NRL09/21/2004_Davis.1

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

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 Integrated Ocean Observing System (IOOS) Yvette Spitz, data assimilation, HAB models COAST Participants: Paul Bissett, FERI, optical products, data management Bob Arnone, NRL, optical products, calibration, atmospheric correction, data management Dariusz Stramski, SIO, optical products Oscar Schofield, Rutgers U., product validation, IOOS, coastal models Steve Lohrenz suspended sediments, HABs Raphael Kudela HABs, IOOS Other COAST members, as needed, in future years

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Status and Schedule

•

The Coastal Ocean Applications and Science Team (COAST) was created in August 2004 to support NOAA to develop coastal ocean applications to optimize the use of the HES-Coastal Water Imager planned for GOES-R.

–

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

–

CIOSS/COAST invited to become part of GOES-R Risk Reduction Activity beginning in FY 2006

– –

Here we present an overview of our planned activities.

We have edited the GOES-R Risk Reduction Plan to reflect our activities.

– –

Early draft proposal submitted to Paul Menzel.

Detailed proposal to be submitted by the end of April 2005.

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Presentation Outline

• • • • •

Background and Experience with ocean color sensors (CZCS, SeaWiFS, MODIS) Overview of HES-CW requirements and goals Approach to Algorithm Development

–

Experience with Hyperion and airborne hyperspectral sensors Planned Risk Reduction activities:

–

Calibration and vicarious calibration

– –

Atmospheric correction Optical properties

– –

Phytoplankton chlorophyll, chlorophyll fluorescence and productivity Coastal carbon budget

– – –

Harmful algal blooms Data access and visualization Education and public outreach Summary

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Ocean Color Remote Sensing Background

•

First satellite ocean color measurements were made in 1978 with the Coastal Zone Color Scanner (CZCS) on Nimbus-7.

–

CZCS was a technology demonstration instrument with 4 ocean color channels and one for SST.

–

Worked well for the open ocean, not coastal due to ringing from bright land, and limited set of channels

–

Limited duty, demonstrated utility, lasted eight years to 1984, inspired NASA to develop SeaWiFS

•

SeaWiFS 1996-present

–

Designed as a dedicated ocean color instrument, also produced excellent land biosphere products

–

Excellent calibration and product validation and data distribution system has made SeaWiFS the international standard for ocean color measurements.

•

MODIS on Terra and Aqua general purpose instrument with ocean channels

–

9 ocean color channels including chlorophyll fluorescence channels

•

VIIRS operational follow-on to MODIS

–

Does not include chlorophyll fluorescence channels

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SeaWiFS Global Ocean Chlorophyll

Seven year composite of the global distribution of chlorophyll from SeaWiFS data (blue low and yellow high concentrations). SeaWiFS has been highly successful for addressing NASA goals to better understand the global ocean carbon cycle and climate change.

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Example products from MODIS

Visible Infrared Imaging Radiometer Suite (VIIRS)

•

Being built by Raytheon SBRS

–

SeaWiFS and MODIS heritage Channel Name M1 M2

•

First flight on NPOESS Preparatory Project (NPP) in 2007 then NPOESS satellites starting in 2009 M3 M4 M5 M6 M7

•

Seven ocean color channels and 2 SST channels

M

M15 M16

•

Approximately 1 km GSD ocean color channel Center

412 nm 445 nm 488 nm 555 nm 672 nm 751 nm 865 nm 10.8  m 12.0  m

Channel Width

20 nm 18 nm 20 nm 20 nm 20 nm 15 nm 39 nm 1.0  m 1.0  m

L typical ocean

44.9 40 32 21 10 9.6 6.4 300K 300K

Required SNR/NE



T VIIRS SNR/NE



T

352 380 415 361 242 199 215 .070 .072 –

Maximum revisit frequency of twice a day at 1030 and 1530

670 506 515 446 ~ 400 ~ 400 314 .041 .041 –

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

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Why HES-CW given VIIRS?

• • • • •

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.

VIIRS daily sampling at the same time cannot resolve tides, diurnal winds, etc.

HES-CW will provide the capability to view coastal waters from a geostationary platform that will provide the management and science community with a unique capability to observe the dynamic coastal ocean environment. HES-CW will provide higher spatial resolution (300 m vs. 1000 m) HES-CW will provide additional channels to measure solar stimulated fluorescence, suspended sediments, CDOM and improved atmospheric correction.

These improvements are critical for the analyses of coastal waters.

Example tidal cycle from Charleston, OR. Black arrows VIIRS sampling, red arrows HES-CW sampling.

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HES-CW higher spatial resolution critical to monitor complex coastal waters

MODIS 1 km water clarity Modeled HES-CW (250 m)

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Fluorescence provides better phytoplankton measurements in optically-complex coastal waters

MODIS Terra l2 scene from 3 October 2001.

The ratio of fluorescence line height to chlorophyll changes as a function of the physiological state of the phytoplankton. This can be exploited to assess the health and productivity of the phytoplankton populations.

Fluorescence line height not available from VIIRS.

