Length Of Available Data Research Articles

Evidence of climate-driven changes on atmospheric, hydrological, and oceanographic variables along the Chilean coastal zone

The Chilean coastal zone (CCZ) is subjected to a complex spectrum of anthropogenic, geophysical, biogeochemical, and climate-driven perturbations. Potentially affected variables including atmospheric sea level pressure (Pa), alongshore wind, sea surface temperature (SST), chlorophyll-a, rainfall, river discharge, relative mean sea level (RMSL), and wave climate are studied using in situ and satellite records, hindcasts, and reanalysis datasets. Linear temporal trends and correlations of anomalies are estimated between 18°S and 55°S along the CCZ. The comparison of some of the variables is achieved by means of a strict homogenization procedure on a monthly basis for 35 years. Our findings show that the poleward drift and strengthening of the Southeast Pacific Subtropical Anticyclone (SPSA) partially explains the increase in Pa and reduction in rainfall and river discharge. The enhancement of alongshore winds, also attributable to changes in the SPSA, increases coastal upwelling, which in turn could reduce SST and increase chlorophyll-a. Despite differential latitudinal responses, increasing wave heights and a southward rotation are evidenced. RMSL does not show significant variation as it is presumably affected by seafloor changes during the seismic cycle. Though some correlations are evidenced, the influence of climate variability at decadal scale (PDO, SAM) may be affecting the detected trends due to the short length of available data. Impacts on coastal communities, infrastructure, and ecosystems are discussed, aiming to highlight that coastal vulnerabilities and risk management should be based on the cumulative impacts of these variables.

Tools for investigating the prior distribution in Bayesian hydrology

Summary Bayesian inference is one of the most popular tools for uncertainty analysis in hydrological modeling. While much emphasis has been placed on the selection of appropriate likelihood functions within Bayesian hydrology, few researchers have evaluated the importance of the prior distribution in deriving appropriate posterior distributions. This paper describes tools for the evaluation of parameter sensitivity to the prior distribution to provide guidelines for defining meaningful priors. The tools described here consist of two measurements, the Kullback-Leibler Divergence (KLD) and the prior information elasticity. The Kullback-Leibler Divergence (KLD) is applied to calculate differences between the prior and posterior distributions for different cases. The prior information elasticity is then used to quantify the responsiveness of the KLD values to the change of prior distributions and length of available data. The tools are demonstrated via a Bayesian framework using an MCMC algorithm for a conceptual hydrologic model with both synthetic and real cases. The results of the application of this toolkit suggest the prior distribution can have a significant impact on the posterior distribution and should be more routinely assessed in hydrologic studies.

Assessment of land use and water management induced changes in flow regime of the Upper Narew

Previous studies have shown that it is very difficult to distinguish human-induced changes from those caused by natural forcing. In this paper we try to quantify the influence of land use and water management on flows of the Upper Narew River in north-east Poland. Apart from climatic and land use changes, the Upper Narew catchment was changed by the construction of a storage reservoir at Siemianówka, near Bondary, on the upstream reach of the river. We apply four different approaches to analysing the changes in flow regime and catchment response for the periods before and after reservoir construction. First we estimate the cumulative distribution functions for low and high flow events. The second approach is a time series analysis of flow variation over the whole length of available data and the derivation of cumulative distribution functions for the flows and 0.25–0.75 quantiles followed by a statistical analysis of the number of events below and above the thresholds and their duration. The third approach consists of the application of the Wittenberg baseflow separation method and tests for changes in baseflow indices. In the fourth approach an analysis of changes in flow regime is performed by studying the changes in transfer function-based flow model parameters. Long-term changes in land use are assessed using previous studies of the catchment and the analysis of Corine land cover data and government yearbooks. The results show that different methods explain different aspects of changes in the catchment and flow regime due to climatic changes and changes in land use and water management practices. The analysis of cumulative distribution functions gave evidence of the influence of Siemianówka reservoir on low flows which was also confirmed by the low flow analysis using the Wittenberg approach. The STF analysis of flows indicates the existence of changes in flow regime that can be attributed to the roughness changes in the channel.

