We have a new paper that has been accepted for publication. It is
Wu., Y., U.S. Nair, R.A. Pielke Sr., R.T. McNider, S.A. Christopher, and V. Anantharaj, 2009: Impact of land surface heterogeneity on mesoscale atmospheric dispersion. Bound.-Layer Meteor., accepted.
The abstract reads
“Prior numerical modelling studies show that atmospheric dispersion is sensitive to surface heterogeneities. However, past studies do not consider the impact of realistic distribution of surface heterogeneities on mesoscale atmospheric dispersion. While past studies focused on dispersion in the convective boundary layer, the present work also considers dispersion in the nocturnal boundary layer and above. Using a Lagrangian Particle Dispersion Model (LPDM) coupled to the Eulerian Regional Atmospheric Modeling System (RAMS), the impact of topographic, vegetation, and soil moisture heterogeneities on daytime and nighttime atmospheric dispersion is examined in the present study. In addition, sensitivity to the use of satellite-derived, realistic spatial distribution of vegetation characteristics on atmospheric dispersion is also
The impact of vegetation and terrain heterogeneities on atmospheric dispersion is strongly modulated by soil moisture, with the nature of dispersion switching from non-Gaussian to near-Gaussian behaviour for wetter soils (fraction of saturation soil moisture content exceeding 40%). At drier soil moisture conditions, vegetation heterogeneity causes differential heating and formation of mesoscale circulation patterns that are primarily responsible for non- Gaussian dispersion patterns. Nighttime dispersion is very sensitive to topographic, vegetation, soil moisture, and soil type heterogeneity and is distinctly non-Gaussian for heterogeneous land surface conditions. Sensitivity studies show that soil type and vegetation heterogeneities have the most dramatic impact on atmospheric dispersion. To provide more skilful dispersion calculations, we recommend the utilisation of satellite-derived vegetation characteristics coupled with data assimilation techniques that constrain soil-vegetation-atmosphere transfer (SVAT) models to generate realistic spatial distributions of surface energy fluxes.”
This is another research study that documents why landscape heterogeneity matter in weather and climate.