Comparisons of JU2003 observations with four diagnostic urban wind flow and Lagrangian particle dispersion models

2011 ◽  
Vol 45 (24) ◽  
pp. 4073-4081 ◽  
Author(s):  
Steven Hanna ◽  
John White ◽  
James Trolier ◽  
Rebecca Vernot ◽  
Michael Brown ◽  
...  
2016 ◽  
Author(s):  
Huda Mohd. Ramli ◽  
J. Gavin Esler

Abstract. A rigorous methodology for the evaluation of integration schemes for Lagrangian particle dispersion models (LPDMs) is presented. A series of one-dimensional test problems are introduced, for which the Fokker-Planck equation is solved numerically using a finite-difference discretisation in physical space, and a Hermite function expansion in velocity space. Numerical convergence errors in the Fokker-Planck equation solutions are shown to be much less than the statistical error associated with a practical-sized ensemble (N = 106) of LPDM solutions, hence the former can be used to validate the latter. The test problems are then used to evaluate commonly used LPDM integration schemes. The results allow for optimal time-step selection for each scheme, given a required level of accuracy. The following recommendations are made for use in operational models. First, if computational constraints require the use of moderate to long time steps it is more accurate to solve the random displacement model approximation to the LPDM, rather than use existing schemes designed for long time-steps. Second, useful gains in numerical accuracy can be obtained, at moderate additional computational cost, by using the relatively simple "small-noise" scheme of Honeycutt.


2014 ◽  
Vol 7 (6) ◽  
pp. 2817-2829 ◽  
Author(s):  
W. M. Angevine ◽  
J. Brioude ◽  
S. McKeen ◽  
J. S. Holloway

Abstract. Lagrangian particle dispersion models require meteorological fields as input. Uncertainty in the driving meteorology is one of the major uncertainties in the results. The propagation of uncertainty through the system is not simple, and it has not been thoroughly explored. Here, we take an ensemble approach. Six different configurations of the Weather Research and Forecast (WRF) model drive otherwise identical simulations with FLEXPART-WRF for 49 days over eastern North America. The ensemble spreads of wind speed, mixing height, and tracer concentration are presented. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30–40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15–20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis.


2021 ◽  
Vol 927 ◽  
Author(s):  
J.G. Esler

It is well established that Lagrangian particle dispersion models, for inhomogeneous turbulent flows, must satisfy the ‘well-mixed condition’ of Thomson (J. Fluid Mech., vol. 180, 1987, pp. 529–556) in order to produce physically reasonable results. In more than one dimension, however, the well-mixed condition is not sufficient to define the dispersion model uniquely. The non-uniqueness, which is related to the rotational degrees of freedom of particle trajectories, permits models with trajectory curvatures and velocity autocorrelation functions which are clearly unphysical. A spin condition is therefore introduced to constrain the models. It requires an ensemble of particles with fixed initial position and velocity to have, at short times, expected angular momentum, measured relative to the mean position and velocity of an ensemble of fluid particles with initially random velocity, equal to the relative angular momentum of the mean flow at the ensemble mean location. The resulting unique model is found explicitly for the canonical example of inhomogeneous Gaussian turbulence and is characterised by accelerations which are exponential in the particle velocity. A simpler unique model with a quadratic acceleration is obtained using a weaker version of the spin condition. Unlike previous models, the unique models defined by the spin condition lead to particles having the correct (ensemble mean) angular speed in a turbulent flow in solid-body rotation. The properties of the new models are discussed in the settings of a turbulent channel flow and an idealised turbulent atmospheric boundary-layer flow.


2016 ◽  
Vol 9 (7) ◽  
pp. 2441-2457 ◽  
Author(s):  
Huda Mohd. Ramli ◽  
J. Gavin Esler

Abstract. A rigorous methodology for the evaluation of integration schemes for Lagrangian particle dispersion models (LPDMs) is presented. A series of one-dimensional test problems are introduced, for which the Fokker–Planck equation is solved numerically using a finite-difference discretisation in physical space and a Hermite function expansion in velocity space. Numerical convergence errors in the Fokker–Planck equation solutions are shown to be much less than the statistical error associated with a practical-sized ensemble (N = 106) of LPDM solutions; hence, the former can be used to validate the latter. The test problems are then used to evaluate commonly used LPDM integration schemes. The results allow for optimal time-step selection for each scheme, given a required level of accuracy. The following recommendations are made for use in operational models. First, if computational constraints require the use of moderate to long time steps, it is more accurate to solve the random displacement model approximation to the LPDM rather than use existing schemes designed for long time steps. Second, useful gains in numerical accuracy can be obtained, at moderate additional computational cost, by using the relatively simple “small-noise” scheme of Honeycutt.


