Crowley, M. F. Rutgers University, email@example.com
Fracassi, J. F. Rutgers University, firstname.lastname@example.org
Glenn, S. M. Rutgers University, email@example.com
USING REAL-TIME REMOTE SENSING AND IN SITU OCEAN DATA FOR ADAPTIVE SAMPLING AND DATA ASSIMILATIVE MODELING
A real-time remote sensing and in-situ observation network centered on the Long-term Ecosytem Observatory (LEO-15) was operated during the July 1998 NOPP-sponsored Coastal Predictive Skill Experiment. Satellite-derived sea surface temperature (SST), CODAR-derived surface currents, locally-observed meteorology, and subsurface temperature, salinity and current profiles from LEO-15 were autonomously acquired, processed and displayed on the World Wide Web. Web access by scientists and the general public peaked at 33,000 hits/day during the experiment. The satellite SST images were individually declouded using customized algorithms to maximize the data available for assimilation by the numerical forecast model. SeaWiFS ocean color data also was acquired in real-time, but its value must be re-assessed pending NASA's release of new processing software. Geographical Information System (GIS) overlays of sea surface temperature, surface currents, and in situ subsurface data used each morning to brief the ship and AUV operators provided the most useful information for adaptive sampling mission planning. Events sampled include the relaxation of a late June upwelling event, a relatively quiescent period between upwellings, the start of a second upwelling event that progressed from north to south along the coast, the formation of a typical upwelling center near LEO-15, and its relaxation and demise in a late July mixing storm.
Day: Friday, Feb. 5
Time: 08:45 - 09:00am
Location: Hilton of Santa Fe