HES-CW Key Threshold and Goal Requirements

Nominal Threshold Channel Center Wavelength (um) 0.412

0.443

0.477

0.49

0.51

0.53

0.55

0.645

0.667

0.678

0.75

0.763

0.865

0.905

Nominal Threshold Resolution 0.02

0.02

0.02

0.02

0.02

0.02

0.02

Nominal Threshold Signal to Noise 300 to 1 all channels Nominal GOAL Channel Center Wavelength (um) 0.407 through 0.987

0.57

1.38

1.61

2.26

11.2

12.3

Nominal Threshold Horiz. Resolution Nominal GOAL Resolution Nominal Goal Signal to Noise Ratio 0.01

0.03

0.06

0.05

0.8

1 900 to 1 all channels Nominal Goal Horiz. Resolution 0.01

0.01

0.02

0.02

0.02

0.035

300-meters all channels (at Equator) 150-meters all channels (at Equator)

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Frequency of Sampling and Prioritizing Goal Requirements

•

Threshold requirement is to sample all Hawaii and Continental U. S. coastal waters once every three hours during daylight

–

Plus additional hourly sampling of selected areas

•

Goal requirement is hourly sampling of all U.S. coastal waters is strongly recommended, for cloud clearing and to better resolve coastal ocean dynamics.

•

Goal requirements compete with each other, e.g. higher spatial resolution makes it harder to increase sampling frequency or SNR.

•

COAST top priority goals are:

–

Higher frequency of sampling

– –

Goal channels for atmospheric correction Hyperspectral instead of multispectral HES-CW built to the threshold requirements will be a dramatic improvement over present capabilities for coastal imaging.

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Higher Frequency Sampling to avoid Cloud Cover

REGIONS AROUND THE COUNTRY WHERE DIEL VARIATIONS IN CLOUD COVER ARE IMPORTANT -

Gulf of Maine: Morning fog and haze

- Mid & South Atlantic Bights: Afternoon cumulus, afternoon sea breezes

Gulf of Mexico: Afternoon cumulus, afternoon sea breezes

- West Coast United States: Morning fog and haze

High frequency data will enable researchers by collecting data throughout the day. This maximizes the potential of collecting imagery during the “open” window of time. This window will vary with region and ocean.

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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 Dissolved absorption (phytoplankton, detritus, sediments) (CDOM)

– –

Particulate backscatter Diffuse attenuation (phytoplankton, detritus, sediments) (light availability for seagrasses, corals)

• • • •

Chlorophyll (phytoplankton biomass) Chlorophyll fluorescence Total Suspended Matter (phytoplankton health and productivity) (TSM, material transport) Colored Dissolved Organic Matter track river plumes) (CDOM, organic matter transport,

•

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 Leaving Optical properties Algorithms Water Radiances now-cast and forecast models Data assimilation into models Applications and products Product models and algorithms In-Water Optical Properties Education and outreach Users

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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.

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Example of Existing Data Sets that are Available to Develop Algorithms and Demonstrate Products

SeaWiFS 1 km data PHILLS-2 9 m data mosaic 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 Fronts in AVIRIS 20 m data

Extensive In-situ data for product validation at LEO-15 site

PHILLS Sensor

X 300 250 200 150 100 50 0 0.4

PHILLS-1 Ground Truth ASD

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.) NRL09/21/2004_Davis.21

Profiling Optics and Water Return (POWR) Package

Example Hyperion data sets for Coastal Environments

Chesapeake Bay, 19 Feb ‘02

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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



m)

– – –

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 2007-2008 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.

Recommend three experimental sites, e.g.

–

Hudson River (river input, urban aerosols)

– –

West Florida Shelf (K. Brevis red tides) Monterey Bay (coastal upwelling)

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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 at 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

–

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.

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

r

t

 r

r

 r

A



t

r

wc

algorithm (Gordon and Wang 1994) 

T

r

g



t

r

w

, r  

L

 0

F

0  r

w

is the desired quantity in ocean color remote sensing

.



T

r

g

is the sun glint contribution—avoided/masked and residual contamination is corrected.



t

r

wc

is the whitecap reflectance—computed from wind speed.

 r

r

is the scattering from molecules—computed using the Rayleigh lookup tables.

 r

A =

r

a

+ r

ra

is the aerosol and Rayleigh-aerosol contributions — estimated using

aerosol models

.

 For Case-1 waters in the open ocean, 765 & 865 nm. r

A

r

w

is usually negligible at can be estimated using these two NIR bands.

Menghua Wang, NOAA/NESDIS/ORA

NRL09/21/2004_Davis.27

HES-CW Channels and Atmospheric Transmission Windows 1 0.8

0.6

0.4

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0.2

Total H 2 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 (  m)

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.

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Risk Reduction Plans: In-water Optical Properties 1

Remote-sensing reflectance (R

rs

, water-leaving radiance normalized by the downwelling irradiance) is a function of properties of the water column and the bottom, R rs (



) = f[a(



) , b b (



) ,

r

(



) , H], (1) where a(



) is the absorption coefficient, b b (



) is the backscattering coefficient,

r

(



) is the bottom albedo, H is the bottom depth. In optically deep waters (when the bottom is not imaged), R rs (



) = f[b b (



) /a(



) + b b (



) ] and the water itself.

