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Results for Garver-Siegel-Maritorena semi-analytic model (GSM01) when applied to NOMAD dataset.

Results for Garver-Siegel-Maritorena semi-analytic model (GSM01) when applied to NOMAD dataset.

jmcmillen
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Results for Garver-Siegel-Maritorena semi-analytic model (GSM01) when applied to NOMAD dataset.

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  1. Results for Garver-Siegel-Maritorena semi-analytic model (GSM01) when applied to NOMAD dataset. Figures 1-6. The GSM01 model was “linearized” and the values of chl, acdm(443), and bbp(443) were obtained by a least-squares fit (matrix inversion) applied to the Rrs values in bands 1-5. (For details see separate Readme file)

  2. In a paper that will soon appear in GRL, Siegel et al. applied the GSM01 algorithm to global SeaWiFS data and compared the derived chlorophyll distributions with OC4 chlorophyll maps. They found that the “normalized percent difference” between the two chlorophylls had a distribution similar to that of the derived acdm distribution. That is, when OC4 >> GSM chlorophyll, the acdm was high (see two bottom panels in their figure 1 below).

  3. This result is consistent with the results described in the forthcoming GRL article (Siegel et al. 2005).

  4. Chlorophyll derived by inverting the “linearized” GSM01 model did not agree well with the in situ chlorophyll (Fig. 1). I sent these results to Stephane for his comments. He inverted the GSM01 model using a nonlinear optimization method (He gave me a copy of his code) and got much better results (see next 2 slides).

  5. n: 2189 int: -0.330 slope: 0.614 R2: 0.808 rms: 0.395 bias: -0.235 Nonlinear inversion Linearized model (as in fig. 1)

  6. The conclusion is that the method of inverting the GSM01 model is important! The linearized method (the one I used) has been used by others, e.g., Hoge and Lyon (JGR, 1996), and more recently, Wang et al. (Applied Optics, 2005). Apparently the nonlinear optimization method is better – so we should make sure to get this point across, maybe even figure out why.

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