Publication Date:
2022-05-26
Description:
Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ocean Dynamics 66 (2016): 1209–1229, doi:10.1007/s10236-016-0976-5.
Description:
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 5-glider optimal sampling study is set up for a 400 km x
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.
Description:
This work was supported in part by a Space and Naval Warfare Center (SPAWAR) SBIR
program. PFJL and PJH are also grateful to the Office of Naval Research for partial support under grants N00014-14-1-0476
(Science of Autonomy LEARNS), N00014-11-1-0701 (MURI-IODA) and N00014-12-1-0944 (ONR6.2) to the Massachusetts
Institute of Technology. TFD’s contribution was funded by the SBIR and grant N00014- 11-1-0701 (MURI-IODA).
Description:
2017-08-19
Keywords:
Genetic algorithms
;
Ocean technology
;
Optimization methods
;
Sampling methods
;
Adaptive sampling
;
Computational ocean modeling
;
Data assimilation
;
Error subspace statistical estimation
;
OSSE
Repository Name:
Woods Hole Open Access Server
Type:
Preprint
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