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Course Criteria
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2.00 Credits
Prerequisite: 3213, Mathematics 3113, or permission of instructor. Theory of radiative transfer, spectra of gaseous molecules, use of band models for radiative calculations, interaction of solar radiation with atmospheres, infrared radiative transfer in atmospheres, radiative cooling and heating, scattering, climate and radiation, remote sensing. (Sp)
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3.00 Credits
Prerequisite: 3223, Mathematics 3113. Development of thermodynamical relationships and generalized Clausius-Clapeyron equation, phase diagrams, atmospheric aerosols, review of hydrodynamics of flow past particles, collision and coalescence efficiency, theory of nucleation, precipitation growth, observations with radar, electrical state of the atmosphere. (F)
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4.00 Credits
Prerequisite: permission of instructor. Global electrical circuit, fair-weather electricity, storm electrification, charging mechanisms, electrical discharges, lightning, thunder, instrumentation and observing systems, meteorological applications.
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0.00 Credits
Prerequisite: 4133, Mathematics 3113, Engineering 3723, or equivalent. Introduction to techniques used in objective analysis of meteorological data; polynomial fitting; method of successive corrections; weighting functions; statistical methods; optimum interpolation; filter design; four-dimensional data assimilation. (F)
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2.00 Credits
Prerequisite: Mathematics 4733 or 4753, computer programming. Data collected from geophysical phenomena are considered as stochastic processes. The resulting time series are decomposed into autovariance spectra using Fourier, autocovariance and autoregressive methods. The spectra are interpreted from the viewpoint of estimation theory. Applications and practical aspects of these methods are examined. (Irreg.)
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3.00 Credits
Prerequisite: Grade of C or better in Computer Science 1313 or permission of instructor. The use of computers and networks to process the information of meteorology. Workstation skills, computer operating systems, programming languages, the Internet, computer graphics, analysis and display of meteorological data. No student may earn credit for both 4330 and 5330. (Irreg.)
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4.00 Credits
Prerequisite: 3113 or Engineering 3223; Engineering 3723; Mathematics 3123; permission of instructor. Application of fine difference, spectral, and semi-Lagrangian methods to multidimensional Newtonian fluid flow problems, including well-posedness, consistency, stability, convergence, accuracy, boundary conditions, conservation, grid systems, and filtering. In addition, code development practices and the use of high-performance vector and parallel supercomputers will be addressed.
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5.00 Credits
Prerequisite: graduate standing or permission of instructor. This course provides a thorough overview of the sub-grid scale physical process parameterization schemes used in numerical models and how these schemes influence numerical forecasts of the weather. Various well-known parameterization schemes for mesoscale and cloud-scale models are reviewed and studied. (Irreg.)
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6.00 Credits
Prerequisite: 4303 or Mathematics 4753, Computer Science 1313 or 1323, or permission of instructor. An in-depth overview of linear and non-linear regression techniques with applications to meteorological data analysis. Topics include linear regression, examination of residuals, confidence intervals, bias estimation, serial correlation issues, polynomial regression, transformation of the response variable, stepwise regression methods, multicollinearity problems, ridge regression, logistic regression and robust/resistant regression techniques. (Irreg.)
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7.00 Credits
Prerequisite: 4303 or Mathematics 4753 and Computer Science 1313 or 1323, or permission of instructor. This course is designed to illustrate how to extract additional information from a data set. With the advent of high-speed, inexpensive computers, tools are now readily available to researchers for just this purpose. These tools will be introduced, described and put to use by the students during this course. Topics include an introduction to the s-plus statistical package, random samples and probability, standard errors and estimated standard errors, bootstrap estimate of standard error, the parametric bootstrap, bootstrap failure, resampling applied regression models, the jackknife, confidence intervals, permutation tests, hypothesis testing, cross-validation, adaptive estimation and assessing errors. (Irreg.)
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