• Wind and Structures
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• v.15 no.3
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• pp.223-245
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• 2012

Multivariate simulation is necessary for cases where non-Gaussian processes at spatially distributed locations are desired. A simulation algorithm to generate non-Gaussian wind pressure fields is proposed. Gaussian sample fields are generated based on the spectral representation method using wavelet transforms method and then mapped into non-Gaussian sample fields with the aid of a CDF mapping transformation technique. To illustrate the procedure, this approach is applied to experimental results obtained from wind tunnel tests on the domes. A multivariate Gaussian simulation technique is developed and then extended to multivariate non-Gaussian simulation using the CDF mapping technique. It is proposed to develop a new wavelet-based CDF mapping technique for simulation of multivariate non-Gaussian wind pressure process. The efficiency of the proposed methodology for the non-Gaussian nature of pressure fluctuations on separated flow regions of different rise-span ratios of domes is also discussed.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.95-106
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• 2013

It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties of atmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need of bias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correction methods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillful techniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regional climate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2 scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the west central Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections) were examined and the variations among the results using different methods were evaluated in various ways. The results for daily temperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correction methods, monthly maximum values showed as significant variation (~2'C) as the mean differences between the retrospective simulations and future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected results appreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to be relevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variation among the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among other processes that may be required for cliamte change impact assessment. This study underscores the need to carefully select a bias-correction method and that the approach for any given analysis should depend on the research question being asked.