1. Gulf of Mexico and East Coast Carbon Research Cruise: A preliminary comparison of in situ and satellite products Amanda M. Plagge Research & Discover Graduate Fellow University of New Hampshire, Durham, NH

2. Introduction  Undergraduate work in engineering and Earth science at Dartmouth College  Masters in Electrical Engineering from Thayer Engineering School at Dartmouth College  Currently in the University of New Hampshire’s Natural Resources and Earth System Science Ph.D. Program

3. Objectives  Long-term  Use of ocean remote sensing to aid in renewable energy development efforts  Use of ocean remote sensing to better understand the Earth system and how it is changing  Short-term  Detailed analyses of satellite data compared to in situ data: ocean winds, fluxes, and productivity measurements

4. Background

5. GOMECC Cruise  Gulf of Mexico and East Coast Carbon Cruise: July 10-Aug 4  Water samples taken at various depths  Air fluxes: Momentum, CO 2, Ozone  Flow-through system measured:  Salinity  Temperature  Chlorophyll  Scattering  Nitrate  Oxygen saturation

6. Original Plan and Changes  Original plan: concentrate on flux data in preparation for building our flux measurement buoy  Problem 1: ozone flux team had data transfer problems, and have not begun analyzing data yet  Problem 2: CO 2 flux team lost sonic anemometer after first two weeks and will have to use data from ozone team’s anemometer; therefore also no data processed yet  Solution: Alternate focus found: comparing data from UNH flow-through system to satellite products

7. Methods  Use of SPIP and QuaTech box to log data  Use of statistical filters back at UNH to read in raw data and create ASCII files with all variables; upload back to ship  Filter data to match ship’s GPS string with flow-through instrument data  Use of MATLAB to process ASCII files  Incorporate SPIP on-off times and remove known bad data (e.g. when water shut off for cleaning)  Use of MATLAB to compare flow-through data to MODIS satellite products (uploaded by Ken Fairchild at UNH)  Difficulties finding clear (cloud-free) data  Choose chlorophyll product as most straight-forward to compare to in situ measurements

8. Cruise Data Chlorophyll units are log(mg m -3 )

9. Results: Satellite image from July 11 Chlorophyll units are log(mg m -3 )

10. Results: July 11 continued Chlorophyll units are log(mg m -3 )

11. Results: Satellite image from July 22 Chlorophyll units are log(mg m -3 )

12. Results: July 22 continued Chlorophyll units are log(mg m -3 )

13. Possible Sources of Error  Satellite chlorophyll in many places is greater than that measured by flow-through sensor  Coastal regions:  Satellite algorithm is basically ratio of reflectance in blue to that in yellow/green  Colored dissolved organic matter (CDOM) also absorbs blue light and are common along coast  Therefore, results in higher satellite measures of chlorophyll along coast  Open ocean:  During summer, optimal depth for phytoplankton would be 20-30 m  Satellite would pick up plankton at this depth  Flow-through seawater inlet is 3-5 m; would not pick up this signal  Errors due to different quantum yields  Quantum yield= measure of efficiency of photosynthetic process  Differs for different water masses  Relationship between fluorescence (measured quantity) and chlorophyll concentration (desired quantity) will change  Instrument errors (satellite, sensor)  Errors in GPS match-ups and co-location

14. Conclusions  Accomplished a fair amount in a short time while learning a lot about ocean productivity  Very reasonable match-ups: matching error should be less than 30% (MODIS specs) but it is routine to find it as high as 100%*  Visual coherence observed between in situ and satellite measurements  Based on above, fluorometer is a reasonable instrument to use to study chlorophyll distributions  Further work will be needed to quantify errors * Joe Salisbury, personal communication

15. Future Work Based on GOMECC  Productivity and fluorescence: use 8-day MODIS composite images to increase probability of pixel matching; compare other MODIS products (bb, cdom, etc); quantify errors  Wind comparison: in situ from R/V Ron Brown vs. satellite scatterometer wind at various resolutions  Fluxes: investigate data from flux equipment on R/V Brown to prepare for data from flux buoy  Temperature comparison: in situ from R/V Brown on-ship data and both temp-monitoring flow-through sensors vs. with MODIS SST data

16. Acknowledgments  Joe Salisbury  Ken Fairchild and Chris Hunt  My committee: Doug Vandemark (chair), Jamie Pringle, John Moisan, Bertrand Chapron, John Kelley  NOAA and AOML  The crew of the Ronald H. Brown  The Ocean Color Group’s MODIS browser  UNH, GSFC, and Research & Discover

17. Questions?

18. Future Work: Buoy  Assemble equipment on bench; test on roof of Morse Hall to ensure data logging properly etc  Mount equipment on Jim Irish’s wave buoy  Deploy for one month  Recover; make any necessary changes  Move equipment to CO2 buoy; redeploy with remote data access.

19. Future Work: Wind  Evaluation of high resolution (3 km) product  Comparison of variance and buoy gustiness  Filtering to degrade resolution: what information lost between 3 km, 12.5 km, 25 km?  Comparison with MODIS True Color images to attempt to account for image variability and apparent fronts  All resolutions: (3 km, 12.5 km, 25 km)  Comparison with CODAR-- current-measuring radar  Comparison of MM5 model  Comparison with SAR images  Further comparison with MODIS SST fronts