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G. Volpe 1 , R. Santoleri 1 , S. Colella 1 , C. Tronconi 1 , S. Marullo 2

Roma 23-24 April. The colour of the Mediterranean Sea: global versus regional bio-optical algorithm evaluation and development of regional chlorophyll dataset. G. Volpe 1 , R. Santoleri 1 , S. Colella 1 , C. Tronconi 1 , S. Marullo 2.

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G. Volpe 1 , R. Santoleri 1 , S. Colella 1 , C. Tronconi 1 , S. Marullo 2

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  1. Roma 23-24 April The colour of the Mediterranean Sea: global versus regional bio-optical algorithm evaluation and development of regional chlorophyll dataset G. Volpe1, R. Santoleri1, S. Colella1, C. Tronconi1, S. Marullo2 1 Istituto di Scienze dell'Atmosfera e del Clima (ISAC), Roma, Italy 2 Ente per le Nuove tecnologie l'Energia e l'Ambiente (ENEA), Frascati, Italy Volpe et al (2007) Remote Sensing of Environment g.volpe@isac.cnr.it – http//gos.ifa.rm.cnr.it

  2. Outline • Background + Aims • In situ dataset • Validation of existing SeaWiFS algorithms ( global and regional) • SeaWiFS new MED Algorithm • Algorithms’ implementation and SeaWiFS data reprocessing • Global versus Regional (MED) domains • MODIS & MERIS MED Algorithms • SeaWiFS - MERIS - MODIS intercomparison • Conclusions

  3. Mediterranean Ocean Color CAL/VAL DATA SETS 28 Mediterranean cruises: from 1997 up to now were organized by ISAC, SZN, ENEA in the framework of Italian National Projects + 2 permanent stations Bio-optical measurements: 155 chl/opt measurements to define the Mediterranean regional algorithm (red points) Optical measurements: 938 SIMBADA for Rrs validation (blue points) In situ chlorophyll-a data: 1144 chlorophyll profiles for satellite data validation

  4. Aims • Validate existing (global & regional) algorithms with an independent dataset • Develop an optimal algorithm for the MED • Provide a regional SeaWiFS reanalysis from 1997 to 2006 • Develop regional operational products for the Mediterranean Sea • Figure out why global algorithms fail in the MED Background • STANDARD algorithms fail in the MED • REGIONAL algorithms have provided better results, but… • In situ datasets NOT fully representative • LACK of satellite validation

  5. N = 156 ALGORITHMS’ INPUT Blue / Green Bio-optical measurements: 156 Chl/Opt measurements to define the Mediterranean regional algorithm Chlorophyll a [mg m-3] ALGORITHMS’ OUTPUT In situ dataset In situ Chlorophyll a data: 1144 chlorophyll profiles for satellite data validation

  6. Global Mediterranean Regional OC4v4 DORMA BRIC O’Reilly et al (2000) D’Ortenzio et al (2002) Bricaud et al (2002) Validation of existing OC algorithms Bio-optical dataset application to existing algorithm

  7. Comparison between all algorithms BRIC DORMA OC4v4 MedOC4 New MED Algorithm – MedOC4 Aligned to 1:1 MedOC4 Volpe et al (2007) BIAS is independent from Chlorophyll value What about the application to SeaWiFS data?

  8. Still overestimation for low Chl values Underestimation for high Chl values Algorithms’ implementation on SeaWiFS data OC4v4 BRIC DORMA MedOC4 NO BIAS NO DEPENDENCE to the Chl value

  9. Algorithms’ implementation on SeaWiFS data BRIC DORMA OC4v4 MedOC4 MedOC4 is the most stable & performing algorithm Why then the standard algorithm fails in the MED?

  10. Global OC4v4 MedOC4 Regional Is the MED “greener or less blue” then the GLOBAL ocean ? Global vs Regional domains Blue / Green Blue / Green Blue / Green Chlorophyll Chlorophyll Chlorophyll

  11. - Blue (30%)+ Green (15%) - Blue (35%)+ Green (18%) - Blue (32%)Green similar Med and Global overlap +Blue (23%)- Green (35%) Global vs Regional domains

  12. The SeaWiFS reanalysis from 1997 to 2006: reprocessing of the entire Mediterranean Sea L1A archive using MedOC4 Algorithm Available at the MERSEA web site

  13. WN:Node01.. ….Node16 SeaWiFS Re-PROCESSING ON ESA-CNR GRID infrastructure 1) Globus-job-submit UI CE 2) Globus-job-status Gridtest03.esrin.esa.int Grid0007.esrin.esa.int 3) Globus-job-get-output Globus-url-copy Globus-url-copy SE UI=User Interface SE=Storage element CE=Computer Element WN=Worker node Se0.artov.rm.cnr.it N° SeaWiFS pass= 6127 Total processing time ~ 4 days

  14. The GRID products output Previous algorithm NEW algorithm Difference OLD-NEW 2 July 2004 0.01 5 0.01 5 0 -0.1 0.2 OC4.V4 Med OC4 (OC4.V4) – (Med OC4)

  15. 1999 yearly average OC4.V4 Med OC4 (OC4.V4) – (Med OC4)

  16. Impact on Primary Productivity Global Model + Chl(OC4) Global Model + Chl(BRIC02) Global Model + Chl(MedOC4) The use of MedOC4 chlorophyll in PP Models reduces the annual PP estimate by about 40% and 10% respect to corresponding estimate made using OC4 and BRIC02

  17. Ctot Csat Ze Ctot Other MED Optical characteristic Antoine and Morel, 1996 (yellow) Morel and Berthon, 1989 (red) Uitz et al., 2006 (green) Colella 2006 (blue) Morel and Berthon, 1989 (red) Morel and Maritorena, 2001 (green) Colella 2006 (blue)

  18. Impact on Primary Productivity: global vs regional PP model (a) Global Model + Chl(OC4) (b) Global Model + Chl(BRIC02) (c) Global Model + Chl(MedOC4) (d) Regional Model + Chl(BRIC02) (e) Regional Model + Chl(MedOC4) MAX PP in SPRING

  19. These results suggest that sensors other than SeaWiFS (MODIS or MERIS) may be affected by the same uncertainties as their OC algorithms have been developed using the same source datasets as for SeaWiFS Therefore we applied the MED bio-optical dataset to MERIS & MODIS

  20. OC4ME MedOC4ME New MED Algorithm for MERIS – MedOC4ME MERIS OC4ME Standard MERIS MedOC4ME MED

  21. OC3 MedOC3 New MED Algorithm for MODIS – MedOC3 MODIS OC3 Standard MODIS MedOC3 MED

  22. April 2004 July 2004 MERIS MODIS SeaWiFS SeaWiFS April 2004 July 2004 MODIS MERIS SeaWiFS SeaWiFS SeaWiFS – MODIS – MERIS intercomparison MODIS vs SeaWiFS MERIS vs SeaWiFS Regional Regional Global Global

  23. Conclusions • MED algorithms perform better than any other algorithm in the MED • Improving model estimates of primary productivity • Improving the quality of ecosystem models which assimilate satellite Chl (MFS) • Peculiar MED bio-optical properties need further investigation • with field campaigns • more refined bio-optical • measurements (IOPs) Levantine Basin Sep 2006 • Good agreement between different sensors (DATA MERGING) THANKS

  24. MBR validation. (2003-2004) MODIS MERIS SeaWiFS

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