Skip to main content
SpringerLink
Log in

Impact of Land Surface Heterogeneity on Mesoscale Atmospheric Dispersion

  • Article
  • Published:
Boundary-Layer Meteorology Aims and scope Submit manuscript

Abstract

Prior numerical modelling studies show that atmospheric dispersion is sensitive to surface heterogeneities, but past studies do not consider the impact of a realistic distribution of surface heterogeneities on mesoscale atmospheric dispersion. While these focussed 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 addition, the sensitivity to the use of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived spatial distributions of vegetation characteristics on atmospheric dispersion is also studied. 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%). For drier soil moisture conditions, vegetation heterogeneity produces differential heating and the 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 a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alapaty K, Niyogi D, Chen F et al (2008) Development of the flux-adjusting surface data assimilation system for mesoscale models. J Appl Meteorol Climatol 47: 2331–2350

    Article  Google Scholar 

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62: 241–252

    Article  Google Scholar 

  • Chen C, Cotton WR (1983) A one-dimensional simulation of the stratocumulus-capped mixed layer. Boundary-Layer Meteorol 25: 289–321

    Article  Google Scholar 

  • Cotton WR, Pielke RA, Walko RL et al (2002) RAMS 2001: current status and future directions. Meteorol Atmos Phys 82: 5–29

    Article  Google Scholar 

  • De Lannoy GJM, Houser PR, Pauwels VRN, Verhoest NEC (2007) State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency. Water Resour Res 43: W06401. doi:10.1029/2006WR005100

    Article  Google Scholar 

  • Eastman JL, Pielke RA, Lyons WA (1995) Comparison of lake-breeze model simulations with tracer data. J Appl Meteorol 34: 1398–1418

    Article  Google Scholar 

  • Eastman JL, Pielke RA, McDonald DJ (1998) Calibration of soil moisture for large eddy simulations over the FIFE area. J Atmos Sci 55: 1131–1140

    Article  Google Scholar 

  • Gifford FA (1982) Horizontal diffusion in the atmosphere: a Lagrangian-dynamical theory. Atmos Environ 16: 505–512

    Article  Google Scholar 

  • Gopalakrishnan S, Avissar R (2000) LES study of the impacts of land surface heterogeneity on dispersion in the convective boundary layer. J Atmos Sci 57: 352–371

    Article  Google Scholar 

  • Gopalakrishnan S, Baidya Roy S, Avissar R (2000) An evaluation of the scale at which topographical features affect the convective boundary layer using large-eddy simulations. J Atmos Sci 57: 334–351

    Article  Google Scholar 

  • Gupta S, McNider RT, Trainer M, Zamora R (1997) Nocturnal wind structure and plume growth rates due to inertial oscillations. J Appl Meteorol 36: 1050–1063

    Article  Google Scholar 

  • Hadfield MG, Cotton WR, Pielke RA (1991) Large-eddy simulations of thermally-forced circulations in the convective boundary layer. Part I: A small-scale circulation with zero wind. Boundary-Layer Meteorol 57: 79–114

    Article  Google Scholar 

  • Hadfield MG, Cotton WR, Pielke RA (1992) Large-eddy simulations of thermally forced circulations in the convective boundary layer. Part II: The effect of changes in wavelength and wind speed. Boundary-Layer Meteorol 58: 307–328

    Article  Google Scholar 

  • Harrington JY, Reisin T, Cotton WR, Kreidenweis SM (1999) Cloud resolving simulations of Arctic stratus: Part II: Transition-season clouds. Atmos Res 51: 45–75

    Article  Google Scholar 

  • Jones AS, Guch IC, Vonder Haar TH (1998a) Data assimilation of satellite-derived heating rates as proxy surface wetness data into a regional atmospheric mesoscale model. Part I: Methodology. Mon Weather Rev 126: 634–645

