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Design Justification v2 overview Samantha Lavender

Design Justification v2 overview Samantha Lavender. Work Packages. 15 Jan 2006. CDR. Design Justification v2. 5.2 In situ Characterisation 5.3 Coastal Waters 5.4 Sensor Cross Characterisation 6.3 Merging Algorithm Sensitivity Analysis.

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Design Justification v2 overview Samantha Lavender

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  1. Design Justification v2 overview Samantha Lavender

  2. Work Packages 15 Jan 2006 CDR

  3. Design Justification v2 • 5.2 In situ Characterisation • 5.3 Coastal Waters • 5.4 Sensor Cross Characterisation • 6.3 Merging Algorithm Sensitivity Analysis

  4. In situ characterisation Samantha Lavender and Yaswant Pradhan

  5. Characterisation review • In situ data • MERIS • MODIS • SeaWiFS • Parasol: data not available at present for characterisation • Overall Conclusions

  6. In situ data sets • NOMAD (2002 onwards) • Publicly available SeaBASS (2002 onwards) • NILU database • Boussole buoy

  7. On-line Database

  8. SeaBASS NOMAD NILU Spatial Coverage

  9. GlobCOLOUR nLw 412 443 490 510 555 670 OBPG nLw NOMAD In Situ Data Conversion to Fully Normalised Water Leaving Radiance

  10. L2 (M)LAC In-situ meta Preparation/Generation Extraction Statistics/Result L3-DDS Generator In-situ data In GC-NOMAD template L3 DDS L3-DDS Reader GC in-situ Reader Match-up Result NO match-up Timediff <24 hrs Y DDS Match-Up Tdiff< 24 hrs FLAG !=NoData Tpix > 5 N N Locationdiff <=0.02° Y Exclude NO match-up N Import to Excel Stat Template Extract 3x3 kernel Data Processing

  11. Data Processing Number of generated DDS

  12. MERIS

  13. MERIS

  14. MODIS

  15. MODIS

  16. SeaWiFS

  17. SeaWiFS

  18. SeaWiFS

  19. Normalised Water Leaving Radiance • Further discussion and analysis is occurring with respect to the derivation of in-situ normalised water leaving radiances as this is a key step in the characterisation process. • Propose that this work should be ongoing and the characterisations will be updated as additional insitu data becomes available. • The results presented so far indicate that it is particularly important to seek out datasets with high normalised water leaving radiances.

  20. Chlorophyll & GlobCOLOUR Kd

  21. DJF V2.0 Coastal Waters Kai Sørensen and Jo Høkedal

  22. Coastal waters - Guianas CoastMERSEA-IP • The provinces, Guianas Coastal (GUIA) and Guinea Current Coastal (GUIN) are both coastal stripes influenced by land and river inputs. • On the African side (GUIN) there is also a strong impact of atmospheric conditions (cloud coverage, biomass burning and desert dust aerosols) on the ocean colour products. • The two provinces are characterized by the largest differences of the provinces (in this study) between sensor products. • Between SeaWiFS and MODIS–Aqua the differences (defined as the root mean square relative difference) was as high a 21.3 % and 24.7 % on average for GUIA and GUIN, respectively. • The differences compared to MERIS are 3-4 % higher.

  23. Coastal water - Baltic SeaMERSEA-IP and FerryBox-EU • An optically complex water with a high load of CDOM, and summer blooming of Cyanobacteria causing large changes in the IOPs. • An average difference of MERIS vs SeaWiFS or MODIS-Aqua of around 25%, while between SeaWiFS and MODIS-Aqua of 19.2 %. • MERIS Algal_1 and Algal_2 show erroneous data in the bloom, but Algal_2 after the 2nd processing gave better agreement. • Even if the MERIS Neural Network Case 2 products can be trained for this area it will be problematic due to the high IOP variability. • The validation will also be a challenge during such extreme blooms.

  24. North Sea – Skagerrak Case1 Chl-a Algorithms, Folkestad, 2005 SeaWiFS vs MODIS/Aqua SeaWiFS vs MERIS MODIS/Aqua vs MERIS

  25. MERIS Skagerrak (2nd processing)Sørensen, 2006. MERIS Algal_2 binned one month vs Chl-a fluorescence from the Ferrybox systems (+/- 1. Stdev.dev.) MERIS Algal_2 vs Chl-a_HPLC Danish Coast Central Skagerrak Oslo Fjord

  26. Coast and Open Sea – Spatial variability Vertical bars: Max-min Vertical bars: Max-min

  27. Summary • It is clear from the findings by many authors that SeaWiFS and MODIS do not resolve the true values in Case 2 water and that multivariate complex Case 2 waters need to have complex algorithms e.g. MERIS NN. • It is presently difficult to give any recommendation on how to solve the issue of combining data from different sensors in coastal water without dealing with all the Case 2 problems. • The only combining possibilities is then to merge MERIS Case 2 products with Case 1 products, but boundaries will probably be present. • Alternative are to use Case 1 algorithms into the coast and flag Case2 water. To be discussed.

  28. Sensor Cross Characterisation Antoine Mangin

  29. Cross characterisation Cross comparison between MERIS/MODIS/SeaWifs – attempt to detect systematic biases: At global scale and regional scale Check of the consistency with JRC results Harmonisation of Kd algorithm

  30. Cross comparison between MERIS/MODIS/SeaWifs – attempt to detect systematic biases: At global scale and regional scale comparison

  31. Summary for Mediterranean Slope of the regression Mediterranean March 12 03 06 09 Determination coeff. r2 12 03 06 09

  32. 12 12 03 03 06 06 09 09 Summary for Global results Slope of the regression Global Determination coeff. r2

  33. Confrontation with other sources From JRC’s assessment: Global Regional: very fluctuant, seasonal dependency – in agreement with our daily results There is a bias between sensors

  34. Not mature enough Recommended Either…. …or…. We get a faithful caracterisation of bias wrt season and region and correct for it prior to merging. We anticipate the impact of using biased data. We apply inputs as is. The impact will be reflected into the error bar estimates wrt to season/region

  35. Harmonisation of Kd algorithm Kd

  36. Overall Conclusions • Used some large databases and produced a large number of DDS files (1387), but as is often the case with ocean colour data the number of match-up points is considerably smaller than the number of original insitu points. • The characterisation will undergo additional work within the next couple of months to tie up the loose ends and come to a final set of conclusions. • For now the merging will use the following characterisation results: • normalised water leaving radiance: GlobCOLOUR • chlorophyll: NASA (will split GlobCOLOUR into low/high groupings) • diffuse attenuation coefficient: GlobCOLOUR • For Case 2 waters, a decision on the alternatives of using (1) MERIS Case2 products for the coast or (2) using Case1 products only with flagging information must be taken.

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