No abstract

It has been suggested that climate change might modify the occurrence rate of large storms and their magnitude, due to a higher availability of energy in the atmosphere-ocean system. Forecasting physical models are commonly used to assess the effects. No one expects the physical model forecasts for one specific day to be accurate; we consider them to be good if they adequately describe the statistical characteristics of the climate. The Peak-Over-Threshold (POT) method is a common way to statistically treat the occurrence and magnitude of hazardous events: here, occurrence is modelled as a Poisson process and magnitude over a given threshold is assumed to follow a Generalized Pareto Distribution (GPD). We restrict our attention to Weibull-related GPDs, which exhibit an upper bound, to comply with the fact that any physical process has a finite upper limit. This contribution uses this framework to model time series of log-significant wave-height constructed joining quasi-collocated hindcast data and buoy measurements. Two of the POT model parameters (inhomogeneous Poisson rate and logarithm of the GPD shape parameter are considered to be a combination of a linear function of time and a series indicator function. The third parameter, logarithm of the GPD upper bound, is considered to have only a series indicator component. The resulting parameters are estimated using Bayesian methods. Using hincast and buoy series, the time span of the data set is extended, enhancing the precision of statistical results about potential linear changes. Simultaneously the statistical behaviour of hincast and buoy series are compared. At the same time, the step function allows to calibrate the statistical reproduction of storms by hindcasting.

In this note a procedure to deduce new identities involving elementary rational sums is presented and applying this technique some sums including binomial coefficients and harmonic numbers are obtained.

The main goal of this paper is to go some steps further to improve the understanding and manageability of air quality. Quality of atmospheric air in large cities is a matter of great importance because of its impact on the environment and on the health of the population. Recently, measures restricting access of private vehicles to the centre of large cities and other measures to prevent atmospheric air pollution are currently topical. The knowledge of air quality acquires special relevance to be able to evaluate the impact of those great social and economic measures. There are many indices to express air quality. In fact, quite every country has its own, depending on the main pollutants. In general, all indices ignore the compositional nature of the concentrations of air pollutants and do not apply methods of Compositional Data Analysis and have some other weak points such as leak of standardized scale. Therefore, the methodology used is founded on Compositional Data Analysis. The air quality index has an adequate correlation between input (concentrations) and output (air quality index), it distinguishes between air pollution and air quality and it has a 0-100 reference scale which makes easier interpretation and management of air quality expression. To illustrate the proposed method, an application is made to a series of air pollution data (Barcelona, 2001-2015). The results show the effectiveness of the 2008 European directive on ambient air quality.

A fault detection innovation to wind turbines’ pitch actuators is an important subject to guarantee the efficiency wind energy conversion and long lifetime operation of these rotatory machines. Therefore, a recent and effective fault detection algorithm is conceived to detect faults on wind turbine pitch actuators. This approach is based on the interval observer framework theory that has proved to be an efficient tool to measure dynamic uncertainties in dynamical systems. It is evident that almost any fault in any actuator may affect its historical-time behavior. Hence, and properly conceptualized, a fault detection system can be successfully designed based on interval observer dynamics. This is precisely our main contribution. Additionally, we realize a numerical analysis to evaluate the performance of our approach by using a dynamic model of a pitch actuator device with faults. The numerical experiments support our main contribution.