0

Full Content is available to subscribers

Subscribe/Learn More  >

An Ensemble Kalman Filter Data Assimilation Scheme for Modeling the Wave Climate in Persian Gulf

[+] Author Affiliations
Nima Serpoushan, Mostafa Zeinoddini

K.N. Toosi University of Technology, Tehran, Tehran, Iran

Maziar Golestani

DHI, Horsholm, Denmark

Paper No. OMAE2013-10399, pp. V005T06A028; 9 pages
doi:10.1115/OMAE2013-10399
From:
  • ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 5: Ocean Engineering
  • Nantes, France, June 9–14, 2013
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-5539-3
  • Copyright © 2013 by ASME

abstract

In recent years, application and evaluation of the efficiency of different data assimilation methods has been a subject of interest in both wave hindcasting and forecasting systems. The main goal of the current study is to assess the efficiency of an ensemble Kalman filter (EnKF) data assimilation scheme in improving the wave simulation results in Persian Gulf. The so called region plays an important role in the oil and gas industry due to its Geographical and Morphological location and housing a large number of offshore platforms.

A third generation wave model, SWAN, was employed in order to simulate the wave fields in the region. The three hours updated ECMWF wind data were used as the main driving force. The OpenDA toolbox, especially developed for efficient data assimilation purposes, was employed to smooth the chaotic nature of the non-linear wave simulation scheme. The OpenDA utilizes a number of methods that are based on Kalman filter algorithm but do not require the amount of computation efforts that are incurred by the classical filter algorithm. The EnKF is a variant of Kalman filter, where probability density function of a model state is represented by an ensemble of the model state.

Two sets of records for significant wave heights and peak wave periods were used in the analysis process with EnKF to estimate the error covariance matrix. At analysis time, the forecast error covariance was computed by using the model forecasts ensembles. In overall and for the wave climate modeling, the initial conditions of the numerical model were updated using the improved system state, up to the current computing time level. This is achieved by incorporating the previous measurements into the Kalman filter algorithm. The model was then run into the future, driven by the new improved state conditions.

The statistical results and diagrams showed that applying EnKF scheme leads to a noticeable improvement in significant wave heights. However, the accuracy of this technique was subjected to the location and number of observation stations and also ensemble size. With larger ensembles, results of error covariance estimation are more accurate but there is a limitation due to execution time of process and efficiency of the computations.

Copyright © 2013 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In