1 / 18

Quantify IOP quality derived from QAA

Quantify IOP quality derived from QAA. ZhongPing Lee 1 , Robert Arnone 2 1 Northern Gulf Institute, Mississippi State University, Stennis Space Center, MS 39529; zplee@ngi.msstate.edu 2 Naval Research Laboratory, Stennis Space Center, MS 39529.

faunia
Télécharger la présentation

Quantify IOP quality derived from QAA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Quantify IOP quality derived from QAA ZhongPing Lee1, Robert Arnone2 1Northern Gulf Institute, Mississippi State University, Stennis Space Center, MS 39529; zplee@ngi.msstate.edu 2Naval Research Laboratory, Stennis Space Center, MS 39529

  2. Q: How big is the error (or confidence) of inverted IOPs at each pixel? “Average error of an algorithm is insufficient to describe the error of specific locations.” -- Boss et al We should not expect the same error bars for the three pixels.

  3. The Quasi-analytical algorithm (QAA) Rrs() The three stating points: Y (Lee et al. 2002)

  4. If Rrs is error free, and the relationship between Rrs and {bb,a} is accurate, quality of inverted a&bb by QAA depends on the estimation of a(λ0) and Y.

  5. A “Hydrolight” data set a440 distribution a550 distribution bbp440 distribution Y distribution

  6. QAA inverted a440&bbp440 vs known a440&bbp440 -- when a550 and Y are known exactly. a440inv [m-1] bbp440inv [m-1] bbp440known [m-1] a440known [m-1] Indication: the formulation and scheme can get a closure!

  7. QAA inverted a440 vs known a440 -- impact of imprecise a550; Y is known exactly a550 is 10% higher a550 is 10% lower a440inv [m-1] a440known [m-1] Indication: nearly the same impact to all a440 values.

  8. QAA inverted a440 vs known a440 -- impact of imprecise Y; a550 is known exactly Y is 0.4 higher Y is 0.4 lower a440inv [m-1] a440known [m-1] Indication: nearly the same impact to all a440 values.

  9. QAA inverted a440 vs known a440 -- compounding effects of imprecise Yand imprecisea550 Y is 0.4 higher and a550 is 10% higher Y is known; a550 is 10% higher a440inv [m-1] a440known [m-1] Indication: more errors!

  10. QAA inverted a440 vs known a440 -- compensation between imprecise Yand imprecisea550 Y is 0.4 lower; a550 is 10% higher Both Y and a550 known a440inv [m-1] a440known [m-1] Indication: nearly compensating each other.

  11. Q: 1. How big is the a550 error for different a550? 2. How this error propagates to other IOPs? Error at a550 (εa550) vs estimated a550 εa550 Global ocean a550inv [m-1]

  12. Error at a440 vs compound error of a550 and Y (±0.2, ±0.4) δa440 εa440 Error at a440 Error at a550 (εa550) δ: percentage error at 84th percentile or likelihood range

  13. Error at a410 vs compound error of a550 and Y (±0.2, ±0.4) δa440 εa440 Error at a410 Error at a550 (εa550) δ: percentage error at 84th percentile

  14. Data flow to quantify quality of inverted IOP : Rrs aλ0ελ0εIOP & δIOP Note: No Rrs error considered yet; but can be added when quality measure of Rrs is known.

  15. Conclusion: • Qualities of IOPs derived by QAA, in addition to quality of Rrs, rely on the estimation of a(λ0) and Y. • The qualities at each pixel, measured by projected average error and projected likelihood range, can be quantified by evaluating the error propagation in the QAA process. • Higher qualities for IOPs of oceanic waters, as both a(λ0) and Rrs possess higher reliability.

  16. Acknowledgement: The supported from NASA Ocean Biology and Biogeochemistry Program is greatly appreciated.

  17. aw values dominate the longer wavelengths, so a(λ0) can be estimated reasonably well. a550 [m-1] a440 [m-1]

More Related