    Article  Google Scholar 

  • Jones AS, Guch IC, Vonder Haar TH (1998b) Data assimilation of satellite-derived heating rates as proxy surface wetness data into a regional atmospheric mesoscale model. Part II: A case study. Mon Weather Rev 126: 646–667

    Article  Google Scholar 

  • Legg BJ, Raupach MR (1982) Markov-chain simulation of particle dispersion in inhomogeneous flows: the mean drift velocity induced by a gradient in Eulerian velocity variance. Boundary-Layer Meteorol 24: 3–13

    Article  Google Scholar 

  • Lyons WA, Pielke RA, Tremback CJ et al (1995) Modeling the impacts of mesoscale vertical motions upon coastal zone air pollution dispersion. Atmos Environ 29: 283–301

    Article  Google Scholar 

  • Mahrer Y, Pielke RA (1977) The effects of topography on sea and land breezes in a two-dimensional numerical model. Mon Weather Rev 105: 1151–1162

    Article  Google Scholar 

  • Matsui T, Beltran-Przekurat A, Pielke RA Sr et al (2007) Continental-scale multi-objective calibration and assessment of Colorado State University Unified Land Model. Part I: Surface albedo. J Geophys Res 112: G02028. doi:10.1029/2006JG000229

    Article  Google Scholar 

  • Matsui T, Beltran-Przekurat A, Pielke RA Sr et al (2008) Aerosol light scattering effect on terrestrial plant productivity and energy fluxes over the eastern United States. J Geophys Res—Yoram J. Kaufman Symposium Issue 113: D14S14. doi:10.1029/2007JD009658

    Google Scholar 

  • McNider RT (1981) Investigation of the impact of topographic circulations on the transport and dispersion of air pollutants. Ph.D. Dissertation, University of Virginia

  • McNider RT, Pielke RA (1984) Numerical simulation of slope and mountain flows. J Climate Appl Meteorol 23: 1441–1453

    Google Scholar 

  • McNider RT, Moran MD, Pielke RA (1988) Influence of diurnal and inertial boundary layer oscillations on long-range dispersion. Atmos Environ 22: 2445–2462

    Article  Google Scholar 

  • McNider RT, Singh MP, Lin JT (1993) Diurnal wind-structure variations and dispersion of pollutants in the boundary layer. Atmos Environ 27: 2199–2214

    Google Scholar 

  • McNider RT, Song AJ, Casey DM et al (1994) Toward a dynamic-thermodynamic assimilation of satellite surface temperature in numerical atmospheric models. Mon Weather Rev 122: 2784–2803

    Article  Google Scholar 

  • McNider RT, Lapenta WM, Biazar AP et al (2005) Retrieval of model grid-scale heat capacity using geostationary satellite products. Part I: First case-study application. J Appl Meteorol 44: 1346–1360

    Article  Google Scholar 

  • Moran MD (1992) Numerical modelling of mesoscale atmospheric dispersion. Ph.D. Dissertation, Department of Atmospheric Science, Colorado State University, 758

  • Moran MD, Pielke RA (1996) Evaluation of a mesoscale atmospheric dispersion modeling system with observations from the 1980 Great Plains mesoscale tracer field experiment. Part II: Dispersion simulations. J Appl Meteorol 35: 308–329

    Article  Google Scholar 

  • Myneni RB, Knyazikhin Y, Privette JL, Glassy J (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83(1–2): 214–231

    Article  Google Scholar 

  • Ookouchi Y, Segal M, Kessler RC, Pielke RA (1984) Evaluation of soil moisture effects on the generation and modification of mesoscale circulations. Mon Weather Rev 112: 2281–2292

    Article  Google Scholar 

  • Pielke RA (1984) Mesoscale meteorological modeling, 1st edn. Academic Press, New York, p 612

    Google Scholar 

  • Pielke RA (1985) The use of mesoscale numerical models to assess wind distribution and boundary layer structure in complex terrain. Boundary-Layer Meteorol 31: 217–231