(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 b b (



) 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

NRL09/21/2004_Davis.30

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.31

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, In Press.

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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 in water 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.

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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

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MODIS FLH bands: avoid oxygen absorbance at 687 nm

Weighting factor used to compensate for off-center FLH NRL09/21/2004_Davis.36

MODIS Terra FLH vs Oregon optical drifters derived FLH

NRL09/21/2004_Davis.37

0.18

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  m -1 sr -1 0.25

NRL09/21/2004_Davis.38

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.

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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 29º 28º Case example: 27º Detection of K. brevis 27.5º -87º -85º -83º -81º 25º 27º -84º October 2001 EcoHAB Diel Station October 2001 EcoHAB Station -83.5º -83º 26.5º -82.5º NRL09/21/2004_Davis.42

5 0

A)

10 1.6

Karenia brevis

cell abundance 1.2

0.8

0.4

0.0

08:00 14:00 20:00 Time of Day NRL09/21/2004_Davis.43

02:00 0.20

0.10

0.30

0.25

0.20

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.

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HABSOS can immediately utilize improved spatial resolution and frequency of coverage from HES-CW

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.

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Coastal Carbon Cycle

NRL09/21/2004_Davis.46

• •

Detailed studies of the Oregon coastal upwelling system to determine its role as a CO 2 source or sink.

pCO 2 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 pCO 2 from space.

Combine this with winds from either scatterometer(s) or coastal/buoy meteorological stations to facilitate flux calculations.

(Hales

et al

., 2004. Atmospheric CO 2 uptake by a coastal upwelling system.

Global Biogeochemical Cycles

, 19, GB1009, 10.1029/2004GB002295.)

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Coastal Oregon Study Site Cascade Head: Repeat sections Cape Perpetua: Extended sections

Undersaturation of CO

2

in coastal waters

Freshly upwelled water near the Oregon coast is a CO 2 source to the atmosphere. As the water moves offshore the phytoplankton bloom making the same waters a CO 2 sink.

Cascade Head time series

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NRL09/21/2004_Davis.49

Coastal CO

2

: Relationship to physics and biology

Productivity & CO 2 uptake N limitation offshore

Risk Reduction Plans: Coastal CO

2

Fluxes

• • • •

The coastal ocean plays a large and poorly measured role in the global carbon cycle.

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Addresses NOAA’s climate change goals HES-WC will provide valuable data to study this process;

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Temporal sampling of 3 hours will enable basic budgets to be calculated and the tracking of processes such as productivity and subduction.

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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:

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Continue to develop and refine current models and algorithms using SeaWiFS, MODIS and shipboard data.

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Update algorithms to take advantage of HES-CW data.

Adapt approach to take advantage of IOOS and associated modeling efforts.

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Risk Reduction Plans: Now-cast and forecast models

•

Now-cast and forecast models are currently under development for the coastal ocean;

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Model development will be closely coupled with IOOS,

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Current emphasis is on getting the physics right and on assimilating surface currents, wind data and other physical parameters,

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Some bio-optical models that could make excellent use of HES-CW data have been demonstrated,

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Work in this area will require the HES-CW demonstration data set to be collected in 2007-2008,

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Plan to initiate modeling efforts in 2009.

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A second class of prognostic models for HABs are being developed for several coastal regions

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Begin limited effort in 2006 to support those models specifically emphasizing the utility of HES-CW data to improve skill of those models

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Utilize the HES-CW demonstration data set beginning in 2009.

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EcoSim 2 Model Output for July 31, 2001 HyCODE experiment at (LEO-15)

Satellite Measured

July 31 SeaWiFS Chlor-a (mg/m3) .5

2 3 4 5

Node A UCSB 39:30N Large diatoms 39:00N Bissett, et al., Submitted J. Geophys. Res.

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Small diatoms

Risk Reduction Plans: Data Management

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Data processing, distribution and archiving issues.

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Need more processing capacity for atmospheric correction and product algorithms (3-5 X the calibration processing)

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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.

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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 HES CW data system, with specific risk reduction activities beginning in 2008.

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Web based server with the the Simulated HES-CW data from the proposed experiments. Include all ancillary data and access to models for testing.

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Example: CI-CORE Data on GIS Web Server. Airborne hyperspectral data for the Big Sur Coast

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Risk Reduction Plans: Education and Public Outreach

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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 HES CW data.

Educating state and local users to the value and utility of HES CW products. For the general public and state and local users we will work through the Coastal Services Center.

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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.

Summary

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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;

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Initially utilize existing data sets including SeaWiFS and MODIS, 2007-2008 field experiments to develop example HES-CW data for

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algorithm development and testing,

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Coordination with IOOS for in-situ data and coastal ocean models,

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Demonstrate terabyte web-based data system.

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Initially provide SeaWiFS and MODIS heritage calibration and algorithms;

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Calibration approach includes vicarious calibration,

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Heritage band-ratio algorithms.

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Major focus on developing advanced algorithms that take advantage of HES-CW unique characteristics.

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Efforts coordinated with NOAA ORA, NMFS and NOS with a focus on meeting their operational needs.

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