Resources
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Related Research Areas
Carbon Cycle & Ecosystems, Water & Energy Cycles

The efficiency and fidelity of forecast models is dramatically improved by multidimensional data assimilation. We propose to utilize an Ensemble Kalman Filter (ENKF) to assimilate in situ and satellite data such as high-frequency radar currents, optimal interpolated sea surface temperature (OI SST), chlorophyll a, species-specific phytoplankton retrievals, and phytoplankton cell counts into a multi-model simulation of harmful algal blooms (HAB) in the eastern Gulf of Mexico (GOM). HABs occur annually in the eastern GOM causing millions of dollars in economic losses to fisheries and tourism. The Harmful Algal Bloom SIMulation (HABSIM) is an interdisciplinary state-of-the-art regional ocean modeling system designed to predict HAB events. The physical model consists of a USF adaptation of the Regional Ocean Modeling System (ROMS) nested in the Hybrid Coordinate Model (HYCOM) to link the coastal and deep-ocean and the Finite Volume Coastal Ocean Model (FVCOM) to link to the estuaries. The biogeochemical model combines four phytoplankton types, nutrients, spectral light, a benthic submodel, and a dust submodel to evaluate the role of each potential nutrient source for support of red tide blooms in the GOM. This proposal addresses specific research incorporating data assimilation to provide quantitative estimates of key variables to better constrain the dynamical solutions. We, therefore, intend to combine our biophysical models with data assimilation algorithms and observational data to improve error estimation, subsequently improving the HAB predictive efficiency and adaptive sampling protocols on the eastern GOM. The major goal of this proposal is thus to examine the inclusion of data assimilative products as an Ensemble Kalman Filter to improve model performance with transfer of resultant forecast products to state and local management agencies.

Project PI: Jason Lenes/University of South Florida

Center for Prediction of Red Tides College of Marine Science University of South Florida 140 7th Ave S St. Petersburg, FL 33701

Phone: (727) 553-1112

Email: lenes@marine.usf.edu

http://gcoos.tamu.edu/meetingreports/2009_Oct/documents/Lenes_Jason.pdf

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Started: Sep 27, 2010

Last Activity: Jan 04, 2011

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