Wind characterization and potential assessment using spectral analysis

The study of environmental problems usually requires the description of variables with different nature and the assessment of relations between them. In this work, an algorithm for flexible estimation of the joint density for a circular-linear variable is proposed. The method is applied for exploring the relation between wind direction and SO 2 concentration in a monitoring station close to a power plant located in Galicia (NW-Spain), in order to compare the effectiveness of precautionary measures for pollutants reduction in two different years.

Section: Introductionmentioning

confidence: 99%

Exploring wind direction and SO2 concentration by circular–linear density estimation

García‐Portugués

Crujeiras

González–Manteiga

2012

“…The methodology of stationary spectral analysis can be found in detail in the updated work of Bendat and Piersol (2000). Many advanced applications in geosciences for obtaining spectral estimates are also found in the relevant studies (Shih et al 1999Shih 1999aShih , b, 2000Shih , 2002 Lin 2002Lin , 2004Shih 2008;Shih et al 2008a, b). Detailed procedure and computation for spectral analysis can be reviewed on the above-mentioned studies.…”

Section: Spectral Analysismentioning

confidence: 93%

Sea level fluctuations on the east coast of Taiwan that overlie continental shelf break

Shih

Chen

Lin

et al. 2008

Self Cite

Captured CO 2 could be deliberately injected into the ocean at great depth, where most of it would remain isolated from the atmosphere for centuries. CO 2 can be transported via pipeline or ship for release in the ocean or on the sea floor. No matter what for medium depth or deep sea, it appears that a potential area exists between 122-122.5°E and 21.8-22.3°N for CO 2 sequestration. The east coast of Taiwan can be a candidate for CO 2 temporary storage or transmitted plant. To have whole picture of assessment of sea level fluctuation, a completed statistical summary of seasonal sea level at six tidal gauge stations along the east coast of Taiwan is provided herein. Seasonal sea level time series is analyzed using spectral analysis in frequency domain to identify periodic component and phase propagation, especially for the astronomical-driven tidal effects. It identifies that the semi-diurnal and diurnal components in the resultant time series are related to astronomical tides M 2 , and K 1 and O 1 , respectively. It demonstrates a full analysis of sea level variations, and results can be useful when construction of testing or operating facilities on sea surface becomes desirable in the future.

“…The graphics also display noticeable periodic variations with no trend. Additionally, we carried out a spectral analysis of the upwelling time series, which is a very important tool for analysing meteorological data (Shih 2008;Bouette et al 2006). We estimated the spectral density function by the periodogram and studied the statistical significance of its large peaks.…”

Section: Application To Upwelling Datamentioning

confidence: 99%

A comparison between the Linear Regression Model with autocorrelated errors and the Partial Adjustment Model

Mouriño

Barão

2009

In this paper we consider a Linear Regression Model with a design matrix that fits the periodic structure of a time series. As a consequence, the residuals are very often autocorrelated. The main problem is that residual autocorrelation does not necessarily entail error autocorrelation. To analyse the effects of selecting different formulations to accommodate the autocorrelation in the residuals, we consider two seemingly different ways to deal with this problem: the Linear Regression Model with the error terms following an Autoregressive Stationary Process and the Partial Adjustment Model. We study the equivalence between the two formulations. We go over the problem of estimating the parameters and, especially, of making inferences in this framework. After parameter estimation, we analyse the adequacy of the models. We demonstrate that the issue of selecting the most appropriate model to capture the autocorrelation in the residuals is, in this context, a kind of an artefact since the main results concerning the fitted values and forecasting features are the same. These modelling procedures are applied to the Portuguese coastal upwelling data and we compare the estimated models.