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David Antoine; André Morel

Evaluation of Particulate Backscattering Inversion Algorithms in Clear Oceanic Case 1 Waters. David Antoine; André Morel Laboratoire d’Océanographie de Villefranche (LOV), CNRS and Université Pierre et Marie Curie, Paris 06, UMR 7093, Villefranche-sur-Mer , France

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David Antoine; André Morel

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  1. Evaluation of Particulate Backscattering Inversion Algorithms in Clear Oceanic Case 1 Waters • David Antoine; André Morel • Laboratoired’Océanographie de Villefranche (LOV), CNRS and Université Pierre et Marie Curie, Paris 06, UMR 7093, Villefranche-sur-Mer, France • StéphaneMaritorena; David A Siegel; Norm B Nelson • Earth Research Institute (ERI), University of California at Santa Barbara (UCSB), Santa Barbara, California, United States of America. • Department of Geography, University of California at Santa Barbara (UCSB), Santa Barbara, California, United States of America • Hubert Loisel; David Dessailly • Laboratoired’Océanologie et de Géosciences (LOG), CNRS and Université du littoral, côted’opale, Wimereux, France

  2. Rationale (1/3) A number of “bbp algorithms”, i.e., inversion of radiometric quantities in terms of a and bb, exist (see, e.g., IOCCG report N°5, 2006) Validation of the bbp retrieval from these algorithms is still quite limited, because of 1 – A lack of bb measurements (although the situation improves now) 2 – bb measurements are still dominated by either coastal waters or open ocean waters with relatively high [Chl], i.e., bbp(550) > ~0.002 m-1 Therefore, we don’t really know how these algorithms perform for waters that represent more than half of the global ocean, i.e., waters with bbp(550) < ~0.001-0.002 m-1 See, e.g., recent paper by Dall’Olmoet al., 2012, Optics Express 20(19)

  3. Average global repartition of bbp at 550 nm Figs. 5A in Kostadinovet al., 2009, J. Geophys. Res., 114, C09015, doi:10.1029/2009JC005303

  4. Rationale (2/3) This is important in particular in view of reducing uncertainty on the behaviour of particulate backscattering for low-Chl waters Fig. 1A in Huotet al., 2008, Biogeosciences, 5, 495–507 Fig. 1 in Behrenfeldet al., 2005, Global Biogeochem. Cycles, 19, GB1006, doi10.1029 / 2004GB002299 Fig. 5a in Antoine et al., 2011, L&O, 56(3), 955–973

  5. Rationale (3/3) A number of applications exist that use “satellite bbp”  How uncertainties in bbp retrievals for clear waters may affect these results? Fig. 3 in Loiselet al., 2006, J. Geophys. Res., 111, C09024, doi:10.1029 / 2005JC003367 Fig. 4C in Behrenfeldet al., 2005, Global Biogeochem. Cycles, 19, GB1006, doi10.1029 / 2004GB002299 Figs. 5A and 7A in Kostadinovet al., 2009, J. Geophys. Res., 114, C09015, doi:10.1029/2009JC005303

  6. Objectives Use inversion algorithms of quite different nature, and apply them to in situ data sets in order to evaluate uncertainty in the final bbp product for clear waters. Application of the algorithms is performed in different configurations in order to evaluate robustness to: 1 – Loss of some information when using a single input quantity (Rrs) instead of both R and Kd 2 – Behaviour when fed with satellite Rrs, which includes additional errors from atmospheric correction The goal is not to perform an inter-comparison of algorithms with the idea of ranking algorithms

  7. Data sets (1/2) BOUSSOLE: Med. Sea clear waters, Chl from ~0.05-5 mg m-3, bbp(555): 0.0005-0.005 m-1 PnB Stations 2-6: More coastal, still Case 1, Chl from ~0.5-10 mg m-3, bbp(555): 0.0007-0.01 m-1 BIOSOPE: SE Pacific gyre, the most oligotrophic waters in the World ocean Chl from ~0.02-5 mg m-3, bbp(555): 0.0002-0.005 m-1

  8. Data sets (2/2) Data from Hobilabs Hydroscat-4 (BOUSSOLE), Hydroscat-6 (PNB) sensors (Maffione and Dana,1997. Appl. Opt. 36: 6057–6067), Wetlabs EcoBB3 (BIOSOPE) Measurements & data analysis protocols: Antoine et al. (2011)L&O, 56(3), 955–973 for BOUSSOLE Kostadinovet al., 2007, J. Geophys. Res. 112 for PNB Twardowskiet al. 2007, Biogeosciences4, 1041-1058 for BIOSOPE

  9. Algorithms The GSM Model (Garver & Siegel, 1997; Maritorena et al., 2002) Non-water components of absorption and scattering are expressed as known shape functions with unknown magnitudes For the 3 algorithms, bbw(l) is computed following Zhang et al. (2009, Opt. Exp. 17: 5698-5710) and Zhang and Hu (2009, Opt. Exp. 17: 1671-1678), as a function of temperature and salinity New version of the Loisel and Stramskimethod (Loisel and Stramski, 2000; Loisel et al., 2001). No assumptions about spectral shapes for absorption and scattering (“No Spectral Assumption Algorithm” or “NSAA”) LOV: (Morel et al., 2006, Deep-Sea Res. I, 53, 1439-1559) Just based on 2 equations: Kd(l) = 1.0395 [a(l) + bb(l)] / md (Gordon, 1989, L&O 34) R(l) = f' bb(l) / [a(l) + bb(l)] LUTs are used for md and f’ (Morel & Gentili, 2004, J. Geophys. Res.)

