Ganix Esnaola

Reconstruction of sea surface temperature by means of DINEOF: a case study during the fishing season in the Bay of Biscay

The Spanish surface fishery operates mainly during the summer season in the waters of the Bay of Biscay. Sea surface temperature (SST) data recovered from satellite images are being used to improve the operational efficiency of fishing vessels (e.g. reduce search time and increase catch rate) and to improve the understanding of the variations in catch distribution and rate needed to properly manage fisheries. The images used for retrieval of SST often present gaps due to the existence of clouds or satellite malfunction periods. The data gaps can totally or partially affect the area of interest. Within this study, an application of a technique for the reconstruction of missing data called DINEOF (data interpolating empirical orthogonal functions) is analysed, with the aim of testing its applicability in operational SST retrieval during summer months. In this case study, the Bay of Biscay is used as the target area. Three months of SST Moderate Resolution Imaging Spectroradiometer (MODIS) images, ranging from 1 May 2006 to 31 July 2006, were used. The main objective of this work is to test the overall performance of this technique, under potential operational use for the support of the fleet during the summer fishing season. The study is designed to analyse the sensitivity of the results of this technique to several details of the methodology used in the reconstruction of SST, such as the number of empirical orthogonal functions (EOFs) retained, the handling of the seasonal cycle or the length (number of images) of the SST database used. The results are tested against independent SST data from International Comprehensive Ocean–Atmosphere Data Set (ICOADS) ship reports and standing buoys and estimations of the error of the reconstructed SST fields are given. Conclusions show that over this area three months of data are enough for efficient SST reconstruction, which yields four EOFs as the optimal number needed for this case study. An extended EOF experiment with SST and SST with a lag of one day was carried out to analyse whether the autocorrelation of the SST data allows better performance in the SST reconstruction, although the experiment did not improve the results. The validation studies show that the reconstructed SSTs can be trusted, even when the amount of missing data is very high. The mean absolute deviation maps show that the error is greatest near to the coast and mainly in the upwelling areas close to the French and north-western Spanish coasts.

Probabilistic relationships between wind and surface water circulation patterns in the SE Bay of Biscay

Non-linear K-means classification algorithm was used to obtain a comprehensive description of the winds and high-frequency radar-derived currents in the SE Bay of Biscay (study area), taking into account a wide range of scales, from several days to interannual variability. The results in the study area show that in summer, a stronger variability in winds and surface currents can be expected, while in winter, intense southwesterly winds and a cyclonic circulation prevail. In addition to the seasonal component of the currents, a significant spatial variability in terms of current patterns and a temporal variability at shorter and interannual scales were also identified, highlighting the complexity of the surface current dynamics. Moreover, the probabilistic relationships between wind and current patterns were explored, obtaining conditional probabilities. Most of the surface current patterns are clearly related to specific wind patterns that are recurrent in the study area. However, other common current patterns are not so clearly related to specific wind conditions. The presence of a seasonal slope current (Iberian Poleward Current, IPC) is one of the most relevant features of the local circulation. An IPC occurrence time series based on Sea Surface Temperature satellite imagery was used to obtain conditional probabilities with the high-frequency radar surface current patterns, showing a relation between the strongest IPC events and closed cyclonic currents, which are not linked to specific winds.

Wave Energy Forecasting at Three Coastal Buoys in the Bay of Biscay

In 2008, the first commercial wave farm came online in Portugal. As with other types of renewable energy, the electricity obtained from waves has the drawback of intermittency. Knowing a few hours ahead how much energy waves will hold can contribute to a better management of the electricity grid. In this work, three types of statistical models have been used to create up to 24-h forecasts of the zonal and meridional components of wave energy flux (WEF) levels at three directional buoys located off the coast in the Bay of Biscay. Each models performance has been compared at a 95% confidence level with the simplest prediction (persistence of levels), along with the forecasts provided by the physics-based WAve Modeling (WAM) wave model at the nearest grid point. The results indicate that for forecasting horizons between 3 and roughly 16 h ahead, the statistical models built on random forests (RFs) outperform the rest, including WAM and persistence.

Multi-objective environmental model evaluation by means of multidimensional kernel density estimators

We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to single and multi-core processors. They compute multivariate PDFs by means of kernels, the optimal bandwidth using smoothed bootstrap and the index of agreement between multidimensional PDFs. Their use is illustrated with two case-studies. The first one assesses the ability of seven global climate models to reproduce the seasonal cycle of zonally averaged temperature. The second case study analyzes the ability of an oceanic reanalysis to reproduce global Sea Surface Temperature and Sea Surface Height. Results show that the proposed methodology is robust to variations in the optimal bandwidth used. The technique is able to process multivariate datasets corresponding to different physical dimensions. The methodology is very sensitive to the existence of a bias in the model with respect to observations. The performance index based on the area under two PDFs is extended to several dimensions.The evaluation of the performance of models can be done for several variables, resulting in a single skill score.A fast and parallel implementation that allows to apply the method with highly dimensional problems is presented.The method is illustrated with two case-studies.The sensitivity of the results to the bias between models and observations or the bandwidth is presented.