1. AQUA AMSR-E MODIS POES AVHRR TRMM TMI ENVISAT AATSR Multi-satellite, multi-sensor data fusion: global daily 9 km SSTs from MODIS, AMSR-E, and TMI gentemann@remss.com www.misst.org

2. NRT 9km global SSTs Global OI SSTs Pre-processing data –Validation –Data flagging –Diurnal Warming –Skin effect Optimum Interpolation Processing Validation Remote Sensing Systems www.remss.com

3. 3 Global SST products ReynoldsRTGRSS MWRSS MW+IR WeeklyDaily 100km50km25km9km AVHRR AMSRE TMI MODIS AMSRE&TMI

4. Monthly Average SST Long Term Stability 1982 1986 1990 1994 1998 2000 SST (C) Year

5. Near-land SSTs AMSREMODIS9km OI SST www.misst.org Remote Sensing Systems www.remss.com

6. Diurnal Modeling 2)Parameterization of IR and MW retrieval differences, with consideration of diurnal warming and cool-skin effects required for multi-sensor blending.

7. Data processing Process TMI, AMSR-E, MODIS SSTs –Find wind retrieval for each observation and calculate diurnal warming –For MODIS SSTs, AMSR-E SSTs are used, but MODIS has a wider swath and retrieves near land. Look for any AMSRE wind within 100km Get NWP wind Remote Sensing Systems www.remss.com

8. Validation Blending two very different measurements of SSTs requires some understanding of differences MW Bias/STD IR Bias/STD

9. Mean difference

10. JPDFs Day looks better than night, low SST biasing Wind bias in opposite direction than expected skin- bulk relationship Low vapor biasing

11. Mean difference MODIS - AMSRE SST - Reynolds SST Vapor MODIS - Reynolds AMSRE - Reynolds

12. MW-IR Need to account for regional differences due to unexplained algorithm errors in MW and IR SSTs Calculate 20-day average difference, smooth, subtract from IR

13. Optimum interpolation 1st guess field: currently previous days OI Correlations in time/space: invariant Careful A is not singular, restrict size of A Observation errors Correlations less important in ocean Remote Sensing Systems www.remss.com

14. Global 9 km NRT SST Processing on 4 processors: Master, 3 servants –Master checks to make sure last day of fusion = current date if this is up to date, then –Master checks for files that should go from RT to final versions, if none of these need processing, then –Reprocesses current date –Output file with date to process, servants find file, process data, delete file, wait for next file Remote Sensing Systems www.remss.com

15. Mean Bias/STD 2006 days 105-200 Remote Sensing Systems www.remss.com Data from Latitudes: 40S – 40N OI SST Bias (C)STD (C)# Collocations Dates included TMI 0.120.59198,622,6016/2002-2/2004 AMSR-E-0.030.53202,317,8436/2002-2/2004 TMI+AMSR-E 0.010.56196,485,2676/2002-2/2004 TMI+AMSR-E+MODIS-0.120.58 18,292,0934/15/2006-6/11/2006 Data from Latitudes: 90S – 90N OI SST Bias (C)STD (C)# Collocations Dates included TMI AMSR-E-0.020.64313,865,2306/2002-2/2004 TMI+AMSR-E 0.010.65319,671,0576/2002-2/2004 TMI+AMSR-E+MODIS-0.130.65 25,913,7704/15/2006-6/11/2006

16. Data Release Ice Mask developed for SSTs –SSM/I Uses 9 channel combinations total Different tests used for : –Open ocean –Near ice edge –Near land –Lake (not implemented yet) Remote Sensing Systems www.remss.com

20. Sea ice in Hudson Bay May 21, 2005 MODIS imagery, AMSR-E RSS exp

21. Remote Sensing Systems www.remss.com

22. www.misst.org Remote Sensing Systems www.remss.com Thank you! 2006 almost finished (10 days missing) 2007 – present Google Earth year of satellite data w/ NASA