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Operational Implementation Strategies

Topic 3. Operational Implementation Strategies. Moderator: Bryan Franz. Goals. Identification of issues unique to satellite retrieval of IOPs Understanding of satellite R rs generation Agreement on common IOP model inputs (a w & b bw )

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Operational Implementation Strategies

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  1. Topic 3 Operational Implementation Strategies Moderator: Bryan Franz IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  2. Goals • Identification of issues unique to satellite retrieval of IOPs • Understanding of satellite Rrs generation • Agreement on common IOP model inputs (aw & bbw) • Agreement on algorithm failure conditions & masking • Understanding impact of IOP inversion at L2 versus L3 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  3. Satellite Focus • Multiple sensors - varying wavelength sets • SeaWIFS, MODIS, MERIS --> OCM-2, VIIRS, OLCI • Multiple data processing systems (NASA, ESA, ISRO) • Global application • wide range of water classes, distribution dominated by low-Ca water • large data volumes, want best IOP algorithm that is “practical” • Imperfect Lw retrieval • satellite sensor calibration & noise • atmospheric correction error • Rrs normalization • wide range of viewing geometry (0 < v < 60) • transition through interface IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  4. Multi-Sensor Processing Framework atmospheric correction Lw normalization derived products flags (failure & quality) Level-1 to Level-2 (common algorithms) observed Lt() , 0,  SeaWiFS L1A - or - MODISA L1B MODIST L1B OCTS L1A MOS L1B OSMI L1A CZCS L1A MERIS L1B OCM L1B observed radiances AOPs Rrs() IOPs a(), bb() Level-2 File Level-2 File Level-2 File spatial averaging temporal averaging masking Level-2 to Level-3 Level-3 Global Product IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  5. full-band water-leaving radiance normalized to non-attenuating atmosphere with Sun overhead Sun fb nLw() = Lw() / td0() 0 f0 fb correct from full-band to nominal 10-nm center-band via Morel model nLw() = nLw() f correct for Fresnel reflection refraction and inhomogeneity of subsurface light field via LUT 0 (f/Q)0 ex nLw() = nLw()  (f/Q) solar irradiance from Thuillier 2002 10-nm square-band-pass average ex ex Rrs() = nLw() / F0 () Rrs from Satellite Radiances TOA gas pol glint whitecap air aerosol td() Lw() = Lt() / tg() /fp() - TLg()- tLf() - Lr() - La() IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  6. IOP Model Implementation Issues • transition across air/sea interface • Lee et al. 2002 • pure sea-water values (aw & bbw) • aw: Pope & Fry, Kou et al. 1993, bbw: Smith & Baker 1981 • 10-nm square band-pass average (consistent with Rrs retrieval) • salinity & temperature sensitivity • significant impact on IOP retrieval when aw & bbw = f(T,S) • need to identify ancillary data sources IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  7. Inversion Methods and Efficiency • sequential (class 1 & 3) • model-specific (wavelength-specific) • may be iterative • simultaneous (class 2) • Matrix inversion • Lower-Upper Decomposition (LUD) • Singular-Valued Decomposition (SVD) • Iterative cost-function minimization • Levenburg-Marquart (LM) • Downhill Simplex (Amoeba, AMB) Algorithm Time (secs) one SeaWIFS GAC orbit IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  8. IOP Models Implemented at NASA • GSM (Garver-Siegel-Maritorena) • a, aph, adg, bb, bbp, Ca • QAA (Quasi-Analytical Algorithm) • a, aph, adg, bb, bbp • LAS (Loisel and Stramski) • a, b, c, bb, bbp • PML (Plymouth Marine Labs) • a, aph, adg, bb, bbp • HAL (Hoge & Lyon, via GIOP) • a, aph, adg, bb, bbp • GIOP (Generalized IOP Model) • a, aph, adg, bb, bbp, Ca, flags, , S • TBD: • NIWA • Boss & Roesler IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  9. 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 Generalized IOP Model (GIOP) IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  10. 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) quadtratic: 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 Generalized IOP Model (GIOP) 5-Band GSM IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  11. 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 Generalized IOP Model (GIOP) Hoge & Lyon IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  12. Flags & Masks IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  13. Multi-Sensor Processing Framework atmospheric correction Lw normalization derived products flags (failure & quality) Level-1 to Level-2 (common algorithms) observed Lt() , 0,  SeaWiFS L1A - or - MODISA L1B MODIST L1B OCTS L1A MOS L1B OSMI L1A CZCS L1A MERIS L1B OCM L1B observed radiances AOPs Rrs() IOPs a(), bb() Level-2 File Level-2 File Level-2 File spatial averaging temporal averaging masking Level-2 to Level-3 Level-3 Global Product IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  14. Level-2 Flags & Level-3 Masking Level-2 flags used as masks in Level-3 processing IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  15. Proposed Conditions for IOP Product Failure • Rrs < 0 in any required band? • not required for Rrs() minimization • required for matrix inversion (no positive roots in Gordon quad.) • required for band ratio component algorithms (e.g., QAA, HAL) • Failure within model computation • e.g., inputs out of range of LUTs, divide by zero errors • Tests on IOP retrievals (for 400 <  < 600) or Rrs < -() initial proposal employed in some of our global analyses for this workshop IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  16. L2 vs L3 Inversion IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  17. IOP Inversion at Level-2 Standard NASA Approach atmospheric correction Lw normalization derived products flags (failure & quality) Level-1 to Level-2 (common algorithms) observed Lt() , 0,  SeaWiFS L1A - or - MODISA L1B MODIST L1B OCTS L1A MOS L1B OSMI L1A CZCS L1A MERIS L1B OCM L1B observed radiances AOPs Rrs() IOPs a(), bb() Level-2 File Level-2 File Level-2 File Level-2 to Level-3 Level-3 Global Product IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  18. IOP Inversion at Level-3 Alternative Approach atmospheric correction Lw normalization derived products flags (failure & quality) Level-1 to Level-2 (common algorithms) observed Lt() , 0,  SeaWiFS L1A - or - MODISA L1B MODIST L1B OCTS L1A MOS L1B OSMI L1A CZCS L1A MERIS L1B OCM L1B AOPs Rrs() IOPs a(), bb() averaged Rrs() , 0,  Level-2 File Level-2 File Level-2 File Level-3 Global Product Level-2 to Level-3 Level-3 Global Product IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  19. L2 vs L3 Inversion: GSM Model IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  20. L2 vs L3 Inversion: GSM Model bb() < 0.015 mask GSM: Largest differences in eutrophic bb. IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  21. L2 vs L3 Inversion: QAA Model QAA: Largest differences in eutrophic a (band ratio algorithm, mean of ratio not same as ratio of means). IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  22. L2 vs L3 Inversion: PML Model Eutrophic Mesotrophic Oligotrophic a(443) bb(443) PML: differences everywhere (f/Q from mean geometry) IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  23. Model-to-Model Differences Mesotrophic Oligotrophic Eutrophic a(443) bb(443) IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  24. My View • I like simultaneous solutions (class-1) • take advantage of full spectral suite, readily adapted to multiple sensors, easy to incorporate new ideas or alternative basis functions. • I prefer Rrs minimization to matrix inversion • can handle negative Rrs (small Rrs +/- noise) • seems less sensitive to noise (perhaps a weighting issue) • Efficiency in algorithm/inversion selection is not a primary concern • satellite data processing is i/o intensive (exception Boss & Roesler) • Inversion at Level-3 vs Level-2 is not a primary concern • differences between popular models are much greater • Mask all IOP products at Level-3 if: • any one product exceeds valid (TBD) range IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  25. Discuss ... IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  26. Goals • Identification of issues unique to satellite retrieval of IOPs • Understanding of satellite Rrs generation • Agreement on common IOP model inputs (aw & bbw) • Agreement on algorithm failure conditions & masking • Understanding impact of IOP inversion at L2 versus L3 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  27. IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  28. Inversion Method IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  29. Inversion Methods • sequential • model-specific • may be iterative • simultaneous • Iterative cost-function minimization • Levenburg-Marquart (LM) • Downhill Simplex (Amoeba, AMB) • Matrix inversion • Lower-Upper Decomposition (LUD) • Singular-Valued Decomposition (SVD) A x = b IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  30. Rrs Minimization vs Matrix Inversion a(443), 6-Band GSM Model, SVD Fit a(443), 6-Band GSM Model, LM Fit a(443), 5-Band GSM Model, SVD Fit a(443), 5-Band GSM Model, LM Fit 5-Band = 412,443,490,510,555 a(443) 0.01 1.0 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG 6-Band = 5-Band + 670

