Long-term radiometric validation of the ATSR SST record using the M-AERI

1. Long-term radiometric validationof the ATSR SST recordusing the M-AERI Gary Corlett, David Llewellyn-Jones University of Leicester and Peter Minnett RSMAS, University of Miami

2. Thermal and Vis/IR imaging radiometer Similar Thermal channels to AVHRR, MODIS. Dual view (nadir and 55º to nadir) Along-track scanning, two views of same scene at different angles, for better atmospheric correction On board calibration 2 on-board black bodies for IR calibration VISCAL unit for visible channel calibration 500 km swath 1 km IFOV at nadir Stirling Cycle Coolers, cooling low noise detectors to 80K, for optimum signal-to-noise ratios What is an ATSR?

3. V2.0 ATSR 18 Year SST Climatology

4. Marine - Atmospheric Emitted Radiance Interferometer (M-AERI) Spectral range: 500 – 3300 cm-1 (3-18 m) Resolution: 1.0 cm-1 Field of View: 1.3 ° Measurement cycle: ~ 11 minutes Views ahead of the ship’s bow wave to avoid disturbance M-AERI Instrument http://www.rsmas.miami.edu/rccl/maeri.html

5. M-AERI SSTs • SST calculated from 10 spectral points near 7.7 µm • Corrected for reflected sky radiance and atmosphere between the sea surface and the M-AERI instrument (~1 km atmospheric path length) • Emissivity value used in SST retrieval was derived from previous M-AERI measurements made at sea • Calibration using two internal black bodies

6. M-AERI Accuracies • Traceability to NIST through the NIST EOS Transfer Radiometer (TXR) and NIST traceable reference thermometers. • Residual uncertainties <0.03K

7. M-AERI Cruises

8. AATSR pixel closest to lat and long of in situ measurement 3 x 3 block AATSR Overpass -15 mins +15 mins Match-up Criteria 1 x 1 block -120 mins +120 mins All results in following slides are for clear-sky match-ups to the nearest 1-km pixel within 2 hours for ATSR version 2.0 data

9. Match-up Locations (AATSR) Day time match-ups Night time match-ups

10. Histograms of differences D2 D3 N2 N3 N2 D2 N2: Nadir-only, 11 & 12 N3: Nadir-only, 3.7, 11 & 12 D2: Dual-view, 11 & 12 D3: Dual-view, 3.7, 11 & 12 Without filter of M-AERI SD < 0.1 K

11. Wind Speed Dependence D2/N2 – red D3/N3 – black LH axis – Median difference plus/minus standard error RH axis – Number of match-ups (solid line) Asterisk is 90% confidence from Student’s T-test

12. Which Statistics? All D3 retrievals versus drifting buoys • Simple Arithmetical • Mean: -0.09 K • SD: 0.49 K • 3-sigma • Mean: -0.09 K • SD: 0.35 K • D-N (GHRSST L2P) • Mean: -0.08 K • SD: 0.37 K • Robust • Median: -0.08 K • RSD: 0.33 K • Gaussian fit • Mean: -0.07 K • SD: 0.27 K Uncertainty usually quoted as Standard Deviation, which is 1 σ (k=1) or 68.27% of the distribution

13. AATSR/M-AERI Match-up Stats

14. Compare AATSR/M-AERI and AATSR/drifters Drifter match-ups limited to wind speeds > 6 ms-1 Expect ‘constant’ offset of -0.17 K Robust statistics Note: Radiometer night time RSD smaller than drifters, but radiometer day time RSD larger than drifters. Similar results seen for ISAR match-ups. Likely due to higher natural variability in day time ocean skin due to small scale changes in wind speed and solar insolation. Impact of such changes will be lower below the ocean skin. Note: Radiometer night time RSD smaller than drifters, but radiometer day time RSD larger than drifters. Similar results seen for ISAR match-ups. Likely due to higher natural variability in day time ocean skin due to small scale changes in wind speed and solar insolation. Impact of such changes will be lower below the ocean skin.

