Transcript CIOSS/COAST GOES-R Risk Reduction Activities for HES-CW CIOSS: Cooperative Institute for Oceanographic Satellite Studies, College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, Oregon COAST:

CIOSS/COAST GOES-R Risk
Reduction Activities for HES-CW
CIOSS: Cooperative Institute for Oceanographic
Satellite Studies, College of Oceanic and
Atmospheric Sciences, Oregon State University,
Corvallis, Oregon
COAST: Coastal Ocean Applications and Science
Team, Mark Abbott Team Leader, Curtiss Davis
Executive Director
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Risk Reduction Activities:
Principal Roles of Co-Investigators
• Curtiss Davis, program management, calibration, atmospheric correction
• Mark Abbott, COAST Team Leader, phytoplankton productivity, chlorophyll
and chlorophyll fluorescence
• Ricardo Letelier, phytoplankton productivity and chlorophyll fluorescence,
data management
• Peter Strutton, coastal carbon cycle, Harmful Algal Blooms (HABs)
• Ted Strub, CIOSS Director, coastal dynamics, links to IOOS
COAST Participants:
• Bob Arnone, NRL, optical products, calibration, atmospheric correction,
data management
• Paul Bissett, FERI, optical products, data management
• Heidi Dierssen, U. Conn., benthic productivity
• Raphael Kudela, UCSC, HABs, IOOS
• Steve Lohrenz, USM, suspended sediments, HABs
• Oscar Schofield, Rutgers U., product validation, IOOS, coastal models
• Heidi Sosik, WHOI, productivity and optics
• Ken Voss, U. Miami, calibration, atmospheric correction, optics
• Other COAST members, as needed, in future years
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COAST 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 for
HES-CW:
– 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.
• Paul Menzel Presented GOES-R Risk Reduction Program at the first COAST
meeting in September 2004 and invited COAST to participate.
– Curt Davis and Mark Abbott presented proposed activities in Feb. 2005.
– CIOSS/COAST invited to become part of GOES-R Risk Reduction Activity
beginning in FY 2006.
– Proposal Submitted to NOAA Sept 6, 2005.
– Here we present an overview of our planned Risk Reduction Activities.
NRL09/21/2004_Davis.3
Presentation Outline
• Approach to Algorithm Development
– Experience with Hyperion and airborne hyperspectral sensors
– Field Experiments to collect prototype HES-CW data
• Planned Risk Reduction activities:
– Calibration and vicarious calibration
– Atmospheric correction
– Optical properties
– Phytoplankton chlorophyll, chlorophyll fluorescence and productivity
– Benthic productivity
– Coastal carbon budget
– Harmful algal blooms
– Data access and visualization
– Education and public outreach
• Summary
NRL09/21/2004_Davis.4
NRL09/21/2004_Davis.5
HES-CW Measurements
• Calibrated at sensor radiances for all channels
– For the threshold 14 channels and possibly the
additional goal channels
• Measurements are geo-located to approximately 1 Ground
Sample Distance (GSD)
• Methods for on-orbit calibration and validation of products
are not clearly defined at this time.
• Methods for atmospheric correction are not clearly defined
at this time.
HES-CW Products
• Water-leaving radiance (the product of atmospheric correction, all
other products are calculated from this one)
• Optical properties
– Turbidity (water clarity)
– Particulate absorption (phytoplankton, detritus, sediments)
– Dissolved absorption (CDOM)
– Particulate backscatter (phytoplankton, detritus, sediments)
– Diffuse attenuation (light availability for seagrasses, corals)
• Chlorophyll (phytoplankton biomass)
• Chlorophyll fluorescence (phytoplankton health and productivity)
• Total Suspended Matter (TSM, material transport)
• Colored Dissolved Organic Matter (CDOM, organic matter transport,
track river plumes)
• These products tie directly into NOS requirements for coastal ocean
remote sensing.
