The GSM merging model.Previous achievements and application to GlobCOLOUR

Globcolour / Medspiration user consultation, Dec 4-6, 2006, Villefranche/mer

Stéphane Maritorena, Dave SiegelICESS

University of California, Santa Barbara

OCEAN COLOR DATA MERGING AT UCSBA BRIEF HISTORY

1997 - 2003: NASA SIMBIOS ProgramDevelop merging proceduresTest them with SeaWiFS and MODIS

2004 - : NASA Research, Education, and Applications Solution Network (REASoN) Develop an Ocean Color Time-series Develop and distribute merged data sets from SeaWiFS and MODIS: Chl and other products

2005 - : ESA GlobCOLOUR ProjectGSM merging procedure is one of the methods tested.

Why do ocean color data merging ?• Several simultaneous global ocean color missions• Several versions of the same product

Benefits:• Development of unified, consistent ocean color time-series from

multiple sensors (ESDRs, CDRs)• Improved spatial and temporal coverage

• More diverse ocean color products with lower uncertainties

Difficulties:• Sensors are not created equal: different designs, calibrations,

algorithms, accuracies,….

• Large volumes of data to deal with

• Merging procedure should not create biases, discontinuities,

artifacts,…

THE GSM01 MERGING MODEL (Garver & Siegel, 1997; Maritorena et al., 2002; Maritorena & Siegel, 2005).

THE GSM01 MERGING MODEL (Garver & Siegel, 1997; Maritorena et al., 2002; Maritorena & Siegel, 2005).

Non-water components of absorption and scattering are expressed as known shape functions with an unknown magnitudes:

• aph() = Chl aph*()• acdm() = acdm(443) exp(-S( -443))• bbp() = bbp(443) (/443)-

Fixed parameters were optimized for global applications using a large in situ data set

Non-linear least-square technique is used to solve for the unknowns (chl, acdm(443) and bbp(443)) from LwN() data at 4 or more wavelengths.

Uncertainties of the input LwN() can be taken into account.

Confidence intervals of the retrievals are estimated

GSM01 is a Case I non-polar water model

LwN () = t F0 ()

nw2

gi bbw () + bbp ()

bbw () + bbp () + aw ()+ aph ()+ acdm ()

i = 1

2

i

LSM = LwN-i( j)mod. - LwN-i( j)meas.

i( j)

2

j=1

Ni

i=1

Nsat

RESULTS WITH THE NOMAD DATA SETRESULTS WITH THE NOMAD DATA SET

Merging technique = GSM model (Maritorena & Siegel, 2005)

L wN() source 1 LwN() source 2 L wN() source n

LwN () uncertainties source 1 LwN () uncertainties source 2 LwN () uncertainties source n

[L wN() source 1, L wN() source 2, ..., LwN() source n]

LwN () uncertainties source 1, LwN () uncertainties source 2, ..., LwN () uncertainties source n[ ]

GSM01 MODEL INVERSION

wNL bb

bb= f(

a + )

wNL

SeaWiFSMODIS

[Chl] (443)acdm (443)bbp

DATA SOURCES

CONCATENATED ARRAYS

[Chl] confidence interval (443) confidence intervala cdm (443) confidence intervalb bp

RETRIEVALS

412 443 490 510 555412 443 488 531 551412 443 490 510 560

412 443 490 510 531 551 555 560

SeaWiFSAquaMeris

Merged

9 km grid (REASoN)4.6 km grid (GlobCOLOUR)

GSM main features:

• Exploits band differences to extend spectral information

• Utilizes band redundancies• Uncertainties of the input LwN() can be taken into account.• Can provide uncertainty estimates for the output products

GSM01 forward

LwN() QC

SeaWiFS

Aqua

Merged

Daily 9 km data - March 21, 2003

Meris

GSM - Merged SeaWiFS/Aqua/Meris - Daily (3/21/2003)GSM - Merged SeaWiFS/Aqua/Meris - Daily (3/21/2003)

Chl

acdm(443)

1.00

0.1

0.01

GSM

Chl [m

g m-3]

GSM

acdm (443) [m

-1]

1.00

0.01

0.010.001

0.1

GSM - Merged SeaWiFS/Aqua/Meris8-Day (May 24-31, 2003)

Chl

acdm(443)

1.00

0.1

0.01

GSM

Chl [m

g m-3]

1.00

0.1

0.001

0.01

GSM

acdm (443) [m

-1]

GSM - Merged SeaWiFS/Aqua/MerisMonthly Chl (July, 2002)

SeaWiFS vs. Aqua

SeaWiFS vs. Meris

Aqua vs. Meris

412 nm412 nm 443 nm443 nm 490 nm490 nm 555 nm555 nm

LwN() comparisons (Jan. 1, 2003)

SeaWiFS only MODIS only SeaWiFS and MODIS

GSM Merging Model

Coverage map

Chl product

Issues with the particulate backscattering productIssues with the particulate backscattering product

Meris issue

Mostly a SeaWiFS issue

Noise in the SeaWiFS and Meris Lwns around gaps caused by clouds. In these areas, the Lwn() are sometimes higher than in nearby gap-free areas and this results in enhanced bbp (443) values.

