1. ECOOP-WP3 Better use of remote-sensing data and in situ measurements Francis Gohin, Ifremer T3.1 Optimal synergy between altimetry and tide gauge data CLS, CNRS-EPOC (LEGOS), DNSC, MHI, Puertos Del Estado T3.2 Improved Ocean Colour algorithms and products for case-II waters JRC, CNR, Ifremer T3.3 Improved Sea Surface Temperature products in coastal seas Ifremer, CNR, DMI End of the Work Package 3 on 31/07/08 then most of the partners will join WP2 for the operational phase

2. ECOOP-WP3 T3.1 Optimal synergy between altimetry and tide gauge data CLS, CNRS-EPOC (LEGOS), DNSC, MHI, Puertos Del Estado -Report on comparison between tide gauges and altimetry : 31/01/2008 -Report on the merging technique of tide gauges and altimetry : 31/07/2008 -Report on the New Mean Dynamic Topography for Black Sea and IBIROOS region : “” T3.2 Improved Ocean Colour algorithms and products for case-II waters JRC, CNR, Ifremer -Report on comparison between R/S and in-situ data (Adriatic) 31/01/2008 -Report on comparison between R/S and in-situ data (Gulf of Biscay) “” -Report on multi-sensor merging and dynamic bio-optical algorithm selection (Adriatic Sea) 31/07/2008 -Report on the merging technique between OC R/S and in-situ data (Bay of Biscay) T3.3 Improved Sea Surface Temperature products in coastal seas Ifremer, CNR, DMI -3 Reports on comparison between SST R/S and in-situ data (Adriatic Sea, Bay of Biscay, Baltic Sea 31/10/2007 -3 reports on the merging technique between SST R/S and in-situ data (Adriatic Sea, Bay of Biscay, Baltic Sea) 31/07/2008

3. ECOOP-WP3 T3.3 Improved Sea Surface Temperature products in coastal seas Links with the GHRSST GODAE (Global Ocean Data Assimilation Experiment) High Resolution SST Pilot Project (www.ghrsst-pp.org) By Ifremer, CNR, DMI The definitions of SST developed by the GHRSST-PP SST Science Team achieve the closest possible coincidence between what is defined and what can be measured operationally, bearing in mind current scientific knowledge and understanding of how the near surface thermal structure of the ocean behaves in nature.

4. ECOOP-WP3 Sea Surface Temperature ; Infra-red instruments  Infrared radiometers (IR)  Geostationary or polar-orbiting satellites  Skin SST (10-20 µm)  Problems: clouds, aerosols, diurnal cycle, …  High resolution (5km > 1km) MSG/SEVIRI ENVISAT/AATSR NOAA16&17/AVHRR AQUA/AMSR-E TRMM/TMI

5. ECOOP-WP3 Micro wave radiometers –Polar-orbiting satellites –Subskin SST (~1 mm) –Rain, land (<100 km) contamination –Diurnal cycle –Lower resolution (25-50km) –Low sampling over some areas MSG/SEVIRI ENVISAT/AATSR NOAA16&17/AVHRR AQUA/AMSR-E TRMM/TMI Sea Surface Temperature : µ-wave instruments

6. ECOOP-WP3 All satellite data used are subskin Level 2 (L2P) from the GHRSST-pp project (www.ghrsst.pp.org)www.ghrsst.pp.org Satellite data used in the validation are: AATSR 1 km AVHRR 2 km (Ocean & Sea Ice SAF, NAR 17 + 18 ) Seviri hourly 0.05 degrees Modis 1 km Terra Modis 1 km Aqua AMSR-E 25 km METOP_A 1 km Validation of GHRSST-pp SST products for the North Sea, Baltic Sea and Danish Straits, Jacob L. Høyer

7. ECOOP-WP3 In situ data In situ observations are obtained from drifter, buoys, ships and fixed stations Danish monitoring network: And other observations from e.g Marnet buoy network

8. ECOOP-WP3 SST comparison Validation period Jan 2006-Oct 2007 Monthly validation statistics (stddev and bias) Separate statistics for day and night Separate statistics for each sensor Separate statistics for each GHRSST-pp L2P quality flag (3,4 and 5) Depth dependent error statistics Matchup windows: 2 hours and 25 km Report delivered to Progecta A few examples…

9. ECOOP-WP3 Individual statistics Standard deviation Bias Red = quality flag 3 Blue = quality flag 4 Green = quality flag 5

10. ECOOP-WP3 1) Comparison of seven data sets of different satellite sensors in the IBIROOS area sensors with in situ data in 2006 2 different analyses have been carried out 2) Comparison of 21 years of AVHRR/Pathfinder data and in situ, including coastal stations and mooring. Building of a climatology and reconstruction of weekly SST fields (OI by kriging) on the English Channel Validation of GHRSST-pp SST products for the Bay of Biscay Francis Gohin Ifremer

11. ECOOP-WP3 Confidence levelNumber of obsBiasStd. deviation 114126-0.050.45 121432-0.020.50 1373-0.740.50 AMSRE µwave Seven data sets tested on the IBIROOS area in 2006 Confidence levelNumber of obsBiasStd. deviation 37255-0.230.53 4478-0.020.50 555330.050.46 SEVIRI Geostationnary Confidence levelNumber of obsBiasStd. deviation 34761-0.410.56 4630-0.070.54 528660.030.45 NAR18 AVHRR Confidence levelNumber of obsBiasStd. deviation 31075-0.140.52 4183-0.300.46 5507-0.180.31 AATSR

