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1. Introduction Atmospheric dispersion of an airborne contaminant is a complex physical process involving a three-dimensional wind field complicated by turbulence effects over a wide range of spatial scales. This wind field is affected by the local meteorology and local forcings by terrain, heating, land use, and coastlines as well as the larger-scale atmospheric flow. The nonlinear physics underlying these wind field responses makes the instantaneous, local state of the wind field chaotic and
1. Introduction Atmospheric dispersion of an airborne contaminant is a complex physical process involving a three-dimensional wind field complicated by turbulence effects over a wide range of spatial scales. This wind field is affected by the local meteorology and local forcings by terrain, heating, land use, and coastlines as well as the larger-scale atmospheric flow. The nonlinear physics underlying these wind field responses makes the instantaneous, local state of the wind field chaotic and
1. Introduction Understanding horizontal dispersal and stirring in the oceans is important for a wide range of problems, and a variety of diagnostics have been used to quantify these processes in oceanic (and other) flows. These include the mean-square separation (“relative dispersion”) of particles and finite-size Lyapunov exponents (FSLEs; e.g., Lacorata et al. 2001 ; Haza et al. 2008 ; LaCasce 2008 ), finite-time Lyapunov exponents (FTLEs; e.g., Abraham and Bowen 2002 ; Waugh et al
1. Introduction Understanding horizontal dispersal and stirring in the oceans is important for a wide range of problems, and a variety of diagnostics have been used to quantify these processes in oceanic (and other) flows. These include the mean-square separation (“relative dispersion”) of particles and finite-size Lyapunov exponents (FSLEs; e.g., Lacorata et al. 2001 ; Haza et al. 2008 ; LaCasce 2008 ), finite-time Lyapunov exponents (FTLEs; e.g., Abraham and Bowen 2002 ; Waugh et al
1. Introduction A dispersion model as a user of meteorological information poses specific requirements of quality and content of the input data ( Fisher et al. 1998 ). First, fields of concentrations of atmospheric pollutants are often much more irregular than the meteorological ones (except, maybe, convective precipitation fields) and therefore the air quality (AQ) models might need to cover a wider range of scales than numerical weather prediction (NWP) models (“meteorological drivers”) used
1. Introduction A dispersion model as a user of meteorological information poses specific requirements of quality and content of the input data ( Fisher et al. 1998 ). First, fields of concentrations of atmospheric pollutants are often much more irregular than the meteorological ones (except, maybe, convective precipitation fields) and therefore the air quality (AQ) models might need to cover a wider range of scales than numerical weather prediction (NWP) models (“meteorological drivers”) used
1. Introduction The transport and dispersion of dissolved and particulate materials in the ocean, such as salts, gases, marine organisms, and various marine pollutants, determine the pathways and the concentration of those materials, respectively. Some particulate materials, such as spilled oil, marine debris, and some marine organisms, are positively buoyant and are confined in the ocean surface boundary layer. Understanding the transport and dispersion of oceanic buoyant materials is
1. Introduction The transport and dispersion of dissolved and particulate materials in the ocean, such as salts, gases, marine organisms, and various marine pollutants, determine the pathways and the concentration of those materials, respectively. Some particulate materials, such as spilled oil, marine debris, and some marine organisms, are positively buoyant and are confined in the ocean surface boundary layer. Understanding the transport and dispersion of oceanic buoyant materials is
transport can be studied using particle (Lagrangian) dispersion. This includes the study of single particles (absolute dispersion) and groups of particles (relative dispersion). Relative dispersion is also of interest, as its properties depend on the kinetic energy spectrum ( Bennett 1984 ). As such, pair dispersion gives insight into the Eulerian velocity statistics. Relative dispersion was studied on large scales in the 1970s using constant-level balloons in the lower stratosphere. Two such
transport can be studied using particle (Lagrangian) dispersion. This includes the study of single particles (absolute dispersion) and groups of particles (relative dispersion). Relative dispersion is also of interest, as its properties depend on the kinetic energy spectrum ( Bennett 1984 ). As such, pair dispersion gives insight into the Eulerian velocity statistics. Relative dispersion was studied on large scales in the 1970s using constant-level balloons in the lower stratosphere. Two such
