María Luisa Novo Martín

Sensitivity Analysis of the WRF Model: Wind-Resource Assessment for Complex Terrain

AbstractWind energy requires accurate forecasts for adequate integration into the electric grid system. In addition, global atmospheric models are not able to simulate local winds in complex terrain, where wind farms are sometimes placed. For this reason, the use of mesoscale models is vital for estimating wind speed at wind turbine hub height. In this regard, the Weather Research and Forecasting (WRF) Model allows a user to apply different initial and boundary conditions as well as physical parameterizations. In this research, a sensitivity analysis of several physical schemes and initial and boundary conditions was performed for the Alaiz mountain range in the northern Iberian Peninsula, where several wind farms are located. Model performance was evaluated under various atmospheric stabilities and wind speeds. For validation purposes, a mast with anemometers installed at 40, 78, 90, and 118 m above ground level was used. The results indicate that performance of the Global Forecast System analysis and Eu...

Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

During recent decades, the use of probabilistic forecasting methods has increased markedly. However, these predictions still need improvement in uncertainty quantification and predictability analysis. For this reason, the main aim of this paper is to develop tools for quantifying uncertainty and predictability of wind speed over the Iberian Peninsula. To achieve this goal, several spread indexes extracted from an ensemble prediction system are defined in this paper. Subsequently, these indexes were evaluated with the aim of selecting the most appropriate for the characterization of uncertainty associated to the forecasting. Selection is based on comparison of the average magnitude of ensemble spread (ES) and mean absolute percentage error (MAPE). MAPE is estimated by comparing the ensemble mean with wind speed values from different databases. Later, correlation between MAPE and ES was evaluated. Furthermore, probability distribution functions (PDFs) of spread indexes are analyzed to select the index with greater similarity to MAPE PDFs. Then, the spread index selected as optimal is used to carry out a spatiotemporal analysis of model uncertainty in wind forecasting. The results indicate that mountainous regions and the Mediterranean coast are characterized by strong uncertainty, and the spread increases more rapidly in areas affected by strong winds. Finally, a predictability index is proposed for obtaining a tool capable of providing information on whether the predictability is higher or lower than average. The applications developed may be useful in the forecasting of wind potential several days in advance, with substantial importance for estimating wind energy production.

Inteligencia conectiva para la educación matemática infantil = Connective intelligence for childhood mathematics education

La construccion de un cerebro conectivo comienza en las edades mas tempranas del desarrollo humano. Sin embargo, el conocimiento que ya se tiene sobre los cerebros individual y colectivo apenas se ha incorporado en el desarrollo del pensamiento matematico en Educacion Infantil, donde comienzan a gestarse elementos clave para tomar decisiones, resolver problemas de la vida cotidiana, tratar con datos y comprender el entorno. Desde esta perspectiva la presente investigacion marca como objetivo general analizar el proceso de ensenanza-aprendizaje de las matematicas en Educacion Infantil a partir del conexionismo, considerando como objetivos especificos, por un lado, determinar las caracteristicas de una practica matematica que promueva las conexiones y, por otro lado, identificar los distintos tipos de conexiones matematicas para fomentar la inteligencia conectiva. La investigacion se lleva a cabo a lo largo de dos anos consecutivos bajo un paradigma interpretativo con un enfoque metodologico basado en el uso combinado de Investigacion-Accion y Teoria Fundamentada. Los resultados han permitido concretar un prototipo de actividad o conjunto de actividades que, en forma de secuencia didactica, promueve tres tipos de conexiones matematicas para desarrollar la inteligencia conectiva en Educacion Infantil: conceptuales, que producen nexos entre contenidos matematicos diversos; docentes, que vinculan diversos conceptos matematicos a traves de una metodologia activa y de vivenciar las experiencias matematicas con otras materias; y practicas, que relacionan las matematicas con el entorno.The construction of a connective brain begins at the earliest ages of human development. However, knowledge about individual and collective brains provided so far by research has been rarely incorporated into Maths in Early Childhood classrooms. In spite of that, it is obvious that it is at these ages when the learning of mathematics acts as a nuclear element for decision- making, problem -solving, data- processing and the understanding of the world. From that perspective, this research aims to analyse the mathematics teaching-learning process at early ages based on connectionism, with the specific objectives being, on the one hand, to determine the features of mathematics practices which promote connections and, on the other hand, to identify different types of mathematics connections to enhance connective intelligence. The research was carried out over two consecutive academic years under an interpretative paradigm with a methodological approach combining Action Research and Grounded Theory. The results obtained allow the characterization of a prototype of a didactic sequence that promotes three types of mathematics connections for the development of connective intelligence in young children: conceptual, giving rise to links between mathematics concepts, teaching, linking mathematics concepts through an active methodology, and practical ones connecting maths with the environment.