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Numerical stochastic simulation of joint non-Gaussian meteorological series
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V. A. Ogorodnikov
Published/Copyright:
November 25, 2009
Abstract
Numerical stochastic simulation algorithms for vector stationary non-Gaussian series are considered. These algorithms allow one to reproduce variations in time for a set of continuous and discrete meteorological values of the daily resolution, such as surface air temperature, indicators of precipitation, wind velocity components, and overall cloudiness. Representations in the form of a mixture of normal distributions and specific threshold transformations of Gaussian processes are used for adequate description of one-dimensional distributions. The model can be used in interpretations of results of climatic simulation for estimation of climate change consequences and in solving various problems of applied climatology.
Published Online: 2009-11-25
Published in Print: 2009-November
© de Gruyter 2009
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Articles in the same Issue
- A posteriori error estimates for a Maxwell type problem
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- Convergence of a full multigrid algorithm for quadratic finite elements in a domain with a curvilinear boundary
- Variances of estimates of a diffusion process functional and its derivatives with respect to parameters
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- Numerical simulation of halos in crystal clouds by Monte Carlo method
- Statistical modelling of the solution of the nonlinear Boltzmann equation in the spatially inhomogeneous case