    Article  Google Scholar 

  • Pielke RA Sr (2006) The partnership of weather and air quality—an essay. Atmospheric Science Paper No. 770, Colorado State University, Fort Collins, 44 pp

  • Pielke RA, Uliasz M (1993) Influence of landscape variability on atmospheric dispersion. J Air Waste Manage 43: 989–994

    Google Scholar 

  • Pielke RA, Uliasz M (1998) Use of meteorological models as input to regional and mesoscale air quality models - Limitations and strengths. Atmos Environ 32: 1455–1466

    Article  Google Scholar 

  • Pielke RA, Arritt RW, McNider RT (1986) Screening estimation of maximum 24-h average pollution concentrations in mountain valleys during synoptic stagnation. In: 1986 conference on science in the national parks. AWRA symposium proceedings

  • Poulos GS, Pielke RA (1994) A numerical analysis of Los Angeles basin pollution transport to the Grand Canyon under stably stratified, southwest flow conditions. Atmos Environ 28: 3329–3357

    Article  Google Scholar 

  • Reichle RH, McLaughlin DH, Entekhabi D (2002) Hydrologic data assimilation using the Ensemble Kalman filter. Mon Weather Rev 130: 103–115

    Article  Google Scholar 

  • Reichle RH, Koster RD, Liu P et al (2007) Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR). J Geophys Res 112: D09108. doi:10.1029/2006JD008033

    Article  Google Scholar 

  • Reichle RH, Crow WT, Keppenne CL (2008) An adaptive ensemble Kalman filter for soil moisture data assimilation. Water Resour Res 44(3): W03423. doi:10.1029/2007WR006357

    Article  Google Scholar 

  • Schaaf CB, Gao F, Strahler AH et al (2002) First operational BRDF, albedo and Nadir reflectance products from MODIS. Remote Sens Environ 83: 135–148

    Article  Google Scholar 

  • Segal M, Garratt JR, Pielke RA, Ye Z (1991) Scaling and numerical model evaluation of snow-cover effects on the generation and modification of daytime mesoscale circulations. J Atmos Sci 48: 1024–1042

    Article  Google Scholar 

  • Walko RL, Band LE, Baron J et al (2000) Coupled atmosphere-biophysics-hydrology models for environmental modeling. J Appl Meteorol 39: 931–944

    Article  Google Scholar 

  • Whiteman CD (1982) Breakup of temperature inversions in deep mountain valleys: Part I. Observations. J Appl Meteorol 21: 270–289

    Article  Google Scholar 

  • Wolyn PG, McKee TB (1989) Deep stable layers in the intermountain western United States. Mon Weather Rev 117: 461–472

    Article  Google Scholar 

  • Zannetti P (1990) Air pollution modeling: theories, computational methods, and available software. Computation Mechanics Publications, Boston, p 444

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Earth System Science Center, University of Alabama in Huntsville, 320 Sparkman Drive, Huntsville, AL, 35805, USA

    Yuling Wu & Udaysankar S. Nair

  2. CIRES, University of Colorado, Boulder, CO, 80309, USA

    Roger A. Pielke Sr.

  3. Department of Atmospheric Science, University of Alabama in Huntsville, 320 Sparkman Drive, Huntsville, AL, 35805, USA

    Richard T. McNider & Sundar A. Christopher

  4. Geosystems Research Institute, Mississippi State University, Box 9652, Mississippi State, MS, 39762, USA

    Valentine G. Anantharaj

Authors
  1. Yuling Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Udaysankar S. Nair
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roger A. Pielke Sr.
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard T. McNider
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sundar A. Christopher
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Valentine G. Anantharaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Udaysankar S. Nair.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, Y., Nair, U.S., Pielke, R.A. et al. Impact of Land Surface Heterogeneity on Mesoscale Atmospheric Dispersion. Boundary-Layer Meteorol 133, 367–389 (2009). https://doi.org/10.1007/s10546-009-9415-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10546-009-9415-1

Keywords

Search

Navigation