- Email: [email protected]
The impact of using different land cover data on wind-blown desert dust modeling results in the southwestern United States
ARTICLE IN PRESS
Atmospheric Environment 41 (2007) 2214–2224 www.elsevier.com/locate/atmosenv
The impact of using different land cover data on wind-blown desert dust modeling results in the southwestern United States Dazhong Yina,, Slobodan Nickovicb, William A. Sprigga a
Department of Atmospheric Sciences, University of Arizona, 1118 E 4th Street, Tucson, AZ 85721, USA Weather Modification Program, World Meteorological Organization, 7 bis, Avenue de la Paix, BP2300, 1211 Geneva 2, Switzerland
b
Received 7 December 2005; received in revised form 31 July 2006; accepted 30 October 2006
Abstract 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. r 2006 Elsevier Ltd. All rights reserved. Keywords: Desert dust modeling; Air-borne particulate matter pollution; PM2.5 concentrations; Model performance
1. Introduction Studies show that breathing air with high concentrations of fine particles can trigger heart attack and worsen respiratory disease in vulnerable populations. Every year, an estimated 60,000 premature deaths in the United States (US) can be attributed to fine particle pollution (Kaiser, 2005). Corresponding author. Tel.: +1 520 626 1534; fax: +1 520 621 6833. E-mail address: [email protected] (D. Yin).
1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.10.061
The US Centers for Disease Control also found that elevated airborne particle concentrations contribute to increased emergency room visits, hospital admissions, and work and school absences [http:// www.epa.gov/geoss/fact_sheets/newmexico.html]. In the arid and semiarid southwestern US, desert dust storms can cause significant airborne particle pollution in the region. Dust pollution events in the US Southwest usually start with the formation of a cold front and its associated surface low-pressure center over the Pacific Ocean west of the US Pacific Northwest. The cold front moves southeastward
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2215
Table 1 Four dust categories and their particle properties Dust category
Size bin (mm)
Typical particle radius (mm)
Particle density (kg m3)
Associated soil component
1 2 3 4
0–3.4 3.4–12 12–28 428
0.73 6.10 18.00 38.00
2500 2650 2650 2650
Clay Small silt Large silt Sand
and sweeps through the southwestern US and northern Mexico. Strong gusty winds associated with this system cause saltation and sandblasting over the desert (Alfaro et al., 1997) and eventually create dust storms. According to air pollution records (http://www.tceq.state.tx.us/nav/data/air_ met_data.html), 78 air pollution events occurred in Texas in 2004. Among these events, 16, or 21%, were caused by dust storms. Numerical models with capability of predicting dust storm occurrence and subsequent elevated airborne dust concentrations are important tools in giving early warning and protecting people from unnecessary exposure to heavy dust particle pollution. Usually, land cover data are used in dust models to specify dust sources across the model domain. Land cover data also affect land and atmosphere water and energy exchange simulation in the models (Chen and Dudhia, 2001). In the work presented in this paper, the impact of using two different land cover data sets on modeling results of the Dust REgional Atmospheric Modeling (DREAM) system (Nickovic et al., 2001) was studied. Model results from two model runs for a December 2003 case using different land cover data sets were compared. Measurements and observational analyses such as surface synoptic data, surface Aerodrome Report (METAR), upper-air radiosonde, satellite images, measured visibility distributions, and surface PM2.5 data were used in model comparisons to show differences in model performance. 2. A brief description of the model In the DREAM system, a dust cycle simulation module is coupled online with the National Centers for Environmental Prediction (NCEP) operational Eta Model (Janjic, 1984, 1994). The dust cycle module simulates dust production, dust advection and turbulent diffusion, and dry and wet deposition (Nickovic et al., 2001; Shao et al., 1993; Georgi,
1986). Horizontally, the model uses semistaggering Arakawa E grid (Arakawa and Lamb, 1977). Vertically, it uses a step-mountain representation (Mesinger et al., 1988). Locations of desert dust sources are determined using land cover data. First, land cover data are mapped into a horizontal model grid. Grid cells that have arid and semiarid land cover categories are considered dust source areas. Then the arid and semiarid land cover data points in a grid cell are counted. The fraction of these points to total land cover data points in the cell is calculated and used as the fraction of dust sources in the cell for dust flux calculation. The physical properties of the dust particles from these dust source areas are associated with clay, silt, and sand components of the soil texture types specified by the Food and Agriculture Organization (FAO)/United Nations Educational, Scientific and Cultural Organization (UNESCO) soil map data. As listed in Table 1, four dust categories are modeled. They reflect properties of dust particles in four dust bins, with respective typical radius and density. They are associated with soil components of clay, silt and sand. The surface dust fluxes are parameterized according to Shao et al. (1993) wind tunnel experiments. Where frictional velocity u is greater than threshold frictional velocity ut, the dust flux Fs is F s ¼ const u3 ½1 ðut =u Þ2 . The threshold friction velocity depends on soil wetness and dust particle sizes of each dust category. The dust flux of an individual dust category is calculated using Fs and the fraction of desert surface, the fractions and the dust productivity factors of the clay/sand/silt components of the soil texture types in a grid cell (Nickovic et al., 2001). 3. Land cover data sets Two land cover data sets were used, respectively, in this study: the Olson World Ecosystem (OWE) land cover global data and the land cover product
ARTICLE IN PRESS 2216
D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the National Aeronautics and Space Agency (NASA) of the US satellite Terra. We used MODIS data covering the southwestern US. The OWE land cover data set (Olson, 1992) is based on numerous collected maps, references, and observations in the 1970s and 1980s. In the early 1990s, the greenness indices from Advanced Very High Resolution Radiometer (AVHRR) satellite data were used for certain ecosystems. The OWE data are a product based on various data sources and represents the land cover conditions of the 1980s. There are 74 land cover categories including water body, but only 59 categories occur in the data set (http://biodi.sdsc. edu/Doc/WhyWhere/meta/owe14d.txt). The spatial resolution of the OWE data is 10-min. MODIS land cover data used in our modeling studies identify 17 categories (Table 2) in the International Geosphere–Biosphere Program (IGBP) vegetation classification scheme. The classification was produced using a supervised approach (Hodges et al. 2001). A decision tree algorithm (Quinlan, 1993) and a boosting technique (Freund and Shapire, 1997) were used to improve classification efficiency and accuracies. The MODIS data represent land cover conditions of 2001 with 30-s spatial resolution. Land cover data sets are used in both the land–atmosphere water and energy flux and the
Table 2 MODIS land cover categories MODIS category
Description
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Water Evergreen needleleaf forest Evergreen needleleaf forest Deciduous needleleaf forest Deciduous broadleaf forest Mixed forest Closed shrubland Open shrubland Woody savannas Savannas Grasslands Permanent wetlands Croplands Urban and built-up Crops, natural vegetation mosaic Permanent snow/ice Barren/sparsely vegetated
15 16
dust source flux simulation in the modeling. As one land cover data set replaces the other, the land cover information used in meteorological and dust source flux modeling is changed according to the new data set. When OWE land cover data are used, the land cover that can contribute to dust source flux is categories 8 (desert, mostly bare stone, clay or sand), 50 (sand desert, partly blowing dunes), 51(semidesert/desert scrub/spars grass), and 52 (cool/cold shrub semidesert/steppe). When MODIS land cover data are used, land cover 16 (Table 2) can contribute to dust source flux. 4. Model scenarios Over 15–17 December 2003, a strong Pacific cold front moved southeastward through the southwest US and northern Mexico. It generated strong gusty winds and caused a significant dust storm in southern New Mexico and western Texas. Two model scenarios were designed to study the impact of using two different land cover data sets on the dust modeling results in the southwest US. The modeling period is from 00:00 Z 14 December to 23:00 Z 17 December 2003. The first day is treated as model spin-up time. The modeling area is shown in Fig. 1. From sea surface level to 100 hPa, each scenario used 24 vertical half eta levels with the first level about 86 m above the sea level. Horizontally, for both scenarios, the grid spacing between neighboring mass grid points and wind grid points was 1/31 (Arakawa and Lamb, 1977). European Center for Medium Range Forecast (ECMWF) data were used to generate initial and boundary conditions. The United States Geological Survey (USGS) 30-s spatial resolution terrain elevation data were used to specify model topography. Model soil texture was obtained from the FAO/UNESCO (http://www.fao.org/ag/agl/agll/ wrb/wrbmaps/htm/soilres.htm) soil map data with 134 categories and 2-min spatial resolution. The difference between the two scenarios is that the first scenario (S1) used OWE land cover data and the second scenario (S2) used MODIS land cover data instead. 5. Results and discussions The modeling domain was designed to capture the synoptic weather patterns and provide realistic meteorological fields for southeastern New Mexico, western Texas, and part of northern Mexico, where
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2217
Fig. 1. Modeled mean sea level pressure of S1 (left panel) and S2 (right panel) for 00:00 Z 15 December 2003.
Fig. 2. Surface weather map for 00:00 Z 15 December 2003 (http://weather.unisys.com, returns of a weather radar also shown).
the dust storm occurred. In the following comparisons, the modeled dust variables in this dust storm affected area were used, while the modeled meteorological fields were compared over the whole domain. No comparisons were made for the model spin-up time. 5.1. Meteorological variables Fig. 1 gives an example of S1 and S2 modeled sea level pressure fields. It depicts the modeled sea level pressure for 00:00 Z 15 December. Compared to the surface weather map for the same valid time as
shown in Fig. 2, both modeled patterns match the observed one. The cold front location along Nevada, Utah and Wyoming and the stationary front location along Texas and the Kansas–Colorado border can be identified from the S1 and S2 modeled fields. Comparisons showed that patterns of S1 and S2 500 hPa geopotential height and temperature fields were all similar to patterns of corresponding observational analyses. Meteorological variable vertical profiles of wind speed, wind direction, temperature and specific humidity were not significantly affected by using different land cover
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2218
Table 4 lists some of the differences of S2 and S1 modeled hourly 10-m wind speed and 2-m temperature at abovementioned surface synoptic and surface METAR sites. This shows that the difference of S2 and S1 modeled surface wind speed at some time can be big (maximum relative difference exceeding 800% and absolute difference over 1 m s1) although the average difference are small. The maximum absolute difference of S2 and S1 modeled surface temperature is over 2 K.
data. Fig. 3 shows the S1 and S2 modeled temperature and wind speed profiles for 12:00 Z 16 December 2003 at Santa Teresa, New Mexico. The S1 and S2 modeled profiles are similar and they compared well with observations. In comparing to observations, performance statistics (Steyn and McKendry, 1988) were calculated for modeled 10 m wind speed, wind direction, and 2 m temperature. In the calculation, hourly observations at 89 surface synoptic and 612 surface METAR sites located in the modeling domain were used. Performance statistics (Table 3) for S1 and S2 modeled surface variables are similar.
Obs. wind speed
S1 wind speed
S2 wind speed
Obs. Temperature
S1 temperature
S2 temperature
5.2. Dust concentrations Snapshots of S1 and S2 modeled near-surface dust concentrations for 20:00 Z 15 December are shown in Fig. 4. They include dust particles of all sizes. Both S1 and S2 modeled dust plumes cover an area from Southern New Mexico/El Paso to the Texas panhandle (see Fig. 6 for site locations). However, the detailed dust concentration distributions are quite different. The S1 high dust concentrations stretch across southern New Mexico to eastern Texas. S2 high dust concentrations are scattered at Lubbock and El Paso, Texas. The right panel of Fig. 5 is the satellite dust cloud image for 19:50 Z 15 December. The dust clouds are in the El Paso/Mexico border and Lubbock, Texas regions. The locations of S2 modeled dust plumes match satellite dust clouds locations better than those of S1. High airborne dust concentrations cause reduced visibility. The left panel of Fig. 5 is the observed visibility analysis based on surface METAR prevailing visibility at 20:00 Z 15 December. White
Temperature (K) 0
50
100
150
200
250
300
35000 30000 Height (m)
25000 20000 15000 10000 5000 0 0
20
40 60 Wind speed (m/s)
80
100
Fig. 3. Modeled and observed vertical profiles at Santa Teresa, New Mexico (31.871N, 106.701W) for 12:00 Z 16 December 2003.
Table 3 Performance statistics for S1 and S2 modeled surface variables Metrics
Wind speed
Wind direction
Temperature
Definition (M: modeled; O: observed)
S1
S2
S1
S2
S1
S2
Mean observed
5.53 (m s1)
5.53 (m s1)
231.40 (degree)
231.40 (degree)
276.74 (K)
276.74 (K)
Mean modeled
4.65 (m s1)
4.65 (m s1)
226.60 (degree)
226.53 (degree)
275.56 (K)
275.53 (K)
Mean bias
0.88 (m s1)
0.88 (m s1)
4.80 (degree)
4.87 (degree)
1.20 (K)
1.23 (K)
Mean error
1.97 (m s1)
1.97 (m s1)
51.76 (degree)
51.77 (degree)
4.09 (K)
4.09 (K)
Agreement index
0.74
0.74
0.74
0.74
0.71
0.71
N 1P Oi N i¼1 N 1P Mi N i¼1 N 1P ðM i Oi Þ N i¼1 N 1P jM i Oi j N i¼1 PN ðM i Oi Þ2 1 PN i¼1 i¼1
ðjM i OjþjOi OjÞ2
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2219
Table 4 Average, maximum, and minimum S2 to S1 differences and relative differences
Wind speed Temperature
Avg. Dif.
Max. Dif.
Min. Dif.
Avg. Rel. Dif. (%)
Max. Rel. Dif. (%)
Min. Rel. Dif. (%)
0.0001 (m s1) 0.0251 (K)
1.0077 (m s1) 1.4480 (K)
1.0536 (m s1) 2.6610 (K)
0.04 0.01
824.65 0.55
42.55 0.97
Fig. 4. Modeled dust concentrations (mg m3) of S1 (left panel) and S2 (right panel) for 20:00 Z 15 December 2003.
Fig. 5. Observed visibilities (mile) and satellite image of the dust clouds for 20:00 Z 15 December 2003.
areas in this panel indicate no visibility observations are available, and much of Mexico is without observations. Thus, looking only on the US side, the areas with the most reduced visibilities are near Lubbock and El Paso, Texas. The S2 modeled high dust concentration areas compare more favorably with measurements than S1 results.
The modeled surface PM2.5 concentrations at the locations of 40 in situ measurement sites in the dust storm area are compared. Fig. 6 shows the locations of some of the sites. The hourly PM2.5 observational data are the tapered-element oscillation (TEOM) sampler measurements of the US Environmental Protection Agencies (EPA) Air Quality System
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2220
Fig. 6. Surface PM2.5 site locations.
200 100
Difference (µg/m3)
0 -100 -200 -300 -400 -500 -600 -700 -800
Avg Max Min
-900 -1000 Fig. 7. The average, maximum, and minimum S2 and S1 modeled PM2.5 difference.
(AQS). Details about AQS data can be found on [http://www.epa.gov/ttn/airs/airsaqs/index.htm]. Fig. 7 shows that the average difference of S2 to S1 modeled PM2.5 concentrations at these sites is about 20 mg m3. However, the maximum and minimum differences reach about 90 and 883 mg m3, respectively. At most sites, S2 modeled PM2.5 concentrations better than S1 when compared with the observations. Fig. 8 is an example showing the comparison of S1 and S2 modeled PM2.5 concentrations against
observations at Odessa. S1 obviously overestimated PM2.5 concentration at this site. S2 modeled concentrations are much closer to observed values. The two observed peaks at 18:00 Z on 15 December and 00:00 Z on 16 December are shown in the S2 modeled concentrations as well, although discrepancies in magnitude and timing still exist. Performance statistics (Table 5) were calculated for modeled PM2.5 concentrations at these 40 sites. In general, S1 overestimated PM2.5 concentrations, with large mean bias and mean error. The agreement index is low at 0.12. The S2 results have smaller mean bias and mean error. The agreement index value increases considerably to 0.58. Figs. 9 and 10 show S1 and S2 modeled PM2.5 contours and observed PM2.5 values for 23:00 Z, 15 December. S1 overestimated PM2.5 concentration in the El Paso and Mission, Texas region. The 640 mg m3 contour passes El Paso with measured concentration of 44.3 mg m3. At Mission and Laredo Texas, the observed PM2.5 concentrations are 13.7 and 14.0 mg m3. However, at these two sites, the S1 modeled PM2.5 concentrations are larger than 100 mg m3. The modeled PM2.5 concentrations in the rest of the area are close to the measurements. S2 results show obvious improvements in areas where S1 overestimated concentrations. The S2 modeled 40 mg m3 contour is close to
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2221
Fig. 8. Comparison of modeled and measured PM2.5 concentrations at Odessa, Texas; Left panel for S1 and right panel for S2 (dots show measured values and lines show modeled values).
Table 5 Performance statistics for S1 and S2 modeled surface PM2.5 concentrations Metrics
PM2.5 S1
Mean observed Mean modeled Mean bias Mean error Agreement index
8.66 26.33 17.67 26.51 0.12
S2 (mg m3) (mg m3) (mg m3) (mg m3)
8.66 6.00 2.66 8.94 0.58
(mg m3) (mg m3) (mg m3) (mg m3)
El Paso and Mission, Texas. The S2 modeled 10 mg m3 contour is near Laredo Texas. 5.3. Discussion The S1 and S2 model result comparisons showed that, generally, the impact of using the two land cover data sets on modeled meteorological field patterns and vertical profiles were not significant. The S1 and S2 performance statistics of model surface wind and temperature were similar. The differences of surface wind and temperature at a specific time can be big. However, the impact on modeled dust concentrations was much more significant. This is also seen in our model simulations for other dust events. The dust storm events usually last several days or less. Probably, for land cover data having systematic impact on meteorological fields, it requires a model simulation for longterm events. These results also show although meteorological fields, especially wind fields are not
systematically different, dust concentrations can be considerably different. This is different than what some people believe that wind field forecasting determines dust forecasting. The dust source areas based on the OWE and MODIS land cover data are shown in Figs. 11 and 12. The overall patterns of dust sources based on the two different data sets are similar. But there are many differences in detailed distribution. The scattered small source areas in Fig. 12 do not appear in Fig. 11. In this dust storm affected area, dust source areas in the Texas panhandle do not show up in Fig. 11. However, the MODIS land cover data set captures these source areas (Fig. 12). The significant improvements in S2 model performance in modeled dust concentration distributions, time series, and agreement index of modeled PM2.5 can be attributed to the better representation of dust sources in the modeling. Differences between the two land cover data sets are due to several possible reasons. As previously mentioned, the OWE data set was compiled in the early 1990s and was mainly based on data sources of 1970s and 1980s. The MODIS data set was based on 2001 data. Because of agricultural activities, urbanization, climate variations, and other reasons such as drought and wildfire, land cover changed over these years. For this dust storm case, MODIS data are more up-to-date than OWE data. Spatial resolution also can cause differences in dust source representations using the two data sets. The OWE data set has 10-min spatial resolution and misses some of the dust source areas that can be resolved by the MODIS data set, which has a 30-s resolution.
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2222
Fig. 9. S1 modeled surface PM2.5 contours and observed PM2.5 site values (mg m3) for 23:00 Z, 15 December 2003.
Fig. 10. S2 modeled surface PM2.5 contours and observed PM2.5 site values (mg m3) for 23:00 Z, 15 December 2003.
6. Conclusions The 10-min OWE and 30-s MODIS land cover data were used, respectively, in modeling windblown desert dust in the southwest US.
Modeled sea level pressure, 500 hPa potential height and temperature patterns, and wind, temperature, and specific humidity vertical profiles in both model scenarios matched the measurement analyses. Using different land cover data did not
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
Fig. 11. Dust source areas (red) when using OWE data.
Fig. 12. Dust source areas (red) when using MODIS data.
cause systematic changes in meteorological fields. Performance statistics of the modeled surface meteorological variables from the two scenarios were similar. At a specific time and location, the differences of model surface wind and temperature can be big. Using different land cover data had a much larger and systematic influence on the modeled dust concentrations. The modeled near-ground dust concentration distribution using MODIS land cover matched with the satellite observed dust clouds and reduced visibility distributions better than those results when using OWE land cover. Comparisons of modeled and observed surface PM2.5 time series showed that MODIS land cover improved model results over OWE land cover. This was confirmed by performance statistics for modeled PM2.5 concentrations. The agreement index of modeled surface PM2.5 concentration increased from 0.12 to 0.58. Comparisons of modeled PM2.5 fields against
2223
observed ones also showed improved model performance by using MODIS land cover data to replace OWE land cover data. This demonstrates that, since land cover data determine dust source locations, the updates and spatial resolution of land cover data sets in desert dust modeling play an important role in predicting wind-blown dust particle spatial and temporal distributions and dust particle concentrations. Although land cover data sets attempt to represent what is in the real world, and they will never be 100% accurate, data like MODIS land cover from NASA Earth Observing System can give more upto-date and higher resolution land cover representation. In addition, studies and observations show that desert sand dune regions could be covered by ephemeral vegetations with occasional precipitation. Grazers could change grassland and shrubland to bare sand areas. These can happen in a relatively short time. There seems to be no better measure than using satellite remote sensing to resolve these changes. With careful testing of land cover classification methodologies, such as supervised, unsupervised, combinations of supervised and unsupervised methods (Friedl et al., 2002) to ensure maximum resolution of dust source areas, timely update of land cover data based on satellite remote sensing should help improve wind-blown desert dust modeling and forecasting. Acknowledgments This work is funded by NASA under an Earth Science Research, Education, and Applications Solutions Network (REASoN) project (CA#NNS04AA19A). MODIS land cover data are provided by Dr. Karl Benedict at the Earth Data Analysis Center of the University of New Mexico. We thank Professor Jim Koermer of Plymouth State University and the US EPA for providing meteorological and air quality observational data. References Alfaro, S.C., Gaudichet, A., Gomes, L., Maille, M., 1997. Modeling the size distribution of a soil aerosol produced by sandblasting. Journal of Geophysical Research 102 (D10), 11239–11249. Arakawa, A., Lamb, V.R., 1977. Computational design of the basic dynamical processes of the UCLA general circulation model. Methods of Computational Physics 17, 173–265. Chen, F., Dudhia, J., 2001. Coupling an advanced land-surface/ hydrology model with the Penn State/NCAR MM5 modeling