15. Compare AATSR/ISAR and AATSR/M-AERI All match-ups limited to wind speeds > 3 ms-1 < 9 ms-1 Median difference and standard error Note difference in D-N in Caribbean compared to BoB/EC

16. Compare AATSR/M-AERI and ATSR-2/M-AERI All match-ups from Explorer of the Seas M-AERI Full records shown, not just overlap periods Robust statistics

17. Conclusions (1) • Long-term reference data set for satellite validation provided by M-AERI on Explorer of the Seas • Radiometers are only reference data that is fully traceable to national standards (NIST) at all times • Used to validate both ATSR-2 and AATSR • Small number of three-way match-ups during overlap period (not shown here) • Results show good agreement for D3 match-ups • AATSR warm relative to ATSR-2 • Evidence that observed 12 micron difference is indeed due to AATSR SRF and not ATSR-2 • N3 match-ups have lowest noise • Evidence of poor cloud screening and alignment uncertainty in dual view retrievals

18. Conclusions (2) • Good agreement between M-AERI, AATSR & drifters • All biases ~ 0.1 K for D3 • Good agreement between radiometer/drifter results between Caribbean Sea and Bay of Biscay/English channel • M-AERI and ISAR match-ups have lower noise at night compared to drifters but higher noise during the day • Evidence of increased skin variability in day time • Clearly an issue with outliers • Very small (few %) number of outliers have large impact on statistics • Robust statistics provide more realistic uncertainties for users and are recommended

19. Looking Forward • AATSR is now expected to operate until the end of 2013 at the latest • The ATSR follow-on instrument (SLSTR) on the ESA Sentinel 3 satellite is not expected to fly before 2013 and so there may be an enforced break in the ATSR record. • Any break will mean that an overlap between AATSR and Sentinel 3 will not be possible. • This raises two fundamental SST data continuity questions: • How can the calibration of the ATSR dataset record be transferred to Sentinel 3 if there is a gap? • How might the data gap between the two missions be filled using alternative SST data with the minimum loss of quality? • The former is by far the most critical issue, as climate scientists need to be assured that there are no biases between AATSR and Sentinel 3 and that any observed change in temperature is real. • Situation not just true for AATSR/SLSTR • Need to protect existing and future satellite SST records

20. Reference Data Sources • Satellite SST validation uses the various types of ref. Data available. Ideally we require combination of these measurements • Radiometers • Provide SST-skin; only traceable reference source; expensive; very poor coverage owing to low number of deployments; long-term deployments preferred; funding not guaranteed • Argo floats • Provide SST-depth; excellent calibration (0.005 K); only surface every 10 days; funding not guaranteed; provides global uncertainties • Drifting buoys • Provide SST-depth; depth unknown; not calibrated; main workhorse for global uncertainties • Moored buoys • Provide SST-depth; reasonable calibration; often in tough regions for validation (e.g. coasts); good for regional uncertainties • Ship thermometry • Provide SST-depth; usually poor calibration; can be useful for areas without drifters

21. Generic SST validation • Need to move to generic validation • Validation should not be mission specific • Provides protection for data gaps • Costs can be shared • Offers maximum benefit to all • Work with GHRSST • To agree best practices and methods for the “community” • Work with CEOS • To implement QA4EO guidelines • For intercomparisons • Maintain links to other communities • Argo; DBCP; JCOMM SOT

22. Potential Next Steps • Get ship-borne radiometry on firmer footing • Need long-term funding for several deployments • Get more secure funding for Argo • Ideally obtain near-surface profiles • Improve calibration of drifting buoys • Pilot project underway • Use ships more opportunistically • But need improved measurements (e.g. IMOS)

23. NASA for funding M-AERI operations Personnel at RSMAS for M-AERI support DECC for funding AATSR sensor and support teams, including ISAR Programme at the University of Southampton ESA for funding the Envisat Mission and for provision of AATSR data GKC and the PI/Val Team at the University of Leicester are funded by DECC through Space ConneXions contract 2004-03-001/CPEG10 Acknowledgementsare due to: