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Source Characterization and Meteorology Retrieval Including Atmospheric Boundary Layer Depth Using a Genetic Algorithm

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Source Characterization and Meteorology Retrieval Including Atmospheric Boundary Layer Depth Using a Genetic Algorithm Source Characterization and Meteorology Retrieval Including Atmospheric Boundary Layer Depth Using a Genetic Algorithm Andrew J. Annunzio*, Sue E Haupt, and George S. Young The Pennsylvania State University, Department of Meteorology, University Park, Pennsylvania 1. Introduction This current work is unique, because back calculation includes boundary layer depth that impacts surface An important issue in homeland and military concentration further from the source. security is characterizing the source of a harmful contaminant in the atmosphere. Characterizing the 2. Model Formulation source allows appropriate response to and mitigation of the contaminant. Further, finding the appropriate This goal of this study is to determine the source initial conditions permits proper determination of of a contaminant and meteorological parameters, where it will disperse. Characterizing the source including wind speed, wind direction, and boundary provides initial conditions for the location and strength layer depth, in order to accurately model the transport of the release, but it does not give enough insight to and dispersion of a contaminant. The data inversion is model the spread of the contaminant. Ultimately, accomplished with an optimization technique. Such a meteorological parameters such as wind speed and calculation requires a robust optimization technique; wind direction govern the dispersion of contaminants therefore we implement a hybrid Genetic Algorithm allowing one to compute surface concentrations. The (GA) for the task. Prior to any back calculation, a depth of the boundary layer is another meteorological forward model computes concentration data at grid parameter that impacts surface concentration by points in the domain. The GA optimizes the number reflecting contaminants back to the surface. The of parameters in question in the forward model to best capping inversion at the top of the boundary layer acts match the concentration data at grid points. as a lid to rising thermals that result from surface heating and also limits the domain of turbulence (Stull, 2.1 Forward model 1998). The impact of reflected contaminants on surface concentration depends on the depth of the For this problem we use an identical twin boundary layer and atmospheric stability. A parameter experiment where the model itself creates the that helps assess the influence that a capping inversion observations (Daley, 1991). This approach is useful has on the dispersion of a contaminant is the for technique development because it eliminates model ventilation factor given by the product of wind speed errors that are a source of uncertainty. The model and boundary layer depth (Hsu, 2003). A shallow chosen to create synthetic data is the three dimensional boundary layer coupled with calm winds implies trapped Gaussian puff model given by stagnant air and hence low ventilation. Deep boundary layers with fast winds, on the other hand qt∆−() x ut22 y C=−×−(exp( ) exp( ) entail rigorous motions and high values of ventilation 3 22σσ22 σσσ π 2 xy (Eagleman, 1996). While ventilation may describe u xyz(2 ) how efficiently the atmosphere disperses ()()zz−+22 zz contaminants, it does not provide insight into how the ×−(expsensor release ) + exp( sensor release ) (1) σσ22 boundary layer depth impacts surface concentrations. 22zz Further, before use of the ventilation factor, the height N (2)(2)z++ z nz22 z +− z nz ++(exp(sensor release i ) exp( sensor release i ) of the capping inversion and wind speed must be ∑ 22 = 22σσ determined, which are unknowns for this problem. n 1 zz (zz− +−−2)nz22 ( z z 2) nz This work uses the concept of assimilating +exp( sensor release i)exp(+ sensor release i )) σσ22 surface concentration measurements to back calculate 22zx the source characteristics of the release as well as meteorological parameters. This is an extension of Where σ ,σ , and σ represent the standard prior work where wind speed and direction were back x y z calculated along with the (x,y) source location, source deviations of the puff in each of the three dimensions, z is the boundary layer depth, z is the strength, and time of release (Allen 2006, Long 2007). i sensor height of the sensor, zrelease is the height of the release, q is the release rate over a time span ∆t , u is the wind *Corresponding author address: Andrew J. Annunzio, Department of Meteorology, The Pennsylvania State University, speed, and N is the number of reflection terms University Park, PA 16802; [email protected] included. Experiments show that two or three reflection terms describe almost all of the dispersion, Gaussian (Beychok, 2005). In order to determine however, we will use five reflection terms in creating where contaminants reflecting off the base of the the synthetic data (Beychock, 2005). The forward inversion impact surface concentrations, we extend model computes the concentration at the sensors given Turner’s equation to “guesses” of the seven parameters to be optimized. −=σ The hybrid GA optimizes the parameters by 22.15zzi release z (5) minimizing the squared difference between the concentrations produced by the synthetic data and the If we plug (3) into (5) and solve for x, we obtain the concentrations calculated with the current guess to the equation variables summed over each grid point and each time JIzzIJ2 − 1 x =−±exp( (( ln(i release ) −+ ) ( )2 )2 ) (6) step. 22.152KK K K 2.2 Domain Considerations Then, the distance x becomes the radius of our domain if we only want a minimal amount of reflected In the Gaussian Puff model, at a distance near contaminants in surface con centration. For stability σ z = 1.2 (2) classes C and D this distance can be large, and z i therefore the distance x is taken as the domain radius. vertical dispersion becomes uniform (Beychok, 2005). For stability classes A and B, x is small, and making σ In (1) and (2) the vertical dispersion coefficient z the domain larger necessitates adjusting (5) to −=σ describes the vertical spread of the puff which is given 22.15nzirelease z z (7) by Although our domain is now larger, the risk of having σ =+ + 2 z exp(IJ ln( x ) K (ln( x )) ) (3) uniform concentration at the surface is now greater. To ascertain this distance, we plug (2) into (7) and The constants I, J, and K in (3) are functions of divide by zi to obtain the n that determines the Pasquill stability classification. In general a more distance where concentration is uniform. From this stable atmosphere produces dispersion coefficients relation, one can also predict how many of the sensors that limit the spread of the puff (Beychok, 2005). will report equal concentration values. Rigorous vertical motions accompany unstable conditions causing the puff to spread very quickly: the 2.3 The Hybrid Genetic Algorithm dispersion coefficients are larger in this case. Uniform concentration close to the source is the result for the A genetic algorithm is a global optimization most unstable case, which poses a problem for the technique that is quite robust (Haupt, 2004). The back calculation because many sensors possess the crossover scheme used here blends the variables same value. Sensors reporting the same reading make instead of using the more standard single point the back calculation more difficult since the GA has crossover. The cost function employed is logarithmic less distinguishing information to work with. To and is given by combat this problem, we can adjust the size of the 5 R 1 +−εε + 2 2 domain in the forward model depending on the ∑∑( log10 (aCrr ) log 10 ( aR )) ) tr==11 boundary layer depth and the atmospheric stability. 1 (8) 5 R ((log()))aR + ε 2 2 Initially, the domain size is small so that contaminant ∑∑tr==1110 r concentration does not become uniform. Then we will expand our domain so that there is a large portion where Cr is the concentration given by placing current of the domain where dispersion is uniform. This guesses to the variables into (1) and R is the receptor situation only occurs in the unstable cases (Stability r classes A and B). In the nearly unstable and neutral concentration from the identical twin experiment. The cases (Stability classes C and D), we need to enlarge constant ε is placed in the logarithm to avoid taking instead of shorten the domain so that back calculation the logarithm of zero. After the GA finds the region of is possible. In order to determine domain size we use the global minimum, the Nelder Meade Downhill the Turner approximation Simplex Algorithm cascades down the global minimum to the solution (Allen, 2006, Long, 2007). zz=+2.15σ (4) i release z to compute the horizontal distance at which the reflection terms start to play a role or the distance at which the upper portion of the plume is no longer 2.4 Testing the model reflection terms are necessary to accurately ascertain all four parameters. For stability class D, the back In the identical twin experiment the “truth” data is calculation is successful for all boundary layer depths computed with the (x,y) source set at (0.0 ,0.0) m, the considered when two or more reflections terms are source strength set at 1.0 kg/m3, while the source considered. The back calculation for Stability class B height, zrelease , is allowed two different values in yielded skill scores greater than .1 when two and three different runs: 1 m and 50 m. Although a source reflection terms are considered. The unsuccessful height is possible above 1000 m, the GA only searches result is attributed to the model finding a source height for a source height between 0 and 1000 m since of 0 m instead of 1 m. When a 50 m release is sources above this range are unlikely to require an considered results improve.
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