2013 ◽  
Vol 52 (12) ◽  
pp. 2623-2637 ◽  
Author(s):  
Jennifer Hegarty ◽  
Roland R. Draxler ◽  
Ariel F. Stein ◽  
Jerome Brioude ◽  
Marikate Mountain ◽  
...  

AbstractThree widely used Lagrangian particle dispersion models (LPDMs)—the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Stochastic Time-Inverted Lagrangian Transport (STILT), and Flexible Particle (FLEXPART) models—are evaluated with measurements from the controlled tracer-release experiments Cross-Appalachian Tracer Experiment (CAPTEX) and Across North America Tracer Experiment (ANATEX). The LPDMs are run forward in time driven by identical meteorological inputs from the North American Regional Reanalysis (NARR) and several configurations of the Weather Research and Forecasting (WRF) model, and the simulations of tracer concentrations are evaluated against the measurements with a ranking procedure derived from the combination of four statistical parameters. The statistical evaluation reveals that all three LPDMs have comparable skill in simulating the tracer plumes when driven by the same meteorological inputs, indicating that the differences in their formulations play a secondary role. Simulations with HYSPLIT and STILT demonstrate the benefit of using customized hourly WRF fields with 10-km horizontal grid spacing over the use of 3-hourly NARR fields with 32-km horizontal grid spacing. All three LPDMs perform better when the WRF wind fields in the planetary boundary layer are nudged to NARR, with FLEXPART benefitting the most. Case studies indicate that the nudging corrects an overestimate in plume transport speed possibly caused by a positive bias in WRF wind speeds near the surface. All three LPDMs also benefit from the use of time-averaged velocity and convective mass flux fields generated by WRF, but the impact on HYSPLIT and STILT is much greater than on FLEXPART. STILT backward runs perform as well as their forward counterparts, demonstrating this model’s reversibility and its suitability for application to inverse flux estimates.


2014 ◽  
Vol 7 (4) ◽  
pp. 4603-4643 ◽  
Author(s):  
W. M. Angevine ◽  
J. Brioude ◽  
S. McKeen ◽  
J. S. Holloway

Abstract. Lagrangian particle dispersion models require meteorological fields as input. Uncertainty in the driving meteorology is one of the major uncertainties in the results. The propagation of uncertainty through the system is not simple, and has not been thoroughly explored. Here, we take an ensemble approach. Six different configurations of the Weather Research and Forecast (WRF) model drive otherwise identical simulations with FLEXPART for 49 days over eastern North America. The ensemble spreads of wind speed, mixing height, and tracer concentration are presented. Uncertainty of tracer concentrations due solely to meteorological uncertainty is 30–40%. Spatial and temporal averaging reduces the uncertainty marginally. Tracer age uncertainty due solely to meteorological uncertainty is 15–20%. These are lower bounds on the uncertainty, because a number of processes are not accounted for in the analysis.


Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1369
Author(s):  
Alice Crawford

Atmospheric Lagrangian particle dispersion models, LPDM, simulate the dispersion of passive tracers in the atmosphere. At the most basic level, model output consists of the position of computational particles and the amount of mass they represent. In order to obtain concentration values, this information is then converted to a mass distribution via density estimation. To date, density estimation is performed with a nonparametric method so that output consists of gridded concentration data. Here we introduce the use of Gaussian mixture models, GMM, for density estimation. We compare to the histogram or bin counting method for a tracer experiment and simulation of a large volcanic ash cloud. We also demonstrate the use of the mixture model for automatic identification of features in a complex plume such as is produced by a large volcanic eruption. We conclude that use of a mixture model for density estimation and feature identification has potential to be very useful.


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