ARTICLE IN PRESS 2224
D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
system. Part I: model implementation and sensitivity. Monthly Weather Review 129, 569–585. Freund, Y., Shapire, R.E., 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139. Friedl, M.A., McIver, D., Brodley, C.E., 2002. Integration of domain knowledge in the form of ancillary map data into supervised classification of remotely sensed data. In: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), vol. II, pp. 1038–1040. Georgi, F., 1986. A particle dry-deposition parameterization scheme for use in tracer transport models. Journal of Geophysical Research 91, 9794–9806. Hodges, J.C.F., Friedl, M.A., Strahler, A.H., 2001. The MODIS global land cover product: new data sets for global land surface parameterization. In: Proceedings of the Global Change Open Science Conference, Amsterdam, 2001. Kaiser, J., 2005. Mounting evidence indicts fine-particle pollution. Science 307, 1858–1861. Janjic, Z.I., 1984. Non-linear advection schemes and energy cascade on semi-staggered grids. Monthly Weather Review 118, 1234–1245. Janjic, Z.I., 1994. The step-mountain coordinate model: further developments of the convection, viscous sublayer and
turbulence closure schemes. Monthly Weather Review 122, 927–945. Mesinger, F., Janjic, Z.I., Nickovic, S., Gavrilov, D., Deaven, D.G., 1988. The step-mountain coordinate: model description and performance for cases of Apline lee cyclogenesis and for a case of an Appalachian redevelopment. Monthly Weather Review 116, 1493–1518. Nickovic, S., Kallos, G., Papadopoulos, A., Kakaliagou, O., 2001. A model for prediction of desert dust cycle in the atmosphere. Journal of Geophysical Research 106 (D16), 18113–18129. Olson, J.S., 1992. World Ecosystems (WE1.4). Digital Raster Data on a 10-minute Cartesian Orthonormal Geodetic 1082 2160 grid. In: Global Ecosystems Database, Version 2.0. National Geophysical Data Center, Boulder, CO. Quinlan, J.R., 1993. Programs for Machine Learning. Morgan Kauffman, Los Altos, CA. Shao, Y., Raupach, M.R., Findlater, P.A., 1993. Effect of saltation bombardment on the entrainment of dust by wind. Journal of Geophysical Research 98, 12719–12726. Steyn, D.G., McKendry, I.G., 1988. Quantitative and qualitative evaluation of a three-dimensional mesoscale numerical model simulation of a sea breeze in complex terrain. Monthly Weather Review 116, 1914–1926.
Atmospheric Environment 41 (2007) 2214–2224 www.elsevier.com/locate/atmosenv
The impact of using different land cover data on wind-blown desert dust modeling results in the southwestern United States Dazhong Yina,, Slobodan Nickovicb, William A. Sprigga a
Department of Atmospheric Sciences, University of Arizona, 1118 E 4th Street, Tucson, AZ 85721, USA Weather Modification Program, World Meteorological Organization, 7 bis, Avenue de la Paix, BP2300, 1211 Geneva 2, Switzerland
b
Received 7 December 2005; received in revised form 31 July 2006; accepted 30 October 2006
Abstract 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. r 2006 Elsevier Ltd. All rights reserved. Keywords: Desert dust modeling; Air-borne particulate matter pollution; PM2.5 concentrations; Model performance
1. Introduction Studies show that breathing air with high concentrations of fine particles can trigger heart attack and worsen respiratory disease in vulnerable populations. Every year, an estimated 60,000 premature deaths in the United States (US) can be attributed to fine particle pollution (Kaiser, 2005). Corresponding author. Tel.: +1 520 626 1534; fax: +1 520 621 6833. E-mail address: [email protected] (D. Yin).
1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.10.061
The US Centers for Disease Control also found that elevated airborne particle concentrations contribute to increased emergency room visits, hospital admissions, and work and school absences [http:// www.epa.gov/geoss/fact_sheets/newmexico.html]. In the arid and semiarid southwestern US, desert dust storms can cause significant airborne particle pollution in the region. Dust pollution events in the US Southwest usually start with the formation of a cold front and its associated surface low-pressure center over the Pacific Ocean west of the US Pacific Northwest. The cold front moves southeastward
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2215
Table 1 Four dust categories and their particle properties Dust category
Size bin (mm)
Typical particle radius (mm)
Particle density (kg m3)
Associated soil component
1 2 3 4
0–3.4 3.4–12 12–28 428
0.73 6.10 18.00 38.00
2500 2650 2650 2650
Clay Small silt Large silt Sand
and sweeps through the southwestern US and northern Mexico. Strong gusty winds associated with this system cause saltation and sandblasting over the desert (Alfaro et al., 1997) and eventually create dust storms. According to air pollution records (http://www.tceq.state.tx.us/nav/data/air_ met_data.html), 78 air pollution events occurred in Texas in 2004. Among these events, 16, or 21%, were caused by dust storms. Numerical models with capability of predicting dust storm occurrence and subsequent elevated airborne dust concentrations are important tools in giving early warning and protecting people from unnecessary exposure to heavy dust particle pollution. Usually, land cover data are used in dust models to specify dust sources across the model domain. Land cover data also affect land and atmosphere water and energy exchange simulation in the models (Chen and Dudhia, 2001). In the work presented in this paper, the impact of using two different land cover data sets on modeling results of the Dust REgional Atmospheric Modeling (DREAM) system (Nickovic et al., 2001) was studied. Model results from two model runs for a December 2003 case using different land cover data sets were compared. Measurements and observational analyses such as surface synoptic data, surface Aerodrome Report (METAR), upper-air radiosonde, satellite images, measured visibility distributions, and surface PM2.5 data were used in model comparisons to show differences in model performance. 2. A brief description of the model In the DREAM system, a dust cycle simulation module is coupled online with the National Centers for Environmental Prediction (NCEP) operational Eta Model (Janjic, 1984, 1994). The dust cycle module simulates dust production, dust advection and turbulent diffusion, and dry and wet deposition (Nickovic et al., 2001; Shao et al., 1993; Georgi,
