OSU_08/1/2005_Davis.1 COAST Risk Reduction Activities Curtiss O. Davis College of Oceanic and Atmospheric Sciences Oregon State University, Corvallis, Oregon

OSU_08/1/2005_Davis.2 HES-CW Products and Applications Products: Spectral water leaving radiances Chlorophyll Chlorophyll fluorescence Turbidity Spectral absorption and scattering Applications: Water quality monitoring Coastal hazard assessment Navigation safety Human and ecosystem health awareness (Harmful Algal Blooms) Natural resource management in coastal and estuarine areas Climate variability prediction (e.g., role of the coastal ocean in the carbon cycle) Nowcast and Forecast models of the coastal ocean

OSU_08/1/2005_Davis.3 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. Initial activity to evaluate HES-CW requirements and suggest improvements 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 –CIOSS/COAST invited to become part of GOES-R Risk Reduction Activity beginning in FY –Proposal Submitted to NOAA Sept 6, –Activities to begin in May, 2006.

OSU_08/1/2005_Davis.4 Risk Reduction Activities: Principal Roles of Co-Investigators Curtiss Davis, program management, calibration, atmospheric correction Mark Abbott, COAST Team Leader 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 NOAA/STAR Menghua Wang, atmospheric correction Mike Ondrusek, calibration, MOBY

OSU_08/1/2005_Davis.5 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 Calibration Atmospheric Correction Optical properties Algorithms Product models and algorithms now-cast and forecast models Data assimilation into models Education and outreach

OSU_08/1/2005_Davis.6 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 –Documented in Technical reports, publications and 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.

OSU_08/1/2005_Davis.7 Risk Reduction Activities Approach to Algorithm Development –Experience with SeaWiFS and MODIS –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

OSU_08/1/2005_Davis.8 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.

OSU_08/1/2005_Davis.9 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).

OSU_08/1/2005_Davis.10 Current Atmospheric Correction Algorithms  w is the desired quantity in ocean color remote sensing.  T  g is the sun glint contribution—avoided/masked and residual contamination is corrected.  t  wc is the whitecap reflectance—computed from wind speed.  r is the scattering from molecules—computed using the Rayleigh lookup tables.  A =  a +  ra is the aerosol and Rayleigh-aerosol contributions — estimated using aerosol models.  For Case-1 waters in the open ocean,  w is usually negligible at 765 & 865 nm.  A can be estimated using these two NIR bands.  w is the desired quantity in ocean color remote sensing.  T  g is the sun glint contribution—avoided/masked and residual contamination is corrected.  t  wc is the whitecap reflectance—computed from wind speed.  r is the scattering from molecules—computed using the Rayleigh lookup tables.  A =  a +  ra is the aerosol and Rayleigh-aerosol contributions — estimated using aerosol models.  For Case-1 waters in the open ocean,  w is usually negligible at 765 & 865 nm.  A can be estimated using these two NIR bands. Menghua Wang, NOAA/NESDIS/ORA SeaWiFS and MODIS algorithm (Gordon and Wang 1994)

OSU_08/1/2005_Davis.11 HES-CW Channels and Atmospheric Transmission Windows 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

OSU_08/1/2005_Davis.12 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.

OSU_08/1/2005_Davis.13 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 ( ),  ( ), H],(1) where a( ) is the absorption coefficient, b b ( ) is the backscattering coefficient,  ( ) 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 ( ) ](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 and the water itself.

OSU_08/1/2005_Davis.14 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.

OSU_08/1/2005_Davis.15 Bathymetry, Bottom Type and Optical Properties Example Approach: Semi-Analytical Models 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.) Seagrass beds Sand bars Navigation channel Semi-analytical model developed to resolve the complex optical signature from shallow waters. Simultaneously produces bathymetry, bottom type, water optical properties.

OSU_08/1/2005_Davis.16 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.

OSU_08/1/2005_Davis.17 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.