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NOAA HES-CW Applications
• Water quality monitoring (e.g. Harmful Algal Blooms,
suspended sediments, CDOM)
• Coastal hazard assessment
• Navigation safety
• Human and ecosystem health awareness (HABs)
• Natural resource management in coastal and estuarine
areas
• Climate variability prediction (sea level rise, carbon cycle)
• Landscape changes
• Coral reef detection and health appraisal
HES-CW Data flow and Risk Reduction
Activities
Raw sensor
data
Calibration
Calibrated
radiances
at the
sensor
Atmospheric
Correction
Water
Leaving
Radiances
Optical
properties
Algorithms
now-cast and
forecast
models
NRL09/21/2004_Davis.8
Applications
and products
Data
assimilation
into models
Education
and outreach
Users
Product
models and
algorithms
In-Water
Optical
Properties
Approach to Algorithm Development
• Directly involve the ocean color community which has extensive algorithm
development experience with SeaWiFS and MODIS
– NASA funded science teams developed, tested and validated calibration,
atmospheric correction and product algorithms
– Additional product development and testing funded by U. S. Navy
– SeaWiFS procedures and algorithms documented in series of NASA Tech
memos and numerous publications
– MODIS algorithms documented in Algorithm Theoretical Basis
Documents (ATBDs)
– Algorithms are continuously evaluated and updated; SeaWiFS and
MODIS data routinely reprocessed to provide Climate Data Records with
latest algorithms
• Design program to assure compatibility of HES-CW products with VIIRS
– VIIRS algorithms based on MODIS ATBDs
– Similar calibration and atmospheric correction approaches
– Use the same ocean calibration sites for vicarious calibration
• Initial plans and algorithms based on SeaWiFS and MODIS experience
modified to fit HES-CW in geostationary orbit.
• Advanced algorithms tested and implemented when available.
• Early tests planned using airborne hyperspectral data.
NRL09/21/2004_Davis.9
Example of Existing Data Sets that are Available to
Develop Algorithms and Demonstrate Products
SeaWiFS 1 km data
Near-simultaneous data
from 5 ships, two
moorings, three Aircraft
and two satellites
collected to address
issues of scaling in the
coastal zone. (HyCODE
LEO-15 Experiment July
31, 2001.)
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Sand waves in
PHILLS-1 1.8 m data
PHILLS-2 9 m data mosaic
Fronts in AVIRIS 20 m data
Extensive In-situ data for product validation at
LEO-15 site
PHILLS Sensor
X
Profiling Optics and Water
Return (POWR) Package
300
250
Reflectance X 10
4
PHILLS-1
200
Ground Truth ASD
150
100
50
0
0.4
0.5
0.6
0.7
Wavelength (microns)
0.8
0.9
Comparison at the X. (C. O. Davis, et al.,
(2002), Optics Express 10:4, 210--221.)
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Chesapeake Bay,
19 Feb ‘02
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Example Hyperion data sets for Coastal
Environments
Chesapeake Bay,
6 Sep ‘02
Gulf of Maine,
27 Aug ‘02
Apalachicola, FL
15 Aug ‘02
Bahrain
26 Aug ‘02
Proposed Experiments to Collect Simulated
HES-CW data (1 of 2)
• There are no existing data sets that include all the key attributes of HES-CW
data:
– Spectral coverage (.4 – 2.4 mm)
– High signal-to-noise ratio (>300:1 prefer 900:1, for ocean radiances)
– High spatial resolution (<150 m, bin to 300 m)
– Hourly or better revisit
• Propose field experiments in FY2006-2008 to develop the required data sets
for HES-CW algorithm and model development.
• Airborne system:
– Hyperspectral imager that can be binned to the HES-CW bands
– Flown at high altitude for minimum of 10 km swath
– Endurance to collect repeat flight lines every half hour for up to 6 hours
– Baseline AVIRIS on ER-2 which can meet all of these requirements.
• Propose three experimental sites:
– 2006 Monterey Bay (coastal upwelling, HABs)
– 2007 New York/Mid Atlantic Bight (river input, urban aerosols)
– 2008 Mississippi River Plume (Sediment input, HABs)
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Proposed Experiments to collect simulated
HES-CW data (2 of 2)
• Experimental Design
– Choose sites with IOOS or other long term monitoring and modeling
activities
– Intensive effort for 2 weeks to assure that all essential parameters are
measured:
- Supplement standard measurements at the site with shipboard or
mooring 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.
– 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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Risk Reduction Plans: Calibration
• Develop plan for on-orbit calibration:
– At sensor radiance calibration must be +/- 0.3% to meet proposed
Chlorophyll product accuracy requirement of +/- 30%
– Follow SeaWiFS and MODIS approach using moon imaging, solar
diffuser and vicarious calibration to achieve this accuracy
– Risk reduction activity includes planning for highly accurate water
leaving radiance measurements (MOBY follow-on) at one or two clear
water ocean sites (NOAA ORA led effort)
– Additional coastal sites for validation of atmospheric correction in
coastal waters and validation of coastal products (IOOS sites?)