Predictability of SST-Modulated Westerly Wind Bursts

AbstractWesterly wind bursts (WWBs), a significant player in ENSO dynamics, are modeled using an observationally motivated statistical approach that relates the characteristics of WWBs to the large-scale sea surface temperature. Although the WWB wind stress at a given location may be a nonlinear function of SST, the characteristics of WWBs are well described as a linear function of SST. Over 50% of the interannual variance in the WWB likelihood, zonal location, duration, and fetch is explained by changes in SST. The model captures what is seen in a 17-yr record of satellite-derived winds: the eastward migration and increased occurrence of wind bursts as the western Pacific warm pool extends. The WWB model shows significant skill in predicting the interannual variability of the characteristics of WWBs, while the prediction skill of the WWB seasonal cycle is limited by the record length of available data. The novel formulation of the WWB model can be implemented in a stochastic or deterministic mode, where the deterministic mode predicts the ensemble-mean WWB characteristics. Therefore, the WWB model is especially appropriate for ensemble prediction experiments with existing ENSO models that are not capable of simulating realistic WWBs on their own. Should only the slowly varying component of WWBs be important for ENSO prediction, this WWB model allows a shortcut to directly compute the slowly varying ensemble-mean wind field without performing many realizations.

EOF-Based Linear Prediction Algorithm: Theory

This study considers the theory of a general three-dimensional (space and time) statistical prediction/extrapolation algorithm. The predictor is in the form of a linear data filter. The prediction kernel is based on the minimization of prediction error and its construction requires the covariance statistics of a predictand field. The algorithm is formulated in terms of the spatiotemporal EOFs of the predictand field. This EOF representation facilitates the selection of useful physical modes for prediction. Limited tests have been conducted concerning the sensitivity of the prediction algorithm with respect to its construction parameters and the record length of available data for constructing a covariance matrix. Tests reveal that the performance of the predictor is fairly insensitive to a wide range of the construction parameters. The accuracy of the filter, however, depends strongly on the accuracy of the covariance matrix, which critically depends on the length of available data. This inaccuracy implies suboptimal performance of the prediction filter. Simple examples demonstrate the utility of the new algorithm.

Improving the performance of f–x prediction filtering at low signal‐to‐noise ratios

The conventional method of f-x filtering for random noise reduction suffers from three drawbacks. Firstly, the wavenumber response of the filter does not peak exactly at the wavenumbers of the signal components. Secondly, the amplitude of the filter response is less than one at the signal component wavenumbers, causing attenuation of the signal. Finally, sidelobes in the filter response cause noise at wavenumbers well separated from the signal components to leak into the filtered output. Singular value decomposition (SVD) of the data matrix shows that the problems may be reduced by using a transient-free formulation of the data matrix; that is, minimizing the squared errors over a finite data length rather than minimizing the expected value of the squared errors under the assumption of an infinite length of available data. The SVD analysis of the transient-free case shows that the noise-reduction performance may be improved further at all signal-to-noise ratios (SNRs). Tests on synthetic data show that the SNR gain may typically be 10 dB for the selected eigenvector method, as opposed to 5 dB to 0 dB at different input signal-to-noise ratios for the other methods. The optimal filter lengths were about twice the optimal lengths found for themore » conventional method. Tests on real stacked data also show considerable improvement in performance. Care must be taken in areas of complicated structure, particularly when strongly curved events are present, to select sufficient eigenvectors, but this may be achieved at the price of a slight loss in noise reduction.« less

The Experimental Classifications of the Linear Models of the Lathe Cutting Mechanisms

A preparatory stage to achieve “smart machining” is addressed, namely a forecasting model for the oscillation at the tool/workpiece interface is reported in this text. The nonlinear dynamics of the mentioned interface is suggested to be put into a very similar discretized linear format: Autoregressive Moving Average Model (ARMA). This linearization operation is normally carried on as an off-line procedure. The following questions are addressed for the ARMA models:a)What are the ARMA forms to achieve the best representation? This question leads to a library of plausible coefficients which are envisioned to have a powerful place in “knowledge based expert system representations”. A comparative study is included in the text for a broader understanding of the overall nature of the cutting tool dynamic modeling.b)The ARMA procedure is performed over a set of data. How dependent are coefficients of the model on the length of available data points? And can the data collection period be shortened without jeopardizing the health of the respresentation? And going a step further: can the ARMA modeling be made into an on-line procedure so that true meaning of predictive and adaptive control concepts hold?Additional to the temporal concerns of ARMA coefficients a spatial dependency of the model is scrutinized. Results are tabulated considering the flexural properties of the workpiece with respect to the cutting tool dynamics.

Exact Formulation of Window Functions for Analog Network Spectral Analyzers

An equation for the spectral window function for analog network analyzers is derived without assumptions on length of data interval available. The equation, given in terms of filter pole-zero locations for both the filter-bank and heterodyned-input type analyzers, can be programmed on a digital computer without difficulty. As an illustration, the growth of a spectral window with increasing length of available data is shown for a special case: a single-tuned circuit followed by a squaring circuit and perfect integrator. The error in using the usual approximation also is indicated. Use of the derived equation for quantitative comparisons of analog and digital analyzers is noted.