### Abstract

Ocean currents are an important consideration throughout the life cycle of the many offshore projects. These currents are complex, three dimensional, dynamic and as yet poorly characterized in a statistical sense. Numerical ocean circulation models are increasingly sophisticated and are beginning to capture the structure and variability of complex ocean current systems. The starting point for model-based characterization of currents is a long time series of model outputs obtained at high spatial and temporal resolution. There are an ever-increasing number of model products, but it is not clear how to identify suitable products for a given application. Frequently, a familiar product is chosen that may not be the best choice. Here, we present an alternative approach wherein a collection of model runs, referred to as an ensemble, is used to estimate ocean current statistics at points of interest. Unlike other ensemble methods where the ensemble is used to estimate the statistics directly, we use the ensemble to construct a surrogate ocean model or an emulator using polynomial expansions. This emulator is computationally inexpensive to run and is used to sample the model outputs for large numbers of model inputs to generate full probability distributions of the model state, which can then be used to derive statistics required for design criteria. We have used the above technique to build an emulator for a numerical circulation model of the Gulf of Mexico. We present statistics of the Loop Current derived from this approach and briefly compare it with statistics obtained from measurements and other available long time-series of model outputs. Probability distributions for a sample point in the vicinity of the Loop Current are presented. It is shown that the technique can provide robust statistics and complements existing techniques.

Original language | English (US) |
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Title of host publication | Offshore Technology Conference 2019, OTC 2019 |

Publisher | Offshore Technology Conference |

ISBN (Electronic) | 9781613996416 |

State | Published - Jan 1 2019 |

Event | Offshore Technology Conference 2019, OTC 2019 - Houston, United States Duration: May 6 2019 → May 9 2019 |

### Publication series

Name | Proceedings of the Annual Offshore Technology Conference |
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Volume | 2019-May |

ISSN (Print) | 0160-3663 |

### Conference

Conference | Offshore Technology Conference 2019, OTC 2019 |
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Country | United States |

City | Houston |

Period | 5/6/19 → 5/9/19 |

### Fingerprint

### ASJC Scopus subject areas

- Safety, Risk, Reliability and Quality
- Ocean Engineering
- Energy Engineering and Power Technology
- Mechanical Engineering

### Cite this

*Offshore Technology Conference 2019, OTC 2019*(Proceedings of the Annual Offshore Technology Conference; Vol. 2019-May). Offshore Technology Conference.

**Ocean current statistics in the Gulf of Mexico derived from an ensemble approach.** / Srinivasan, Ashwanth; Sharma, Neha; Gustafson, Drew; Iskandarani, Mohamed; Knio, Omar; Thacker, Carlisle.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Offshore Technology Conference 2019, OTC 2019.*Proceedings of the Annual Offshore Technology Conference, vol. 2019-May, Offshore Technology Conference, Offshore Technology Conference 2019, OTC 2019, Houston, United States, 5/6/19.

}

TY - GEN

T1 - Ocean current statistics in the Gulf of Mexico derived from an ensemble approach

AU - Srinivasan, Ashwanth

AU - Sharma, Neha

AU - Gustafson, Drew

AU - Iskandarani, Mohamed

AU - Knio, Omar

AU - Thacker, Carlisle

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Ocean currents are an important consideration throughout the life cycle of the many offshore projects. These currents are complex, three dimensional, dynamic and as yet poorly characterized in a statistical sense. Numerical ocean circulation models are increasingly sophisticated and are beginning to capture the structure and variability of complex ocean current systems. The starting point for model-based characterization of currents is a long time series of model outputs obtained at high spatial and temporal resolution. There are an ever-increasing number of model products, but it is not clear how to identify suitable products for a given application. Frequently, a familiar product is chosen that may not be the best choice. Here, we present an alternative approach wherein a collection of model runs, referred to as an ensemble, is used to estimate ocean current statistics at points of interest. Unlike other ensemble methods where the ensemble is used to estimate the statistics directly, we use the ensemble to construct a surrogate ocean model or an emulator using polynomial expansions. This emulator is computationally inexpensive to run and is used to sample the model outputs for large numbers of model inputs to generate full probability distributions of the model state, which can then be used to derive statistics required for design criteria. We have used the above technique to build an emulator for a numerical circulation model of the Gulf of Mexico. We present statistics of the Loop Current derived from this approach and briefly compare it with statistics obtained from measurements and other available long time-series of model outputs. Probability distributions for a sample point in the vicinity of the Loop Current are presented. It is shown that the technique can provide robust statistics and complements existing techniques.

AB - Ocean currents are an important consideration throughout the life cycle of the many offshore projects. These currents are complex, three dimensional, dynamic and as yet poorly characterized in a statistical sense. Numerical ocean circulation models are increasingly sophisticated and are beginning to capture the structure and variability of complex ocean current systems. The starting point for model-based characterization of currents is a long time series of model outputs obtained at high spatial and temporal resolution. There are an ever-increasing number of model products, but it is not clear how to identify suitable products for a given application. Frequently, a familiar product is chosen that may not be the best choice. Here, we present an alternative approach wherein a collection of model runs, referred to as an ensemble, is used to estimate ocean current statistics at points of interest. Unlike other ensemble methods where the ensemble is used to estimate the statistics directly, we use the ensemble to construct a surrogate ocean model or an emulator using polynomial expansions. This emulator is computationally inexpensive to run and is used to sample the model outputs for large numbers of model inputs to generate full probability distributions of the model state, which can then be used to derive statistics required for design criteria. We have used the above technique to build an emulator for a numerical circulation model of the Gulf of Mexico. We present statistics of the Loop Current derived from this approach and briefly compare it with statistics obtained from measurements and other available long time-series of model outputs. Probability distributions for a sample point in the vicinity of the Loop Current are presented. It is shown that the technique can provide robust statistics and complements existing techniques.

UR - http://www.scopus.com/inward/record.url?scp=85066616387&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066616387&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85066616387

T3 - Proceedings of the Annual Offshore Technology Conference

BT - Offshore Technology Conference 2019, OTC 2019

PB - Offshore Technology Conference

ER -