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The Gaussian distribution is a good approximation for transient (instantaneously released) puff concentration distributions within a short period of time after release. Artificial neural network (ANN) models for puff dispersion coefficients were developed, based on observations from field experiments covering a wide range of meteorological conditions (in March, May, August and November). Their average predictions were in very good agreement with measurements, having high correlation coefficients (r > 0.99). A non-linear multi-variable regression model for dispersion coefficients was also developed, under the assumption that puff dispersion coefficients increase with time, and follow power laws. Both ANN-based and multi-regression non-linear models were able to use easily measured atmospheric parameters directly, without the necessity of predefining the Pasquill stability category. Predictions of ANN-based and multi-regression-based Gaussian puff models were compared with those of Gaussian puff models using Slade's dispersion coefficients and COMBIC, a sophisticated model based on Gaussian distributions. Predictions from our two new models showed better agreement with concentration measurements than the other Gaussian puff models, by having a much higher fraction within a factor of two of measured values, and lower normalized mean square errors.

Snow precipitation interaction with a generic 3D lidar is modeled. The randomness and the intensity of the signal as a function of the visibility and snowflake size and density distribution are reproduced. To do so, a representative snow density distribution is modeled as a function of visibility. Taking into account the laser beam and pulse characteristics, the probability to have one or many snowflakes of a given size in the lidar sampling cell is calculated. Knowing the number and the size of the snowflakes, the magnitude of the lidar signal is calculated. Finally, a filtering algorithm based on the relative intensity of the snowflakes is discussed.

A neural network model was developed to predict the short-term (<150 s) concentration distributions of aerosols released from point sources over very short time periods (approximately 2 s). The model was based on data from field experiments covering a wide range of meteorological conditions. The study focused on relative dispersion about the puff centroid, with puff/cloud meander and large-scale gusts not being considered. The artificial neural network (ANN) model included explicitly a number of meteorological and turbulence parameters, and was compared with predictions from two Gaussian-based puff models to the measurements of four independent trials representing different stability conditions. The performance of the neural network model was comparable (in stable conditions) or better (in unstable and neutral conditions) than these two models when high concentration predictions were considered. Simulations of concentration distributions under different stability conditions were also generated using the developed neural network model, with the result that Gaussian distributions provided good descriptors for puff dispersion in the downwind and crosswind directions, and for particles close to the centroid in the vertical when dealing with short dispersion times.