scholarly journals Study on Characteristic of Standard Deviation of Wind Direction Fluctuation for Atmospheric Dispersion Simulation

2021 ◽  
Vol 46 (2) ◽  
pp. 33-41
Author(s):  
Kazutaka GOTO ◽  
Hiroshi TAKIMOTO ◽  
Takeshi KISHIDA ◽  
Hiroki ONO ◽  
Ayumu SATO
Keyword(s):  
Standard Deviation ◽  
Wind Direction ◽  
Atmospheric Dispersion

A routine to compute mean and standard deviation of fluctuating wind direction

1984 ◽  
Vol 18 (2) ◽  
pp. 473-474 ◽  
Author(s):  
R. Sundaresan ◽  
V.V. Shirvaikar
Keyword(s):  
Standard Deviation ◽  
Wind Direction ◽  
Fluctuating Wind

scholarly journals On the Algorithms Used to Compute the Standard Deviation of Wind Direction

2009 ◽  
Vol 48 (10) ◽  
pp. 2144-2151 ◽  
Author(s):  
Pierre S. Farrugia ◽  
James L. Borg ◽  
Alfred Micallef
Keyword(s):  
Standard Deviation ◽  
Wind Direction ◽  
Atmospheric Stability ◽  
Angular Distance ◽  
Circular Statistics ◽  
Horizontal Wind ◽  
Stability Class ◽  
Mathematical Expressions ◽  
Analytical Work

Abstract The standard deviation of wind direction is a very important quantity in meteorology because in addition to being used to determine the dry deposition rate and the atmospheric stability class, it is also employed in the determination of the rate of horizontal diffusion, which in turn determines transport and dispersion of air pollutants. However, the computation of this quantity is rendered difficult by the fact that the horizontal wind direction is a circular variable having a discontinuity at 2π radians, beyond which the wind direction starts again from zero, thus preventing angular subtraction from being a straightforward procedure. In view of such a limitation, this work is meant to provide new mathematical expressions that simplify both the computational and analytical work involved in handling the standard deviation of wind direction. This is achieved by deriving a number of Fourier series and Taylor expansions that can represent the minimum angular distance and its powers. Using these expressions, the relation between two algorithms commonly used to determine the standard deviation of wind direction is analyzed. Furthermore, given that these trigonometric expansions effectively reduce the mathematical complexity involved when dealing with circular statistics, their potential application to solve other problems is discussed.

Variance of Atmosphere Dispersion Factor for Accident Release from Nuclear Power Plant

2012 ◽  
Vol 518-523 ◽  
pp. 1242-1246 ◽  
Author(s):  
Rui Ping Guo ◽  
Chun Lin Yang
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Probability Distribution ◽  
Wind Direction ◽  
Nuclear Power ◽  
Power Plants ◽  
Atmospheric Dispersion ◽  
Radioactive Material ◽  
The Difference ◽  
Release Height

The growing concern over the effect of atmosphere dispersion resulted from radioactive material was noticeable. This paper demonstrated the variance of atmosphere dispersion factor for accident release from nuclear power plant through running PAVAN (Atmospheric Dispersion of Radioactive Releases from Nuclear Power Plants) model. Also, we investigated the effect of release height (short for H) on atmosphere dispersion factor and compared the correlation between atmosphere dispersion factor and dispersion distance. Our results showed that atmosphere dispersion factor would descend with increased release height. As dispersion distance increasing, the tendency of atmosphere dispersion factor expressed complicated status caused by the difference of wind direction. It was illustrated that the phenomena was caused by the integrated action between the wind direction and release height. The probability distribution of atmosphere dispersion factor also validated that the distribution was depend on the wind direction. Probability analysis indicated that the atmosphere dispersion factor under H=100m was overall less than that under H=75m.

scholarly journals A Marine Radar Wind Sensor

2007 ◽  
Vol 24 (9) ◽  
pp. 1629-1642 ◽  
Author(s):  
Heiko Dankert ◽  
Jochen Horstmann
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Wind Direction ◽  
Ocean Surface ◽  
Image Sequences ◽  
Wind Vector ◽  
Sea State ◽  
Wind Speeds ◽  
X Band ◽  
Wind Measurements

Abstract A new method for retrieving the wind vector from radar-image sequences is presented. This method, called WiRAR, uses a marine X-band radar to analyze the backscatter of the ocean surface in space and time with respect to surface winds. Wind direction is found using wind-induced streaks, which are very well aligned with the mean surface wind direction and have a typical spacing above 50 m. Wind speeds are derived using a neural network by parameterizing the relationship between the wind vector and the normalized radar cross section (NRCS). To improve performance, it is also considered how the NRCS depends on sea state and atmospheric parameters such as air–sea temperature and humidity. Since the signal-to-noise ratio in the radar sequences is directly related to the significant wave height, this ratio is used to obtain sea state parameters. All radar datasets were acquired in the German Bight of the North Sea from the research platform FINO-I, which provides environmental data such as wind measurements at different heights, sea state, air–sea temperatures, humidity, and other meteorological and oceanographic parameters. The radar-image sequences were recorded by a marine X-band radar installed aboard FINO-I, which operates at grazing incidence and horizontal polarization in transmit and receive. For validation WiRAR is applied to the radar data and compared to the in situ wind measurements from FINO-I. The comparison of wind directions resulted in a correlation coefficient of 0.99 with a standard deviation of 12.8°, and that of wind speeds resulted in a correlation coefficient of 0.99 with a standard deviation of 0.41 m s−1. In contrast to traditional offshore wind sensors, the retrieval of the wind vector from the NRCS of the ocean surface makes the system independent of the sensors’ motion and installation height as well as the effects due to platform-induced turbulence.

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