A Dynamically Adapting Weather and Dispersion Model: The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA)

Bacon, David; Ahmad, Nashat N.; Boybeyi, Zafer; Dunn, Thomas J.; Hall, Mary S.; Lee, Pius C. S.; Sarma, Ripunjoy; Turner, Mark; Waight, Kenneth; Young, Steve H.; Zack, John W.

doi:10.1175/1520-0493(2000)128<2044:adawad>2.0.co;2

Cited by 136 publications

(144 citation statements)

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“…Adaptive grid algorithms for meteorological and air quality models were developed more than a decade ago, e.g., [28][29][30][31][32][33][34][35][36], but their use in functional or operational models is more recent and still evolving. Initial applications of the adaptive grid approach in an air quality model for an ozone episode are described by Odman et al [37] and Odman and Khan [38] who incorporated the Dynamic Solution Adaptive Grid Algorithm-Piecewise Parabolic Method (DSAGA-PPM) algorithm of Srivastava et al [34][35][36] in the Multiscale Air Quality Simulation Platform (MAQSIP) model [39], the prototype for CMAQ.…”

Section: Adaptive Grid Modelingmentioning

confidence: 99%

Sub-Grid Scale Plume Modeling

2011

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Multi-pollutant chemical transport models (CTMs) are being routinely used to predict the impacts of emission controls on the concentrations and deposition of primary and secondary pollutants. While these models have a fairly comprehensive treatment of the governing atmospheric processes, they are unable to correctly represent processes that occur at very fine scales, such as the near-source transport and chemistry of emissions from elevated point sources, because of their relatively coarse horizontal resolution. Several different approaches have been used to address this limitation, such as using fine grids, adaptive grids, hybrid modeling, or an embedded sub-grid scale plume model, i.e., plumein-grid (PinG) modeling. In this paper, we first discuss the relative merits of these various approaches used to resolve sub-grid scale effects in grid models, and then focus on PinG modeling which has been very effective in addressing the problems listed above. We start with a history and review of PinG modeling from its initial applications for ozone modeling in the Urban Airshed Model (UAM) in the early 1980s using a relatively simple plume model, to more sophisticated and state-of-the-science plume models, that include a full treatment of gas-phase, aerosol, and cloud chemistry, embedded in contemporary models such as CMAQ, CAMx, and WRF-Chem. We present examples of some typical results from PinG modeling for a variety of applications, discuss the implications of PinG on model predictions of source attribution, and discuss possible future developments and applications for PinG modeling. OPEN ACCESSAtmosphere 2011, 2 390

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Section: Adaptive Grid Modelingmentioning

confidence: 99%

Sub-Grid Scale Plume Modeling

2011

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“…Pioneered by the OMEGA model [1] for forecasting high-impact weather, air quality, and environmental hazard, there has been a growing interest in modelling atmospheric flows on unstructured meshes and utilising flexible mesh adaptivity. Interest in unstructured meshing per se dates back to the nineteen sixties [58], in the context of homogeneous discretisation for global flows.…”

Section: Introductionmentioning

confidence: 99%

“…An unstructured-mesh atmospheric model for nonhydrostatic dynamics: towards optimal mesh resolution 1 …”

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confidence: 99%

An unstructured-mesh atmospheric model for nonhydrostatic dynamics: Towards optimal mesh resolution

Szmelter

Zhang

Smolarkiewicz

2015

Journal of Computational Physics

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The paper advances the limited-area anelastic model [Smolarkiewicz et al., J. Comput. Phys., 254 (2013) 184] for investigation of nonhydrostatic dynamics in mesoscale atmospheric flows. New developments include the extension to a tetrahedral-based median-dual option for unstructured meshes and a static mesh adaptivity technique using an error indicator based on inherent properties of the Multidimensional Positive Definite Advection Transport Algorithm (MPDATA). The model employs semi-implicit nonoscillatory forward-in-time integrators for soundproof PDEs, built on MPDATA and a robust non-symmetric Krylov-subspace elliptic solver. Finitevolume spatial discretisation adopts an edge-based data structure. Simulations of stratified orographic flows and the associated gravity-wave phenomena in media with uniform and variable dispersive properties, verify the advancement and demonstrate the potential of heterogeneous anisotropic discretisation with large variation in spatial resolution for study of complex stratified flows that can be computationally unattainable with regular grids.

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“…Staniforth & Mitchell 1978;Berger & Oliger 1984;Skamarock et al 1989;Dietachmayer & Droegemeier 1992;Fiedler & Trapp 1993;Skamarock & Klemp 1993) but is still mostly under research rather than operational (e.g. Jablonowski et al 2006;Läuter et al 2007;St-Cyr et al 2008), a noteworthy exception being the study of Bacon et al (2000). If adaptive meshing is to be used to resolve deep convection, mesh density requirements must be predicted before convection would be likely to break out on the refined mesh.…”

Section: Introductionmentioning

confidence: 99%

“…Bacon et al 2000;Jablonowski et al 2006;Läuter et al 2007;St-Cyr et al 2008). However, re-meshing can be expensive and have a detrimental effect on the accuracy, reducing conservation of high-moment properties of the flow and altering the partition between balanced and unbalanced flow.…”

Section: Introductionmentioning

confidence: 99%

Predicting mesh density for adaptive modelling of the global atmosphere

Weller

2009

Phil. Trans. R. Soc. A.

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The shallow water equations are solved using a mesh of polygons on the sphere, which adapts infrequently to the predicted future solution. Infrequent mesh adaptation reduces the cost of adaptation and load-balancing and will thus allow for more accurate mapping on adaptation.We simulate the growth of a barotropically unstable jet adapting the mesh every 12 h. Using an adaptation criterion based largely on the gradient of the vorticity leads to a mesh with around 20 per cent of the cells of a uniform mesh that gives equivalent results. This is a similar proportion to previous studies of the same test case with mesh adaptation every 1-20 min.The prediction of the mesh density involves solving the shallow water equations on a coarse mesh in advance of the locally refined mesh in order to estimate where features requiring higher resolution will grow, decay or move to. The adaptation criterion consists of two parts: that resolved on the coarse mesh, and that which is not resolved and so is passively advected on the coarse mesh. This combination leads to a balance between resolving features controlled by the large-scale dynamics and maintaining fine-scale features.

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A Dynamically Adapting Weather and Dispersion Model: The Operational Multiscale Environment Model with Grid Adaptivity (OMEGA)

Cited by 136 publications

References 50 publications

Sub-Grid Scale Plume Modeling

Sub-Grid Scale Plume Modeling

An unstructured-mesh atmospheric model for nonhydrostatic dynamics: Towards optimal mesh resolution

Predicting mesh density for adaptive modelling of the global atmosphere

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