1. A semi-analytical ocean color inherent optical property model: approach and application. Tim Smyth, Gerald Moore, Takafumi Hirata and Jim Aiken Plymouth Marine Laboratory, UK

2. Overview Model description Implementation Validation Sensitivity study - summary Application to satellite data Further work

3. 1) Model description Morel (1980): “ … the inverse system can be theoretically solved using simultaneous equations, if a spectral law is assumed for backscattering.” –Not implemented (or implementable!) for either in situ or satellite data Sugihara & Kishino (1988) and Roesler & Perry (1995): implemented schemes for in-water reflectance data –Not scaled up to satellite data (problems with Q) We have developed a scheme using simultaneous equations: solved using empirically derived spectral slopes in combination with radiative transfer modelling. Interface terms (Fresnel and f/Q): angular and IOP dependency Total absorption: a ph (λ), a d (λ), a y (λ) Two unknowns of b bp (λ) and a(λ): therefore require two equations … achieve this using two neighbouring wavelengths (i, j) and empirically derived spectral slopes.

4. Simultaneous equations 1) Model description Spectral slope in total absorption Spectral slope in total backscatter solve equations simultaneously for a(j) and b bp (j) – nasty maths! then can solve for a(i) and b bp (i) using spectral slopes. need to work out which wavelength pairing to use for spectral slopes – based on empirical data.

5. 1) Model description Spectral slopes from COLORS dataset (predominantly coastal stations) ε a (490,510) converged to narrow range of values with low σ ε a (490,510) = 1.268; ε bb (490,510) = 1.0202 Is this because only 20 nm difference between bands? observationally: chlorophyll-a has greatest effect between 400 – 470 nm; minor effect between 490 and 510 nm. N=216

6. 1) Model description Once have a(490,510) and b bp (490,510), then use assumed shape of backscatter to extrapolate to other wavelengths: can then work out the entire spectrum of a(λ) bio-geochemical parameters of a dy (λ) and a ph (λ) can be determined using spectral slope method ε dy (412,443) and ε ph (412,443) selected as they are distinct with low variance. Used in combination with standard CDOM exponential function to extrapolate to other wavelengths.

7. 2) Implementation

8. 3) Validation Validation using NOMAD in situ dataset: –Points selected on basis that each entry contained ρ w (SeaWiFS); a(λ); a ph (λ) and a dy (λ); –439 data points met this criterion; –88 data points for b bp (λ). –Comparison with Lee et al. (2002) model

9. 3) Validation: Total absorption (PML model), a(λ) R 2 : 0.835 RMS: 0.192 R 2 : 0.851 RMS: 0.161 R 2 : 0.840 RMS: 0.202 R 2 : 0.819 RMS: 0.118 R 2 : 0.637 RMS: 0.148 R 2 :0.061 RMS: 0.362 N=418 Signal to noise ratio? Raman scattering? Good retrievals over 2 orders of magnitude

10. 3) Validation: Total absorption (Lee model), a(λ) R 2 : 0.549 RMS: 0.362 N=439 R 2 : 0.464 RMS: 0.376 R 2 : 0.206 RMS: 0.415 R 2 : 0.006 RMS: 0.435 R 2 : 0.509 RMS: 0.475 R 2 : 0.556 RMS: 0.308 Limitation at higher absorption

11. 3) Validation: Total backscatter, b b (λ) N=88 R 2 : 0.400 RMS: 0.148 R 2 : 0.395 RMS: 0.148 R 2 : 0.387 RMS: 0.192 R 2 : 0.383 RMS: 0.217 R 2 : 0.375 RMS: 0.270 R 2 : 0.354 RMS: 0.390 Increasing bias with increasing λ: problem with assumed spectral shape?

12. 3) Validation: CDOM absorption, a dy (λ) R 2 : 0.568 RMS: 0.507 R 2 : 0.532 RMS: 0.500 R 2 : 0.477 RMS: 0.516 R 2 : 0.453 RMS: 0.519 R 2 : 0.406 RMS: 0.538 R 2 : 0.313 RMS: 0.595 Noisy at low a dy (λ): possible measurement error?

13. 3) Validation: phytoplankton absorption, a ph (λ) R 2 : 0.666 RMS: 0.298 R 2 : 0.759 RMS: 0.230 R 2 : 0.744 RMS: 0.263 R 2 : 0.677 RMS: 0.358 R 2 : 0.394 RMS: 0.857 R 2 : 0.099 RMS: 0.178 Problems with retrievals at 555 and 670

14. 4) Sensitivity study - summary a(λ) and b b (λ): –Model most sensitive to ε a (490,510) NOMAD 1.317 cf. COLORS 1.268 –Relatively insensitive to ε bb (490,510) NOMAD 1.040 cf. COLORS 1.0202 a dy (λ) and a ph (λ) –Most sensitive to ε ph (412,443) NOMAD 1.065 cf. COLORS 0.954 –Relatively insensitive to ε dy (412,443) and S NOMAD 1.638 cf. COLORS 1.579

15. 5) Application to satellite data Implemented on HRPT and GAC SeaWiFS imagery using an Intel Xeon 1.8 GHz processor: –15 mins proc HRPT –1.5 mins process on GAC entire orbit

16. Phytoplankton – fine eddy structure SedimentCoccolithophoresCDOMbloom? Clear blue ocean 18 May 1998 13.14 GMT“True color” composite - qualitative

17. a)a(443): high values (0.2 – 0.5 m -1 ) in coastal seas; ca. 0.3 m -1 in bloom. b)a dy (443): coastal seas dominated by CDOM; c)a ph (443): phytoplankton bloom off W. Ireland; d)b bp (555): Emiliana huxleyi bloom in Western Approaches (in situ confirmed this) IOP model allows us to quantify these features. a(443) b bp (555)a ph (443) a dy (443)

18. 10 Oct 2002 10.26 GMT a(443)a ph (443)a dy (443) BENCAL experiment (October 2002) upwelling combination of a dy and a ph ; offshore bloom dominated by a ph South Africa Namibia

19. 5) Further work 490:510 pairing used for SeaWiFS / MERIS –Develop 488:532 spectral slopes for use with MODIS 412:443 pair for a dy and a ph subject to ρ w (412) problems (atmospheric correction); could use 443:490 pairing instead IOP models can form building block for many applications: –Determination of phytoplankton functional types; –Data assimilation into process oriented models: address rate equations, cf. chlorophyll which is a model derived variable; –Primary production modelling without recourse to chlorophyll