Dispersion models often require meteorological inputs which are not routinely measured, such as surface heat flux or boundary layer depth (or mixing depth), which have to be inferred from other measurements. Some quantities, such as wind speed and direction, although routinely measured are available usually only at isolated sites not necessary close to the area for dispersion calculations. Thus, dispersion parameters have to be estimated before the dispersion calculation can be performed. The activity of inferring meteorological parameters needed in dispersion models using routinely available meteorological data and parameterisation schemes is known as pre-processing. We present here the meteorological pre-processor ABLE (Acquisition of Boundary Layer paramEters), newly developed at the Department of Physics, University of Genoa, Italy for air pollution simulations in local and regional scales. ABLE could be applied not only at one specific site of interest, but also for retrieving the horizontal distribution of the desired dispersion parameters. . The former point is more or less 'classical' for the pre-processing activity and is widely treated in the pre- processors developed during the last decade. The latter is related to application of dispersion models in complex terrain (strong horizontal inhomogeneity and terrain obstacles), as well as in urban environment. Under such conditions simple parameterisations are rarely valid and new approaches are currently under development. The present version of ABLE makes use of a mass-consistent flow model in order to compute the horizontal distribution of the sensible heat flux, the mixing height and other boundary layer parameters for non-homogeneous terrain. The pre-processor is based on the surface energy balance method and incorporates a wide variety of state-of-the-art algorithms and formulae. Boundary layer parameters, relevant to applied and regulatory dispersion codes, include the height of the mixing layer (the mixing height MH), the Monin Obukhov length scale , the friction velocity , the convective scaling velocity , the turbulent temperature scale . The mixing height is a key parameter for air pollution models and we shall focus our attention mainly on its estimation. The growth and the structure of the mixing height are driven by the fluxes of heat and momentum which depend on surface energy balance and surface characteristics such as roughness of the underlying surface, albedo, availability of moisture. At first we will describe the adopted approach for estimating the sensible heat flux, which is an essential parameter for the growth of the MH. Then the schemes for estimating the MH during convective and stable conditions will be presented. ABLE incorporates different techniques for the computation of the MH and other boundary layer scaling parameters in different meteorological conditions, starting from routinely available meteorological measurements. The input data consist of values for the wind speed. the temperature at 10m, the cloud cover the albedo and the atmospheric pressure, averaged either on one hour, or half an hour or fifteen minutes, as well as sounding profile data to estimate the lapse rate above the mixed layer. Some site-specific parameters like roughness length, geographical coordinates, surface moisture parameters are also required.
ABLE Release 1.3 User's Guide
E Canepa;
2006
Abstract
Dispersion models often require meteorological inputs which are not routinely measured, such as surface heat flux or boundary layer depth (or mixing depth), which have to be inferred from other measurements. Some quantities, such as wind speed and direction, although routinely measured are available usually only at isolated sites not necessary close to the area for dispersion calculations. Thus, dispersion parameters have to be estimated before the dispersion calculation can be performed. The activity of inferring meteorological parameters needed in dispersion models using routinely available meteorological data and parameterisation schemes is known as pre-processing. We present here the meteorological pre-processor ABLE (Acquisition of Boundary Layer paramEters), newly developed at the Department of Physics, University of Genoa, Italy for air pollution simulations in local and regional scales. ABLE could be applied not only at one specific site of interest, but also for retrieving the horizontal distribution of the desired dispersion parameters. . The former point is more or less 'classical' for the pre-processing activity and is widely treated in the pre- processors developed during the last decade. The latter is related to application of dispersion models in complex terrain (strong horizontal inhomogeneity and terrain obstacles), as well as in urban environment. Under such conditions simple parameterisations are rarely valid and new approaches are currently under development. The present version of ABLE makes use of a mass-consistent flow model in order to compute the horizontal distribution of the sensible heat flux, the mixing height and other boundary layer parameters for non-homogeneous terrain. The pre-processor is based on the surface energy balance method and incorporates a wide variety of state-of-the-art algorithms and formulae. Boundary layer parameters, relevant to applied and regulatory dispersion codes, include the height of the mixing layer (the mixing height MH), the Monin Obukhov length scale , the friction velocity , the convective scaling velocity , the turbulent temperature scale . The mixing height is a key parameter for air pollution models and we shall focus our attention mainly on its estimation. The growth and the structure of the mixing height are driven by the fluxes of heat and momentum which depend on surface energy balance and surface characteristics such as roughness of the underlying surface, albedo, availability of moisture. At first we will describe the adopted approach for estimating the sensible heat flux, which is an essential parameter for the growth of the MH. Then the schemes for estimating the MH during convective and stable conditions will be presented. ABLE incorporates different techniques for the computation of the MH and other boundary layer scaling parameters in different meteorological conditions, starting from routinely available meteorological measurements. The input data consist of values for the wind speed. the temperature at 10m, the cloud cover the albedo and the atmospheric pressure, averaged either on one hour, or half an hour or fifteen minutes, as well as sounding profile data to estimate the lapse rate above the mixed layer. Some site-specific parameters like roughness length, geographical coordinates, surface moisture parameters are also required.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
