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Jawad S. Touma, William M. Cox, Harold Thistle, and James G. Zapert

MARCH 1995 TOUMA ET AL. 603Performance Evaluation of Dense Gas Dispersion Models JAWAD S. TOUMA*Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, Research Triangle Park, North Carolina WILLIAM M. COXOffice of Air Quality Planning and Standards, U.S. Environmental

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Prem Woli and Joel O. Paz

). In accord with this emphasis, several agricultural research activities are carried out in this region and need data on R g . Although a large number of models exist that can estimate R g from commonly available meteorological variables, researchers have used only a limited number of methods for generating R g or have explored the performance of only a few methods for the southeastern United States. For instance, Thornton and Running (1999) evaluated the reformulated Bristow and Campbell

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Pius Lee, Daiwen Kang, Jeff McQueen, Marina Tsidulko, Mary Hart, Geoff DiMego, Nelson Seaman, and Paula Davidson

Atmospheric Chemistry, Seattle, WA, Amer. Meteor. Soc., J2.10 . Eder , B. , and S. Yu , 2006 : A performance evaluation of the 2004 release of Models-3 CMAQ. Atmos. Environ. , 40 , 4811 – 4824 . EPA , 2003 : User’s guide to MOBILE6.1 and MOBILE6.2 (Mobile Source Emission Factor Model). U.S. Environmental Protection Agency Rep. EPA420-R-03-010, 262 pp . EPA , cited . 2005 : 2005 summer ozone season—Archive. [Available online at http

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Qing Yang, Larry K. Berg, Mikhail Pekour, Jerome D. Fast, Rob K. Newsom, Mark Stoelinga, and Catherine Finley

features in the UW scheme are likely to improve boundary layer wind predictions. The goal of this research is to characterize the ramp occurrence over the CBWES site that is within an operating wind farm, to evaluate the WRF model's capability in ramp prediction, and to test the sensitivity of the model's performance to the choice of PBL schemes. Results of this study are also intended to provide recommendations to the wind-energy community regarding the choice of PBL schemes in WRF in areas of complex

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Lei Zhang, YinLong Xu, ChunChun Meng, XinHua Li, Huan Liu, and ChangGui Wang

simulation relative to observation. The systematic comparisons of simulations are performed between GCMs and PRECIS, between statistical and dynamic downscaling, as well as raw outputs and bias-corrected outputs, at the spatial and temporal dimensions in the baseline period of 1961–90. An overall performance of one model can be evaluated by a comprehensive ranking metric (MR) ( Jiang et al. 2015 ; Ahmed et al. 2019 ), which is defined as (9) MR = 1 − ∑ i = 1 n rank i m n , where m is the number of

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Jonathan E. Pleim

still essential to validate overall performance of three-dimensional modeling systems with the ACM2 used for the PBL parameterization. Section 2 describes the implementation of the ACM2 in the MM5, including the detailed formulation of the eddy diffusivities used and the numerical integration techniques. The specifics of the MM5 simulations used for testing and evaluation are summarized in section 3 . This section also presents the evaluation of the MM5 applications of the ACM2 through comparison

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Jiali Wang and Veerabhadra R. Kotamarthi

R-2 data. All experiments employed the same model domain, relaxation zones, physics options, initial conditions, and boundary conditions, and the simulation periods were identical to that of the control experiment. In section 3 , we use the experiment numbers shown in Table 1 to represent different nudging experiments. Table 1. Summary of experimental design. 3. Results For a better evaluation of model performance, we divided the portion of the CONUS without oceans and lakes (30°–49°N, 122

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Weiguo Wang, William J. Shaw, Timothy E. Seiple, Jeremy P. Rishel, and Yulong Xie

are among the most important variables that determine where the pollutants will be carried. In this regard, overall statistical evaluation may be insufficient to assess whether a model could be useful to help decision makers respond to individual events. Therefore, we further examined the performance of CALMET by comparing the trajectories of individual pollutant parcels driven by the wind fields from CALMET and from the reference data. Trajectory analyses integrate the effects of temporally and

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Julia Andrys, Thomas J. Lyons, and Jatin Kala

.3, driven by ERA-Interim ( Dee et al. 2011 ) boundary conditions. A 3-month model spinup period was used. The outer 50-km domain ( Fig. 1a ) was based on the CORDEX Australia domain. The two nested domains, at 10- and 5-km resolution, were chosen to evaluate the influence of spatial scale on model skill and also to compare the performance of parameterized convection with convection that is explicitly resolved in the model. We note that the downscaling ratio of 2 used between our two nested domains is a

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Robert Nedbor-Gross, Barron H. Henderson, Justin R. Davis, Jorge E. Pachón, Alexander Rincón, Oscar J. Guerrero, and Freddy Grajales

1. Introduction Meteorological model performance is critical for successful air quality modeling and necessary for regulatory purposes. The standard model performance evaluation (sMPE) techniques for judging model fidelity are based on statistical thresholds developed by the community from various regional studies such as Emery et al. (2001) . However, these types of evaluations are likely to show failure for high-resolution modeling in regions of complex topography. Despite sMPE failure, a

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