Error characterization of infrared and microwave satellite sea surface temperature products for merging and analysis
Article first published online: 11 MAR 2008
Copyright 2008 by the American Geophysical Union.
Journal of Geophysical Research: Oceans (1978–2012)
Volume 113, Issue C3, March 2008
How to Cite
2008), Error characterization of infrared and microwave satellite sea surface temperature products for merging and analysis, J. Geophys. Res., 113, C03010, doi:10.1029/2006JC003829., , , and (
- Issue published online: 11 MAR 2008
- Article first published online: 11 MAR 2008
- Manuscript Accepted: 24 OCT 2007
- Manuscript Revised: 28 AUG 2007
- Manuscript Received: 20 JUL 2006
- error characterization;
- data fusion and blending
 The complementary nature of infrared and microwave sea surface temperature (SST) products provides opportunities for merging the data, but significant differences among the products must first be understood and characterized. This paper presents a method for specifying the uncertainty in existing infrared and microwave SST products in terms of available satellite-derived parameters, explores which environmental and sensor parameters contribute most to the errors, and determines if the derived uncertainty estimates can enable significant reductions in the differences among the SST products. Collocations among the satellite retrievals and SST measurements from buoys are used to derive the error estimates. Simple multidimensional lookup tables expressing the expected bias and standard deviation of individual retrievals as functions of various combinations of parameters provide an effective characterization of the uncertainty suitable for operational applications. Bias adjustments derived from the satellite zenith angle, 11–12 μm brightness temperature difference, and SST for the infrared, and wind speed, water vapor content, and SST for the microwave products significantly reduce the variability of differences both between the satellite products and with buoy observations. Further improvements are possible through inclusion of additional independent corrections based on the climatological SST anomaly and aerosol optical depth. These corrections reduce the monthly rms difference between the products by as much as 42% and reduce differences with independent buoys by as much as 10%. The largest individual corrections are to the infrared data in regions of low SST, but the microwave corrections are more geographically distributed and affect more retrievals.