1986). Horizontally, the model uses semistaggering Arakawa E grid (Arakawa and Lamb, 1977). Vertically, it uses a step-mountain representation (Mesinger et al., 1988). Locations of desert dust sources are determined using land cover data. First, land cover data are mapped into a horizontal model grid. Grid cells that have arid and semiarid land cover categories are considered dust source areas. Then the arid and semiarid land cover data points in a grid cell are counted. The fraction of these points to total land cover data points in the cell is calculated and used as the fraction of dust sources in the cell for dust flux calculation. The physical properties of the dust particles from these dust source areas are associated with clay, silt, and sand components of the soil texture types specified by the Food and Agriculture Organization (FAO)/United Nations Educational, Scientific and Cultural Organization (UNESCO) soil map data. As listed in Table 1, four dust categories are modeled. They reflect properties of dust particles in four dust bins, with respective typical radius and density. They are associated with soil components of clay, silt and sand. The surface dust fluxes are parameterized according to Shao et al. (1993) wind tunnel experiments. Where frictional velocity u is greater than threshold frictional velocity ut, the dust flux Fs is F s ¼ const u3 ½1 ðut =u Þ2 . The threshold friction velocity depends on soil wetness and dust particle sizes of each dust category. The dust flux of an individual dust category is calculated using Fs and the fraction of desert surface, the fractions and the dust productivity factors of the clay/sand/silt components of the soil texture types in a grid cell (Nickovic et al., 2001). 3. Land cover data sets Two land cover data sets were used, respectively, in this study: the Olson World Ecosystem (OWE) land cover global data and the land cover product
ARTICLE IN PRESS 2216
D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the National Aeronautics and Space Agency (NASA) of the US satellite Terra. We used MODIS data covering the southwestern US. The OWE land cover data set (Olson, 1992) is based on numerous collected maps, references, and observations in the 1970s and 1980s. In the early 1990s, the greenness indices from Advanced Very High Resolution Radiometer (AVHRR) satellite data were used for certain ecosystems. The OWE data are a product based on various data sources and represents the land cover conditions of the 1980s. There are 74 land cover categories including water body, but only 59 categories occur in the data set (http://biodi.sdsc. edu/Doc/WhyWhere/meta/owe14d.txt). The spatial resolution of the OWE data is 10-min. MODIS land cover data used in our modeling studies identify 17 categories (Table 2) in the International Geosphere–Biosphere Program (IGBP) vegetation classification scheme. The classification was produced using a supervised approach (Hodges et al. 2001). A decision tree algorithm (Quinlan, 1993) and a boosting technique (Freund and Shapire, 1997) were used to improve classification efficiency and accuracies. The MODIS data represent land cover conditions of 2001 with 30-s spatial resolution. Land cover data sets are used in both the land–atmosphere water and energy flux and the
Table 2 MODIS land cover categories MODIS category
Description
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Water Evergreen needleleaf forest Evergreen needleleaf forest Deciduous needleleaf forest Deciduous broadleaf forest Mixed forest Closed shrubland Open shrubland Woody savannas Savannas Grasslands Permanent wetlands Croplands Urban and built-up Crops, natural vegetation mosaic Permanent snow/ice Barren/sparsely vegetated
15 16
dust source flux simulation in the modeling. As one land cover data set replaces the other, the land cover information used in meteorological and dust source flux modeling is changed according to the new data set. When OWE land cover data are used, the land cover that can contribute to dust source flux is categories 8 (desert, mostly bare stone, clay or sand), 50 (sand desert, partly blowing dunes), 51(semidesert/desert scrub/spars grass), and 52 (cool/cold shrub semidesert/steppe). When MODIS land cover data are used, land cover 16 (Table 2) can contribute to dust source flux. 4. Model scenarios Over 15–17 December 2003, a strong Pacific cold front moved southeastward through the southwest US and northern Mexico. It generated strong gusty winds and caused a significant dust storm in southern New Mexico and western Texas. Two model scenarios were designed to study the impact of using two different land cover data sets on the dust modeling results in the southwest US. The modeling period is from 00:00 Z 14 December to 23:00 Z 17 December 2003. The first day is treated as model spin-up time. The modeling area is shown in Fig. 1. From sea surface level to 100 hPa, each scenario used 24 vertical half eta levels with the first level about 86 m above the sea level. Horizontally, for both scenarios, the grid spacing between neighboring mass grid points and wind grid points was 1/31 (Arakawa and Lamb, 1977). European Center for Medium Range Forecast (ECMWF) data were used to generate initial and boundary conditions. The United States Geological Survey (USGS) 30-s spatial resolution terrain elevation data were used to specify model topography. Model soil texture was obtained from the FAO/UNESCO (http://www.fao.org/ag/agl/agll/ wrb/wrbmaps/htm/soilres.htm) soil map data with 134 categories and 2-min spatial resolution. The difference between the two scenarios is that the first scenario (S1) used OWE land cover data and the second scenario (S2) used MODIS land cover data instead. 5. Results and discussions The modeling domain was designed to capture the synoptic weather patterns and provide realistic meteorological fields for southeastern New Mexico, western Texas, and part of northern Mexico, where
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2217
Fig. 1. Modeled mean sea level pressure of S1 (left panel) and S2 (right panel) for 00:00 Z 15 December 2003.