  10. The test steps 1st step (ideal case): For algorithms using R and Kd as inputs: both R and Kd are independently derived from field radiometry measurements For algorithms using Rrs as input: Rrsderived from field radiometry measurements 2nd step (algorithms using R and Kd as inputs): R is still derived from field radiometry measurements, but Kd is now modelled (either from Rrs or Chl) 3rdstep : satellite Rrsare used for all algorithms (for algorithms using R and Kd as inputs: both R and Kdare derived from satellite Rrs)

  11. 2 10-2 Results using R-Kd when feasible, and Rrs otherwise 1 10-4 PNB BOUSSOLE BIOSOPE

  12. Results using R-Kdbut with Kd=f(Chl) and Rrs otherwise PNB BOUSSOLE BIOSOPE

  13. Results using SeaWiFS Rrs PNB BOUSSOLE

  14. Results using SeaWiFS Rrs All bands pooled together PNB BOUSSOLE

  15. Respective importance of seawater / particles In determining total bb (solid lines) and a (dashed lines) 443 nm 550 nm Using Morel & Maritorena (2001)

  16. Getting accurate bbp in the field for clear waters BOUSSOLE data (Hydroscat-IV measurements) Effect of accounting for field determinations of dark currents on the bbp spectral slope (g) (blue: with dark records included)

  17. to conclude • Still very difficult to get accurate bbp from radiometry in clear waters • Still difficult as well to get accurate bbp field measurements in clear waters. The bbp values < ~0.001 m-1 determined in situ using currently available instrumentation have to be carefully considered • Overall the best results are obtained in the green, around l=550 nm • Seems illusory (at least difficult) to get bbp in the blue (l~440nm) by using only blue bands. Need methods that constrain to some extent the bbp derivation using more bands (e.g., GSM) • Degradation of results when using a modelled Kd instead of the measured one is not so dramatic • Which Kd is to be used? Field determination or modelled value from Rrs or Chl ? Using a model could actually decrease the noise of the inversion. The best thing to do might be to improve our determination of Kd from field radiometry.

  18. Thank you for your attention Questions?

  19. Algorithms (1/3) The GSM Model(Garver & Siegel, 1997; Maritorena et al., 2002) Gordon et al. (1988) • Non-water components of absorption and scattering are expressed as known shape functionswith unknown magnitudes(=unknowns): • aph(λ) = Chlaph*(λ) • acdm(λ) = acdm(443)exp(-S(λ -443)) • bbp(λ) = bbp(443) (λ /443)-η • aph*(λ), S and η were optimized for global applications using a large in situ data set. • Unknowns and their confidence intervals are retrieved by fitting the model to the observed Rrs using a non-linear least-square technique.

  20. Algorithms (2/3) New version of the Loisel and Stramskimethod (Loisel and Stramski, 2000; Loisel et al., 2001). No assumptions about spectral shapes for absorption and scattering (“No Spectral Assumption Algorithm” or “NSAA”) • Rrs is used as input parameter instead of R(0-) • New formulation between : bb and (Rrs, Kd, mw, h) • Kd(l) is retrieved from NN (Jamet et al., 2011) • More realistic h and b/a combinations How ? Radiative transfer simulations with no spectral assumptions (a=1, b/a [0.02 to 30], h[0.01-0.20 %] Performance using the synthetic (error free) IOCCG data set with the true Kd

  21. Algorithms (3/3) LOV: (Morel et al., 2006, Deep-Sea Res. I, 53, 1439-1559) Just based on 2 equations: Kd(l) = 1.0395 [a(l) + bb(l)] / md(Gordon, 1989, Limnol. Oceanogr. 34: 1389-1409) R(l) = f' bb(l) / [a(l) + bb(l)] fromwhich a(l) = 0.962 Kd(l) md(l, qs, Chl) x [1 – R(l) / f'(l, qs, Chl)] bb(l) = 0.962 Kd(l) md(l, qs, Chl) x [R(l) / f'(l, qs, Chl)] LUTs are used for md and f’ (Morel & Gentili, 2004, J. Geophys. Res., 109, C6) For the 3 algorithms, bbw(l) is computed following Zhang et al. (2009, Opt. Exp. 17: 5698-5710) and Zhang and Hu (2009, Opt. Exp. 17: 1671-1678), as a function of temperature and salinity

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