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Data merging benefits Antoine Mangin, Stéphane Maritorena

Data merging benefits Antoine Mangin, Stéphane Maritorena Session 4 –GlobColour applications – November 21, 2007. for the users. Background. Data merging benefits ? Improvement of spatial/temporal coverage Error bar estimates Trends analysis. For the benefit of every user.

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Data merging benefits Antoine Mangin, Stéphane Maritorena

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  1. Data merging benefits Antoine Mangin, Stéphane Maritorena Session 4 –GlobColour applications – November 21, 2007 for the users

  2. Background • Data merging benefits ? • Improvement of spatial/temporal coverage • Error bar estimates • Trends analysis For the benefit of every user For assimilation into models (but also to understand reliability of products) For the benefit of reliable environmental reporting and carbon cycle studies

  3. Outlines • Data merging • Improvement of spatial/temporal coverage • Error bar estimates • Trends analysis It is a truism.

  4. Improvement of spatial/temporal coverage

  5. Outlines • Data merging benefits ? • Improvement of spatial/temporal coverage • Error bar estimates • Trends analysis It requests a careful analysis of error estimates as inputs of GSM

  6. Estimates of the uncertainties on input LwN Estimates of the error model Estimates of the uncertainties on outputs Chla, bbp,cdm (Co-variance matrix between all ingredients) Error bar estimates

  7. Error bar estimates Estimates of the uncertainties on input LwN This is a direct result from the characterisation at sensors level (GC phase 1 ++)

  8. Error bar estimates Estimates of the full uncertainties as input of GSM

  9. Error bar estimates Discussion Interest: Relative importance (weight) of each wavelength in the inversion is a key element Assumption: Main assumption is that there is no bias – input error is considered as a pure deviation defined by its standard deviation Future: When we will reach the million match up points (or maybe before) – we should go to error estimates by classes of LwN

  10. Nomad DB / EO data Extract samples of concomitent LwN, Chla bbp and cdom samples Use of the estimates If the Chla error estimates is reliable should be close to a standard normal distribution Estimates of the uncertainties on outputs Chla, bbp,cdm (Co-variance matrix between all ingredients) Error bar estimates - Validation

  11. Inputs: In situ observations (Nomad) Results: very close to expectancy – no significant bias Inputs: GC products Results: very close to expectancy – a small bias is detected – the error estimates by GSM (with ad hoc inputs) is slightly underestimated. Error bar estimates - Validations

  12. GlobColour Chla-merged product – May,15 2006 GlobColour Chla-merged product relative uncertainties – May,15 2006 100 50 0 Example of products uncertainties - daily

  13. GlobColour Chla-merged product – May 2006 GlobColour Chla-merged product relative uncertainties– May 2006 100 50 0 Example of products uncertainties

  14. Outlines • Data merging benefits ? • Improvement of spatial/temporal coverage • Error bar estimates • Trends analysis Differences between individual sensor time series for each sensors may (will?) lead to disturbances in merged time series. One aspect which is however not yet fully exploited is the correlation between individual sensor products which is rather good.

  15. Background Context for this trend analysis: EEA reporting In the frame of GSE Marcoast, reliability of EO to help environmental reporting is explored as well as consistency between missions to ensure continuity of the reporting. Today the reporting is based on in situ observation and the metric for trend identification is, for a given area, the number of stations that have showed a significant increase/decrease of observed Chla (*) during the last 10 years. (*) Observed Chla is an average seasonal value built on a very strict protocol.

  16. … and thus the report Background – ingredients for reporting About 6800 Chla samples in 2003-2005 14 eco-regions Within Marcoast we are working to replace/complement in situ sampling by EO (and here more specifically by GlobColour)

  17. Trends analysis Important distinction: We are not trying here to quantify trends but to identify the probable ones. Method used Setting up of a non parametric test for detection of trends at GC pixel level. The test is based on summation of sign of difference between one status and the previous ones (eg. season 2005 compared to 2004, 2003 etc..) Statistical variance s2 of a white noise on such times series is analytically known. So … any departure above (resp. below) 2s (resp. –2s) from this law would indicate that a trend exist with a 95% significance level

  18. 2.5% 2.5% Trends detection Standard normal distribution White noise at a level of significance of 95%

  19. 2.5% 2.5% Discrepancies andConsistencies between instruments 2003-2006 MERIS alone MODIS alone

  20. GlobColour 2003-2006 SeaWiFS MODIS Possible trends are very consistent from one single sensor to the other Patchiness of MERIS results is probably due to coverage MERIS

  21. 2.5% 2.5% Trends detection – weighted average – the full game Spatial consistency of possible trends are evidences of trends

  22. Trends detection – GSM – the full game

  23. .8/.9/.9 .2/.5/.2 .3/.6/.4 .8/.8/.8 .9/.9/.8 .6/.8/.6 .9/.9/.9 .8/.9/.8 .8/.9/.8 .7/.8/.7 .9/.9/.9 MERSWF/SWFMOD/MERMOD Correlation coefficient for the seasonal figures This gives a reasonible confidence level (or caution level!) in the merging of all sensors in order to identifiy trends

  24. Final reporting for EEA 0% 20% 40% 60% 80% 100% GC - 1998-2006 trends 2003-2005 trends

  25. Outcomes/conclusions • Data merging benefits ? • Improvement of spatial/temporal coverage • Yes, by construction • Error bar estimates • A reliable error estimates has been derived through GSM – about to be submitted for publication • Trends analysis • Although GC has not yet the right quality for Climate change studies, it already provides means to detect evidence of trends for environmental reporting – under iteration with EEA within GSE-Marcoast

  26. Acknowledgments A special thanks to Christophe Lerebourg and Julien Demaria for data handling, impossible, hair-splitting and after-hours computations. …. and thank you for your attention

  27. 2-sigma 2 -  « sigma » Error bar estimates – Validation – inputs : GC products

  28. Impact of taking into account input Lwn uncertainties on: 1. Retrieved Lwn (from GSM forward) 2. Retrieved Chla, CDM, Bbp GSM forward)

  29. Nomad DB Extract samples of LwN Estimates of the error model Use of direct bio optical model to derive new LwN Error bar estimates Estimates of the model uncertainties

  30. Correlation coefficient for the seasonal figures MERSWF SWFMOD MERMOD A: Greenland and Iceland Seas B: Barents Sea C: Faroes D: Norwegian Sea E: Celtic Sea F: North Sea G: South European Atlantic Shelf H: Western Mediterranean Sea I: Adriatic-Ioanan Seas J: Aegean-Levantine Seas K: Oceanic Northeast Atlantic L: Baltic Sea M: Black Sea N: Azov Sea J L E A B C D F G H I K M N

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