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

BackgroundBackground

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

Cruise DataCruise DataChlorophyll units are log(mg m-3)

Results: Satellite image from July 11Results: Satellite image from July 11

Chlorophyll units are log(mg m-3)

Results: July 11 continuedResults: July 11 continued

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)

Results: July 22 continuedResults: July 22 continued

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

Questions?

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