1. Walton McBride U.S. Naval Research Lab, Stennis Space Center MS Robert Arnone University of Southern Mississippi, Stennis Space Center MS Jean-François Cayula Qinetiq North America, Stennis Space Center MS May 1, 2013 Improvements of Satellite SST Retrievals at Full Swath 1

2. 2 A FRESH PERSPECTIVE Search for Clues on how to Improve SST Retrievals SST algorithms Used to Create Clues Scatter Plots Used to Identify Clues Model Run Results to Discern Clues Putting the Clues Together Interpretation of Results Conclusions

3. 3 SST ALGORITHMS MC SST NL SST Tfield SST (same as MC SST, except for addition of T field as separate predictor) First guess temperature field

4. 4 BUOY DATA SET NAVOCEANO buoy data set for the month of June 2012: 115,036 daytime points After routine NAVOCEANO filtering: 61,782 points from 0° to 53° zenith angle (53.70%) 97,496 points from 0° to 70° zenith angle (84.75%) Linear regression coefficients using MC SST: In the original spirit behind the MC SST formulation: where

5. MCSST Pinch effect No scatter Filtering Parameters |T field -Buoy| < 2.0° |Zenith| < 53 deg  distance < 25 km  time < 4 hours SST MC -T11 vs. T11-T12

6. MCSST Pinch effect Data displays scatter No scatter No Pinch effect Filtering Parameters |T field -Buoy| < 2.0° |Zenith| < 53 deg  distance < 25 km  time < 4 hours Goal is to capture the features and structure of the distribution. BUOY a b BUOY-T11 vs. T11-T12 SST MC -T11 vs. T11-T12

7. NLSST MCSST Pinch effect Data displays scatter No scatter Signs of scatter Pinch effect No Pinch effect Filtering Parameters |T field -Buoy| < 2.0° |Zenith| < 53 deg  distance < 25 km  time < 4 hours Goal is to capture the features and structure of the distribution. BUOY a b c BUOY-T11 vs. T11-T12 SST NL -T11 vs. T11-T12

8. Clim K100 K10 |T field -Buoy| < 0.1 0.021 0.057 0.026 5.921 0.978 0.999 0.07175 |T field -Buoy| < 0.2 0.074 0.194 0.088 20.33 0.927 1.001 0.12447 |T field -Buoy| < 0.5 0.245 0.643 0.261 66.82 0.759 1.004 0.25600 |T field -Buoy| < 1.0 0.471 1.218 0.535 128.5 0.541 1.012 0.36675 |T field -Buoy| < 2.0 0.649 1.663 0.736 177.0 0.364 1.013 0.45077 |T field -Buoy| < 5.0 0.752 1.914 0.853 204.9 0.259 1.011 0.49914 rmserror Clim c 1 + c 5 |T field -Buoy| < 0.1 0.009 0.018 0.005 2.458 0.991 1.000 0.06968 |T field -Buoy| < 0.2 0.043 0.096 0.055 11.86 0.957 1.000 0.12447 |T field -Buoy| < 0.5 0.195 0.475 0.229 53.23 0.806 1.001 0.25554 |T field -Buoy| < 1.0 0.389 0.958 0.467 106.1 0.615 1.004 0.36104 |T field -Buoy| < 2.0 0.527 1.310 0.633 143.6 0.479 1.006 0.42815 |T field -Buoy| < 5.0 0.585 1.462 0.697 159.5 0.420 1.005 0.46373 rmserror K100 c 1 + c 5 |T field -Buoy| < 0.1 0.013 0.032 0.011 3.600 0.986 0.999 0.06991 |T field -Buoy| < 0.2 0.055 0.130 0.063 15.10 0.945 1.000 0.12362 |T field -Buoy| < 0.5 0.188 0.454 0.239 51.21 0.814 1.002 0.24467 |T field -Buoy| < 1.0 0.326 0.810 0.406 89.01 0.677 1.003 0.33348 |T field -Buoy| < 2.0 0.419 1.049 0.518 114.3 0.585 1.004 0.39268 |T field -Buoy| < 5.0 0.467 1.169 0.571 127.2 0.539 1.006 0.43082 rmserror K10 c 1 + c 5 Filtering Table1: Effects of pre-filtering and increased spatial resolution of Tfield. T field AS SEPARATE PREDICTOR

9. Blue  Red COLOR PLOTTING Red  Blue COLOR PLOTTING a c b d DATA MC SST

10. a b c d Blue  Red COLOR PLOTTING Red  Blue COLOR PLOTTING DATA Tfield SST with K10

