Since the early 1990s, sensors on offshore moorings have been collecting continuous, long-term measurements of physical oceanographic and meteorological variables on the west Florida shelf.
What do you with over 20 years of data collected by University of South Florida College of Marine Science Coastal Ocean Monitoring and Prediction System (USF-CMS COMPS)? The answer is quite a lot, but it all begins with creating climatologies which are used to analyze seasonal and long-term variability about the climatological averages. An example of this type of analysis is found in Liu and Weisberg (2012), a peer reviewed paper that analyzed the seasonal variability of circulation and sea level on the west Florida continental shelf using the first 10 years of USF COMPS data.
SECOORA is committed to continuing long-term observations off the west Florida shelf. These ever-lengthening datasets collected by USF-CMS are opening opportunities for students to look past seasonal cycles and into multi-decadal changes that may reflect a changing climate (Figure 1). As part of his PhD dissertation, USF CMS graduate student and mooring technician, Jay Law, is updating this prior 10-year climatology work to include the full, updated twenty-plus year data set, which will allow for long term changes to be studied.
Webinar: Developing low-cost and open-source technologies for smart coastal communities
Join us Thursday, February 22nd at 12 PM ET for SECOORA's Coastal Observing in Your Community Webinar Series! Our speaker this month is Dr. Phil Bresnahan from the University of North Carolina Wilmington.
SECOORA Request for Quotes: Operate Nationwide Coastal Web Camera Network
SECOORA is soliciting proposals focused on installing, maintaining, and operating web cameras to scale from a regional to a national network of coastal web cameras (WebCOOS).
Meet the Winner of the 2023 SECOORA Data Challenge
Kaylee Mooney, a graduate student from Florida Gulf Coast University, is the winner of the SECOORA Data Challenge for her proposal Implementing Vulnerability into Historic Hurricane Normalizations.