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- Author or Editor: Thomas K. Flesch x
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Abstract
Lagrangian stochastic (LS) dispersion models often use trajectory reflection to limit the domain accessible to a particle. It is shown how the well-mixed condition (Thomson) can he expressed in the Chapman-Kolmogorov equation for a discrete-time LS model to provide a test for the validity of a reflection algorithm. By that means it is shown that the usual algorithm (perfect reflection) is exactly consistent with the wmc when used to bound Gaussian homogeneous turbulence, but that no reflection scheme can satisfy the wmc when applied at a location where the probability distribution for the normal velocity is asymmetric, or locally inhomogeneous. Thus, there is no well-mixed reflection scheme for inhomogeneous or skew turbulence.
Abstract
Lagrangian stochastic (LS) dispersion models often use trajectory reflection to limit the domain accessible to a particle. It is shown how the well-mixed condition (Thomson) can he expressed in the Chapman-Kolmogorov equation for a discrete-time LS model to provide a test for the validity of a reflection algorithm. By that means it is shown that the usual algorithm (perfect reflection) is exactly consistent with the wmc when used to bound Gaussian homogeneous turbulence, but that no reflection scheme can satisfy the wmc when applied at a location where the probability distribution for the normal velocity is asymmetric, or locally inhomogeneous. Thus, there is no well-mixed reflection scheme for inhomogeneous or skew turbulence.
Abstract
Practical problems in predicting the spread of plant diseases within and between fields require knowledge of the rate of release Q of pathogenic spores into the air. Many plant pathogenic fungus spores are released into the air from plant surfaces inside plant canopies, where they are produced, or from diseased plant debris on the ground below plant canopies, where they have survived from one growing season to the next. There is no direct way to specify Q for naturally released microscopic fungus spores. It is relatively easy to measure average concentrations of spores above a source, however. A two-dimensional Lagrangian stochastic (LS) simulation model for the motion of spores driven by atmospheric turbulence in and above a plant canopy is presented. The model was compared 1) with measured concentration profiles of Lycopodium spores released from line sources at two heights inside a wheat canopy and 2) with concentration profiles of V. inaequalis ascospores measured above ground-level area sources in a grass canopy. In both cases, there was generally good agreement between the shapes of the modeled and measured concentration profiles. Modeled and measured concentrations were compared to yield estimates of spore release rates. These, in turn, were compared to release rates estimated independently from direct measurements. The two estimates of spore release rate were in good agreement both for 1) the 30-min artificial releases of Lycopodium spores [significance level P = 0.02 (upper source) and P = 0.02 (lower source)] and for 2) the daily total release of V. inaequalis ascospores (P < 0.002). These results indicate that the LS model can yield accurate values of Q (or, conversely, of concentration). Thus, LS models allow a means of attacking a nearly intractable problem and can play an important role in predicting disease spread and in helping to reduce pesticide use in disease-management decisions.
Abstract
Practical problems in predicting the spread of plant diseases within and between fields require knowledge of the rate of release Q of pathogenic spores into the air. Many plant pathogenic fungus spores are released into the air from plant surfaces inside plant canopies, where they are produced, or from diseased plant debris on the ground below plant canopies, where they have survived from one growing season to the next. There is no direct way to specify Q for naturally released microscopic fungus spores. It is relatively easy to measure average concentrations of spores above a source, however. A two-dimensional Lagrangian stochastic (LS) simulation model for the motion of spores driven by atmospheric turbulence in and above a plant canopy is presented. The model was compared 1) with measured concentration profiles of Lycopodium spores released from line sources at two heights inside a wheat canopy and 2) with concentration profiles of V. inaequalis ascospores measured above ground-level area sources in a grass canopy. In both cases, there was generally good agreement between the shapes of the modeled and measured concentration profiles. Modeled and measured concentrations were compared to yield estimates of spore release rates. These, in turn, were compared to release rates estimated independently from direct measurements. The two estimates of spore release rate were in good agreement both for 1) the 30-min artificial releases of Lycopodium spores [significance level P = 0.02 (upper source) and P = 0.02 (lower source)] and for 2) the daily total release of V. inaequalis ascospores (P < 0.002). These results indicate that the LS model can yield accurate values of Q (or, conversely, of concentration). Thus, LS models allow a means of attacking a nearly intractable problem and can play an important role in predicting disease spread and in helping to reduce pesticide use in disease-management decisions.
Abstract
This study examines simulations of two flooding events in Alberta, Canada, during June 2005, made using the Weather Research and Forecasting Model (WRF). The model was used in a manner readily accessible to nonmeteorologists (e.g., accepting default choices and parameters) and with a relatively large spatial resolution for rapid model runs. The simulations were skillful: strong storms were developed having the correct timing and location, generating precipitation rates close to observations, and with precipitation amounts near that observed. The model was then used to examine the sensitivity of the two storms to the topography of the Rocky Mountains. Comparing model results using the actual topographic grid with those of a reduced-mountain grid, it is concluded that a reduction in mountain elevation decreases maximum precipitation by roughly 50% over the mountains and foothills. There was little sensitivity to topography in the precipitation outside the mountains.