– Coordinated effort between NOAA ORA, CICS, CIOSS
• Good on-orbit calibration is only possible if the instrument is properly
designed to provide stable accurate radiances over its lifetime. This must
be demonstrated with good pre-launch calibration and characterization for
MTF, stray light, etc.
– Support NOAA/NASA to provide feed back to instrument builders.
– Pre-launch calibration requirement is +/- 5% absolute, +/- 0.5% channel to
channel. The needed higher accuracies on-orbit can only be achieved
with vicarious calibration.
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Atmospheric Correction Challenges
We anticipate three major challenges in developing the
atmospheric correction algorithms for HES-CW.
1. Adaptation of the current algorithms for SeaWiFS and
MODIS to the geostationary viewing geometry, including
addressing BRDF issues.
2. Dealing with Absorbing Aerosols which are common
downwind from urban and industrial areas.
3. In coastal waters with high levels of suspended sediments,
or large phytoplankton blooms the contributions at the NIR
bands are not negligible. This can lead to significant
underestimation of the satellite-derived water-leaving
radiance spectrum (SeaWiFS, MODIS).
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Current Atmospheric Correction Algorithms
SeaWiFS and MODIS algorithm (Gordon and Wang 1994)
rt  rr  r A  t rwc  Trg  t rw, r   L m0 F0
rw is the desired quantity in ocean color remote sensing.
Trg is the sun glint contribution—avoided/masked and residual
contamination is corrected.
trwc is the whitecap reflectance—computed from wind speed.
rr is the scattering from molecules—computed using the Rayleigh
lookup tables.
rA = ra + rra is the aerosol and Rayleigh-aerosol contributions —
estimated using aerosol models.
For Case-1 waters in the open ocean, rw is usually negligible at
765 & 865 nm. rA can be estimated using these two NIR bands.
NRL09/21/2004_Davis.17
Menghua Wang, NOAA/NESDIS/ORA
HES-CW Channels and Atmospheric
Transmission Windows
1
Transmittance
0.8
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0.6
0.4
0.2
Total
H2 O
Ozone
0
0.3 0.4 0.5 0.6 0.7 0.8 0.9
1
1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8
Wavelength (mm)
UV channels can be used for detecting the absorbing aerosol cases
Two long NIR channels (1000 & 1240 nm) are useful for of the Case-2 waters
Menghua Wang, NOAA/NESDIS/ORA
Risk Reduction Plans: Atmospheric correction
• Atmospheric correction needed to produce water-leaving radiance.
• Approach:
– Evolution of algorithms from the current SeaWiFS, MODIS algorithms.
– Adjustments for Geostationary orbit geometry
– Adaptation to different spectral channels
– Development of coastal atmospheric correction algorithm:
- Address absorbing aerosols,
- Address high reflectance in coastal waters where NIR channels cannot
be used for aerosol calculations.
– Current effort between NOAA ORA, CICS, CIOSS
- Providing feedback to NOAA/NASA and instrument and spacecraft
vendors to assure spectral channel characteristics, etc.
– Would like to expand effort to include collaborative efforts with CIMSS,
CIRA, others?
– Explore advantages of using HES sounder and ABI data to improve
atmospheric correction.
NRL09/21/2004_Davis.19
Risk Reduction Plans: In-water Optical Properties 1
Remote-sensing reflectance (Rrs, water-leaving radiance normalized by
the downwelling irradiance) is a function of properties of the water
column and the bottom,
Rrs() = f[a(), bb(), r(), H],
(1)
where a() is the absorption coefficient, bb() is the backscattering
coefficient, r() is the bottom albedo, H is the bottom depth. In optically
deep waters (when the bottom is not imaged),
Rrs() = f[bb()/a() + bb()]
(2)
Where f is a proportionality constant that varies slightly as a function of
the shape of the volume scattering function and the angular distribution
of the light field. The backscattering coefficient bb() is the sum of the
backscattering from the phytoplankton, detritus, suspended sediments
and the water itself. The absorption coefficient a() is the sum of the
absorption by CDOM, phytoplankton, detritus, suspended sediments
and the water itself.
NRL09/21/2004_Davis.20
Risk Reduction Plans: In-water Optical Properties 2
• Algorithms for SeaWiFS and MODIS use spectral channel ratios to calculate
specific products, such as suspended sediments, chlorophyll and CDOM.
– This approach does not work if the bottom is imaged (e.g. West Florida
Shelf), or in the presence of high levels of suspended sediments (e.g.
Mississippi River Plume)
• Excellent Radiative Transfer Models (e.g. HYDROLIGHT) are available to
model the light field – the challenge for remote sensing is to invert those
models to go from remote sensing reflectance to estimates of the in-water
constituents.
