Trajectory Models for Heavy Particles in Atmospheric Turbulence: Comparison with Observations Export

@article{citeulike:5899516, abstract = {The simplest \&\#8220;random flight\&\#8221; models for the paths of heavy particles in turbulence have been tested against previous observations of the deposition of glass beads from an elevated source in the atmospheric surface layer. For the bead sizes examined (diameter 50\&\#8211;100 \&\#956;m), for which the ratio of particle inertial timescale to turbulence timescale \&\#964;p/\&\#915;L \&\#8810; 1, it was found sufficient to adapt, as others earlier have done, a well-mixed first-order Lagrangian stochastic (\&\#8220;Langevin\&\#8221;) model of fluid element trajectories, simply by superposing a gravitational settling velocity wg and reducing the velocity autocorrelation timescale along the heavy particle trajectory (\&\#915;p) relative to the fluid-Lagrangian timescale (\&\#915;L). That is to say, unless details of the particle distribution very close to ground (where \&\#964;p/\&\#915;L is not small) are of interest, no advantage other than conceptual clarity can be found in the more faithful approach of explicitly modeling particle acceleration by means of the particle equation of motion.With the timescale reduction parameter \&\#946; \&\#8764; 2, the Langevin model estimated the location and width of the bead deposit swath very well and fixed the peak deposit density to within (at worst) about 100\&\#37; error (in very stable stratification), but more generally to within about 20\&\#37;. In the case where trajectories intersected a tall crop canopy, uncertainties in the treatment of deposition proved more significant than nuances of the trajectory algorithm.}, author = {Wilson, John D.}, citeulike-article-id = {5899516}, citeulike-linkout-0 = {http://dx.doi.org/10.1175/1520-0450(2000)039%3C1894:TMFHPI%3E2.0.CO;2}, day = {1}, doi = {10.1175/1520-0450(2000)039%3C1894:TMFHPI%3E2.0.CO;2}, journal = {Journal of Applied Meteorology}, keywords = {atmosphere, stochastic-methods}, month = {November}, number = {11}, pages = {1894--1912}, posted-at = {2009-10-06 17:49:41}, priority = {2}, title = {Trajectory Models for Heavy Particles in Atmospheric Turbulence: Comparison with Observations}, url = {http://dx.doi.org/10.1175/1520-0450(2000)039%3C1894:TMFHPI%3E2.0.CO;2}, volume = {39}, year = {2000} }

TY - JOUR ID - citeulike:5899516 L3 - citeulike-article-id:5899516 N2 - The simplest “random flight” models for the paths of heavy particles in turbulence have been tested against previous observations of the deposition of glass beads from an elevated source in the atmospheric surface layer. For the bead sizes examined (diameter 50–100 μm), for which the ratio of particle inertial timescale to turbulence timescale τp/ΓL ≪ 1, it was found sufficient to adapt, as others earlier have done, a well-mixed first-order Lagrangian stochastic (“Langevin”) model of fluid element trajectories, simply by superposing a gravitational settling velocity wg and reducing the velocity autocorrelation timescale along the heavy particle trajectory (Γp) relative to the fluid-Lagrangian timescale (ΓL). That is to say, unless details of the particle distribution very close to ground (where τp/ΓL is not small) are of interest, no advantage other than conceptual clarity can be found in the more faithful approach of explicitly modeling particle acceleration by means of the particle equation of motion.With the timescale reduction parameter β ∼ 2, the Langevin model estimated the location and width of the bead deposit swath very well and fixed the peak deposit density to within (at worst) about 100% error (in very stable stratification), but more generally to within about 20%. In the case where trajectories intersected a tall crop canopy, uncertainties in the treatment of deposition proved more significant than nuances of the trajectory algorithm. IS - 11 JF - Journal of Applied Meteorology EP - 1912 TI - Trajectory Models for Heavy Particles in Atmospheric Turbulence: Comparison with Observations VL - 39 SP - 1894 KW - atmosphere KW - stochastic-methods AU - Wilson, John PY - 2000/11/01/ UR - http://dx.doi.org/10.1175/1520-0450(2000)039%3C1894:TMFHPI%3E2.0.CO;2 ER -