Problem seems much less important in Aqua data

This noise is not uniformly distributed but seems to mostly affect mid-latitudes in the southern hemisphere.

Meris shows some high bbp values on the western edge of some swaths.

SeaWiFSLwN(555)

SeaWiFSbbp(443)

Noise in the backscattering product with SeaWiFS

• “Ringing” issue

• High LwN() values around cloud gaps

• Can be filtered but will result in loss of coverage

• Does not happen with Aqua data

Daily coverage using SeaWiFS/Aqua and Meris (2003 data)

Sensor(s) Coverage (%) Std. Dev. (%)

SeaWiFS 16.65 2.01

Aqua 13.76 1.15

Meris 8.51 1.48

SeaWiFS/Aqua 24.22 1.94

SeaWiFS/Meris 22.24 2.40

Aqua/ Meris 19.92 1.74

SeaWiFS/Aqua/Meris 28.85 2.241

Ocean color sensorsaverage daily coverage (2003 data)

Ocean color sensorsaverage daily coverage (2003 data)

coverage with composite imagescoverage with composite images

Matchups w/ NOMAD (97-03) + SeaBASS (04-06) dataMatchups w/ NOMAD (97-03) + SeaBASS (04-06) data

Nomad data:Rrs, Chl: 2203acdm(443): 535bbp(443): 181

Chl acdm(443)

bbp(443)

2004-2006 data:Rrs, Chl: 884acdm(443): 370bbp(443): 178

Chl - latitudinal comparisonChl - latitudinal comparisonSeaWiFS Aqua Meris Merged SeaWiFS OBPG

Comparison of GSM and Meris products - Daily

January 15, 2003

April 15, 2003

Validation and analyses of the model and products

What to do with the merged data sets ?What to do with the merged data sets ?

What we are currently doing at UCSB:Eddies and open ocean biogeochemistry in the

Sargasso Sea Export particle source regions and the

horizontal advection of sinking particles Spring bloomsCDOM in the Southern Ocean Spatial structure/decorrelation scales

What we are currently doing at UCSB:Eddies and open ocean biogeochemistry in the

Sargasso Sea Export particle source regions and the

horizontal advection of sinking particles Spring bloomsCDOM in the Southern Ocean Spatial structure/decorrelation scales

NASA REASoN GSM Merged data sets

• global 9 km• daily• weekly (8D)• monthly

• Chl (+ confidence intervals for dailies)• acdm(443) (+ confidence intervals for dailies)• bbp(443) (+ confidence intervals for dailies)• coverage map

• Product files in HDF format, compressed (4320 x 2160 arrays)

• Merged SeaWiFS-Aqua, SeaWiFS only, Aqua only.

• Available at: • ftp:ftp.oceancolor.ucsb.edu/pub/org/oceancolor/REASoN• OPeNDAP server:

http://dap.oceancolor.ucsb.edu/cgi-bin/nph-dods/data/oceancolor/

NASA REASoN GSM Merged data sets

• global 9 km• daily• weekly (8D)• monthly

• Chl (+ confidence intervals for dailies)• acdm(443) (+ confidence intervals for dailies)• bbp(443) (+ confidence intervals for dailies)• coverage map

• Product files in HDF format, compressed (4320 x 2160 arrays)

• Merged SeaWiFS-Aqua, SeaWiFS only, Aqua only.

• Available at: • ftp:ftp.oceancolor.ucsb.edu/pub/org/oceancolor/REASoN• OPeNDAP server:

http://dap.oceancolor.ucsb.edu/cgi-bin/nph-dods/data/oceancolor/

SeaWiFS

Aqua

Merged SeaWiFS Aqua

NASA REASON future developments:

• 3- or 4-Day composites

• Merge Terra data if/when available

• More validation and analyses of the merged products

• More user-friendly interface

• sub-sampling, slicing

• Integration into NASA Giovanni system

• New products

Thank youfor yourattention

The GSM01 model

Rrs() t nw

2 gi bbw() + bbp()

aw() + aph() + ad() + ag() + bbw() + bbp()

i = 1

2

i

Gordon et al., 1988

• aph() = Chl aph*()

• acdm() = acdm(0) exp[-S( - 0)]

• bbp() = bbp(/ 0)