11. From GUI results Using global coefficients 97507 0.41055 3.66e-12 0.294 0.610 0.388 80.10 0.719 1.013 no K10 0.65727 1.09e-11 1.034 2.204 1.363 281.8 0.000 1.034 +60 to +70 21213 0.41834 -8.37e-13 0.230 0.292 0.296 62.7 0.794 1.024 +50 to +60 19667 0.39780 -2.03e-13 0.296 0.621 0.349 80.9 0.721 1.017 +40 to +50 15353 0.39463 2.83e-13 0.343 0.814 0.447 93.4 0.665 1.008 +30 to +40 12415 0.38942 -9.51e-13 0.407 1.004 0.742 110.8 0.594 1.001 +20 to +30 10231 0.38372 -2.92e-12 0.469 1.264 -0.011 127.7 0.533 1.002 +10 to +20 9477 0.37942 7.47e-13 0.521 1.325 1.087 141.9 0.482 1.003 0 to +10 9151 0.37119 -6.32e-13 0.493 1.292 0.922 134.3 0.506 0.999 |Zenith| Band #pts rmserror bias c 1 c 2 c 3 c 4 c 5 (c 1 +c 5 ) |Zenith| Band #pts rmserror bias rmserror bias +60 to +70 21213 0.44234 -0.04142 0.86911 -0.16031 +50 to +60 19667 0.39847 -0.00778 0.63142 0.01057 +40 to +50 15353 0.39828 0.00676 0.58992 0.05601 +30 to +40 12415 0.40274 0.02223 0.58093 0.06392 +20 to +30 10231 0.40334 0.01332 0.53541 0.05429 +10 to +20 9477 0.40560 0.03403 0.50201 0.06380 0 to +10 9151 0.39493 0.02127 0.52607 0.04228 Table 2: Coefficients Dependence on Zenith DEPENDENCE ON ZENITH ANGLE

12. From GUI results Using global coefficients 61795 0.3894 3.05e-12 0.416 1.039 0.515 113.4 0.589 1.005 no K10 0.5305 6.62e-12 1.006 2.550 1.174 274.1 0.000 1.006 +50 to +70 7112 0.36861 -1.38e-12 0.375 0.980 0.314 102.2 0.626 1.001 +30 to +50 17404 0.39976 5.79e-13 0.513 1.191 0.626 139.8 0.497 1.010 +10 to +30 12910 0.3532 -6.44e-13 0.279 0.672 0.339 76.3 0.731 1.010 -10 to +10 4856 0.3521 -1.66e-13 0.242 0.795 0.240 63.9 0.672 0.914 -30 to -10 8947 0.34733 -1.81e-13 0.432 1.080 0.554 117.9 0.580 1.012 -50 to -30 9098 0.38322 -6.67e-13 0.514 1.054 0.612 140.0 0.501 1.015 -70 to -50 1468 0.36599 -2.07e-14 0.359 0.777 0.359 98.1 0.673 1.032 Latitude Band #pts rmserror bias c 1 c 2 c 3 c 4 c 5 (c 1 +c 5 ) Latitude Band #pts rmserror bias rmserror bias +50 to +70 7112 0.37013 -0.02832 0.5056 -0.07924 +30 to +50 17404 0.40451 0.12566 0.51462 0.11358 +10 to +30 12910 0.36430 -0.01473 0.57448 -0.11061 -10 to +10 4856 0.38496 -0.11888 0.58587 -0.11294 -30 to -10 8947 0.34863 -0.07716 0.47300 0.00595 -50 to -30 9098 0.38908 -0.06359 0.49011 0.05780 -70 to -50 1468 0.37408 0.03549 0.47590 -0.00664 Table 3: Coefficients Dependence on Latitude, Zenith 0 to 53 DEPENDENCE ON LATITUDE

13. Table 4: Percent reduction in rms error Zenith MCSST Tfield = Clim Tfield = K100 Tfield = K10 0 o to 53 o 53 o to 70 o 0 o to 70 o 0.53052 0.45077 15.0% 0.42551 19.8% 0.38937 26.6% 0.73712 0.51667 29.9% 0.45622 38.1% 0.41298 43.9% 0.65627 0.49989 23.8% 0.45507 30.7% 0.41053 37.4% RMS ERROR REDUCTION

14. c1 c2 c3 c4 c5 rmserror MCSST TfieldSST 1.006 2.541 1.173 274.10 ---- 0.5058 0.251 0.617 0.312 68.42 0.752 0.2869 1.000 2.467 1.243 272.58 ---- c1 c2 c3 c4 c5 rmserror MCSST TfieldSST 1.034 2.20 1.360 281.99 ---- 0.6382 0.163 0.326 0.221 44.48 0.844 0.2987 1.000 2.00 1.356 272.88 ---- Zenith angles from 0 to 53 degrees |Tfield-Tbuoy|=0.7° Zenith angles from 0 to 70 degrees |Tfield-Tbuoy|=0.7° ANOTHER CLUE! Clue 2:

