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Science web site marine.rutgers/cool

Science web site marine.rutgers/cool

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Science web site marine.rutgers/cool

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  1. Development of a Shelf-Wide Observatory in the Mid-AtlanticBightOscar Schofield & Scott GlennRutgers University Science web site http://marine.rutgers.edu/cool Operational web site http://thecoolroom.org

  2. New Jersey Coastal Upwelling July 6, ’98 - AVHRR July 11, ‘98 - SeaWiFS Chlor-a (mg/m3) Temperature oC 19 20 21 22 24 .1 .3 .5 1 2 4 40N 40N Historical Hypoxia/Anoxia Field Station Field Station LEO LEO 39N 39N 75W 74W 75W 74W Barnegat Cape May

  3. Causes of Hypoxia/Anoxia Surface bloom wind SW upwelling Stratification favorable wind Decay Bacteria Depletes Oxygen

  4. 12 m 15 m 20 m 25 m 30 m 35 m 40 m 50 m 100 m 500 m 1000m 2500m Hypoxia/Anoxia & Bottom Bathymetry Warsh – NOAA 1989

  5. Modeled Effect of Bathymetric Variability on Upwelling 1 m/s current velocity Along shore subsurface deltas cause upwelling to be 3d, not 2d. North wind Barnegat delta LEO delta Cape May delta

  6. Coastal Predictive Skill Experiment Study Site: 1998-2001

  7. Research Sponsors and Partners Academic California PolyTechnic State University Cornell University Dalhousie University Florida Environmental Research Institute Harbor Branch Louisiana State University Oregon State University University of California Santa Barbara University of Rhode Island University of Puerto Rico University of Southern Mississippi Woods Hole Oceanographic Institution Industry Bosch Aerospace Inc. CODAR Ocean Systems HOBI Labs MetOcean Prariestarfish Filming Satlantic SeaSpace Corporation Sequoia Scientific SPECTIR Webb Research Government National Weather Service Naval Air Warfare Center Naval Research Lab Office of Naval Research

  8. 30 X 30 km Research Space

  9. Atmosphere/Ocean Forecast Models 3-D visualization Forecast Briefing Operational Low-Res COAMPS Atmospheric Model Experimental High-Res COAMPS Atmospheric Model Air-Sea Interaction Model ROMS Ocean Model (KPP and MY 2.5 Turbulent Closure) Bottom Boundary Layer Model

  10. Sat. Sun. Mon. Tue. Wed. Thu. Fri. Atmospheric Forecasts Ocean Forecast Ensemble Continuous Remote Sensing, Node, Mooring, Glider Data Collected Briefing Aircraft, Ship, AUV Adaptive Sampling Atmospheric Forecasts Ocean Forecast Ensemble Briefing Aircraft, Ship, AUV Adaptive Sampling Coastal Predictive Skill Experiment Forecast Cycle

  11. Real-Time Validation of the Ensemble Forecasts HR COAMPS / ROMS 2 4 6 8 10 12 KPP Depth (m) 2 4 6 8 10 12 26 24 22 20 18 16 14 12 10 8 Depth (m) 2 4 6 8 10 12 Depth (m) MY2.5 18 18 18 19 19 19 20 20 20 21 21 21 July, 2001 July, 2001 July, 2001 • In an observationally rich • environment, ensemble forecasts • can be compared to real-time data • to assess which model is closer to reality • and try to understand why.

  12. Month Long Experimental Effort

  13. Adaptive Sampling Platforms

  14. Ship-to-Shore Communications

  15. The COOLroom Operational Collaboratory COOLroom Skunk Works Model COOLroom War Room Model Each experiment averaged 150-200 scientists, 20-25 institutions, up to 13 ships, up to 5 aircraft, up to 5 AUVs

  16. Scientific Accomplishments Atmospheric Model Validation Ocean Model Development and Validation Physical, Chemical, and Biological Process Studies Cross calibration of Satellite Data Algorithm Development for Turbid Coastal Waters Hyperspectral Remote Sensing Adaptive Sampling of Physical/Biological Features AUV, Bio-Optical sensors, and CODAR development Results highlighted in 30 presentations at AGU 2 Weeks ago. See http://marine.rutgers.edu/cool Adaptive Sampling of Physical/Biological Features