• Two approaches are demonstrated that solve the full problem and produce
values for water column optical properties, bathymetry and bottom type.
– A predictor-corrector approach is used to invert a semi-analytical model
– A look-up table approach has been used to invert HYDROLIGHT.
NRL09/21/2004_Davis.21
Bathymetry, Bottom Type and Optical Properties
Example Approach: Semi-Analytical Models
• Semi-analytical model developed to resolve the complex optical signature
from shallow waters.
• Simultaneously produces bathymetry, bottom type, water optical
properties.
Seagrass beds
Sand bars
Navigation channel
a) Bottom type and b) bathymetry derived from an AVIRIS image of
Tampa Bay, FL using automated processing of the hyperspectral data.
Accurate values were retrieved in spite of the fact that water clarity
varies greatly over the scene.
(Lee, et al., J. Geophys. Research, 106(C6), 11,639-11,651, 2001.)
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Bathymetry, Bottom Type and Optical Properties
Example Approach: Look-up Tables
Interpretation of hyperspectral remote-sensing imagery via spectrum matching
and look-up tables. Mobley, C. D., et al., Applied Optics, 2005.
NRL09/21/2004_Davis.23
Risk Reduction Plans: In-water Optical Properties 3
Planned Risk Reduction Activities:
• NASA and the Navy have a set of band ratio type algorithms to produce inwater optical properties from SeaWiFS and MODIS data.
• Initial approach will be to adapt those algorithms for use with HES-CW.
• Main Risk Reduction effort will be to develop comprehensive methods along
the lines of the Lee et al. and Mobley, et al. approaches that have been
demonstrate for airborne hyperspectral data.
– Will work in all conditions even when the bottom is imaged
• Algorithm work can be initiated immediately with existing data sets but the
HES-CW demonstration data set will be essential for the full demonstration
of the algorithms.
• Initiate effort in 2006 to use existing data sets and to participate in the
planning of the HES-CW demonstration experiment to assure that all of the
essential data is collected.
• Expanded effort in 2009 utilizing the demonstration data set and Web based
data system.
NRL09/21/2004_Davis.24
Risk Reduction Plans: Phytoplankton chlorophyll,
chlorophyll fluorescence and productivity
• Chlorophyll and Chlorophyll fluorescence
– Fluorescence unambiguously associated with chlorophyll
– Signal is small, but use of baseline approach greatly reduces impact of
atmosphere on retrievals
– Amount of fluorescence per unit chlorophyll varies as function of light,
phytoplankton physiology, and species composition
• Validation relies on long time series of high quality measurements to ensure
consistency
– IOOS, MOBY sites
– Analysis of MODIS Aqua and Terra data sets
– AVIRIS or other overflights
• Estimates of chlorophyll and productivity
– Continued field and satellite data analysis
– Modeling of quantum yield of fluorescence based on laboratory
analyses, comparison with field measurements
– Incorporate quantum yield into productivity models
– Compare with recent chlorophyll/backscatter models using SeaWiFS
NRL09/21/2004_Davis.25
MODIS FLH bands: avoid oxygen absorbance at 687 nm
NRL09/21/2004_Davis.26
Weighting factor
used to compensate
for off-center FLH
MODIS Terra FLH vs Oregon optical drifters
derived FLH
MODIS Terra FLH, W m-2 mm-1 sr-1
0.18
NRL09/21/2004_Davis.27
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
0
0.05
0.1
0.15
0.2
Oregon Drifters FLH, W m-2 mm-1 sr-1
0.25
NRL09/21/2004_Davis.28
Testing the MODIS FLH Algorithm
FLH vs.
chlorophyll
From Hoge et al.
FLH vs.
CDOM
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Frequent measurements in morning can
elucidate quantum yield of fluorescence
Initial slope proportional to F
Risk Reduction Plans: Phytoplankton chlorophyll,
chlorophyll fluorescence and productivity
• Proposed activities:
- Development of chlorophyll and fluorescence algorithms based on SeaWiFS
and MODIS legacy and modified to fit HES-CW in geostationary orbit.
- Characterization of chlorophyll and chlorophyll fluorescence algorithm
sensitivity based on HES-CW (waveband position and SNR) characteristics
(i.e. Letelier and Abbott 1996)
- Generation of HES-CW synthetic chlorophyll and fluorescence products in
coastal (case II) waters using Hyperion and PHILLS data, and data from field
experiments in 2007-2008.
