(7) Nonstationary fuzzy forecasting of wind and wave climate in very long-term scales
Ch.N. Stefanakos a , ∗, E. Vanem b
a SINTEF Ocean, Department of Energy and Transport, Postboks 4762 Torgarden, Trondheim NO-7465, Norway
b DNV GL, Group Technology and Research, P.O Box 300, 1322, Høvik, Norway
Received 6 November 2017; received in revised form 12 February 2018; accepted 13 April 2018
Available online 19 April 2018
Abstract
Global climate change may have serious impact on human activities in coastal and other areas. Climate change may affect the degree of storminess and, hence, change the wind-driven ocean wave climate. This may affect the risks associated with maritime activities such as shipping and offshore oil and gas. So, there is a recognized need to understand better how climate change will affect such processes. Typically, such understanding comes from future projections of the wind and wave climate from numerical climate models and from the stochastic modelling of such projections. This work investigates the applicability of a recently proposed nonstationary fuzzy modelling to wind and wave climatic simulations. According to this, fuzzy inference models (FIS) are coupled with nonstationary time series modelling, providing us with less biased climatic estimates. Two long-term datasets for an area in the North Atlantic Ocean are used in the present study, namely NORA10 (57 years) and ExWaCli (30 years in the present and 30 years in the future). Two distinct experiments have been performed to simulate future values of the time series in a climatic scale. The assessment of the simulations by means of the actual values kept for comparison purposes gives very good results.
© 2018 Shanghai Jiaotong University. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license. ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Keywords: Fuzzy time series; Wind and wave data; Forecasting; Nonstationary; Ocean wind and wave climate.