The impact of noise barriers on gaseous air-pollution dispersion was examined using the high-resolution CLMM (Charles University LES (Large Eddy Simulation) Microscale Model). The dispersion of a mixture of nitrogen o...The impact of noise barriers on gaseous air-pollution dispersion was examined using the high-resolution CLMM (Charles University LES (Large Eddy Simulation) Microscale Model). The dispersion of a mixture of nitrogen oxides (denoted as NOx—a mix of NO and NO2) was computed, providing the simulation in which wind direction is approximately perpendicular to the noise barriers. The barriers were assumed to be straight and infinitely long, with a height of 3 m. Dispersion of NOx was modeled for situations with no noise barriers along the highway, barriers on both sides, and for a single barrier on the upwind and downwind sides of the highway. The modelling results are presented and discussed in relation to previous studies and the implications of the results are considered for pollution barriers along highways.展开更多
The growth of small errors in weather prediction is exponential on average. As an error becomes larger, its growth slows down and then stops with the magnitude of the error saturating at about the average distance bet...The growth of small errors in weather prediction is exponential on average. As an error becomes larger, its growth slows down and then stops with the magnitude of the error saturating at about the average distance between two states chosen randomly.This paper studies the error growth in a low-dimensional atmospheric model before, during and after the initial exponential divergence occurs. We test cubic, quartic and logarithmic hypotheses by ensemble prediction method. Furthermore, the quadratic hypothesis suggested by Lorenz in 1969 is compared with the ensemble prediction method. The study shows that a small error growth is best modeled by the quadratic hypothesis. After the error exceeds about a half of the average value of variables, logarithmic approximation becomes superior. It is also shown that the time length of the exponential growth in the model data is a function of the size of small initial error and the largest Lyapunov exponent. We conclude that the size of the error at the least upper bound(supremum) of time length is equal to 1 and it is invariant to these variables. Predictability, as a time interval, where the model error is growing, is for small initial error, the sum of the least upper bound of time interval of exponential growth and predictability for the size of initial error equal to 1.展开更多
文摘The impact of noise barriers on gaseous air-pollution dispersion was examined using the high-resolution CLMM (Charles University LES (Large Eddy Simulation) Microscale Model). The dispersion of a mixture of nitrogen oxides (denoted as NOx—a mix of NO and NO2) was computed, providing the simulation in which wind direction is approximately perpendicular to the noise barriers. The barriers were assumed to be straight and infinitely long, with a height of 3 m. Dispersion of NOx was modeled for situations with no noise barriers along the highway, barriers on both sides, and for a single barrier on the upwind and downwind sides of the highway. The modelling results are presented and discussed in relation to previous studies and the implications of the results are considered for pollution barriers along highways.
基金supported by Research Plan(No.MSM0021620860)by project(No.SVV-2013-267308)
文摘The growth of small errors in weather prediction is exponential on average. As an error becomes larger, its growth slows down and then stops with the magnitude of the error saturating at about the average distance between two states chosen randomly.This paper studies the error growth in a low-dimensional atmospheric model before, during and after the initial exponential divergence occurs. We test cubic, quartic and logarithmic hypotheses by ensemble prediction method. Furthermore, the quadratic hypothesis suggested by Lorenz in 1969 is compared with the ensemble prediction method. The study shows that a small error growth is best modeled by the quadratic hypothesis. After the error exceeds about a half of the average value of variables, logarithmic approximation becomes superior. It is also shown that the time length of the exponential growth in the model data is a function of the size of small initial error and the largest Lyapunov exponent. We conclude that the size of the error at the least upper bound(supremum) of time length is equal to 1 and it is invariant to these variables. Predictability, as a time interval, where the model error is growing, is for small initial error, the sum of the least upper bound of time interval of exponential growth and predictability for the size of initial error equal to 1.