- These field experiments will serve to:
1) Validate a chlorophyll algorithm for case II waters based on chlorophyll
fluorescence.
2) Assess diurnal changes in algal physiology affecting carbon:chlorophyll
ratio and the chlorophyll fluorescence efficiency.
3) Evaluate how water column stability and CDOM concentrations affect the
apparent relationship between chlorophyll concentration and the
chlorophyll fluorescence in algorithms inherited from SeaWiFS and MODIS.
4) Develop improved productivity models incorporating laboratory
estimates of quantum yield.
NRL09/21/2004_Davis.30
Risk Reduction Plans: Benthic Productivity
Benthic habitats are degrading and seagrass beds are decreasing at an
alarming rate. To better understand and monitor that process we propose to
develop procedures and algorithms for using GOES-R HES-CW data to
quantify benthic productivity.
• Develop algorithms for estimating benthic productivity from seagrass and
sediment across the large optically shallow carbonate sediment basins
(Florida Bay, Bahamas, etc.).
• Conduct sensitivity analysis identifying U.S. coastal regions (e.g.,
Chesapeake Bay, Monterey Bay) with optically shallow water ecosystems
that can be resolved by the GOES-R HES-CW. Potential benthic
constituents for analysis include seagrasses, kelp, and benthic algal mats.
• Use seagrass canopy model (Zimmerman 2003), bathymetry and remotely
derived estimates of diel and seasonal water column optical properties to
develop predictive maps of the optically shallow regions that could support
seagrass habitats based on light availability.
• Use the field data collected during the process studies to extrapolate
benthic productivity algorithms from the carbonate systems to other coastal
ecosystems identified in the sensitivity analysis.
NRL09/21/2004_Davis.31
Risk Reduction Plans: Harmful Algal Blooms
Background
• 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
• HES-CW 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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Frequent sampling can assist in detection
and classification of HABs
Some properties have a diel cycle associated with it.
Documenting the diel dynamics can thus potentially
assist in documenting and identifying material in the ocean
31º
28º
Case example:
29º
Detection of K. brevis
27º
-87º
-85º
-83º
-81º
25º
27.5º
27º
October 2001 EcoHAB Diel Station
October 2001 EcoHAB Station
26.5º
-84º
-83.5º
-83º
-82.5º
NRL09/21/2004_Davis.33
0
0.20
atotal 676 nm
Depth (m)
A)
5
0.10
10
1.6 A)
B)
Karenia brevis cell abundance
Cells L-1
(x105)
0.8
0.25
0.4
0.20
0.0
NRL09/21/2004_Davis.34
08:00
14:00
20:00
Time of Day
02:00
dissolved a(440) (m-1)
0.30
1.2
When K. brevis
Blooms, conditions tend
to be calm. Under
these
Conditions the cells
exhibit a dramatic diel
migration. The net
result
is a 10X increase in
cells
at the air-sea
interface
over a several
hour period. This unique
feature will be readily
detected in HES-CW
data.
HABSOS can immediately utilize improved spatial
resolution and frequency of coverage from HES-CW
NRL09/21/2004_Davis.35
Risk Reduction Plans: Harmful Algal Blooms
Proposed Risk Reduction Activities:
• Improve methods for early detection of HABs from optical remote sensing
data
– Not all HABs have a unique optical signature – use additional
information, e.g. vertical migration to identify blooms.
– Specific methods needed for each region of the country to identify local
species, etc.
• Continue development of models of HAB dynamics
– Higher frequency of HES-CW data critical for cloud clearing and to
include vertical migration in the models
• Prepare to use HES-CW data in warning systems, such as, HABSOS
– Increased frequency of sampling for cloud clearing will provide faster
updates allowing more precise system for warnings
- Avoid unnecessary costly beach and shellfish bed closures
• Strong education component to educate the state and local managers and
the public as to the benefits of HES-CW data and improved models and
forecasts.
NRL09/21/2004_Davis.36
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Coastal Carbon Cycle
• Detailed studies of the Oregon coastal upwelling system to
determine its role as a CO2 source or sink.
• pCO2 in coastal (and other) environments is associated with
characteristic chlorophyll and SST signatures.
• Using multiple satellite products and techniques, such as
multiple linear regression, we have developed an approach
to determine sea surface pCO2 from space.
• Combine this with winds from either scatterometer(s) or
coastal/buoy meteorological stations to facilitate flux
calculations.
(Hales et al., 2004. Atmospheric CO2 uptake by a coastal upwelling
system. Global Biogeochemical Cycles, 19, GB1009,
10.1029/2004GB002295.)