12. ECOOP-WP3 ROSCOFF 89.6% of the variance explained by 2 harmonics FLAMANVILLE 95.2% of variance explained by 2 harmonics Building the climatology on the English Channel, reconstruction of the weekly SST fileds, comparisons of observed and interpolated SST with in situ data SST(t)= P0+P1*t - P2*cos[2pi/365(t-P3)]-P4*cos[2pi/182.5(t-P5)]

13. Parameters P0 and P1 calculated from AVHRR data (1986-2006) P1, warming slopeP0, average SST 1986 SST(t)= P0+P1*t - P2*cos[2pi/365(t-P3)]-P4*cos[2pi/182.5(t-P5)] Explained Variance

14. ECOOP-WP3 Validation on the English Channel of AVHRR/Pathfinder data over the period 1986-2006 Satellite SSTKriged satellite SST Station Nb match- ups Bias Sat- Situ St. dev. Sat-Situ Nb match-upsBias Sat-Situ St. dev. Sat- Situ Profiles data11-0.470.9250-0.390.84 Thermosalinograph data 2057-0.170.4611286-0.170.46 Validation on the English Channel of AVHRR/Pathfinder data: satellite to cruise Satellite SSTKriged satellite SST Station Nb match- ups Bias Sat- Situ St. dev. Sat-Situ Nb match-upsBias Sat-Situ St. dev Sat - Situ Wimereux_l20-0.470.43105-0.290.44 Boulogne_316-0.20.78103-0.430.98 Roscoff_Astan3-0.341.2345-0.10.49 Dunkerque_312-0.230.5757-0.320.64 Validation on the English Channel of AVHRR/Pathfinder data: satellite to coastal stations (relatively offshore as available from GHRSST sensors) 2 validations : 1 direct night satellite SST : 2 interpolated satellite SST

15. ECOOP-WP3 Validation of the satellite interpolated SST on the in situ observations at coastal stations Notion of transfer function offshore to coastal (scale 5 km) Stations of Wimereux Coast (C) and offshore (L) 1997-2006 Offshore to coastal temperature model.

16. ECOOP-WP3 Validation period Oct 2002- Dec 2007 Validation statistics separated by product: Non interpolated SST (single-sensor NOAA) AREG at 5km resolution Optimally interpolated SST (single sensor NOAA) OISST @ 7 km Delayed Time SST (GHRSST L2P) DTSST @7km resolution Only nighttime satellite data used Separate validations against XBT and CTD Matchup window: 1 day and 5 or 7km depending on product Report delivered to Progecta SST comparison (CNR-ISAC)

17. ECOOP-WP3 Example of Adriatic SST image Coastal current has length scale same order of resolution of model grid (several km)

18. ECOOP-WP3 Example of Mediterranean NRT SST products Multi sensors

19. ECOOP-WP3 CTD XBT Non interpolated SST (AREG)

20. ECOOP-WP3 CTD XBT Optimally interpolated SST (OISST)

21. ECOOP-WP3 CTD XBT Delayed Time SST (DTSST)

22. ECOOP-WP3 Summary statistics for Adriatic Sea Note that for depths less than 50 m SST vs CTD has worse stats than vs XBT, while for depths greater than 50m the two stats are very similar indicating higher shallow water variability (both in space and time).

23. ECOOP-WP3 CNR-ISAC R/S vs in situ SST conclusions Comparison of operational satellite-derived products of the Adriatic Sea with in situ observations for the period 2002- 2007 indicates that satellite data compare with in situ XBT temperature with a bias between -0.04 °C and 0.14 °C and a standard deviation between 0.45 °C and 0.70 °C, state of the art values given by the multi-sensor DTSST data comparison. Decreased quality validation vs CTD that includes shallow water and coastal regions (bias -0.38 to +0.38 °C and standard deviation 0.86 to 0.99°C) resulting from higher frequency variability indicates a need for improvement of the CNR-ISAC Adriatic fast delivery processing system that includes refined cloud detection of coastal areas and a higher resolution interpolation scheme.

24. ECOOP-WP3 Conclusions 1)Results -All the data sets of the different sensors give correct results if the highest level quality flag is chosen, bias is often slightly negative (cloud-contaminated pixels?), standard error of the order of 0.5°C. AATSR data are the best, but smaller swath. -Better matching would be possible from a more accurate selection on the depth of in situ measurements. From DMI’s experience, In situ obs. from 0-3.5 meters compare similar, differences increase below 4 meters. Another question concerning the use of the in situ data in the merging (essentially satellite-based) would be the consequent difficulty to validate the products at coast ! 2) Next phase : Merging Each partner has already one or several methods for merging satellite SSTs offshore. SST at stations in shallow waters (for instance less than 50 m in the Adriatic) or closer to the coast in tide-mixed waters show a stronger variability than offshore; which makes less acceptable the hypotheses of stationarity required for the Optimal Interpolation. Transfer functions to derive coastal SST from offshore could be a solution, but local. Validations of merged satellite products at lower resolution have still to be carried out at coast