1. Introduction NOAA’s Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is one of the most commonly used tools to simulate the transport and dispersion of pollutants for a variety of atmospheric applications. An objective performance evaluation against independent measurement datasets, such as tracer experiments, is fundamental to assess the model reliability to simulate transport and dispersion features under different meteorological conditions. For this reason, NOAA
1. Introduction NOAA’s Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is one of the most commonly used tools to simulate the transport and dispersion of pollutants for a variety of atmospheric applications. An objective performance evaluation against independent measurement datasets, such as tracer experiments, is fundamental to assess the model reliability to simulate transport and dispersion features under different meteorological conditions. For this reason, NOAA
1. Introduction Atmospheric dispersion of pathogenic spores is one of the key steps in the development of plant disease epidemics ( Aylor 1990 ). The dispersion of pathogenic spores from infected fields frequently creates disease frontal boundaries that move rapidly throughout large growing regions, causing important crop losses ( Hogg et al. 1969 ; Roelfs 1978 , 1986 ). Consequently, prediction of spore deposition with distance has important implications for implementing integrated pest
1. Introduction Atmospheric dispersion of pathogenic spores is one of the key steps in the development of plant disease epidemics ( Aylor 1990 ). The dispersion of pathogenic spores from infected fields frequently creates disease frontal boundaries that move rapidly throughout large growing regions, causing important crop losses ( Hogg et al. 1969 ; Roelfs 1978 , 1986 ). Consequently, prediction of spore deposition with distance has important implications for implementing integrated pest
, and green infrastructure (e.g., trees, grass, or ponds) on the radiation budget, as well as on heat, momentum, and moisture transport in the UCL. These parameterizations enable mesoscale models to respond to the presence of the urban forcing without a need to explicitly resolve urban processes. These urban parameterizations, however, do not resolve the UCL flow and cannot be used for building-scale micrometeorological behavior and dispersion. A different approach that enables detailed
, and green infrastructure (e.g., trees, grass, or ponds) on the radiation budget, as well as on heat, momentum, and moisture transport in the UCL. These parameterizations enable mesoscale models to respond to the presence of the urban forcing without a need to explicitly resolve urban processes. These urban parameterizations, however, do not resolve the UCL flow and cannot be used for building-scale micrometeorological behavior and dispersion. A different approach that enables detailed
1. Introduction Understanding transport properties of turbulent convective flow (e.g., transport of particles, chemical species, temperature, etc.) is of significant importance for a number of geoscience fields (meteorology, oceanology, geophysics) covering many practical applications, including pollutant dispersion, extreme events (bushfires, volcanic eruptions, technological catastrophes), cloud formation, and climate change ( Franzese et al. 1999 ; Luhar et al. 2000 ; Fedorovich 2004
1. Introduction Understanding transport properties of turbulent convective flow (e.g., transport of particles, chemical species, temperature, etc.) is of significant importance for a number of geoscience fields (meteorology, oceanology, geophysics) covering many practical applications, including pollutant dispersion, extreme events (bushfires, volcanic eruptions, technological catastrophes), cloud formation, and climate change ( Franzese et al. 1999 ; Luhar et al. 2000 ; Fedorovich 2004
1. Introduction For homeland and defense security it is necessary to model accurately the atmospheric transport and dispersion (AT&D) of chemical, biological, radiological, or nuclear (CBRN) contaminants from accidental or deliberate releases. Reliable forecasts of both contaminant concentrations and their inherent uncertainty are critical for situational awareness. Accurate, reliable AT&D forecasts are difficult to make, however. Contaminant dispersion in the atmosphere is a complex process
1. Introduction For homeland and defense security it is necessary to model accurately the atmospheric transport and dispersion (AT&D) of chemical, biological, radiological, or nuclear (CBRN) contaminants from accidental or deliberate releases. Reliable forecasts of both contaminant concentrations and their inherent uncertainty are critical for situational awareness. Accurate, reliable AT&D forecasts are difficult to make, however. Contaminant dispersion in the atmosphere is a complex process