  31. Matrix Inversion: Linearization Issue Rrs[0-] = g1 u + g2 u2 where u  bb/(a+bb) - or - Rrs[0-] =  f/Q u 1) Traditional Approach: System of Equations Proportional to 1/Rrs where v = 1 - 1/u a = -v bb aph() + adg ( + v bbp() = -[aw() + v bbw()] 2) Alternate Approach: System of Equations Proportional to Rrs u a = (1 - u) bb u aph() + u adg ( + (u-1) bbp() = -[u aw() + (u-1) bbw()] IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  32. Alternate Linearization Improves Inversion Consistency Linearization Method 1 a(443) GSM 6-Band a(443) Global bb(443) Global LM LM SVD SVD Linearization Method 2 a(443) GSM 6-Band a(443) Global bb(443) Global LM LM SVD SVD IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  33. Matrix Inversion Still Missing Highs in a & bb NOMAD aph(443) Eutrophic Waters, 5-Band GSM LM vs SVD SVD Amoeba 6-Band a(443) 5-Band 4-Band bb(443) 3-Band IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  34. Uncertainties IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  35. Standard Deviation of Rrs DistributionSeaWiFS March 2005 443 412 490 510 555 670 0.005 0.0 reflectance units IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  36. Misc IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  37. Trophic Subsets Deep-Water (Depth > 1000m) Oligotrophic (Chlorophyll < 0.1) Eutrophic (1 < Chlorophyll < 10) Mesotrophic (0.1 < Chlorophyll < 1) IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  38. Salinity IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  39. ap* I used the Bricaud function to compute aph* for 25 chl concentrations between 0.05 and 3 (evenly distributed in log space), then computed the average spectra and spit out the 10nm wide aph* values for SeaWiFS wavelengths: In the attached plot, the average aph* spectra is the white line, the 10nm (wvl-5 <= wvl < wvl+5) version is in blue, and GSM is in red. IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  40. Model Differences: Global View IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  41. QAA vs 6-Band GSM: a(443) & a(555) Eutrophic Mesotrophic Oligotrophic GSM 443 QAA 555 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG 1.05 aw

  42. QAA - GSM: a(443) & a(555) Eutrophic Mesotrophic Oligotrophic 443 555 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  43. QAA vs 6-Band GSM: bb(443) & bb(555) Eutrophic Mesotrophic Oligotrophic GSM GSM 443 QAA QAA 555 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

  44. QAA - GSM: bb(443) & bb(555) Eutrophic Mesotrophic Oligotrophic 443 555 IOP Algorithm Workshop, Ocean Optics XIX, 3-4 Oct 2008, B. Franz, NASA/OBPG

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