Analysis Of Environmental Time Series Research Articles

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time–period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results. Program summaryProgram title: EnvironmentalWaveletToolCatalogue identifier: AETL_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AETL_v1_0.htmlProgram obtainable from: CPC Program Library, Queen’s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 43223No. of bytes in distributed program, including test data, etc.: 9835648Distribution format: tar.gzProgramming language: MATLAB (MathWorks Inc.) version 7.8.0.347 (R2009a) or higher. Wavelet Toolbox is required.Computer: Developed on a MacBook Pro. Tested on Mac and PC. No computer-specific optimization was performed.Operating system: Any supporting MATLAB (MathWorks Inc.) v7.8.0.347 (R2009a) or higher. Tested on Mac OS X 10.6.8 and Vista.Classification: 13.Nature of problem:Wavelet analysis has not been widely employed in environmental data since an easy program to specifically process this kind of time series does not exist. In recent years, wavelet analysis has been applied to some situations focused on specific problems and signals, but the extension of these wavelet-based methods to other particular problems is not always a trivial work. In fact, the application to other different situations requires a basic knowledge of the wavelet analysis and their implementation using some standard toolboxes, packages or libraries, such as the MathWorks’ Wavelet Toolbox for MATLAB.Solution method:The developed MATLAB-based computer code provides a user-friendly and fast program for the simultaneous wavelet analysis and filtering of several environmental time series. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to proffer a fast and precise time–period examination of the time series at different period (or frequency) bands. Moreover, other useful tools have been specifically included for the analysis of the environmental time series obtained from cave monitoring, such as filtering, cross-correlation, entropy of curves or splines analyses.Additional comments:A README file giving the names and a brief description of all the files that make up the package and clear instructions on the installation and execution of the program is included in the distribution package. Also included are Sample input files.Running time:Interactive.

Application of non-linear time-alignment and integration methods to environmental time series

This article presents methods for the analysis of environmental time series in cases where non-linear time alignment is anticipated. First, we present simple, robust propagation methods for cases where data are archived as event lists rather than standard time series. Then, we describe alignment and analysis methods based on procedures that are traditionally associated with laboratory-instrument-data time series, namely correlation optimized warping and classical chromatographic peak integration. We provide examples to illustrate routine operation and current limitations of these methods. We also discuss possible future applications of these methods.

Harmonic Analysis of Environmental Time Series with Missing Data or Irregular Sample Spacing

The Lomb periodogram and discrete Fourier transform are described and applied to harmonic analysis of two typical data sets, one air quality time series and one water quality time series. The air quality data is a 13 year series of 24 hour average particulate elemental carbon data from the IMPROVE station in Washington, D.C. The water quality data are from the stormwater monitoring network in Milwaukee, WI and cover almost 2 years of precipitation events. These data have irregular sampling periods and missing data that preclude the straightforward application of the fast Fourier transform (FFT). In both cases, an anthropogenic periodicity is identified; a 7-day weekday/ weekend effect in the Washington elemental carbon series and a 1 month cycle in several constituents of stormwater. Practical aspects of application of the Lomb periodogram are discussed, particularly quantifying the effects of random noise. The proper application of the FFT to data that are irregularly spaced with missing values is demonstrated on the air quality data. Recommendations are given when to use the Lomb periodogram and when to use the FFT.

Environmental time series analysis and forecasting with the Captain toolbox

The Data-Based Mechanistic (DBM) modelling philosophy emphasises the importance of parametrically efficient, low order, ‘dominant mode’ models, as well as the development of stochastic methods and the associated statistical analysis required for their identification and estimation. Furthermore, it stresses the importance of explicitly acknowledging the basic uncertainty in the process, which is particularly important for the characterisation and forecasting of environmental and other poorly defined systems. The paper focuses on a Matlab ® compatible toolbox that has evolved from this DBM modelling research. Based around a state space and transfer function estimation framework, Captain extends Matlab ® to allow, in the most general case, for the identification and estimation of a wide range of unobserved components models. Uniquely, however, Captain focuses on models with both time variable and state dependent parameters and has recently been implemented with the latest methodological developments in this regard. Here, the main innovations are: the automatic optimisation of the hyper-parameters, which define the statistical properties of the time variable parameters; the provision of smoothed as well as filtered parameter estimates; the robust and statistically efficient identification and estimation of both discrete and continuous time transfer function models; and the availability of various special model structures that have wide application potential in the environmental sciences.

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Rejoinder

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Rejoinder

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone: Comment: Are Ozone Exceedance Rates Decreasing?

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone: Comment: Are Ozone Exceedance Rates Decreasing?

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone]: Comment

Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone

Several methods of analyzing extreme values are now known, most based on the extreme value limit distributions or related families. This paper reviews these techniques and proposes some extensions based on the point-process view of high-level exceedances. These ideas are illustrated with a detailed analysis of ozone data collected in Houston, Texas. There is particular interest in whether they is any trend in the data. The analysis reveals no trend in the overall levels of the series, but a marked downward trend in the extreme values.