November 28, 2006 Derivation and Evaluation of Multi- Sensor SST Error Characteristics Gary Wick 1 and Sandra Castro 2 1 NOAA Earth System Research Laboratory.

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Presentation transcript:

November 28, 2006 Derivation and Evaluation of Multi- Sensor SST Error Characteristics Gary Wick 1 and Sandra Castro 2 1 NOAA Earth System Research Laboratory 2 CCAR, University of Colorado

MISST Project Review November 28, 2006 Objectives Definition of AVHRR SST error characteristics –Identification of relevant parameters Objective evaluation of effectiveness and significance of different error formulations

MISST Project Review November 28, 2006 Observed Differences Between Infrared and Microwave Products Detailed comparisons between infrared and microwave SST products show complex spatial and temporal differences.

MISST Project Review November 28, 2006 Data Sources Infrared Satellite Data –AVHRR Operational NLSST - Naval Oceanographic Office Microwave Satellite Data –AMSR-E –TRMM Microwave Imager (TMI) Remote Sensing Systems – Wentz and Gentemann Buoys –QC’d GTS buoys via NCEP/CDC

MISST Project Review November 28, 2006 Approach Bias and rms error estimates derived from collocations with buoy data Determined dependence of uncertainties on sensor and environmental parameters Uncertainty estimates expressed through multi-dimensional look-up tables –Can compute for each retrieval –Bin sizes a function of sensor and number of parameters Effectiveness of parameter combinations evaluated through reduction in sensor-buoy and sensor-sensor differences

MISST Project Review November 28, 2006 Uncertainty Sources Considered Wind speed Water vapor Sea surface temperature Air-sea temperature difference Climatological SST anomaly Satellite zenith angle AVHRR channel 4 – channel 5 Aerosol optical depth Number of clear retrievals within 20 km

MISST Project Review November 28, 2006 AVHRR Uncertainty Sources Nighttime NLSST,

MISST Project Review November 28, 2006 AVHRR Uncertainty Sources (2)

MISST Project Review November 28, 2006 AVHRR Uncertainty Sources (3)

MISST Project Review November 28, 2006 Formulation Evaluation Impact on satellite – buoy SST differences Impact on microwave – infrared satellite SST product differences –Evaluates derived biases only Impact on simplified infrared/microwave optimal interpolation analysis –Evaluates effect of both biases and variability estimates

MISST Project Review November 28, 2006 Buoy Evaluation Applied derived bias adjustments to collocated satellite observations Computed change in standard deviation of the satellite – buoy differences Done for both dependent (reanalysis) and independent (operational) periods

MISST Project Review November 28, 2006 Sample Bias Adjustments 6/04

MISST Project Review November 28, 2006 Evaluation of Bias Adjustments AVHRR N-17, Nighttime Dependent Data 2004 Independent Data Oct-Dec 2003 Terms Net STD Reduction (%) STD Change for Points Adjusted (%) Points Adjusted (%) Net STD Reduction (%) STD Reduction for Points Adjusted (%) Points Adjusted (%) SZA, C45, SST SZA, WV, SST

MISST Project Review November 28, 2006 Evaluation of Bias Adjustments AVHRR N-17, Nighttime Dependent Data 2004 Independent Data Oct-Dec 2003 Terms Net STD Reduction (%) STD Change for Points Adjusted (%) Points Adjusted (%) Net STD Reduction (%) STD Reduction for Points Adjusted (%) Points Adjusted (%) SZA, C45, SST SZA, WV, SST SZA, C45, SST + SST-CSST

MISST Project Review November 28, 2006 Evaluation of Bias Adjustments AVHRR N-17, Nighttime Dependent Data 2004 Independent Data Oct-Dec 2003 Terms Net STD Reduction (%) STD Change for Points Adjusted (%) Points Adjusted (%) Net STD Reduction (%) STD Reduction for Points Adjusted (%) Points Adjusted (%) SZA, C45, SST SZA, WV, SST SZA, C45, SST + SST-CSST

MISST Project Review November 28, 2006 Evaluation of Bias Adjustments AMSR-E, Nighttime Dependent Data 2004 Independent Data Oct-Dec 2003 Terms Net STD Reduction (%) STD Change for Points Adjusted (%) Points Adjusted (%) Net STD Reduction (%) STD Reduction for Points Adjusted (%) Points Adjusted (%) WS, WV, SST WS, WV, SST, T s -T a WS, WV, SST + SST-CSST

MISST Project Review November 28, 2006 Evaluation of Bias Adjustments AMSR-E, Nighttime Dependent Data 2004 Independent Data Oct-Dec 2003 Terms Net STD Reduction (%) STD Change for Points Adjusted (%) Points Adjusted (%) Net STD Reduction (%) STD Reduction for Points Adjusted (%) Points Adjusted (%) WS, WV, SST WS, WV, SST, T s -T a WS, WV, SST + SST-CSST

MISST Project Review November 28, 2006 Evaluation of Bias Adjustments AMSR-E, Nighttime Dependent Data 2004 Independent Data Oct-Dec 2003 Terms Net STD Reduction (%) STD Change for Points Adjusted (%) Points Adjusted (%) Net STD Reduction (%) STD Reduction for Points Adjusted (%) Points Adjusted (%) WS, WV, SST WS, WV, SST, T s -T a WS, WV, SST + SST-CSST

MISST Project Review November 28, 2006 Impact on Product Differences Compared impact of different corrections on IR-MW product differences Greatest reduction in STD from: –IR: SZA, C45, SST –MW: WS, WV, SST Before Adjustment After Adjustment Bias: K RMS: 0.58 K Bias: K RMS: 0.45 K

MISST Project Review November 28, 2006 Impact on Product Differences (2) Before Adjustment After Adjustment Bias: K RMS: 0.74 K Bias: K RMS: 0.43 K TMI SST – AVHRR NLSST, June 2000

MISST Project Review November 28, 2006 Impact on Analyzed Product Utilizes modified version of Reynolds and Smith (1994) optimal interpolation method Bias corrections and product uncertainties taken directly from multi- dimensional tables Accuracy evaluated using buoy observations TMI/MCSST August 2000 Bias (K)STD (K) No Corrections Bias and Uncertainty corrections

MISST Project Review November 28, 2006 Conclusions Detailed new error characteristics derived for infrared AVHRR data Multi-dimensional tables provide expected bias and STD as a simultaneous function of several environmental and sensor parameters Objective evaluations demonstrate most effective error formulations –Significant dependence at low SST –Benefit from climatological SST departure In conversation with NAVO to explore operational implementation Derived biases enable significant reductions in product differences (as much as 42%)