15. 15 PUTTING IT ALTOGETHER Clue 2: Clue 1: If the errors in T field and SST MC are uncorrelated, then the variances: Substituting: Simple Linear Weighting and where Past Present - Past or

16. 16 SCATTER PLOT OF ERRORS R 2 < 0.04 Zenith angles from 0 to 70 degrees Blue  Red Color PlottingRed  Blue Color Plotting PRACTICALLY NO CORRELATION!

17. 17 RMS ERRORS RELATIONSHIPS Guarantees that will always be less than or ! If the errors in T field and SST MC are uncorrelated, then the variances are related as: Resistors in Parallel Analogy

18. RMS ERRORS RELATIONSHIPS Interestingly, we have control of Tfield rms error through more aggressive filtering. But at what price?

19. Plot of rms error and % filtered points vs.|Tfield – Tbuoy| Zenith from 0  to 53  TRADE-OFF: rms error vs. # buoy data points SST MC weakly affected by aggressive filtering Stability Low Dropoff

20. Plot of rms error and % filtered points vs.|Tfield – Tbuoy| Zenith from 53  to 70  TRADE-OFF: rms error vs. # buoy data points SST MC weakly affected by aggressive filtering StabilityLow Dropoff

21. Plot of rms error and % filtered points vs.|Tfield – Tbuoy| Zenith from 0  to 70  TRADE-OFF: rms error vs. # buoy data points SST MC weakly affected by aggressive filtering Stability Low Dropoff

22. Zenith from 0 to 53 |Tfield-Tbuoy| = 0.7 MCSST TfieldSST with K10 Blue  Red Color Plotting Red  Blue Color Plotting EVIDENCE OF REAL RMS ERROR REDUCTION

23. Zenith from 53 to 70 |Tfield-Tbuoy| = 0.7 MCSST TfieldSST with K10 Blue  Red Color Plotting Red  Blue Color Plotting EVIDENCE OF REAL RMS ERROR REDUCTION

24. Zenith from 0 to 70 |Tfield-Tbuoy| = 0.7 MCSST TfieldSST with K10 Blue  Red Color Plotting Red  Blue Color Plotting EVIDENCE OF REAL RMS ERROR REDUCTION

25. 25 HELP ME UNDERSTAND, LENA! SST MC

26. 26 SST MC T field + uncorrelated noise HELP ME UNDERSTAND, LENA!

27. 27 SST Tfield SST MC T field = + Always clearer image! HELP ME UNDERSTAND, LENA!

28. 28 A FRESH PERSPECTIVE TWO-PRONGED IMPROVEMENT EFFORTS Using Satellite Data Only! NAVOCEANO K100, K10, K2 IN SITU BUOY DATA HIGH ZENITH ANGLES (emissivity, sea roughness) HARDWARE (2 or more looks) CLOUD MASK

29. 29 CONCLUSIONS Although Tfield is used in NLSST, its additional information is tamed due to its appearance as a multiplier of T11-T12. Use of Tfield as a separate predictor results in a significant increase in accuracy for all existing SST algorithms, daytime and nighttime, due to resulting variance always being less. Tfield now is on equal footing with existing SST algorithms predicitions and improvements in its accuracy will benefit all combinations of existing SST algorithms and Tfield. TfieldSST algorithm was found to be extremely stable. Reduced to a formulation that leads to specific relationships between variances of MCSST and Tfield. NAVOCEANO’s Tfield characterizations, K100 and K10, only use previous satellite data. NAVOCEANO is currently working on K2, at 2km resolution. TfieldSST algorithm allows for rms error under 0.3  K over the full swath (0  to 70  ), while sacrificing a very modest number of buoy data points (from original 84.75%): 75% of buoy data points left from 0  to 70  with TfieldSST versus 55% of buoy data points left from from 0  to 53  PRESENTLY

30. 30 QUESTIONS ?

31. Adding Tdiffoffset to NLSST Tdiffoffset = 0 rmserror = 0.52546Tdiffoffset = -1 rmserror = 0.62069 Tdiffoffset = +10 rmserror = 0.44501 Tdiffoffset = +5 rmserror = 0.45413 Pinch effect moves to +1 Pinch effect Data Pinch effect disappears Scatter is more pronounced rmserror diminishes a c b d INCREASING T field IMPORTANCE