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Comparisons of ocean colour merged data sets Stéphane Maritorena ICESS University of California, Santa Barbara Special thanks to Odile, Antoine and the.

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Presentation on theme: "Comparisons of ocean colour merged data sets Stéphane Maritorena ICESS University of California, Santa Barbara Special thanks to Odile, Antoine and the."— Presentation transcript:

1 Comparisons of ocean colour merged data sets Stéphane Maritorena ICESS University of California, Santa Barbara Special thanks to Odile, Antoine and the ACRI-ST Team

2 OUTLINE Current Ocean Colour merged data sets and associated merging techniques and products. Coverage Time-series Matchups Frequency distributions Error estimates

3 Ocean Color Merged Data Sets REASoN (ICESS/UCSB) NASA OBPG GlobCOLOUR

4 MERGED DATA SETS REASoNNASA OBPGGlobCOLOUR Input Data SeaWiFS MODIS-AQUA SeaWiFS MODIS-AQUA MERIS SeaWiFS MODIS-AQUA Merging method GSM01 model (merges the Lwn( )) Weighted averageGSM01 model (Lwn( ) weighting) Weighted average Products CHL CDM BBP (+ uncertainties) CHL19 products (+ uncertainties for some) Spatial, temporal resolution 9 km Daily, 4-Day, 8- Day, Monthly 9 km Daily, 8-Day, Monthly, Yearly 4.5, 1/4, 1 Daily, 8-Day, Monthly

5 Daily coverage using SeaWiFS/Aqua and Meris (2003 data) GlobCOLOUR NASA REASoN

6 Sensor(s)Coverage (%)Std. Dev. (%) SeaWiFS Aqua Meris SeaWiFS/Aqua SeaWiFS/Meris Aqua/ Meris SeaWiFS/Aqua/Meris Ocean color sensors average daily coverage (2003 data)

7 Days FREQUENCY OF COVERAGE (2005) Merged SeaWiFSAqua

8 MERIS AQUA SeaWiFS Relative contribution of each sensor to an 8-Day composite

9 THE 30 ZONES

10 Time-series CHL - Monthly data (Contd)

11

12 Time-series - CDM - Monthly data

13 Time-series BBP - Monthly data

14 Issues with the particulate backscattering product Meris issue Mostly a SeaWiFS issue Noise in the SeaWiFS Lwn( ) around gaps caused by clouds. In these areas, the Lwn( ) are sometimes higher than in nearby gap-free areas and this results in enhanced b bp (443) values. Problem does not exist in Aqua data MERIS shows some stripes of high BBP values on some swaths.

15 Matchups - CHL In situ data set (NOMAD + SeaBASS) with CHL: ~3100 stations CDM: ~700 stations BBP: ~180 stations Same day matchups, 3x3 box, 9 km data (4.5 km for GlobCOLOUR).

16 GlobCOLOUR REASoN CDM BBP

17 FREQUENCY DISTRIBUTION - CHL - 50N-50S Deep Water

18 FREQUENCY DISTRIBUTION - CDM - 50N-50S Deep Water

19 FREQUENCY DISTRIBUTION - BBP - 50N-50S Deep Water

20 Monthly Chlorophyll May 2006

21 Error estimates at pixel level (%) Chla – May % 50% 0%

22 Summary The GlobCOLOUR data set provides better daily coverage thanks to the use of the MERIS data. GlobCOLOUR also offers more products and several resolutions. CHL The three merged data sets are very consistent most of the time, except in coastal zones (Z < 1000 m). MERIS alone tends to produce higher CHL values than SeaWiFS or AQUA. AQUA alone tends to produce lower CHL values than SeaWiFS or MERIS

23 Summary (Contd) CHL (Contd) The REASoN and GlobCOLOUR (GSM) data contain less low CHL values than the NASA OBPG data. Also true for REASoN at high CHL values but better agreement in summer. CDM The agreement between the GlobCOLOUR and REASoN merged CDM products is excellent, always, everywhere. BBP SeaWiFS and MERIS BBP products are sometimes very (too) high. MODIS-AQUA BBP product appears more stable and reasonable.

24 Conclusion The three merged data sets look good and are in good agreement overall. Better agreement between the merged products than between the products from the individual sensors. Some issues exist (BBP mostly) that need to be looked at.


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