@misc{Shadaydeh18:AGU,
    type = {misc},
    key = {Shadaydeh18:AGU},
    title = {Classification of Spatiotemporal Marine Climate Patterns using Wavelet Coherence and Markov Random Field},
    author = {Maha Shadaydeh and Yanira Guanche Garcia and Joachim Denzler},
    howpublished = {American Geophysical Union Fall Meeting (AGU): Abstract + Oral Presentation},
    year = {2018},
    abstract = {Sea condition characterization and classification is a widely studied topic but rather challenging one due to the spatial and temporal variability in marine climate. The aim of this study is to develop a data-driven method for the classification of marine climate patterns. The proposed method consists of two main steps: i) Feature extraction applied to the time series of each point of the grid independently. ii) Spatiotemporal classification applied on the obtained features over the entire study area. The causal intensity between coupled marine variables can be efficiently visualized at different time-scales using wavelet coherence. To this end, we extract a set of features from the statistically significant wavelet coherence of each pair of the used marine variables: significant wave height (hs), mean wave period (Tm), and wave direction (θm). The obtained features, in addition to the sea level pressure (SLP), over the entire study area are then treated as multi-channel images. For the spatiotemporal classification of these images, we first apply the unsupervised K-means clustering method on the images of each three consecutive time instances. The K clusters represent K different marine climate patterns. Markov Random Fields (MRFs) provide an effective methodology for integrating spatiotemporal dependency between adjacent points into the image classification process. In this study, we use a MRF model for defining the spatiotemporal extent of the detected marine climate patterns, with the statistics of the detected K clusters used for the initial training. Experimental results show that the proposed method allows for a practical classification of marine climate into representative patterns that can be used for an accurate characterization of sea conditions, the analysis of extreme events and its impacts along the coast. A case study in the North Sea will be presented using the coasDat dataset [1].  [1] Weisse, R., H. v. Storch, U. Callies, A. Chrastansky, F. Feser, I. Grabemann, H. Guenther, A. Pluess, Th. Stoye, J. Tellkamp, J. Winterfeldt and K. Woth, (2008): Regional meteo-marine reanalyses and climate change projections: Results for Northern Europe and potentials for coastal and offshore applications. Bull. Amer. Meteor. Soc., 90, 849-860.},
    url = {https://ui.adsabs.harvard.edu/abs/2018AGUFMIN31C0824S/abstract},
}