P4.4 A Construction and Evaluation of Eulerian Dynamic Core for the Air Quality and Emergency Modelling System SILAM

Mikhail Sofiev, Michael Galperin and Eugene Genikhovich

Keywords Advection-diffusion schemes, Eulerian models, numerical modelling

1. Structure of SILAM and the New Items

Historically, most of emergency-response systems are based on Lagrangian advec-tion, often with the Monte-Carlo random-walk diffusion (Saltbones et al., 1996; Stohl et al., 1998; Sørensen et al., 2000; Sofiev et al., 2006). Systems for air quality assessments are mainly based on Eulerian approaches, such as the widely used scheme of Bott (1989) and its numerous variations. For diffusion, the K-theory and its Crank-Nicholson three-diagonal solution are the most universal.

The SILAM system (Figure 1, Sofiev et al., 2006) is a flexible environment made for a wide variety of tasks, including emergency response, air quality, observation analysis, data assimilation and inverse-problem applications. It is a modular system with object-oriented code. Lagrangian and Eulerian dynamic cores utilise the same supplementary routines including meteorological, emission, and I/O servers. At present, the cores are not connected and the user has to select the type of dynamics used for the run.

The advection routine (Galperin, 2000) of the new Eulerian core is connected with the adaptive vertical diffusion algorithm of Sofiev (2002), which makes use of the advection-controlled sub-grid variable (the first moment of the mass located in

Air Pollution Modeling and Its Application XIX.© Springer Science + Business Media B.V. 2008C. Borrego and A.I. Miranda (eds.),

It is based on the original Eulerian advection algorithm combined with extended resistance scheme for vertical diffusion. Apart from the standard advantages of the

inside a grid cell; (ii) robustness to sharp gradients of concentrations and their pre-

for prescribing or dynamically evaluating the horizontal diffusion; (v) vertical

advection for refining the flux values.

Abstract The paper presents a new dynamic core of the SILAM modelling system.

Eulerian environment, it has several unique features: (i) exactly zero numericalviscosity and a possibility to utilise the sub-grid information on mass location

diffusion with thick adaptive layers that utilises the sub-grid information of

servation during the transport, (iii) applicability at high Courant numbers, (iv) options

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References

Bott A (1989) A positive definite advection scheme obtained by non-linear renormalization of the advection fluxes. Mon. Wea. Rev., 117, 1006–1012.

Galperin MV (2000) The Approaches to Correct Computation of Airborne Pollu-tion Advection. In: Problems of Ecological Monitoring and Ecosystem Model-ling, vol. XVII, St. Petersburg, Gidrometeoizdat, pp. 54–68.

Genikhovich E, Sofiev M, Gracheva I (2004) Interactions of meteorological and dispersion models at different scales. In Air Polution Modelling and its Applications XVII (eds. C Borrego, A-LNorman), Springer 2007, pp. 158–166, ISBN-10: 0-387-28255-6

operating at very high Courant numbers. Vertical diffusion scheme serves for the whole column and incorporates dry deposition and re-evaporation via extended resistive algorithm. Its parameterization is based on K-theory after Genikhovich et al. (2004).

Acknowledgments The study was supported by TEKES-KOPRA and EU-GEMS projects, and the Network scale Atmospheric Modelling NETFAM.

the grid cell) for dynamic adaptation of the vertical. The tests of the advection scheme (examples in Figure 2) show that is has zero numerical viscosity and is capable of

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Control unit

Dispersion interface

Lagrangeanpollution

cloud

Euleriandatabuffer

Emissioncomposer

Control flow

Processing

Interface

Data storage

Data flow

Dispersiondynamics

AreaBomb

Pointemission

Dispersionphysics &chemistry

Fig. 1 Schematic picture of the main SILAM components

a b

Fig. 2 Tests of the advection scheme: a point-type and rec-tangular masses in a circular vortex; 1D jet-stream with Cou-rant number (grey) up to 9; relative concentrations are shown in black

Sofiev M, Siljamo P, Valkama I, Ilvonen M, Kukkonen J (2006) A dispersion system SILAM and its evaluation against ETEX data. Atmos. Environ., 40, 674–685.

Sørensen JH, Mackay DKJ, Jensen CØ and Donaldson AI (2000) An integrated model to predict the atmospheric spread of foot-and-mouth disease virus. Epidemiol. Infect. 124, 577–590.

Stohl A, Hittenberger M, Wotawa G (1998) Validation of the Lagrangian particle dispersion model FLEXPART against large scale tracer experiments. Atmos. Environ. 24, 4245–4264.

Sofiev M (2002) Extended resistance analogy for construction of the vertical diffusion scheme for dispersion models. J. of Geophys. Research-Atmosphere, 107, D12, doi:10.1029/2001JD001233.

Saltbones J, Foss A, Bartnicki J (1996) A real time dispersion model for severe nuclear accidents, tested in the European tracer experiment. Syst. Anal. Model. Simul. 25, 263–279.

701A Construction and Evolution of Eluerian Dynamic Core for the Air Quality