Gabriel IBARRA-BERASTEGI

I was awarded my Degree in Engineering at Bilbao´s Faculty of Engineering. Five years later, in 1993, I obtained my PhD degree in Environmental Engineering. Currently, I work as a teacher and researcher at the Department of NE & Fluid Mechanics in the University of the Basque Country and I also act as a coordinator of the activities of EOLO research group www.ehu.es/eolo . Additionally I share with my colleagues of EOLO (0.8 ECTS) the subject Satellite Oceanography and Meteorology in the Erasmus Mundus Master on Environmental Resources (MER) at PiE-UPV/EHU. In the area of, generally speaking, environmental modelling, I have led several research projects in the field of geophysical fluids, renewable energies, air pollution and climate change. I have also taken part in educational and research projects on the fluid mechanics aspects of the biofiltration of waste gases. In my research activities, I use a variety of machine learning algorithms along with CFD for fluid mechanics, meteorology and environmental engineering studies. After many years using a great variety of software, at this moment, for all my educational and research activities, I have eventually come to using only two major tools: SATURNE (in the frame of CAE Linux) for CFD purposes and R for virtually everything else. In some of my research areas, I have also conducted consultancy works for some public institutions. As a result of this work, I have published several research papers in peer-reviewed journals and in 2013, along with other researchers from our University, we were granted a patent. In 2004 I became a fellow of the Wessex Institute of Technology (UK) as “a recognition for his outstanding work”. My research work involves the application of techniques like analogues, random forests and neural networks for classification, downscaling, trend detection, climate analysis and short-term forecasting purposes. The target variables are two groups of geophysical fluids: i) The variables involved in the atmospheric water cycle ii) Fluids associated with renewable energy like wind, and more specifically ocean wave energy flux. This type of energy, like other renewable sources has the problem of intermittency which originates electricity-grid management problems. Being able to forecast with a reasonable accuracy the energy that waves will hold a few hours ahead, can contribute to address this problem. Currently, I am working in two different time scales with both groups of variables. On the one hand, I have developed a set of random forests-based models for short-term prediction of the wave energy flux in the Bay of Biscay. On the other hand, I am trying to learn from current day conditions, how water cycle variables and ocean waves relate with variables commonly accepted to be rather accurately represented in the CMIP5 models like SLP. These results can be used to obtain estimations under other climate scenarios.