Inverse modeling Semi-analytical algorithms : sensitivity analysis of the values of the backscattering spectral dependency n and the a_nap&cdom slope S.

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Presentation transcript:

Inverse modeling Semi-analytical algorithms : sensitivity analysis of the values of the backscattering spectral dependency n and the a_nap&cdom slope S on two stations Maéva DORON, Ocean Optics 2004

To begin with Roesler and Perry ’95 model of inverse modeling 3 basis vectors in input: 1.spectral slope of the absorption of NAP and CDOM S (DL); 2.spectral slope of backscattering n in nm -1 ; 3.spectral absorption of chlorophyll (normalized). 3 parameters in output : 1. chl in mg.m -3 ; 2.a_cdom&nap (440nm) in m -1 ; 3.bb(440nm) in m -1. Data from the second cruise: one station off the DMC dock and one station near the ocean.

Goal First idea Comparison of the data retrieved by the GSM model and the R&P95 model (hyperspectral & multispectral at 412, 442, 490, 510 and 555 nm); Sensitivity to S and n to the retrieved values Comparison of the sensitivities for the hyperspectral and the multispectral model

Original values Station A – Off of DMC Dock a_phiSn/η [chl],ag(443) + anap(443) bbp(443) GSM – Maritorena et al GSM Hyper RPCR avg Hyper RPCR avg GSM lambda CR avg

Original values Station B – SW of Pemaquid Pt. a_phiSn/ η [chl],ag(443) + anap(443) bbp(443) GSM – Maritorena et al GSM Hyper RPCR avg Hyper RPCR avg lambda CR avg

Sensitivity One spectral dependency n,instead of 0 for large particles and 1 for small particles. Varying the n – values : from 0 to 2. Change the values of S : from to 0.04 nm -1 : Realistic range of Scdom = 0.007–0.013nm -1 Realistic range of Snap = 0.016–0.022nm -1

Sensitivity for the n(bb) slope - hyperspectral

Sensitivity for the S(cdom&nap) slope - hyperspectral

Sensitivity for the n(bb) dependency - multispectral

Sensitivity for the S(cdom&nap) slope - multispectral

Sensitivity for the S(cdom&nap) slope – multispectral- different scales

Discussion This was a first qualitative approach. The results are encouraging because for realistic values of S are not erratic. Any idea about how to test the robustness of the algorithm with multispectral data?