Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests
The existence of multiple wave forecasts leads to the question of which one should be used in practical ocean engineering applications. Ensemble forecasts have emerged as an important complement to deterministic forecasts, with better performances at mid-to-long ranges; however, they add another opt...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/9/3/298 |
| _version_ | 1797412322910666752 |
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| author | Ricardo M. Campos Mariana O. Costa Fabio Almeida C. Guedes Soares |
| author_facet | Ricardo M. Campos Mariana O. Costa Fabio Almeida C. Guedes Soares |
| author_sort | Ricardo M. Campos |
| collection | DOAJ |
| description | The existence of multiple wave forecasts leads to the question of which one should be used in practical ocean engineering applications. Ensemble forecasts have emerged as an important complement to deterministic forecasts, with better performances at mid-to-long ranges; however, they add another option to the variety of wave predictions that are available nowadays. This study developed random forest (RF) postprocessing models to identify the best wave forecast between two National Centers for Environmental Protection (NCEP) products (deterministic and ensemble). The supervised learning classifier was trained using National Data Buoy Center (NDBC) buoy data and the RF model accuracies were analyzed as a function of the forecast time. A careful feature selection was performed by evaluating the impact of the wind and wave variables (inputs) on the RF accuracy. The results showed that the RF models were able to select the best forecast only in the very short range using input information regarding the significant wave height, wave direction and period, and ensemble spread. At forecast day 5 and beyond, the RF models could not determine the best wave forecast with high accuracy; the feature space presented no clear pattern to allow for successful classification. The challenges and limitations of such RF predictions for longer forecast ranges are discussed in order to support future studies in this area. |
| first_indexed | 2024-03-09T05:01:31Z |
| format | Article |
| id | doaj.art-dbdd8e93e6a64e19a2b3015fefa499f9 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T05:01:31Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-dbdd8e93e6a64e19a2b3015fefa499f92023-12-03T13:00:20ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-03-019329810.3390/jmse9030298Operational Wave Forecast Selection in the Atlantic Ocean Using Random ForestsRicardo M. Campos0Mariana O. Costa1Fabio Almeida2C. Guedes Soares3Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalCentre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalCentre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalCentre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalThe existence of multiple wave forecasts leads to the question of which one should be used in practical ocean engineering applications. Ensemble forecasts have emerged as an important complement to deterministic forecasts, with better performances at mid-to-long ranges; however, they add another option to the variety of wave predictions that are available nowadays. This study developed random forest (RF) postprocessing models to identify the best wave forecast between two National Centers for Environmental Protection (NCEP) products (deterministic and ensemble). The supervised learning classifier was trained using National Data Buoy Center (NDBC) buoy data and the RF model accuracies were analyzed as a function of the forecast time. A careful feature selection was performed by evaluating the impact of the wind and wave variables (inputs) on the RF accuracy. The results showed that the RF models were able to select the best forecast only in the very short range using input information regarding the significant wave height, wave direction and period, and ensemble spread. At forecast day 5 and beyond, the RF models could not determine the best wave forecast with high accuracy; the feature space presented no clear pattern to allow for successful classification. The challenges and limitations of such RF predictions for longer forecast ranges are discussed in order to support future studies in this area.https://www.mdpi.com/2077-1312/9/3/298wave forecastsrandom forestsdecision treesnumerical wave modelingensemble forecastingextreme events |
| spellingShingle | Ricardo M. Campos Mariana O. Costa Fabio Almeida C. Guedes Soares Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests Journal of Marine Science and Engineering wave forecasts random forests decision trees numerical wave modeling ensemble forecasting extreme events |
| title | Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests |
| title_full | Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests |
| title_fullStr | Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests |
| title_full_unstemmed | Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests |
| title_short | Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests |
| title_sort | operational wave forecast selection in the atlantic ocean using random forests |
| topic | wave forecasts random forests decision trees numerical wave modeling ensemble forecasting extreme events |
| url | https://www.mdpi.com/2077-1312/9/3/298 |
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