Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Data merging benefits Antoine Mangin, Stéphane Maritorena Session 4 –GlobColour applications – November 21, 2007 for the users

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Data merging benefits ? Improvement of spatial/temporal coverage Error bar estimates Trends analysis Background 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

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Data merging Improvement of spatial/temporal coverage Error bar estimates Trends analysis It is a truism. Outlines

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Improvement of spatial/temporal coverage

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, 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 Outlines

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Error bar estimates 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)

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Error bar estimates Estimates of the uncertainties on input LwN This is a direct result from the characterisation at sensors level (GC phase 1 ++)

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Error bar estimates Estimates of the full uncertainties as input of GSM

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, 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 Lw N

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Error bar estimates - Validation Estimates of the uncertainties on outputs Chla, bbp,cdm (Co-variance matrix between all ingredients) Use of the estimates Nomad DB / EO data Extract samples of concomitent Lw N, Chla bbp and cdom samples If the Chla error estimates is reliable should be close to a standard normal distribution

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Error bar estimates - Validations 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.

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Example of products uncertainties - daily GlobColour Chla-merged product – May, GlobColour Chla-merged product relative uncertainties – May,

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, GlobColour Chla-merged product – May 2006 Example of products uncertainties GlobColour Chla-merged product relative uncertainties– May

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, 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. Outlines

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, 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.

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Background – ingredients for reporting 14 eco-regions About 6800 Chla samples in … and thus the report Within Marcoast we are working to replace/complement in situ sampling by EO (and here more specifically by GlobColour)

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, 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  2 of a white noise on such times series is analytically known. So … any departure above (resp. below) 2  (resp. –2  ) from this law would indicate that a trend exist with a 95% significance level Trends analysis Important distinction: We are not trying here to quantify trends but to identify the probable ones.

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, % Standard normal distribution White noise at a level of significance of 95% Trends detection

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Discrepancies and Consistencies between instruments MERIS alone MODIS alone %

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, MODIS SeaWiFS MERIS GlobColour Patchiness of MERIS results is probably due to coverage Possible trends are very consistent from one single sensor to the other

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, % Trends detection – weighted average – the full game Spatial consistency of possible trends are evidences of trends

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Trends detection – GSM – the full game

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Correlation coefficient for the seasonal figures.6/.8/.6.2/.5/.2.8/.9/.9.3/.6/.4.8/.9/.8.9/.9/.9.7/.8/.7.8/.9/.8.8/.8/.8.9/.9/.8 MERSWF/SWFMOD/MERMOD.9/.9/.9 This gives a reasonible confidence level (or caution level!) in the merging of all sensors in order to identifiy trends

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, %60%80%40%100%20% trends GC trends Final reporting for EEA

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21,

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, 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 Outcomes/conclusions

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, A special thanks to Christophe Lerebourg and Julien Demaria for data handling, impossible, hair-splitting and after-hours computations. Acknowledgments …. and thank you for your attention

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, sigma 2 - « sigma » Error bar estimates – Validation – inputs : GC products

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Impact of taking into account input Lwn uncertainties on: 1. Retrieved Lwn (from GSM forward) 2. Retrieved Chla, CDM, Bbp GSM forward)

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, Error bar estimates Estimates of the model uncertaintiesNomad DB Extract samples of Lw N Use of direct bio optical model to derive new Lw N Estimates of the error model

Data merging benefits Globcolour / Medspiration user consultation, Nov , 2007, Oslo Session 4 –GlobColour applications – November 21, ABCD E FGHI J K L MN 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 MERSWF SWFMOD MERMOD Correlation coefficient for the seasonal figures