International audienceIn this paper, non-homogeneous Markov-Switching Autoregressive (MS-AR) models are proposed to describe wind time series. In these models, several au-toregressive models are used to describe the time evolution of the wind speed and the switching between these different models is controlled by a hidden Markov chain which represents the weather types. We first block the data by month in order to remove seasonal components and propose a MS-AR model with non-homogeneous autoregressive models to describe daily components. Then we discuss extensions where the hidden Markov chain is also non-stationary to handle seasonal and inter-annual fluctuations. The different models are fitted using the EM algorithm to a long time series of wind speed measurement on the Island of Ouessant (France). It is shown that the fitted models are interpretable and provide a good description of im-portant properties of the data such as the marginal distributions, the second-order structure or the length of the stormy and calm periods
The knowledge of sea state and wind conditions is of central importance for many offshore and nearshore operations. In this paper, we make a complete survey of stochastic models for sea state and wind time series. We begin with methods based on Gaussian processes, then non-parametric resampling methods for time series are introduced followed by various parametric models. We also propose an original statistical method, based on Monte Carlo goodness-of-fit tests, for model validation and comparison and this method is illustrated on an example of multivariate sea state time series.
SUMMARYIn this article, an original Markov-switching autoregressive model is proposed to describe the space-time evolution of wind fields. At first, a non-observable process is introduced in order to model the motion of the meteorological structures. Then, conditionally to this process, the evolution of the wind fields is described using autoregressive models with time-varying coefficients. The proposed model is calibrated and validated on data in the North Atlantic.
Fiber evanescent wave spectroscopy (FEWS) explores the mid-infrared domain, providing information on functional chemical groups represented in the sample. Our goal is to evaluate whether spectral fingerprints obtained by FEWS might orientate clinical diagnosis. Serum samples from normal volunteers and from four groups of patients with metabolic abnormalities are analyzed by FEWS. These groups consist of iron overloaded genetic hemochromatosis (GH), iron depleted GH, cirrhosis, and dysmetabolic hepatosiderosis (DYSH). A partial least squares (PLS) logistic method is used in a training group to create a classification algorithm, thereafter applied to a test group. Patients with cirrhosis or DYSH, two groups exhibiting important metabolic disturbances, are clearly discriminated from control groups with AUROC values of 0.94+/-0.05 and 0.90+/-0.06, and sensibility/specificity of 8684% and 8787%, respectively. When pooling all groups, the PLS method contributes to discriminate controls, cirrhotic, and dysmetabolic patients. Our data demonstrate that metabolic profiling using infrared FEWS is a possible way to investigate metabolic alterations in patients.
Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. A regime-switching framework is introduced to account for the alternation of intensity and variability that is observed in wind conditions due to the existence of different weather types. This modeling blocks time series into periods in which the series is described by a single model. The regime-switching is modeled by a discrete variable that can be introduced as a latent (or hidden) variable or as an observed variable. In the latter case a clustering algorithm is used before fitting the model to extract the regime. Conditional on the regimes, the observed wind conditions are assumed to evolve as a linear Gaussian vector autoregressive (VAR) model. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes derived from a descriptor of the atmospheric circulation. We also discuss the relative advantages of hidden and observed regime-switching models. For artificial stochastic generation of wind sequences, we show that the proposed models reproduce the average space-time motions of wind conditions, and we highlight the advantage of regime-switching models in reproducing the alternation of intensity and variability in wind conditions. ton, 2010; Ailliot et al., 2006;Wikle et al., 2001;Fuentes et al., 2005). Except in Hering et al. (2015), these models are designed for short-term wind prediction and not for the generation of artificial conditions of {u t , v t }. Consequently they are not focused on reproducing the same statistics we are interested in, namely, the marginal distribution of {u t , v t } and its spatiotemporal dynamics. In Hering et al. (2015), a stochastic generator for multiple temporal and spatial scalesPublished by Copernicus Publications.
We mapped the space-time distribution of stationary and swarmer cells within a growing Proteus mirabilis colony by infrared (IR) microspectroscopy. Colony mapping was performed at different positions between the inoculum and the periphery with a discrete microscope-mounted IR sensor, while continuous monitoring at a fixed location over time used an optical fiber based IR-attenuated total reflection (ATR) sensor, or "optrode." Phenotypes within a single P. mirabilis population relied on identification of functional determinants (producing unique spectral signals) that reflect differences in macromolecular composition associated with cell differentiation. Inner swarm colony domains are spectrally homogeneous, having patterns similar to those produced by the inoculum. Outer domains composed of active swarmer cells exhibit spectra distinguishable at multiple wavelengths dominated by polysaccharides. Our real-time observations agree with and extend earlier reports indicating that motile swarmer cells are restricted to a narrow (approximately 3 mm) annulus at the colony edge. This study thus validates the use of an IR optrode for real-time and noninvasive monitoring of biofilms and other bacterial surface populations.
International audienceIn this paper we propose various Markov-switching auotoregressive models for bivariate time series which describe wind conditions at a single location. The main originality of the proposed models is that the hidden Markov chain is not homogeneous, its evolution depending on the past wind conditions. It is shown that they permit to reproduce complex features of wind time series such as non-linear dynamics and the multimodal marginal distributions
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