Fig. 2. Surface weather map for 00:00 Z 15 December 2003 (http://weather.unisys.com, returns of a weather radar also shown).
the dust storm occurred. In the following comparisons, the modeled dust variables in this dust storm affected area were used, while the modeled meteorological fields were compared over the whole domain. No comparisons were made for the model spin-up time. 5.1. Meteorological variables Fig. 1 gives an example of S1 and S2 modeled sea level pressure fields. It depicts the modeled sea level pressure for 00:00 Z 15 December. Compared to the surface weather map for the same valid time as
shown in Fig. 2, both modeled patterns match the observed one. The cold front location along Nevada, Utah and Wyoming and the stationary front location along Texas and the Kansas–Colorado border can be identified from the S1 and S2 modeled fields. Comparisons showed that patterns of S1 and S2 500 hPa geopotential height and temperature fields were all similar to patterns of corresponding observational analyses. Meteorological variable vertical profiles of wind speed, wind direction, temperature and specific humidity were not significantly affected by using different land cover
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2218
Table 4 lists some of the differences of S2 and S1 modeled hourly 10-m wind speed and 2-m temperature at abovementioned surface synoptic and surface METAR sites. This shows that the difference of S2 and S1 modeled surface wind speed at some time can be big (maximum relative difference exceeding 800% and absolute difference over 1 m s1) although the average difference are small. The maximum absolute difference of S2 and S1 modeled surface temperature is over 2 K.
data. Fig. 3 shows the S1 and S2 modeled temperature and wind speed profiles for 12:00 Z 16 December 2003 at Santa Teresa, New Mexico. The S1 and S2 modeled profiles are similar and they compared well with observations. In comparing to observations, performance statistics (Steyn and McKendry, 1988) were calculated for modeled 10 m wind speed, wind direction, and 2 m temperature. In the calculation, hourly observations at 89 surface synoptic and 612 surface METAR sites located in the modeling domain were used. Performance statistics (Table 3) for S1 and S2 modeled surface variables are similar.
Obs. wind speed
S1 wind speed
S2 wind speed
Obs. Temperature
S1 temperature
S2 temperature
5.2. Dust concentrations Snapshots of S1 and S2 modeled near-surface dust concentrations for 20:00 Z 15 December are shown in Fig. 4. They include dust particles of all sizes. Both S1 and S2 modeled dust plumes cover an area from Southern New Mexico/El Paso to the Texas panhandle (see Fig. 6 for site locations). However, the detailed dust concentration distributions are quite different. The S1 high dust concentrations stretch across southern New Mexico to eastern Texas. S2 high dust concentrations are scattered at Lubbock and El Paso, Texas. The right panel of Fig. 5 is the satellite dust cloud image for 19:50 Z 15 December. The dust clouds are in the El Paso/Mexico border and Lubbock, Texas regions. The locations of S2 modeled dust plumes match satellite dust clouds locations better than those of S1. High airborne dust concentrations cause reduced visibility. The left panel of Fig. 5 is the observed visibility analysis based on surface METAR prevailing visibility at 20:00 Z 15 December. White
Temperature (K) 0
50
100
150
200
250
300
35000 30000 Height (m)
25000 20000 15000 10000 5000 0 0
20
40 60 Wind speed (m/s)
80
100
Fig. 3. Modeled and observed vertical profiles at Santa Teresa, New Mexico (31.871N, 106.701W) for 12:00 Z 16 December 2003.
Table 3 Performance statistics for S1 and S2 modeled surface variables Metrics
Wind speed
Wind direction
Temperature
Definition (M: modeled; O: observed)
S1
S2
S1
S2
S1
S2
Mean observed
5.53 (m s1)
5.53 (m s1)
231.40 (degree)
231.40 (degree)
276.74 (K)
276.74 (K)
Mean modeled
4.65 (m s1)
4.65 (m s1)
226.60 (degree)
226.53 (degree)
275.56 (K)
275.53 (K)
Mean bias
0.88 (m s1)
0.88 (m s1)
4.80 (degree)
4.87 (degree)
1.20 (K)
1.23 (K)
Mean error
1.97 (m s1)
1.97 (m s1)
51.76 (degree)
51.77 (degree)
4.09 (K)
4.09 (K)
Agreement index
0.74
0.74
0.74
0.74
0.71
0.71
N 1P Oi N i¼1 N 1P Mi N i¼1 N 1P ðM i Oi Þ N i¼1 N 1P jM i Oi j N i¼1 PN ðM i Oi Þ2 1 PN i¼1 i¼1
ðjM i OjþjOi OjÞ2
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2219
Table 4 Average, maximum, and minimum S2 to S1 differences and relative differences
Wind speed Temperature
Avg. Dif.
Max. Dif.
Min. Dif.
Avg. Rel. Dif. (%)
Max. Rel. Dif. (%)
Min. Rel. Dif. (%)
0.0001 (m s1) 0.0251 (K)
1.0077 (m s1) 1.4480 (K)
1.0536 (m s1) 2.6610 (K)
0.04 0.01
824.65 0.55
42.55 0.97
Fig. 4. Modeled dust concentrations (mg m3) of S1 (left panel) and S2 (right panel) for 20:00 Z 15 December 2003.
Fig. 5. Observed visibilities (mile) and satellite image of the dust clouds for 20:00 Z 15 December 2003.
areas in this panel indicate no visibility observations are available, and much of Mexico is without observations. Thus, looking only on the US side, the areas with the most reduced visibilities are near Lubbock and El Paso, Texas. The S2 modeled high dust concentration areas compare more favorably with measurements than S1 results.
