TY - JOUR
T1 - Deformed SPDE models with an application to spatial modeling of significant wave height
AU - Hildeman, Anders
AU - Bolin, David
AU - Rychlik, Igor
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work has been supported in part by the Swedish Research Council under grant No. 2016-04187. We would like to thank the European Center for Medium-range Weather Forecast for the development of the ERA-Interim data set and for making it publicly available. The data used was the ERA-Interim reanalysis dataset, Copernicus Climate Change Service (C3S) (accessed September 2018), available from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim.
PY - 2020/5/7
Y1 - 2020/5/7
N2 - A non-stationary Gaussian random field model is developed based on a combination of the stochastic partial differential equation (SPDE) approach and the classical deformation method. With the deformation method, a stationary field is defined on a domain which is deformed so that the field becomes non-stationary. We show that if the stationary field is a Matérn field defined as a solution to a fractional SPDE, the resulting non-stationary model can be represented as the solution to another fractional SPDE on the deformed domain. By defining the model in this way, the computational advantages of the SPDE approach can be combined with the deformation method's more intuitive parameterization of non-stationarity. In particular it allows for independent control over the non-stationary practical correlation range and the variance, which has not been possible with previously proposed non-stationary SPDE models. The model is tested on spatial data of significant wave height, a characteristic of ocean surface conditions which is important when estimating the wear and risks associated with a planned journey of a ship. The model parameters are estimated to data from the north Atlantic using a maximum likelihood approach. The fitted model is used to compute wave height exceedance probabilities and the distribution of accumulated fatigue damage for ships traveling a popular shipping route. The model results agree well with the data, indicating that the model could be used for route optimization in naval logistics.
AB - A non-stationary Gaussian random field model is developed based on a combination of the stochastic partial differential equation (SPDE) approach and the classical deformation method. With the deformation method, a stationary field is defined on a domain which is deformed so that the field becomes non-stationary. We show that if the stationary field is a Matérn field defined as a solution to a fractional SPDE, the resulting non-stationary model can be represented as the solution to another fractional SPDE on the deformed domain. By defining the model in this way, the computational advantages of the SPDE approach can be combined with the deformation method's more intuitive parameterization of non-stationarity. In particular it allows for independent control over the non-stationary practical correlation range and the variance, which has not been possible with previously proposed non-stationary SPDE models. The model is tested on spatial data of significant wave height, a characteristic of ocean surface conditions which is important when estimating the wear and risks associated with a planned journey of a ship. The model parameters are estimated to data from the north Atlantic using a maximum likelihood approach. The fitted model is used to compute wave height exceedance probabilities and the distribution of accumulated fatigue damage for ships traveling a popular shipping route. The model results agree well with the data, indicating that the model could be used for route optimization in naval logistics.
UR - http://hdl.handle.net/10754/662999
UR - https://linkinghub.elsevier.com/retrieve/pii/S2211675320300439
UR - http://www.scopus.com/inward/record.url?scp=85085057688&partnerID=8YFLogxK
U2 - 10.1016/j.spasta.2020.100449
DO - 10.1016/j.spasta.2020.100449
M3 - Article
SN - 2211-6753
SP - 100449
JO - Spatial Statistics
JF - Spatial Statistics
ER -