Browsing by Subject "wavelet analysis"
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Item Statistical analysis in downscaling climate models : wavelet and Bayesian methods in multimodel ensembles(2009-08) Cai, Yihua; Damien, Paul, 1960-; McCulloch, Robert E.Various climate models have been developed to analyze and predict climate change; however, model uncertainties cannot be easily overcome. A statistical approach has been presented in this paper to calculate the distributions of future climate change based on an ensemble of the Weather Research and Forecasting (WRF) models. Wavelet analysis has been adopted to de-noise the WRF model output. Using the de-noised model output, we carry out Bayesian analysis to decrease uncertainties in model CAM_KF, RRTM_KF and RRTM_GRELL for each downscaling region.Item Variations in Nearshore Bar Morphology: Implications for Rip Current Development at Pensacola Beach, Florida from 1951 to 2004(2012-10-19) Barrett, Gemma ElizabethIn 2002, Pensacola Beach was identified by the United States Lifesaving Association as being the most hazardous beach in the continental United States for beach drowning by rip currents. Recent studies suggest that the rip currents at Pensacola Beach are associated with a transverse bar and rip morphology that develops with the migration of the bars and recovery of the beachface following an extreme storm. Combined with an alongshore variation in wave forcing by transverse ridges on the inner-shelf, the bar cycle (of bar response and recovery to extreme storms) is hypothesized to create both rip current hotspots and periods of rip activity. However, it is unknown at what stage, or stages, the bar cycle is associated with the formation of these hotspots and the greatest number of rips. To determine how the accretional rip hazard varies in response to the nearshore bar cycle, this thesis will quantify the alongshore variation in the nearshore bar morphology on Santa Rosa Island from 1951 to 2004. Aerial photographs and satellite images are collected for the study area and nearshore features are digitized in ArcGIS and evaluated using wavelet analysis. Specifically, a continuous wavelet transform is used to the identify times and locations when a transverse bar and rip morphology is present or is in the process of developing. The findings suggest that the rip-scale variation in bar morphology (~100-250m) is superimposed on an alongshore variation consistent with the scale of the transverse ridges (~1000m). From the outer bar to the shoreline, and as the bar migrates landward, the variation becomes increasingly dominated by the rip-scale variation. Hotspots of rip current activity were found consistently between years at Fort Pickens Gate, San Souci, Holiday Inn, Casino Beach, Avenida 18 and Portofino, as clusters of rip-scale variation.Item Wavelets, Self-organizing Maps and Artificial Neural Nets for Predicting Energy Use and Estimating Uncertainties in Energy Savings in Commercial Buildings(2010-01-14) Lei, YafengThis dissertation develops a "neighborhood" based neural network model utilizing wavelet analysis and Self-organizing Map (SOM) to predict building baseline energy use. Wavelet analysis was used for feature extraction of the daily weather profiles. The resulting few significant wavelet coefficients represent not only average but also variation of the weather components. A SOM is used for clustering and projecting high-dimensional data into usually a one or two dimensional map to reveal the data structure which is not clear by visual inspection. In this study, neighborhoods that contain days with similar meteorological conditions are classified by a SOM using significant wavelet coefficients; a baseline model is then developed for each neighborhood. In each neighborhood, modeling is more robust without unnecessary compromises that occur in global predictor regression models. This method was applied to the Energy Predictor Shootout II dataset and compared with the winning entries for hourly energy use predictions. A comparison between the "neighborhood" based linear regression model and the change-point model for daily energy use prediction was also performed. We also studied the application of the non-parametric nearest neighborhood points approach in determining the uncertainty of energy use prediction. The uncertainty from "local" system behavior rather than from global statistical indices such as root mean square error and other measures is shown to be more realistic and credible than the statistical approaches currently used. In general, a baseline model developed by local system behavior is more reliable than a global baseline model. The "neighborhood" based neural network model was found to predict building baseline energy use more accurately and achieve more reliable estimation of energy savings as well as the associated uncertainties in energy savings from building retrofits.