Validation of genetic algorithm-based optimal sampling for ocean data assimilation

Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach...

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Bibliographic Details
Published in:Ocean dynamics 2016-10, Vol.66 (10), p.1209-1229
Main Authors: Heaney, Kevin D., Lermusiaux, Pierre F. J., Duda, Timothy F., Haley, Patrick J.
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric forcing
Atmospheric Sciences
Data assimilation
Data collection
Earth and Environmental Science
Earth Sciences
Fluid- and Aerodynamics
Genetic algorithms
Geophysics/Geodesy
Marine
Monitoring/Environmental Analysis
Oceanography
Sampling techniques
Validation studies
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Summary:Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach to determine optimal adaptive sampling that uses the genetic algorithm (GA) method is presented. The method determines sampling strategies that minimize a user-defined physics-based cost function. The method is evaluated using identical twin experiments, comparing hindcasts from an ensemble of simulations that assimilate data selected using the GA adaptive sampling and other methods. For skill metrics, we employ the reduction of the ensemble root mean square error (RMSE) between the “true” data-assimilative ocean simulation and the different ensembles of data-assimilative hindcasts. A five-glider optimal sampling study is set up for a 400 km × 400 km domain in the Middle Atlantic Bight region, along the New Jersey shelf-break. Results are compared for several ocean and atmospheric forcing conditions.
ISSN:1616-7341
1616-7228
DOI:10.1007/s10236-016-0976-5