ECOOP-WP3

ECOOP - WP3Better 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

ECOOP-WP3

T3.1 Optimal synergy between altimetry and tide gauge dataCLS, 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 watersJRC, 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 seasIfremer, 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

ECOOP-WP3

T3.3 Improved Sea Surface Temperature products in coastal seasLinks 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.

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/SEVIRIENVISAT/AATSR

NOAA16&17/AVHRR

AQUA/AMSR-E

TRMM/TMI

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/SEVIRIENVISAT/AATSR

NOAA16&17/AVHRR

AQUA/AMSR-E

TRMM/TMI

Sea Surface Temperature : µ-wave instruments

ECOOP-WP3

All satellite data used are subskin Level 2 (L2P) from the GHRSST-pp project (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

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

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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…

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Individual statistics

Standard deviation

Bias

Red = quality flag 3

Blue = quality flag 4

Green = quality flag 5

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

ECOOP-WP3

Confidence level Number of obs Bias Std. deviation

11 4126 -0.05 0.45

12 1432 -0.02 0.50

13 73 -0.74 0.50

AMSRE µwave

Seven data sets tested on the IBIROOS area in 2006

Confidence level Number of obs Bias Std. deviation

3 7255 -0.23 0.53

4 478 -0.02 0.50

5 5533 0.05 0.46

SEVIRI Geostationnary

Confidence level Number of obs Bias Std. deviation

3 4761 -0.41 0.56

4 630 -0.07 0.54

5 2866 0.03 0.45

NAR18 AVHRR

Confidence level Number of obs Bias Std. deviation

3 1075 -0.14 0.52

4 183 -0.30 0.46

5 507 -0.18 0.31

AATSR

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)]

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

ECOOP-WP3

Validation on the English Channel of AVHRR/Pathfinder data over the period 1986-2006

Satellite SST Kriged satellite SST

Station Nb match-ups

BiasSat-Situ

St. dev.Sat-Situ

Nb match-ups BiasSat-Situ

St. dev.Sat-Situ

Profiles data 11 -0.47 0.92 50 -0.39 0.84

Thermosalinograph data

2057 -0.17 0.46 11286 -0.17 0.46

Validation on the English Channel of AVHRR/Pathfinder data: satellite to cruise

Satellite SST Kriged satellite SST

Station Nb match-ups

BiasSat-Situ

St. dev. Sat-Situ

Nb match-ups BiasSat-Situ

St. devSat - Situ

Wimereux_l 20 -0.47 0.43 105 -0.29 0.44

Boulogne_3 16 -0.2 0.78 103 -0.43 0.98

Roscoff_Astan 3 -0.34 1.23 45 -0.1 0.49

Dunkerque_3 12 -0.23 0.57 57 -0.32 0.64

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

ECOOP-WP3

Validation of the satellite interpolated SST on the in situ observations at coastal stationsNotion of transfer function offshore to coastal (scale 5 km)

Stations of Wimereux Coast (C) and offshore (L)

1997-2006

Offshore to coastal temperaturemodel.

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)

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Example of Adriatic SST image

Coastal current has length scale same order of resolutionof model grid (several km)

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Example of Mediterranean NRT SST products

Multi sensors

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CTD

XBT

Non interpolated SST (AREG)

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CTD

XBT

Optimally interpolated SST (OISST)

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CTD

XBT

Delayed Time SST (DTSST)

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Summary statistics for Adriatic Sea

CTD CTD < 50 m CTD >50 m XBT Bias RMS Npts Bias RMS Npt Bias RMS Npt Bias RMS Npt AREG 0,1 0,99 1399 0,13 1,05 938 0,05 0,84 461 0,14 0,7 305 OISST -0,38 0,94 2857 -0,5 1,01 2161 0,005 0,51 696 -0,04 0,53 322 DTSST 0,38 0,86 225 0,86 1,03 87 0,08 0,54 138 0,13 0,45 125

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).

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.

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