1. Sandra Castro and Gary Wick

3.  Does direct regression of satellite infrared brightness temperatures to observed in situ skin temperatures result in SST-product accuracy improvements over traditional regression retrievals to the subsurface temperature?

4.  We evaluated parallel skin and subsurface MCSST-type models using coincident in situ skin and SST-at-depth measurements from research-quality ship data.  RMS accuracy of skin and SST-at-depth regression equations directly compared

5. ROYAL CARIBBEAN EXPLORER OF THE SEAS SKIN SST: M-AERI INTERFEROMETER (RSMAS) SST-AT-DEPTH: SEA-BIRD THERMOMETER (SBE-38) @ 2M NOAA R/V RONALD H. BROWN SKIN SST: CIRIMS RADIOMETER (APL) SST-AT-DEPTH: SEA-BIRD THERMOMETER (SBE-39) @ 2M Satellite IR: 2003-2005 AVHRR/N17 GAC and LAC NAVOCEANO BTs

6.  Spatial windows: 25, 8, 4 km  Temporal windows: 4, 1 hrs All Mean Min Time

7. There is no improvement in accuracy for the skin-only SST products in spite of the more direct relationship between the satellite BTs and the in situ skin SST observations!

8.  Regression models fitted to the SST-at-depth almost always resulted in better accuracies (lower RMSE) than skin models regardless of:  Algorithm type (split vs. dual window)  Collocation criteria (window bounds/sampling)  Satellite spatial resolution (GAC vs. LAC)  Radiometric sensor (CIRIMS and M-AERI)  Time of day (day, night, day and night)  Time span (individual years vs. all years combined)  Geographical region

9.  Can differences in measurement uncertainty and spatial variability explain the lack of accuracy improvement in the skin SST retrievals?

10. M-AERI CIRIMS The variogram (Cressie, 1993, Kent et al., 1999) is a means by which it is possible to isolate the individual contributions from the 2 sources of variability, since the behavior at the origin yields an estimate of the measurement error variance, while the slope provides an indication of the changes in natural variability with separation distance.

11. We fitted a linear variogram model by weighted least squares to both skin SST and SST-at-depth with separation distances up to 200 km, and extrapolated to the origin to obtain the variance at zero lag. M-AERI Measurement Uncertainty Spatial Variability

12. MethodVariogramGraphic Error Contribution Measurement Uncertainty Spatial Variability Total Variability Supplemental Noise M-AERIo.100.070.120.15 CIRIMSo.070.070.100.23 C & M0.120.100.160.17 Variogram estimates provide strong support to the notion that the combined role of differences in measurement uncertainty and spatial variability between the skin and SST-at-depth account for the range of required subsurface supplemental noise found graphically. On spatial scales of O(25 km):

13.  Key implication is that differences between the spatial variability of the skin and subsurface temperature matter  Based on these results we started to explore the contribution of spatial variability (and specifically sub-pixel variability) to the total uncertainty of satellite retrievals

14.  Total Uncertainty estimate from 3-D LUT:  Methodology from Castro et al., 2008 (JGR, doi:10.129/2006JC003829)  Wind, WVP, and SST are the main contributors to the MW SST uncertainties  Build Bias and STD LUT as a function of Wind, WVP, and SST from matchups between AMSR-E and buoys for 2006 (predecessor of the hypercube)  Buoy matchups included drifting and moored open-ocean buoys from the GTS. Coastal buoys excluded  Temporal collocation: buoy matchups within 1 hour of satellite overpass.

15. From literature: MW SSTs have a mean bias of -0.07 deg C and STD of 0.57 deg C relative to TAO/TRITON and PIRATA SSTs. Day Night

16.  Total AMSR-E Uncertainty is characterized by the STD LUT, with STDs ~ 0.3 – 0.66 deg C DaytimeNighttime

17.  Sub-pixel Variability:  STD of the 4-km NAVOAVHRR SST pixels within the 25-km AMSR-E SST grid cells DaytimeNighttime

18.  The contribution of sub-pixel variability to the total uncertainty is estimated to be ~20-35% for daytime and ~10-25% at nighttime DaytimeNighttime

19.  Looked at an uncertainty estimate, independent from the buoys, using AMSR-E data alone:  Subtracted first three harmonics from the 2006 AMSRE SST time series at each pixel to remove SST annual cycle  Computed minimum of a moving-average running STD, using a Hanning window of length 13 days  This is not the same as the LUT since the running STD includes additional factors such as temporal variability in addition to spatial variability and instrument noise

20.  Basic noise floor appears on order of 0.1 K  Dynamic features still remain related to temporal variability  Better isolation of effects ideally required DaytimeNighttime

21.  After some iterations to remove the dynamic features, a first-degree approximation for the instrument self-consistency was found

22.  The contribution of self-noise to the total uncertainty is ~30-55% for both daytime and nighttime DaytimeNighttime

24.  Assumption: Retrieval error makes up for the remaining difference of the LUT total uncertainty  Improved isolation of effects ideally required Daytime Nighttime

25.  Removal of the near-surface temperature profile effects through directly fitting the regression models to radiometric skin SSTs did not result in satellite SST estimates with better accuracy than regression models fit to coincident, research-grade ship-borne subsurface SSTs.  Lack of improvement was consistent with the expected impact of both measurement uncertainty and enhanced spatial variability of the skin observations.  Initial attempts were made to isolate components of the spatial variability in AMSR-E retrievals.  Sub-pixel variability estimated to be responsible for~20% of the total uncertainty estimated from collocations against buoy observations.  Additional studies explored isolating the self noise and retrieval error components of the uncertainty, but better separation of the various effects is still required.

26.  Mean “Retrieval” Bias Estimate:  Mean bias of the AMSR-E SST minus the averaged AVHRR SST pixels within the AMSR-E cell  AVHRR not perfect reference. There are biases in both SST products! NighttimeDaytime

27.  Gap at the daytime crossing in the ascending pass due to the way the AMSR-E is gridded

28.  The contribution of retrieval error to the total uncertainty is up to 60% for both daytime and nighttime at the high latitudes