Yanira Guanche

A method for finding the optimal predictor indices for local wave climate conditions

In this study, a method to obtain local wave predictor indices that take into account the wave generation process is described and applied to several locations. The method is based on a statistical model that relates significant wave height with an atmospheric predictor, defined by sea level pressure fields. The predictor is composed of a local and a regional part, representing the sea and the swell wave components, respectively. The spatial domain of the predictor is determined using the Evaluation of Source and Travel-time of wave Energy reaching a Local Area (ESTELA) method. The regional component of the predictor includes the recent historical atmospheric conditions responsible for the swell wave component at the target point. The regional predictor component has a historical temporal coverage (n-days) different to the local predictor component (daily coverage). Principal component analysis is applied to the daily predictor in order to detect the dominant variability patterns and their temporal coefficients. Multivariate regression model, fitted at daily scale for different n-days of the regional predictor, determines the optimum historical coverage. The monthly wave predictor indices are selected applying a regression model using the monthly values of the principal components of the daily predictor, with the optimum temporal coverage for the regional predictor. The daily predictor can be used in wave climate projections, while the monthly predictor can help to understand wave climate variability or long-term coastal morphodynamic anomalies.

Autoregressive logistic regression applied to atmospheric circulation patterns

Autoregressive logistic regression models have been successfully applied in medical and pharmacology research fields, and in simple models to analyze weather types. The main purpose of this paper is to introduce a general framework to study atmospheric circulation patterns capable of dealing simultaneously with: seasonality, interannual variability, long-term trends, and autocorrelation of different orders. To show its effectiveness on modeling performance, daily atmospheric circulation patterns identified from observed sea level pressure fields over the Northeastern Atlantic, have been analyzed using this framework. Model predictions are compared with probabilities from the historical database, showing very good fitting diagnostics. In addition, the fitted model is used to simulate the evolution over time of atmospheric circulation patterns using Monte Carlo method. Simulation results are statistically consistent with respect to the historical sequence in terms of (1) probability of occurrence of the different weather types, (2) transition probabilities and (3) persistence. The proposed model constitutes an easy-to-use and powerful tool for a better understanding of the climate system.

Maximally divergent intervals for anomaly detection

We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat each time step independently from each other without optimizing with respect to all possible intervals.

PROBABILISTIC LONG RANGE FORECAST SYSTEM FOR OIL SPILL TRAJECTORIES IN THE GULF OF BISCAY (SPAIN)

The increasing number of accidental oil spills has motivated the development and implementation of operational oceanography systems (OOS) to help in the decision process during oil spill emergency situations. Currently, most of the national and regional OOS have been setup for short-term (up to 5 days) oil spill forecast. However, recent accidental oil spills such as Prestige in Spain (2002) or Deep Horizon in Gulf of Mexico (2010), have revealed the importance of having larger prediction horizons (up to 30 days) in regional-scale areas. In this work, we have developed a stochastic methodology based on the combination of clustering algorithms and Markov chains of first order to provide medium term (15–30 days) probabilistic oil spill forecasts. The method encompasses the following steps: (1) classification of representative atmospheric patterns using clustering techniques (PCA and k-means [1]); (2) determination the transition probability matrix associated with the Markov chain. The element of the transit...