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...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2016
|
| Online Access: | http://hdl.handle.net/1721.1/106031 https://orcid.org/0000-0002-1869-3883 |
| _version_ | 1826205253575376896 |
|---|---|
| author | Heaney, Kevin D. Duda, Timothy F. Lermusiaux, Pierre Haley, Patrick |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Heaney, Kevin D. Duda, Timothy F. Lermusiaux, Pierre Haley, Patrick |
| author_sort | Heaney, Kevin D. |
| collection | MIT |
| 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 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. |
| first_indexed | 2024-09-23T13:09:47Z |
| format | Article |
| id | mit-1721.1/106031 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:09:47Z |
| publishDate | 2016 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1060312022-09-28T12:22:26Z Validation of genetic algorithm-based optimal sampling for ocean data assimilation Heaney, Kevin D. Duda, Timothy F. Lermusiaux, Pierre Haley, Patrick Massachusetts Institute of Technology. Department of Mechanical Engineering Lermusiaux, Pierre Haley, Patrick 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. Space and Naval Warfare Systems Center San Diego (U.S.). Small Business Innovation Research Program United States. Office of Naval Research (Grants N00014-14-1-0476, N00014-12-1-0944 and N00014-11-1-0701 ) 2016-12-22T15:47:01Z 2017-06-19T21:40:53Z 2016-08 2016-10-08T04:02:31Z Article http://purl.org/eprint/type/JournalArticle 1616-7341 1616-7228 http://hdl.handle.net/1721.1/106031 Heaney, Kevin D. et al. “Validation of Genetic Algorithm-Based Optimal Sampling for Ocean Data Assimilation.” Ocean Dynamics 66.10 (2016): 1209–1229. https://orcid.org/0000-0002-1869-3883 en http://dx.doi.org/10.1007/s10236-016-0976-5 Ocean Dynamics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ Springer-Verlag Berlin Heidelberg application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Heaney, Kevin D. Duda, Timothy F. Lermusiaux, Pierre Haley, Patrick Validation of genetic algorithm-based optimal sampling for ocean data assimilation |
| title | Validation of genetic algorithm-based optimal sampling for ocean data assimilation |
| title_full | Validation of genetic algorithm-based optimal sampling for ocean data assimilation |
| title_fullStr | Validation of genetic algorithm-based optimal sampling for ocean data assimilation |
| title_full_unstemmed | Validation of genetic algorithm-based optimal sampling for ocean data assimilation |
| title_short | Validation of genetic algorithm-based optimal sampling for ocean data assimilation |
| title_sort | validation of genetic algorithm based optimal sampling for ocean data assimilation |
| url | http://hdl.handle.net/1721.1/106031 https://orcid.org/0000-0002-1869-3883 |
| work_keys_str_mv | AT heaneykevind validationofgeneticalgorithmbasedoptimalsamplingforoceandataassimilation AT dudatimothyf validationofgeneticalgorithmbasedoptimalsamplingforoceandataassimilation AT lermusiauxpierre validationofgeneticalgorithmbasedoptimalsamplingforoceandataassimilation AT haleypatrick validationofgeneticalgorithmbasedoptimalsamplingforoceandataassimilation |