Eulerian dispersion modeling with WRF-LES of plume impingement in neutrally and stably stratified turbulent boundary layers

Nunalee, Christopher G.; Kosović, Branko; Bieringer, Paul E.

doi:10.1016/j.atmosenv.2014.09.070

Cited by 17 publications

(21 citation statements)

References 40 publications

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“…where x 0 = 1000 m, and the dispersion coefficient a [m] depends on atmospheric stability as defined by the Pasquill-Gifford atmospheric stability categories (Pasquill, 1961). The solution to Eq.…”

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confidence: 99%

Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes

et al. 2018

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Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ∼10×10 km2 domains with ≤50×50 m2 pixel resolution and 1 %–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50×50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10 m wind speed can infer source rates with an error of 0.07–0.17 t h-1+5 %–12 % depending on instrument precision (1 %–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h-1+8 %–12 %) but a simpler physical basis. For comparison, point sources larger than 0.3 t h−1 contribute more than 75 % of methane emissions reported to the US Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but detrimental for source quantification.

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confidence: 99%

Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes

et al. 2018

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“…Due to its accuracy, efficiency, and, in particular, the capability for high Reynolds number flows, the synthetic inflow turbulence generator (Xie and Castro, 2008) has been implemented and tested on codes developed for engineering applications, such as Star-CD (Xie and Castro, 2009) and Open-FOAM (Kim and Xie, 2016), and the micro-scale meteorology code PALM (PALM, 2017;Maronga et al, 2020). This study focuses on an implementation of this synthetic inflow turbulence generator (Xie and Castro, 2008) in the idealised WRF-LES (v3.6.1) model under neutral atmospheric conditions.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a synthetic inflow turbulence generator in idealised WRF v3.6.1 large eddy simulations under neutral atmospheric conditions

2021

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Abstract. A synthetic inflow turbulence generator was implemented in the idealised Weather Research and Forecasting large eddy simulation (WRF-LES v3.6.1) model under neutral atmospheric conditions. This method is based on an exponential correlation function and generates a series of two-dimensional slices of data which are correlated both in space and in time. These data satisfy a spectrum with a near “-5/3” inertial subrange, suggesting its excellent capability for high Reynolds number atmospheric flows. It is more computationally efficient than other synthetic turbulence generation approaches, such as three-dimensional digital filter methods. A WRF-LES simulation with periodic boundary conditions was conducted to provide prior mean profiles of first and second moments of turbulence for the synthetic turbulence generation method, and the results of the periodic case were also used to evaluate the inflow case. The inflow case generated similar turbulence structures to those of the periodic case after a short adjustment distance. The inflow case yielded a mean velocity profile and second-moment profiles that agreed well with those generated using periodic boundary conditions, after a short adjustment distance. For the range of the integral length scales of the inflow turbulence (±40 %), its effect on the mean velocity profiles is negligible, whereas its influence on the second-moment profiles is more visible, in particular for the smallest integral length scales, e.g. those with the friction velocity of less than 4 % error of the reference data at x/H=7. This implementation enables a WRF-LES simulation of a horizontally inhomogeneous case with non-repeated surface land-use patterns and can be extended so as to conduct a multi-scale seamless nesting simulation from a meso-scale domain with a kilometre-scale resolution down to LES domains with metre-scale resolutions.

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“…The potential temperature soundings are uniform at 290 K from the surface to a mixing depth set at either 500, 800, or 1100 m altitude, with an inversion above that altitude and the model top set 700 m above the inversion. For each of these three mixing depths, we conduct simulations using five initially uniform southerly wind profiles with speeds of 2-8 m s We use the WRF-LES passive tracer transport capability (Nottrott et al, 2014;Nunalee et al, 2014) to generate a plume from a single constant point source in the WRF-LES meteorological environment. From there we integrate the plume over vertical columns and add random noise to produce GHGSat pseudo-observations.…”

Section: Large Eddy Simulations (Les)mentioning

confidence: 99%

Quantifying methane point sources from fine-scale (GHGSat) satellite observations of atmospheric methane plumes

Varon¹,

Jacob²,

McKeever³

et al. 2018

Preprint

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Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over 10 selected ~10×10 km 2 domains with ≤ 50×50 m 2 pixel resolution and 1-5% measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50×50 m 2 pixel resolution for a range of atmospheric conditions using the Weather Research andForecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence 15 cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10-m wind speed can infer source rates with error of 0.07-0.17 t h -1 + 5-12% depending on instrument precision (1-5%).The cross-sectional flux method has slightly larger errors (0.07-0.26 t h -1 + 8-12%) but a simpler physical basis. For 20 comparison, point sources larger than 0.5 t h -1 contribute more than 75% of methane emissions reported to the U.S. Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available, and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but not for source quantification.

show abstract

Eulerian dispersion modeling with WRF-LES of plume impingement in neutrally and stably stratified turbulent boundary layers

Cited by 17 publications

References 40 publications

Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes

Quantifying methane point sources from fine-scale satellite observations of atmospheric methane plumes

Implementation of a synthetic inflow turbulence generator in idealised WRF v3.6.1 large eddy simulations under neutral atmospheric conditions

Quantifying methane point sources from fine-scale (GHGSat) satellite observations of atmospheric methane plumes

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