NRL09/21/2004_Davis.38
Coastal Oregon Study Site
Cascade
Head:
Repeat
sections
Cape
Perpetua:
Extended
sections
Undersaturation of CO2 in coastal waters
Freshly upwelled water near the
Oregon coast is a CO2 source to the
atmosphere. As the water moves
offshore the phytoplankton bloom
making the same waters a CO2 sink.
NRL09/21/2004_Davis.39
Cascade Head
time series
NRL09/21/2004_Davis.40
Coastal CO2: Relationship to physics and biology
Productivity &
CO2 uptake
N limitation
offshore
Risk Reduction Plans: Coastal CO2 Fluxes
• The coastal ocean plays a large and poorly measured role in the global
carbon cycle.
– Addresses NOAA’s climate change goals
• HES-WC will provide valuable data to study this process;
– Temporal sampling of 3 hours will enable basic budgets to be
calculated and the tracking of processes such as productivity and
subduction.
– This is a dynamic environment – any ability to ‘clear’ or alias clouds
will enhance badly-needed coverage.
• Coupling with NASA’s Orbiting Carbon Observatory (2008) will add
significant coupling to atmospheric data.
• Proposed risk reduction activities:
– Continue to develop and refine current models and algorithms using
SeaWiFS, MODIS and shipboard data.
– Update algorithms to take advantage of HES-CW data.
– Adapt approach to take advantage of IOOS and associated modeling
efforts.
NRL09/21/2004_Davis.41
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 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.
NRL09/21/2004_Davis.42
EcoSim 2 Model Output for July 31, 2001
HyCODE experiment at (LEO-15)
Satellite Measured
July 31
SeaWiFS
Chlor-a
(mg/m3)
.5
2
39:30N
3
Node A
Large diatoms
4
UCSB
5
39:00N
Bissett, et al., Submitted J. Geophys. Res.
NRL09/21/2004_Davis.43
Small diatoms
Risk Reduction Plans: Data Management
• Data processing, distribution and archiving issues.
– Need more processing capacity for atmospheric correction and product
algorithms (3-5 X the calibration processing)
– Need for reprocessing with updated calibrations and new algorithms to
make Climate Data Records and the need to archive CDRs
- Planned data system not sized for reprocessing.
• Next generation product generation and delivery services will build on the
notion of “web services,” which are industry standard tools for building
complex services from building block components and multiple data
streams. Web services can provide new capabilities that are not anticipated
in the original systems design. By designing these services as linked
components rather than monolithic systems, GOES-R can provide a much
greater degree of flexibility and evolution within a cost-constrained
environment.
• We propose monitoring and providing advice on current plans for the HESCW data system, with specific risk reduction activities beginning in 2008.
– Web based server with the the Simulated HES-CW data from the
proposed experiments. Include all ancillary data and access to models
for testing.
NRL09/21/2004_Davis.44
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Example: CI-CORE Data on GIS Web Server.
Airborne hyperspectral data for the Big Sur Coast
Risk Reduction Plans: Education and Public
Outreach
For education and outreach CIOSS will support three activities:
• Demonstrating and training users on the algorithms and
products developed during the risk reduction activities.
• Informing the general public as to the value and utility of HESCW data.
• Educating state and local users to the value and utility of HESCW products.
• For the general public and state and local users we will work
through the Coastal Services Center.
– Currently developing a brochure on HES-CW with CSC.
• Initially a very low level effort for the first three years.
• Increase activity according to need and requests from NOAA.
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Summary
• HES-CW will provide an excellent new tool for the characterization and
management of the coastal ocean.
• We will build on extensive experience in calibration, atmospheric correction,
algorithm development from SeaWiFS and MODIS and continuing with VIIRS
to provide the necessary algorithms for HES-CW.
• Planned Activities focus on calibration and algorithm development;
– Initially utilize existing data sets including SeaWiFS and MODIS,
– 2007-2008 field experiments to develop example HES-CW data for
- algorithm development and testing,
- Coordination with IOOS for in-situ data and coastal ocean models,
- Demonstrate terabyte web-based data system.
– Initially provide SeaWiFS and MODIS heritage calibration and algorithms;
- Calibration approach includes vicarious calibration,
- Heritage band-ratio algorithms.
– Major focus on developing advanced algorithms that take advantage of
HES-CW unique characteristics.
• Efforts coordinated with NOAA ORA, NMFS and NOS with a focus on
meeting their operational needs.
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