iop algorithm workshop @ ocean optics xix, 7 oct 2008, pjw nasa/ssai iop algorithm workshop @ ooxix...

IOP Algorithm Workshop @ Ocean Optics XIX, 7 Oct 2008, PJW NASA/SSAI

IOP Algorithm Workshop @ OOXIX

Jeremy Werdell

NASA Ocean Biology Processing Group

7 Oct 2008


attendees:

Antoine Mangin (ACRI) Odile Hembise Fanton d’Andon (ACRI)

Bryan Franz (NASA) Paula Bontempi (NASA)

Catherine Brown (LOV) Samantha Lavender (U. Plymouth)

Emmanuel Boss (U. Maine) Sean Bailey (NASA)

Gene Feldman (NASA) Stephane Maritorena (UCSB)

Hubert Loisel (U. Littoral) Takafumi Hirata (PML)

Jeremy Werdell (NASA) Tim Moore (NURC)

Jill Schwarz (NIWA) Tim Smyth (PML)

Mark Dowell (JRC) Vittorio Brando (CSIRO)

Mike Behrenfeld (OSU) Yannick Huot (LOV)

ZhongPing Lee (MSU)

unable to attend: Andre Morel (LOV), Paul Lyon (NRL)


SAA = semi-analytical algorithmwhat we attempted to do:

extend the IOCCG SAA survey by

(1) evaluating application of SAA algorithms to satellite radiometry

(2) reviewing & consolidating SAA construction

workshop motivation & goal:

achieve community consensus on an effective algorithmic approach for producing global-scale, remotely sensed SAA IOP products

desirable features:

combination of accuracy and geographic coverage

flexible, multi-sensor implementation

computational efficiency to support operational environment

open source software and accompanying LUTs

associated SAA uncertainties

algorithm “shoot out”


pre-workshop achievements (Mar - Sep) - dialog & discussion

1. air-sea transmission, Rrs rrs(0-)

2. calculation of Rrs (bandpass correction, f/Q)

3. temperature & salinity dependence of aw & bbw

4. spectral data products to be considered (adg, bb, etc.)

5. evaluation metrics & SAA failure conditions

6. inversion methods & linearization issues

7. calculation of uncertainties

8. SAA product validation & sensitivity analyses

9. strategies to produce level-3 products

http://oceancolor.gsfc.nasa.gov/forum/oceancolor/board_show.pl?bid=24


pre-workshop achievements (Mar - Sep) - analyses

1. in situ-to-in situ & satellite-to-in situ match-ups

2. global (level-3) comparisons

3. spatial coverage (level-2) comparisions

4. sensitivities to parameterization & noisy input

5. sensitivity to inversion method

6. level-2 vs. level-3 inversion

http://oceancolor.gsfc.nasa.gov/MEETINGS/OOXIX/IOP/analyses.html


€

Rrs ≈ funcbb

a + bb

⎛

⎝ ⎜

⎞

⎠ ⎟

satellite provides Rrs()

a () and bb () are desired products

construction (& deconstruction) of an SAA …

€

a = aw + aii=1

n

∑

total a and bb are sums of coefficients for all components in seawater

€

=aw + M i a ii=1

n

∑

each coefficient expressed as product of magnitude and spectral shape


€

Rrs ≈ funcbb

a + bb

⎛

⎝ ⎜

⎞

⎠ ⎟

satellite provides Rrs()

a () and bb () are desired products

construction (& deconstruction) of an SAA …

Spectral Optimization:

* define shape functions for (e.g.) bbp(), adg(), aph()

* solution via L-M, matrix inversion, etc.

* ex: RP95, HL96, GSM

1

Spectral Deconvolution:

* partially define shape functions for bbp(), adg()

* piece-wise solution: bbp(), then a(), then adg() + aph()

* ex: QAA, PML, NIWA

2

Bulk Inversion:

* no predefined shapes

* piece-wise solution: bbp(), then a(), via (empirical) Kd () via RTE

* ex: LS01

3

correlated

uncorrelated


€

Rrs ≈ funcbb

a + bb

⎛

⎝ ⎜

⎞

⎠ ⎟

our STARTING point:

* dynamic bbp retrieval

* dynamic aph spectral model

* IOP-based f/Q tables

* Raman scattering

* fluorescence

* T/S dependence on aw & bbw

* optical water class parameterization

* uncertainties & propagation of error

metrics defined to evaluate progress

consensus to refine spectral optimization to initiate process …

Spectral Optimization:

* define shape functions for (e.g.) bbp(), adg(), aph()

* optimization via L-M


discussion of uncertainties & their calculation:

Wang et al. 2005GlobColourLee et al. 2008 (OOXIX personal communication)

uncertainties associated with:

* input Rrs* models & shape functions


generalized IOP model (GIOP) in l2gen

• specify sensor wavelengths to fit– e.g., 412,443,490,510,555– e.g., 412,490,555

• select aph form and set params

– tabulated: , ap*()– gaussian: ,

• select adg form and set params– exponential: , S

• select bbp form and set params– power law: , – power law: , via Hoge & Lyon– power law: , via QAA

• select Rrs[0-] to bb/(a+bb)– quadratic: g1, g2– f/Q: (tbd)

• specify inversion method– Levenburg-Marquart– Amoeba (downhill simplex)– Lower-Upper Decomposition– Singular-Value Decomposition

• specify output products– a (), aph (), adg (), bb (), bbp ()

= any sensor wavelength(s)– Ca (given ap* at ) (dynamic model params)– internal flags


summary:

1. consensus was reached on the way forward2. NASA will implement the GIOP w/i next 3-6 months & begin

producing global time-series of IOPs for all missions for which we’re responsible

3. the group will continue our dialog, review results of data processing, & make recommendations for improvements

4. NASA will reintroduce refinements & reprocess the data5. once we have agreement that products are as good as

(currently) possible, full mission reprocessing(s) will be initiated6. all code will be available via SeaDAS7. NASA will implement code for optical water class mapping &

evaluate how to implement this with class-based SAA parameterization


http://oceancolor.gsfc.nasa.gov/MEETINGS/OOXIX/IOP

http://oceancolor.gsfc.nasa.gov/forum/oceancolor/forum_show.pl

iop algorithm workshop @ ocean optics xix, 7 oct 2008, pjw nasa/ssai iop algorithm workshop @ ooxix...

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