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Spatial variability of long-term trends in significant wave height over the Gulf of Gdańsk using System Identification techniques Cover

Spatial variability of long-term trends in significant wave height over the Gulf of Gdańsk using System Identification techniques

Oceanological and Hydrobiological Studies
Volume 47 (2018): Issue 2 (June 2018)
By: Jordan Badur and  Witold Cieślikiewicz  
Open Access
|Jun 2018

Figures & Tables

Figure 1

Flow diagram for wind velocity and significant wave height simulations. Non-simulated data, used for model identification and simulation, are included in solid-line rectangles. The simulated data are denoted by dotted-line rectangles; rectangles with rounded corners represent models. Dataset descriptions are provided in Table 1.
Flow diagram for wind velocity and significant wave height simulations. Non-simulated data, used for model identification and simulation, are included in solid-line rectangles. The simulated data are denoted by dotted-line rectangles; rectangles with rounded corners represent models. Dataset descriptions are provided in Table 1.

Figure 2

Data grids: NOAA 20CRv2 (black squares), UMPL-ICM (blue dots) and 1 NM nested WAM grid (small green dots)
Data grids: NOAA 20CRv2 (black squares), UMPL-ICM (blue dots) and 1 NM nested WAM grid (small green dots)

Figure 3

Root Mean Square Error (left) and scatter index (right) values for significant wave height simulation using the Takagi-Sugeno-Kang simulation method with m-ARX downscaled winds
Root Mean Square Error (left) and scatter index (right) values for significant wave height simulation using the Takagi-Sugeno-Kang simulation method with m-ARX downscaled winds

Figure 4

Significant wave height: Takagi-Sugeno-Kang simulated using downscaled winds (right) and WAM modeled with UMPL-ICM winds (left). Peaks of validation storms no. 16–18
Significant wave height: Takagi-Sugeno-Kang simulated using downscaled winds (right) and WAM modeled with UMPL-ICM winds (left). Peaks of validation storms no. 16–18

Figure 5

NH cumulative change due to the linear trend over the simulation period expressed as a percentage of NH mean
NH cumulative change due to the linear trend over the simulation period expressed as a percentage of NH mean

Datasets used for identification, validation and simulation of local wind velocity and significant wave height Hs

Data set Used…Description
UMPL-ICM winds… to run the WAM model. As an input for the identification of wind scaling models. Half of data used for the verification.9 NM, 3 h, interpolated into 1 NM, 1 h grid, 1998–2001, 28 storms
20CRv2 winds… as an input for the identification and validation of wind scaling models.120 NM, 3 h, means interpolated into 1NM 1 h grid, 1871–2008
Simulated winds… for Hs simulation. Generated using scaling models with 20CRv2 data as an input.1 NM, 1 h, 1871–2008
WAM Hs… to identify Hs simulation models. Half of data used for verification. Generated using a locally nested WAM model and UMPL-ICM winds.1 NM, 3 h, 1998–2001, 28 storms
Simulated Hs… to generate the number of hours with Hs exceeding a given threshold. Simulated using previously identified Hs models and simulated winds.1 NM, 1 h, 1871–2008
DOI: https://doi.org/10.1515/ohs-2018-0018 | Journal eISSN: 1897-3191 | Journal ISSN: 1730-413X
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Language: English
Page range: 190 - 201
Submitted on: Aug 29, 2017
Accepted on: Nov 21, 2017
Published on: Jun 18, 2018
Published by: University of Gdańsk
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Gulf of Gdańsk,
wave climate,
significant wave height,
system identification,
neuro-fuzzy systems,
wave modeling
Related subjects:
Chemistry,
Chemistry, other,
Geosciences,
Geosciences, other,
Life sciences,
Life sciences, other

© 2018 Jordan Badur, Witold Cieślikiewicz, published by University of Gdańsk
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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