Marine Science Faculty Publications

Building an Automated Integrated Observing System to Detect Sea Surface Temperature Anomaly Events in the Florida Keys

C. Hu
Frank E. Muller-Karger, University of South Florida
Brock Murch, University of South Florida
D. Myhre
J. Taylor
R. Luerssen
C. Moses
Mingrui Zhang, University of South Florida
L. Gramer
J. Hendee


Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geo-correction, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47 000 AVHRR images (1993-2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and ±3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent (±0.5 h) data from the SEAKEYS in situ stations in the Florida Keys showed near-zero bias errors (< 0.05 °C) in the weekly anomaly for SST anomalies between -3 °C and 3 °C, with standard deviations < 0.5 °C. The cloud filter was implemented using Interactive Data Language for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential for coral reef stress. The mean and anomaly products are updated weekly, with periodic updates of the DHW products, on a Web site. The SST data at specific geographical locations were also automatically ingested in near real time into National Oceanic and Atmospheric Administration's (NOAA) Integrated Coral Observing Network Web-based application to assist in management and decision making through a novel expert system tool (G2) implemented at NOAA.