The impact of using different land cover data on wind-blown desert dust modeling results in the southwestern United States

Olson World Ecosystem (OWE) land cover data based on data sources of the 1970s and 1980s with a 10-min spatial resolution, and up-to-date Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data with a 30-s resolution, were used, respectively, in modeling wind-blown desert dust in the southwest United States. The model using different land cover data sets preformed similarly in modeling meteorological field patterns, vertical profiles and surface wind and temperature, in comparisons against observations. The differences of wind and temperature at a specific time and location can be big. Compared against satellite and ground measurements, modeled dust spatial distributions using MODIS land cover data were considerably better than those using OWE land cover. Site against site comparisons of modeled and observed surface PM2.5 concentration time series showed that model performance improved significantly using MODIS land cover data. Modeled surface PM2.5 contour distributions using MODIS land cover data compared more favorably against observations. The performance statistics for modeled PM2.5 concentrations at 40 surface sites increased from 0.15 using OWE data, to 0.58 using MODIS data. This demonstrates that the survey updates and spatial resolution of land cover data are critical in correctly predicting dust events and dust concentrations. Using land cover data such as MODIS data from satellite remote sensing is promising in improving wind-blown dust modeling and forecasting.

[1]  F. Giorgi,et al.  A particle dry-deposition parameterization scheme for use in tracer transport models , 1986 .

[2]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[3]  Jocelyn Kaiser,et al.  Mounting Evidence Indicts Fine-Particle Pollution , 2005, Science.

[4]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[5]  Akio Arakawa,et al.  Computational Design of the Basic Dynamical Processes of the UCLA General Circulation Model , 1977 .

[6]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[7]  Slobodan Nickovic,et al.  The Step-Mountain Coordinate: Model Description and Performance for Cases of Alpine Lee Cyclogenesis and for a Case of an Appalachian Redevelopment , 1988 .

[8]  Yaping Shao,et al.  Effect of Saltation Bombardment on the Entrainment of Dust by Wind , 1993 .

[9]  Z. Janjic The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[10]  L. Gomes,et al.  Modeling the size distribution of a soil aerosol produced by sandblasting , 1997 .

[11]  Zavisa Janjic,et al.  Nonlinear Advection Schemes and Energy Cascade on Semi-Staggered Grids , 1984 .

[12]  G. Kallos,et al.  A model for prediction of desert dust cycle in the atmosphere , 2001 .

[13]  D. Steyn,et al.  Quantitative and Qualitative Evaluation of a Three-Dimensional Mesoscale Numerical Model Simulation of a Sea Breeze in Complex Terrain , 1988 .

[14]  Carla E. Brodley,et al.  Integration of domain knowledge in the form of ancillary map data into supervised classification of remotely sensed data , 2002, IEEE International Geoscience and Remote Sensing Symposium.