Interaction Between Meteorological and Dispersion Models at Different Scales

Genikhovich, Eugene; Sofiev, M.; Gracheva, Irene

doi:10.1007/978-0-387-68854-1_18

Cited by 5 publications

(3 citation statements)

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“…Vertical turbulent diffusion coefficient K D is determined using SILAM standard meteorological processing routines, which evaluates K D in a simplified form avoiding the uncertain parameters, such as turbulent kinetic energy or turbulent length scale. The computations of K D start at the surface with evaluation of K D ( z 1 = 1 m) after Genikhovich et al [2004] and the height of the boundary layer H ABL as described by Sofiev et al [2006]. Following the surface‐layer assumptions, it is assumed that K D ∝ z in the range from z 1 up to z 1 = 0.1 H ABL , then K D remains constant up to the height H ABL .…”

Section: Assessment Of the Contribution Of The Effect Of Turbulent Thmentioning

confidence: 99%

Turbulent diffusion and turbulent thermal diffusion of aerosols in stratified atmospheric flows

et al. 2009

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[1] The paper analyzes the phenomenon of turbulent thermal diffusion in the Earth atmosphere, its relation to the turbulent diffusion, and its potential impact on aerosol distribution. This phenomenon was predicted theoretically more than 10 years ago and detected recently in the laboratory experiments. This effect causes a nondiffusive flux of aerosols in the direction of the heat flux and results in formation of long-living aerosol layers in the vicinity of temperature inversions. We applied the theory of turbulent thermal diffusion to the Global Ozone Monitoring by Occultation of Stars (GOMOS) aerosol observations near the tropopause in order to explain the shape of aerosol vertical profiles with elevated concentrations located almost symmetrically with respect to temperature profile. We demonstrate that this theory is in good agreement with the observed profiles of aerosol concentration and temperature in the vicinity of the tropopause. In combination with the derived expression for the dependence of the turbulent thermal diffusion ratio on the turbulent diffusion, these measurements yield an independent method for determining the coefficient of turbulent diffusion at the tropopause. We also derived a practically applicable formulation for dispersion of atmospheric trace species which takes into account the phenomenon of turbulent thermal diffusion. We evaluated the impact of turbulent thermal diffusion to the lower troposphere vertical profiles of aerosol concentration by means of numerical dispersion modeling and found a regular upward forcing of aerosols with coarse particles affected more strongly than fine aerosols.

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Section: Assessment Of the Contribution Of The Effect Of Turbulent Thmentioning

confidence: 99%

Turbulent diffusion and turbulent thermal diffusion of aerosols in stratified atmospheric flows

et al. 2009

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“…This work uses the more recent Eulerian core, where horizontal and vertical advection is computed using the scheme by Galperin (2000). Vertical diffusion is discretised as described in Sofiev (2002) with the diffusion coefficient K z parametrised following Genikhovich et al (2004).…”

Section: Dispersion Modelling With P3d and Smentioning

confidence: 99%

Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release

Winiarek

Vira

Bocquet

et al. 2011

Atmospheric Environment

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International audienceIn the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is required by the decision makers for the preparation of adequate countermeasures. The accuracy of the forecast plume is highly dependent on the source term estimation. On several academic test cases, including real data, inverse modelling and data assimilation techniques were proven to help in the assessment of the source term. In this paper, a semi-automatic method is proposed for the sequential reconstruction of the plume, by implementing a sequential data assimilation algorithm based on inverse modelling, with a care to develop realistic methods for operational risk agencies. The performance of the assimilation scheme has been assessed through the intercomparison between French and Finnish frameworks. Two dispersion models have been used: Polair3D and Silam developed in two different research centres. Different release locations, as well as different meteorological situations are tested. The existing and newly planned surveillance networks are used and realistically large multiplicative observational errors are assumed. The inverse modelling scheme accounts for strong error bias encountered with such errors. The efficiency of the data assimilation system is tested via statistical indicators. For France and Finland, the average performance of the data assimilation system is strong. However there are outlying situations where the inversion fails because of a too poor observability. In addition, in the case where the power plant responsible for the accidental release is not known, robust statistical tools are developed and tested to discriminate candidate release sites

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“…SILAM is a dual-core modelling system with Lagrangian dispersion core based on an iterative advection algorithm of Eerola (1990) and a Monte Carlo random-walk diffusion representation (Sofiev et al, 2006b), and an Eulerian dynamic core that applies the advection routine of Galperin (2000) with vertical diffusion scheme based on extended resistive analogy of Sofiev (2002) and parametrization of vertical diffusivity after Genikhovich et al (2004). The system can directly utilize the meteorological data from the HIRLAM and ECMWF numerical weather prediction (NWP) models, as well as their archives.…”

Section: The Silam Modelmentioning

confidence: 99%

On-Line Chemistry Within WRF: Description and Evaluation of a State-of-the-Art Multiscale Air Quality and Weather Prediction Model

Grell

Fast²,

Gustafson³

et al. 2010

Integrated Systems of Meso-Meteorological and Chemical Transport Models

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Interaction Between Meteorological and Dispersion Models at Different Scales

Cited by 5 publications

References 1 publication

Turbulent diffusion and turbulent thermal diffusion of aerosols in stratified atmospheric flows

Turbulent diffusion and turbulent thermal diffusion of aerosols in stratified atmospheric flows

Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release

On-Line Chemistry Within WRF: Description and Evaluation of a State-of-the-Art Multiscale Air Quality and Weather Prediction Model

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