will greatly improve warning systems like HABSOS
– More frequent data for cloud clearing
– Higher spatial resolution to assess conditions closer to the shell fish beds and beaches
• In the Gulf of Mexico, blooms of the toxic algae Karenia brevis result in shellfish bed closures and lost tourism that cost the state of Florida millions of dollars each year.
• Similar problems in other parts of the country with other toxic species.
• Ship based monitoring very expensive and time consuming
• Inadequate data frequently leads to unnecessary closings.
• HABSOS system being developed to provide early warnings using SeaWiFS data and models
• Geostationary ocean color data will greatly improve warning systems like HABSOS
– More frequent data for cloud clearing
– Higher spatial resolution to assess conditions closer to the shell fish beds and beaches
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HABSOS can immediately utilize improved spatial resolution and frequency of coverage from HES-CWHABSOS can immediately utilize improved spatial
resolution and frequency of coverage from HES-CW
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SummarySummary
• Geostationary ocean color will provide an excellent new tool for the characterization and management of the coastal ocean.
• COAST Risk Reduction activities focus on calibration, algorithm development and bio-optical models.– Completed initial field experiment in Monterey Bay, CA to develop
simulated geostationary data set for algorithm development• Efforts coordinated with NOAA NESDIS/STAR, NMFS and NOS with a
focus on meeting their operational needs. • Need a geostationary ocean color imager to provide hourly satellite
data of the coastal ocean– Use in combination with IOOS in-situ data for the initiation and
validation of coastal bio-optical models
Special thanks to Ted Strub, Amy Vandehey and the COAST for their hard work getting this program started.
Thanks to NOAA for funding and particularly to Stan Wilson, John Pereira, and Paul Menzel for their support and guidance.