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Abstract
The data interpolating empirical orthogonal function (DINEOF) method is applied to concurrent MODIS sea surface temperature (SST) and chlorophyll-a (chl-a) data to produce daily, 4 km, cloud-free SST and chl-a analyses for the Gulf of Mexico (GOM) from 2003 to 2009. Comparisons between SST analysis and in situ buoy temperature measurements indicate that the DINEOF method can accurately resolve temperature variability and solve the cloud-cover problem, which is a typical issue of remote-sensing observations. Based on significant correlations between cloud-free chl-a, SST and sea surface height (SSH) data in the GOM, a simple chl-a statistical prediction model is further developed. Favourable comparisons between model solutions and independent satellite chl-a observations indicate that the statistical model provides a feasible means to predict GOM chl-a field based on existing SST and SSH information, allowing for observational gap fillings when concurrent chl-a data were not available.
Acknowledgements
We are grateful to the funding support provided by NSF through grant OCE-1044573 and by DOE/RPSEA through GOMEX_PPP project. Result analyses presented here are part of Y. Zhao's master thesis research at North Carolina State University.