1. Infrared and Microwave Remote Sensing of Sea Surface Temperature Gary A. Wick NOAA Environmental Technology Laboratory January 14, 2004

2. Outline u Motivation u Basic SST Retrieval Methods u Current Multi-Sensor Merging Efforts

3. Why SST? u Boundary Condition –Weather Models –Estimation of Heat Content and Heat Flux u Climate Monitoring and Change Detection u Naval Operations

4. Climate Anomalies Courtesy: NOAA Climate Diagnostics Center

5. Why Satellites? Courtesy: R. Reynolds, NOAA NCDC

6. Desired Accuracy u WCRP (1985) - Tropics – 0.3 K on 2° grid every 15 days u Robinson et al. (1984) - Global SST Monitoring – 0.05 K on 5° grid every 15 days u NPOESS SST EDR Objectives –0.1 K uncertainty at ~4 km resolution

7. Definition of SST u Interface SST u Skin SST u Sub-skin SST u Near-Surface SST or SST Depth

8. Radiative Transfer Equation

9. Methods for SST Retrieval u Thermal Infrared u Passive Microwave

10. Infrared Retrievals u Strengths –High Accuracy –High Resolution –Long Heritage (over 20 years) u Weaknesses –Obscured by Clouds –Atmospheric Corrections Required

11. Microwave Retrievals u Strengths –Clouds Transparent –Relatively Insensitive to Atmospheric Effects u Weaknesses –Sensitive to Surface Roughness –Poorer Accuracy (?) –Poorer Resolution

12. Spatial Coverage Differences

13. Infrared Retrieval Technique u Cloud Detection u Atmospheric Correction u Multi-Channel SST –T S = T 1 +  (T 1 - T 2 ) –Multi-Frequency –Multiple View

14. Algorithm Refinements u Additional path length term u NLSST u Use of multiple frequencies AND multiple view angles u Independent estimate of water vapor content  Iterative solution for both SST and 

15. Microwave Retrieval Technique Courtesy: Remote Sensing Systems

16. Infrared Sensors u AVHRR u ATSR u GOES Imager u MODIS u Others –GMS –SEVIRI –VIRS

17. Microwave Sensors u TMI u AMSR u WindSat

18. Multi-Sensor Blended SST u Current Projects u Key Issues u Sample Results

19. GODAE High-Resolution SST Pilot Project u Provide rapidly and regularly distributed, global, multi- sensor, high-quality SST products at a fine spatial and temporal resolution –Most promising solution to combine complementary infrared and passive microwave satellite measurements with quality controlled in situ observations from ships and buoys u www.ghrsst-pp.org

20. Next Generation SST u Created by Hiroshi Kawamura, Tohoku University, Japan u http://www.ocean.caos.tohoku.ac.jp/~adeos/sst/

21. Blended SST Issues u Different product resolutions u Different sensor error characteristics u Different sampling times and effective depths u Merging techniques

22. Error Characteristics – Overall Accuracy

23. Observed Differences Between Infrared and Microwave Products Comparisons between the products show complex spatial and temporal differences

24. Sources of Product Differences

25. Diurnal Warming Effects

26. Skin Layer Effects Courtesy: P. Minnett, U. Miami Courtesy: S. Castro, U. Colorado

27. NOAA Environmental Technology Laboratory Blended Infrared and Microwave SST Using derived corrections, the infrared and microwave SST products can be more accurately merged into a new enhanced product. Diurnal warming effects are aliased into the product if not corrected. Strong winds off Somalia cause perceived overcooling and large swath edge effects are visible. Bias (K) RMS (K) w/ Adj-0.010.61 w/o Adj0.150.67 Accuracy of Merged Product vs. Buoys

28. Analyzed SST Product u Daily global (40 N – 40 S) 0.25 degree u Referenced to nighttime predawn value u Based on Reynolds and Smith Optimal Interpolation u Relative product uncertainties derived from difference analyses Analysis Characteristics

29. Analyzed Product Accuracy Summary ProductBias (K)RMS (K) Full Analysis0.130.68 Night Obs Only-0.080.58 AVHRR Obs Only-0.010.56 TMI Obs Only0.220.74 Refined diurnal corrections are the most needed improvement

30. Summary u Complementary infrared and microwave SST products provide the opportunity for cross-validation and improved SST u Multiple sensor-related and geophysical effects lead to complex differences between the products u Optimal blending of the products requires careful treatment of the differences u Is blending correct?