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June 24, 2012 Kay Kilpatrick University of Miami

Miami Comparisons based on RSMAS VIIRS buoy MUDB and VIIRS RT datasets ACSPO and IDPS algorithms and Coefficients. June 24, 2012 Kay Kilpatrick University of Miami. Methods. VIIRS RSMAS buoy MUDB was used to compare the IDPS and ASCPO algorithms Day time 2 band algorithms

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June 24, 2012 Kay Kilpatrick University of Miami

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  1. Miami Comparisons based on RSMAS VIIRS buoy MUDB and VIIRS RT datasets ACSPO and IDPS algorithms and Coefficients June 24, 2012 Kay Kilpatrick University of Miami

  2. Methods • VIIRS RSMAS buoy MUDB was used to compare the IDPS and ASCPO algorithms • Day time 2 band algorithms • ASCPO is a non stratified NLSST • Night time 3 band algorithms -ASCPO is a 5 term SST = a0 + a1*T11 + a2*T37+a3*T12+a4* (T37 – T12) *S θ + a5* S θ, S θ=sec(θ)-1 -IDPS is a 4 term SST = a0 + a1*T11 + a2* (T37 – T12) *(Ts0-273.15) + a3*S θ

  3. Both ACSPO and IDPS algorithms were evaluated using supplied coefficients from ACSPO and IDPS, and RSMAS trained coefficients. • RSMAS coefficients were trained using subsettedReynolds OI SST, every 4th pixel and 50 lines, as the in situ temperature in the regression NOT buoys, data were from Feb thru the 1st week of June,2012. • RSMAS Coefficients should produce a skin temperature compared to buoys • The RSMAS buoy MUDB is completely independent from any coefficient estimation • Only RSMAS quality 0 pixels were used in training and in analysis. RSMAS quality 0 tests for VIIRS are currently the same as those defined for MODIS SST and SST4. We do not use the VIIRS cloud mask at this time. • Buoys are from the GTS via the Navy and not IQUAM. • RT data set is only for month of Jan at satz 0,10,20,45,60

  4. 2 band day time algorithms and coefficientsRSMAS buoy MUDB (Jan thru May 2012 matchups)VIIRS –Buoy Findings: global values for STDEV and RMSE are similar for ACPSO and IDPS RSMAS trained ACSPO coefficients produce a bias very close those supplies by ACSPO. RSMAS trained IDPS coefficients produce a lower day bias than the IDPS supplied coeffs.

  5. Day time algo times series bias ACSPO IDPS ACSPO RSMAS sub0 Coefs IDPS RSMAS Sub0 Coefs Performance of stratified and non stratified NLSST algorithms are similar with time, on a global basis, with the exception of the ~0.2 offset IDPS trained coeffs.

  6. Day time algorithms times series STDEV ACSPO IDPS ACSPO RSMAS sub0 Coefs IDPS RSMAS Sub0 Coefs

  7. 3 band night time algorithms and coefficientsRSMAS buoy MUDB (Jan thru May 2012 matchups)VIIRS –Buoy RSMAS trained ACSPO coefficients produce similar results as ACSPO supplied Coefficients in regard to the stdev and RMS RSMAS trained IDPS produces similar results to ACSPO algorithm RSMAS trained IDPS coefficients have better RMS and bias than the IDPS supplied coffs. Suspect there continues to be an issue with IDPS coefficient estimation not the algorithm.

  8. 3band algorithm times series bias ACSPO IDPS Note scale change Large bias ACSPO RSMAS sub0 Coefs IDPS RSMAS Sub0 Coefs

  9. 3band algorithm times series stdev ACSPO IDPS ACSPO RSMAS sub0 Coefs IDPS RSMAS Sub0 Coefs

  10. 3band Median Bias as a function of latband ACSPO IDPS Note scale change Large bias ACSPO RSMAS sub0 Coefs IDPS RSMAS Sub0 Coefs >40S 40S to 20S 20S to EQ EQ to 20 N 20N to 40N >40N

  11. STDEV night 3 band algorithms as functions of latband ACSPO IDPS ACSPO RSMAS sub0 Coefs IDPS RSMAS Sub0 Coefs >40S 40S to 20S 20S to EQ EQ to 20 N 20N to 40N >40N

  12. 3band and stratified NLSST at night RSMAS sub0 Coefs ACSPO RSMAS sub0 Coefs IDPS RSMAS Sub0 Coefs IDPS Stratified NLSST Night RSMAS Sub0 Coefs >40S 40S to 20S 20S to EQ EQ to 20 N 20N to 40N >40N

  13. Coeffs applied to VIIRS RT data IDPS night time algorithm with RSMAS Coefficients has lower STDEV and RMS than stratified NLSST lower STDEV and RMS for ACSPO algorithm than either stratified nlsst or night 3band IDPS algorithm forms Note: RT is only for 1 month January angles ar 0,10,20,45,60

  14. Conclusions • RT simulated data suggests slightly better performance for the ACSPO night 3 band 5 term algorithm, however no season information for RT dataset in this analysis. • All algorithms perform equally when applied to REAL buoy – VIIRS matchups data Feb-May 2012 • Both the Day ACPSO and day IDPS stratified NLSST perform similarly on a global basis. • RSMAS trained ACPSO coefficients produce same results as ACPSO supplied coefficients. • Both the night 3 banded IDPS and the ACPSO show similar performance using RSMAS train coefficients trained with VIIRS BT’s and subsetted Reynolds SST. • To date, all 3 versions of the IDPS supplied coefficients tested with Miami MUDB have performed poorly. Suggesting a discrepancy with either the estimation method or training dataset used to determine IDPS coeffients and not the algorithm per se. • VIIRS will required 12 months of buoy data to truly evaluate and understand VIIRS sensor and algorithm performance. • Since all Algorithms perform the same on real data may want to wait until we better understand the sensor and have fully tested all potential candidate algorithms before advocating to change to a new algorithm form. Priority would be to update a set of validated IDPS coefficients.

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