gulf of mexico and east coast carbon research cruise: a preliminary comparison of in situ and...
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Gulf of Mexico and East Coast Carbon Research Cruise:
A preliminary comparison of in situ and satellite products
Amanda M. PlaggeResearch & Discover Graduate Fellow
University of New Hampshire, Durham, NH
IntroductionIntroduction
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
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
ObjectivesObjectives
Long-termUse of ocean remote sensing to aid in renewable
energy development effortsUse 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
Long-termUse of ocean remote sensing to aid in renewable
energy development effortsUse 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
GOMECC CruiseGOMECC Cruise Gulf of Mexico and East
Coast Carbon Cruise: July 10-Aug 4
Water samples taken at various depths
Air fluxes: Momentum, CO2, Ozone
Flow-through system measured: Salinity Temperature Chlorophyll Scattering Nitrate Oxygen saturation
Gulf of Mexico and East Coast Carbon Cruise: July 10-Aug 4
Water samples taken at various depths
Air fluxes: Momentum, CO2, Ozone
Flow-through system measured: Salinity Temperature Chlorophyll Scattering Nitrate Oxygen saturation
Original Plan and ChangesOriginal Plan and ChangesOriginal plan: concentrate on flux data in
preparation for building our flux measurement buoyProblem 1: ozone flux team had data transfer
problems, and have not begun analyzing data yetProblem 2: CO2 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
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: CO2 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
MethodsMethods 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
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
Results: Satellite image from July 11Results: Satellite image from July 11
Chlorophyll units are log(mg m-3)
Results: Satellite image from July 22Results: Satellite image from July 22
Chlorophyll units are log(mg m-3)
Possible Sources of ErrorPossible 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
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
ConclusionsConclusions
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
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
Future Work Based on GOMECCFuture 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
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
AcknowledgmentsAcknowledgments
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
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
Future Work: BuoyFuture 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 buoyDeploy for one monthRecover; make any necessary changesMove equipment to CO2 buoy; redeploy with
remote data access.
Assemble equipment on bench; test on roof of Morse Hall to ensure data logging properly etc
Mount equipment on Jim Irish’s wave buoyDeploy for one monthRecover; make any necessary changesMove equipment to CO2 buoy; redeploy with
remote data access.
Future Work: WindFuture 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
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
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