1. Global 1-km Sea Surface Temperature (G1SST) for Real-Time Research and Applications
Yi Chao
Jet Propulsion Laboratory, California Institute of Technology
Pasadena, California, USA
4-Year (FY10-FY13) Project funded by NASA Physical Oceanography Program

2. Global 1-km SST (G1SST) for:
Real-time applications (< 24 hours)
Regional and coastal users (1-km, comparable to model resolution & observational data sets such as coastal radar, glider etc)
Less sophisticated users with limited resources/bandwidth to produce their own GHRSST SST at 1-km resolution over their region of interests

3. Real-time coastal forecasting with a 1-km model:
Data assimilation & model validation
Chao, Y., Z. Li, J. D. Farrara, and P. Huang, 2009: Blended sea surface temperatures from multiple satellites and in-situ observations for coastal oceans. J. Atmos. Oceanic Technology, /2009JTECHO592.1.
Why global?
Request for other regions
If we can do for the California coast, we can do the global ocean

4. A few notes from this morning’s discussion: I hear both sides of the story
Who are the targeted users? What are the processes of interests? (e.g., modelers for data assimilation, feature tracking, real-time synoptic regional/coastal vs CDR low-frequency large-scale)
Recommendations: Data flag (dynamic) to indicate input data sets (1-, 5-, 25-km); Estimated errors
Let “Goldilocks” to pick the right product; no single product meets everyone’s needs (one-page summary should help!)

5. Two sides of the story: not enough 1-km data; ~50% of the world ocean has 1-km data on the daily basis
25-km
5-km
1-km
1-km product & including a data flag

6. Input Data Sets: Microwave (25-km)

7. Input Data Sets: Geostationary Infrared (5-km)

8. Input Data Sets: Polar-Orbiting Infrared (1-km)
AVHRR
AATSR
(not shown)

9. In Situ SST Measurements
Ships, moorings, surface drifters, profiling floats

10. How to combine multi-satellite and in situ SST daily to meet users’ needs?
Each pixel is measured multiple times by the same or different sensors
To use all data that provide independent information
Each pixel is measured by sensors with different resolutions
To combine data that provide information on different scales (e.g., 25-km MW, 5-km Geostationary, 1-km IR)
Different satellite and sensors have different errors including instrument error or representation error
To weight data differently according to their errors
The spatial interpolation/extrapolation cannot be uniform
To specify spatial varying de-correlation scales (e.g., open vs coastal ocean, along-shore vs cross-shore)

11. Multi-Scale 2DAVR Algorithm
L: 25-km; M: 5-km; H: 1-km
i: L, M, H S: input data sets

12. Multi-Scale 2DAVR Algorithm: Gradient Control

13. G1SST: version 1 in Sept 2008

14. G1SST: version 2 in May 2010 http://ourocean.jpl.nasa.gov/SST
Data-Void (24-hour period) Masked

15. Validation: 20% In Situ SST Data reserved as Independent
Data Points: 3856
Bias = -0.06
RMS = 0.59

17. G1SST Data Distribution
CF-Compliant netCDF
OPeNDAP/THREDDS
G1SST data are also available in netCDF format
from GHRSST PO.DAAC

18. G1SST for Applications: Congo River Outflow Survey by Chervon

19. Future Work for G1SST (FY11-FY13)
G1SST version 3 release projected in late 2011
Systematic bias correction (-0.1o)
Data flag for input data used in blending (e.g., 25-km, 5-km, 1-km)
Estimated errors (propagating errors from L2P to L4)
G1SST version 4 and beyond
Diurnal warming correction (e.g., KPP mixed layer model)
More input data sets: AVHRR (HRPT)
Include all data errors and co-variances
Improved spatial-varying error co-variances
Retrospective analysis (before Sept. 2008) – Computational cost
G1SST production is 2x faster than real-time on 16-processor cluster ($10K); reprocessing takes $5K*(data-year/time-year), e.g., $5K*(10 data-year/0.5 time-year) = $100K

20. Global 1-km Sea Surface Temperature (G1SST) for Real-Time Research and Applications
Questions?
Thanks to
The G1SST Team: Zhijin Li, Peggy Li, Benyang Tang, Quoc Vu
Jet Propulsion Laboratory, California Institute of Technology
Pasadena, California, USA
4-Year (FY10-FY13) Project funded by NASA Physical Oceanography Program