  17. 100 meters Case Example: Adaptive Sampling of Red-Tide Filaments Fig. 1

  18. Real-time CODAR Feature Tracking Tracking a filment for 12 hrs prior to the night-time bioluminscence sampling as part of Operation BlackMoon

  19. Photons/sec/ml 1e6 4e10 Ceratium fusus Ship Mapping of CODAR tracked Red-Tide 0 6 12 Depth (m) 18 24 0 1.0 2.0 Distance (km)

  20. Ship Grid Patterns BL Isosurfaces 1E10 ph/s/35L 0 3E11 ph/s/.35L Depth (m) 15 Latitude (~5km) Longitude (~2km)

  21. BL Isosurfaces 5E10ph/s/.35L 1E11ph/s/.35L Depth (m) Latitude (~300m) Longitude (~500m)

  22. Scientists want real-time observational nowcasts and model forecasts …. COOL Web Site Hits Rutgers COOL Web Site Hits 70,000 60,000 50,000 2001 2000 40,000 1999 Average Hits per Day 1998 30,000 1997 1996 1995 20,000 10,000 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Over 35 million to date (Over 13 million in 2001) DO REAL PEOPLE CARE?

  23. Web Users Face-to-Face Contacts - United States Navy - United States Coast Guard - NOAA Weather Radio - GPU Power Company - Value-added Ocean Analysis Companies - NBC TV, NYC and Philly - NJ State Police E-mail Contacts - Commercial Fishermen - Recreational Fishermen - Recreational Boaters - Recreational Divers - Surfers

  24. Data Type NODES 13% CODAR 17% Gulf Stream 10% MET 17% LEO 6% SST 53% East Coast 19% NYB 37% MAB 28% Region What they want:

  25. Where we do go from here?

  26. New Jersey Shelf Observing System (NJ-SOS) 300km x 300km Began in 2001 Satellites, RADAR, Gliders

  27. International Constellation of Ocean Color Satellites X-Band Earth Observing Satellites EOS (MODIS) USA 2001 NEMO (COIS) USA 2004 Orbview-2 (SeaWiFS) USA Op. HY-1 (COCTS/CZI) China 2002 FY1-C (MVISR) China Op. FY1-D (MVISR) China 2002 IRS-P3 (MOS) India Op. IRS-P4 (OCM) India Op. ADEOS-2 Japan 2002 (GLI/POLDER) ENVISAT(MERIS) Europe 2002

  28. In Situ Vicarious Calibration of The Ocean Color Satellites 1.0 1 Tidal Cycle Upwelling 6 Absorption at 440 nm (m-1) Depth (m) 0 12 0 30 60 Time (hr)

  29. Nested Multi-Static CODAR Array Buoys Beach Boats

  30. Long-Duration Glider AUVs RF Repeater ADCP vs. Glider Drift Comparison Temperature Cross Section July 19, 2000

  31. How do we build a Smart Glider Fleet? Use Agent Oriented Software For self-aware & self-controlled robots Collaborative Society of Glider Software Agents KNOWLEDGE REPRESENTATION SENSORS DECISION MAKING KNOWLEDGE BASE PLANNING REASONING COMMUNICATION COMMUNICATION SITUATION PROTOCOLS MODELLING Glider Fleet Mission Status Panel NASA’s Deep Space 1 Fly-by of Comet Borrelly

  32. Get the Gliders to remain in the Red Tide: What phytoplankton do I see? Do I want to map this area? PATTERN RECOGNITION of What Material is in The Ocean (here Toxic Red Tide) Thanks to Gary Kirkpatrick

  33. EcoSim Bio-Optical Model Physical/Biological Models Operational Low-Res COAMPS Atmospheric Model Experimental High-Res COAMPS Atmospheric Model Air-Sea Interaction Model ROMS Ocean Model (KPP and MY 2.5 Turbulent Closure) Bottom Boundary Layer Model

  34. Air/Sea CO2 Physical Mixing and Advection Dust Light Iron CO2 NH4 NO3 PO4 SiO4 Relict DOM Coastal Diatoms Pelagic Diatoms Dino- flagellate Synecho- coccus Excreted DOM Lysed DOM Hetero- Flagellet Viruses Copepod Ciliates Bacteria Sediment Detritus Predator Closure EcoSIM