Abstract
This study examines simulations of two flooding events in Alberta, Canada, during June 2005, made using the Weather Research and Forecasting Model (WRF). The model was used in a manner readily accessible to nonmeteorologists (e.g., accepting default choices and parameters) and with a relatively large spatial resolution for rapid model runs. The simulations were skillful: strong storms were developed having the correct timing and location, generating precipitation rates close to observations, and with precipitation amounts near that observed. The model was then used to examine the sensitivity of the two storms to the topography of the Rocky Mountains. Comparing model results using the actual topographic grid with those of a reduced-mountain grid, it is concluded that a reduction in mountain elevation decreases maximum precipitation by roughly 50% over the mountains and foothills. There was little sensitivity to topography in the precipitation outside the mountains.
Abstract
“Backward” Lagrangian stochastic models calculate an ensemble of fluid element (particle) trajectories that are distinguished by each passing through an observation point. As shown, they can be faster and more flexible in calculating short-range turbulent dispersion from surface area sources than “forward” models, which simulate trajectories emanating from a source. Using a backward model, one may catalog a set of “touchdown” points (where trajectories reflect off the ground) and vertical touchdown velocities w 0 of particles “on their way to” a sensor location. It is then trivial to deduce the average concentration resulting from a surface source using the touchdown catalog: by summing the reciprocal of w 0 for touchdowns occurring within the source boundary. An advantage of this methodology is that while forward model trajectories are linked to a specific source, backward trajectories have no such dependence. In horizontally homogeneous flow, a “library” of touchdown catalogs (for representative surface roughnesses and atmospheric stabilities) would allow concentration (at a given height) to be rapidly calculated at any location from any uniform surface source.
A “well-mixed” backward model is exploited to calculate the touchdown points of particles passing over a small plot on their way to an observation tower and it is shown how to use those data to estimate the plot emission rate from a single measurement of average concentration, wind speed, and wind direction on the tower. The method was evaluated using 36 field experiments. Predicted emission rates using the backward method agreed well with mass balance estimates.
Abstract
“Backward” Lagrangian stochastic models calculate an ensemble of fluid element (particle) trajectories that are distinguished by each passing through an observation point. As shown, they can be faster and more flexible in calculating short-range turbulent dispersion from surface area sources than “forward” models, which simulate trajectories emanating from a source. Using a backward model, one may catalog a set of “touchdown” points (where trajectories reflect off the ground) and vertical touchdown velocities w 0 of particles “on their way to” a sensor location. It is then trivial to deduce the average concentration resulting from a surface source using the touchdown catalog: by summing the reciprocal of w 0 for touchdowns occurring within the source boundary. An advantage of this methodology is that while forward model trajectories are linked to a specific source, backward trajectories have no such dependence. In horizontally homogeneous flow, a “library” of touchdown catalogs (for representative surface roughnesses and atmospheric stabilities) would allow concentration (at a given height) to be rapidly calculated at any location from any uniform surface source.
A “well-mixed” backward model is exploited to calculate the touchdown points of particles passing over a small plot on their way to an observation tower and it is shown how to use those data to estimate the plot emission rate from a single measurement of average concentration, wind speed, and wind direction on the tower. The method was evaluated using 36 field experiments. Predicted emission rates using the backward method agreed well with mass balance estimates.
Abstract
When a particle descends beneath the (nominal) lower boundary of the atmosphere, it may remain there for some time τ before it reemerges into the (resolved) flow. In particle trajectory models, τ is the random duration of unresolved trajectory segments, below the height z
r
at which an artificial reflection boundary condition is applied. By computing such paths, for realistic near-ground flows, it was found that the mean delay per reflection is
Abstract
When a particle descends beneath the (nominal) lower boundary of the atmosphere, it may remain there for some time τ before it reemerges into the (resolved) flow. In particle trajectory models, τ is the random duration of unresolved trajectory segments, below the height z
r
at which an artificial reflection boundary condition is applied. By computing such paths, for realistic near-ground flows, it was found that the mean delay per reflection is
Abstract
The Athabasca oil sands development in northeast Alberta, Canada, has disturbed more than 500 km2 of boreal forest through surface mining and tailings ponds development. In this paper, the authors compare the time series of temperatures and precipitation measured over oil sands and non–oil sands locations from 1994 to 2010. In addition, they analyzed the distribution of lightning strikes from 1999 to 2010. The oil sands development has not affected the number of lightning strikes or precipitation amounts but has affected the temperature regime. Over the past 17 years, the summer overnight minimum temperatures near the oil sands have increased by about 1.2°C compared to the regional average. The authors speculate that this is caused by a combination of the industrial addition of waste heat to the atmosphere above the oil sands and changing the surface type from boreal forest to open pit mines with tailings ponds.
Abstract
The Athabasca oil sands development in northeast Alberta, Canada, has disturbed more than 500 km2 of boreal forest through surface mining and tailings ponds development. In this paper, the authors compare the time series of temperatures and precipitation measured over oil sands and non–oil sands locations from 1994 to 2010. In addition, they analyzed the distribution of lightning strikes from 1999 to 2010. The oil sands development has not affected the number of lightning strikes or precipitation amounts but has affected the temperature regime. Over the past 17 years, the summer overnight minimum temperatures near the oil sands have increased by about 1.2°C compared to the regional average. The authors speculate that this is caused by a combination of the industrial addition of waste heat to the atmosphere above the oil sands and changing the surface type from boreal forest to open pit mines with tailings ponds.