OSU_08/1/2005_Davis.18 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

OSU_08/1/2005_Davis.19 Weighting factor used to compensate for off-center FLH MODIS FLH bands: avoid oxygen absorbance at 687 nm

OSU_08/1/2005_Davis.20 Oregon Drifters FLH, W m -2  m -1 sr -1 MODIS Terra FLH, W m -2  m -1 sr -1 MODIS Terra FLH vs Oregon optical drifters derived FLH

OSU_08/1/2005_Davis.21 From Hoge et al. FLH vs. chlorophyll FLH vs. CDOM Testing the MODIS FLH Algorithm

OSU_08/1/2005_Davis.22 Frequent measurements in morning can elucidate quantum yield of fluorescence

OSU_08/1/2005_Davis.23 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 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.

OSU_08/1/2005_Davis.24 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 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

OSU_08/1/2005_Davis.25 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 -81º-83º-85º-87º -82.5º-83º-83.5º-84º 27º 27.5º 28º 26.5º October 2001 EcoHAB Station October 2001 EcoHAB Diel Station 29º 27º 25º 31º Case example: Detection of K. brevis Frequent sampling can assist in detection and classification of HABs

OSU_08/1/2005_Davis.26 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.

OSU_08/1/2005_Davis.27 HABSOS can immediately utilize improved spatial resolution and frequency of coverage from HES-CW

OSU_08/1/2005_Davis.28 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.

OSU_08/1/2005_Davis.29 Coastal Carbon Cycle 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., Atmospheric CO 2 uptake by a coastal upwelling system. Global Biogeochemical Cycles, 19, GB1009, /2004GB )

OSU_08/1/2005_Davis.30 Cascade Head: Repeat sections Cape Perpetua: Extended sections Coastal Oregon Study Site

OSU_08/1/2005_Davis.31 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

OSU_08/1/2005_Davis.32 Coastal CO 2 : Relationship to physics and biology Productivity & CO 2 uptake N limitation offshore

OSU_08/1/2005_Davis.33 Risk Reduction Plans: Coastal CO 2 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.

OSU_08/1/2005_Davis.34 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 , –Plan to initiate modeling efforts in 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.

OSU_08/1/2005_Davis.35 EcoSim 2 Model Output for July 31, 2001 HyCODE experiment at (LEO-15) July 31 SeaWiFS Chlor-a (mg/m 3 ) :30N 39:00N Node A UCSB Small diatoms Large diatoms Satellite Measured Bissett, et al., Submitted J. Geophys. Res.

OSU_08/1/2005_Davis.36 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 HES- CW data system, with specific risk reduction activities beginning in –Web based server with the the Simulated HES-CW data from the proposed experiments. Include all ancillary data and access to models for testing.

OSU_08/1/2005_Davis.37 BACKUP

OSU_08/1/2005_Davis.38 Summary HES-CW will provide an excellent new tool for the characterization and management of the coastal ocean. Risk Reduction activities focus on calibration and algorithm development; –Initially provide SeaWiFS and MODIS heritage calibration and algorithms; – 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. –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. 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, Eric Bayler and Paul Menzel for their support and guidance.

OSU_08/1/2005_Davis.39 COAST proposed new HES-CW Channel Specifications

OSU_08/1/2005_Davis.40 COAST proposed new HES-CW Channel Specifications

OSU_08/1/2005_Davis.41 Frequency of Sampling and Prioritizing Goal Requirements COAST top priority goals are: –Higher frequency of sampling –Goal channels for atmospheric correction –Hyperspectral instead of multispectral 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. 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. HES-CW built to the threshold requirements will be a dramatic improvement over present capabilities for coastal imaging.

OSU_08/1/2005_Davis.42 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 four 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.

OSU_08/1/2005_Davis.43 Chlorophyll and chlorophyll fluorescence of optically-complex coastal waters MODIS Terra scene from 3 October The ratio of Fluorescence Line Height (FLH) to chlorophyll is a good indicator of the health of the phytoplankton population. FLH separates chlorophyll from suspended sediments in the Columbia River Plume. Fluorescence line height not available from VIIRS. MODIS Terra scene from 3 October The ratio of Fluorescence Line Height (FLH) to chlorophyll is a good indicator of the health of the phytoplankton population. FLH separates chlorophyll from suspended sediments in the Columbia River Plume. Fluorescence line height not available from VIIRS.

OSU_08/1/2005_Davis.44 Monterey Bay Harmful Algal 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. 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)

OSU_08/1/2005_Davis.45 HES-Will support the higher temporal and spatial resolution required for coastal models July 31 SeaWiFS Chlor-a (mg/m 3 ) :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)