  35. + ESSE Flow Diagram ESSE Smoothing via Statistical Approximation ^ DY0/N Field Initialization Central Forecast ^ ^ Y0 Ycf(-) Ymp(-) Shooting Sample Probability Density Measurement Model OA via ESSE Measurement Model Select Best Forecast Options/ Assumptions Mean SVDp Performance/ Analysis Modules Perturbations Minimum Error Variance Within Error Subspace (Sequential processing of Observations) Adaptive Error Subspace Learning + Scalable Parallel Ensemble Forecast Error Subspace Initialization Normalization Peripherals Analysis Modules Convergence Criterion Continue/Stop Iteration Breeding DE0/N + DP0/N - - + Most Probable Forecast + Synoptic Obs A Posteriori Residules dr (+) Historical, Synoptic, Future in Situ/Remote Field/Error Observations d0R0 + - - Data Residuals Measurement Error Covariance ^ d-CY(-) Ensemble Mean + + ^ eq{Yj(-)} Gridded Residules ^ Y(-) + - ^ ^ j=1 Y(+) Y(+) Y1 Yj Yq ^ - Y1 Yj Yq + 0 + - E(-) P(-) ^ - + 0 + + - +/- ^ E0 P0 j=q 0 uj(o,Ip) with physical constraints Continuous Time Model Errors Q(t) Key Ea(+) Pa(+) E(+) P(+) Field Operation Assumption

  36. Hindcast EcoSim sensitivity & validation studies Large diatoms July 31 SeaWiFS Chlor-a 3 (mg/m ) .5 2 39:30N 3 Node A 4 UCSB 5 Small diatoms 39:00N EcoSim Modeled Satellite Measured

  37. July 31, 2001 Model Forecasts Real-Time Observations THE GOAL: Shelf-Wide Real-Time Maps and Predictions of Physics, Chemistry and Biology

  38. Initial Goals in 1998 Mission: Improving nowcast skill via data assimilation, improve forecast skill via boundary forcing. Characterizing the spatial/temporal variability in the inherent optical properties. Use these improved modeling/observations for rapid environmental assessment of coastal frontal features.

  39. July 31 Oceansat Chlor-a 3 (mg/m ) .5 1 39:30N 2.5 Node A UCSB 4 39:00N Use models to effectively sample frontal boundaries in a nested grid of remote sensing imagery

  40. Front Prediction of Bioluminescence Leaving Radiance in the Littoral Zone. • Operation Blackmoon & Blackmoon II (July 2000 & 2001) Physics Optics Biology Physics

  41. Prediction of Bioluminescence Leaving Radiance in the Littoral Zone. • Blackmoon & Blackmoon II (July 2000 & 2001) Visibility Threshold?

  42. Research Insititutions Scientists 250 40 Initiation of the NSF Instrumentation Award 200 30 # of participating scientists 150 # of Research Institutions 20 100 Cable design & installation 10 50 0 0 1991 1993 1995 1997 1999 The Collaboratory Experience Rich scientific environment, where groups can leverage off each other A training ground for operational oceanography In the present form has been maintained for 1 month and is heavily dependent on human involvement Observationally-rich allowing for model improvement

  43. Measure IOPs using the observation network Hyperspectral Gliders are coming Satellites algorithms

  44. EcoSim 2.0 Model Formulation Air/Sea CO2 Dust Physical Mixing and Advection Light N2 Iron CO2 NH4 NO3 PO4 SiO4 Relict DOM Cocco-litho-phores Benthic Flora Pelagic Diatoms Dino- flagellate Tricho-desmium Synecho- coccus G. breve Excreted DOM Lysed DOM Hetero- Flagellet Viruses Copepod Ciliates Bacteria Sediment Detritus Predator Closure

  45. Hindcast sensitivity studies Large diatoms July 31 SeaWiFS Chlor-a 3 (mg/m ) .5 2 39:30N 3 Node A 4 UCSB 5 Small diatoms 39:00N Measured Total Chlorophyll Measured 3-5 mg Chl a m-3 Diatom Chlorophyll Modeled 2-3 mg Chl a m-3