Detecting anomalous sea-level states in North Sea tide gauge data using an autoassociative neural network


The sea level in the North Sea is densely monitored by tide gauges. The data they provide can be used to solve different scientific and practical problems, including the validation of numerical models and the detection of extreme events. This study focuses on the detection of sea-level states with anomalous spatial correlations using autoassociative neural networks (AANNs), trained with different sets of observation- and model-based data. Such sea-level configurations are related to nonlinear ocean dynamics; therefore, neural networks appear to be the right candidate for their identification. The proposed network can be used to accurately detect such anomalies and localize them. We demonstrate that the atmospheric conditions under which anomalous sea-level states occur are characterized by high wind tendencies and pressure anomalies. The results show the potential of AANNs for accurately detecting the occurrence of such events. We show that the method works with AANNs trained on tide gauge records as well as with AANN trained with model-based sea surface height outputs. The latter can be used to enhance the representation of anomalous sea-level events in ocean models. Quantitative analysis of such states may help assess and improve numerical model quality in the future as well as provide new insights into the nonlinear processes involved. This method has the advantage of being easily applicable to any tide gauge array without preprocessing the data or acquiring any additional information.
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