The modeled surface PM2.5 concentrations at the locations of 40 in situ measurement sites in the dust storm area are compared. Fig. 6 shows the locations of some of the sites. The hourly PM2.5 observational data are the tapered-element oscillation (TEOM) sampler measurements of the US Environmental Protection Agencies (EPA) Air Quality System
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2220
Fig. 6. Surface PM2.5 site locations.
200 100
Difference (µg/m3)
0 -100 -200 -300 -400 -500 -600 -700 -800
Avg Max Min
-900 -1000 Fig. 7. The average, maximum, and minimum S2 and S1 modeled PM2.5 difference.
(AQS). Details about AQS data can be found on [http://www.epa.gov/ttn/airs/airsaqs/index.htm]. Fig. 7 shows that the average difference of S2 to S1 modeled PM2.5 concentrations at these sites is about 20 mg m3. However, the maximum and minimum differences reach about 90 and 883 mg m3, respectively. At most sites, S2 modeled PM2.5 concentrations better than S1 when compared with the observations. Fig. 8 is an example showing the comparison of S1 and S2 modeled PM2.5 concentrations against
observations at Odessa. S1 obviously overestimated PM2.5 concentration at this site. S2 modeled concentrations are much closer to observed values. The two observed peaks at 18:00 Z on 15 December and 00:00 Z on 16 December are shown in the S2 modeled concentrations as well, although discrepancies in magnitude and timing still exist. Performance statistics (Table 5) were calculated for modeled PM2.5 concentrations at these 40 sites. In general, S1 overestimated PM2.5 concentrations, with large mean bias and mean error. The agreement index is low at 0.12. The S2 results have smaller mean bias and mean error. The agreement index value increases considerably to 0.58. Figs. 9 and 10 show S1 and S2 modeled PM2.5 contours and observed PM2.5 values for 23:00 Z, 15 December. S1 overestimated PM2.5 concentration in the El Paso and Mission, Texas region. The 640 mg m3 contour passes El Paso with measured concentration of 44.3 mg m3. At Mission and Laredo Texas, the observed PM2.5 concentrations are 13.7 and 14.0 mg m3. However, at these two sites, the S1 modeled PM2.5 concentrations are larger than 100 mg m3. The modeled PM2.5 concentrations in the rest of the area are close to the measurements. S2 results show obvious improvements in areas where S1 overestimated concentrations. The S2 modeled 40 mg m3 contour is close to
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2221
Fig. 8. Comparison of modeled and measured PM2.5 concentrations at Odessa, Texas; Left panel for S1 and right panel for S2 (dots show measured values and lines show modeled values).
Table 5 Performance statistics for S1 and S2 modeled surface PM2.5 concentrations Metrics
PM2.5 S1
Mean observed Mean modeled Mean bias Mean error Agreement index
8.66 26.33 17.67 26.51 0.12
S2 (mg m3) (mg m3) (mg m3) (mg m3)
8.66 6.00 2.66 8.94 0.58
(mg m3) (mg m3) (mg m3) (mg m3)
El Paso and Mission, Texas. The S2 modeled 10 mg m3 contour is near Laredo Texas. 5.3. Discussion The S1 and S2 model result comparisons showed that, generally, the impact of using the two land cover data sets on modeled meteorological field patterns and vertical profiles were not significant. The S1 and S2 performance statistics of model surface wind and temperature were similar. The differences of surface wind and temperature at a specific time can be big. However, the impact on modeled dust concentrations was much more significant. This is also seen in our model simulations for other dust events. The dust storm events usually last several days or less. Probably, for land cover data having systematic impact on meteorological fields, it requires a model simulation for longterm events. These results also show although meteorological fields, especially wind fields are not
systematically different, dust concentrations can be considerably different. This is different than what some people believe that wind field forecasting determines dust forecasting. The dust source areas based on the OWE and MODIS land cover data are shown in Figs. 11 and 12. The overall patterns of dust sources based on the two different data sets are similar. But there are many differences in detailed distribution. The scattered small source areas in Fig. 12 do not appear in Fig. 11. In this dust storm affected area, dust source areas in the Texas panhandle do not show up in Fig. 11. However, the MODIS land cover data set captures these source areas (Fig. 12). The significant improvements in S2 model performance in modeled dust concentration distributions, time series, and agreement index of modeled PM2.5 can be attributed to the better representation of dust sources in the modeling. Differences between the two land cover data sets are due to several possible reasons. As previously mentioned, the OWE data set was compiled in the early 1990s and was mainly based on data sources of 1970s and 1980s. The MODIS data set was based on 2001 data. Because of agricultural activities, urbanization, climate variations, and other reasons such as drought and wildfire, land cover changed over these years. For this dust storm case, MODIS data are more up-to-date than OWE data. Spatial resolution also can cause differences in dust source representations using the two data sets. The OWE data set has 10-min spatial resolution and misses some of the dust source areas that can be resolved by the MODIS data set, which has a 30-s resolution.
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
2222
Fig. 9. S1 modeled surface PM2.5 contours and observed PM2.5 site values (mg m3) for 23:00 Z, 15 December 2003.
Fig. 10. S2 modeled surface PM2.5 contours and observed PM2.5 site values (mg m3) for 23:00 Z, 15 December 2003.
6. Conclusions The 10-min OWE and 30-s MODIS land cover data were used, respectively, in modeling windblown desert dust in the southwest US.
Modeled sea level pressure, 500 hPa potential height and temperature patterns, and wind, temperature, and specific humidity vertical profiles in both model scenarios matched the measurement analyses. Using different land cover data did not
ARTICLE IN PRESS D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
Fig. 11. Dust source areas (red) when using OWE data.
Fig. 12. Dust source areas (red) when using MODIS data.
cause systematic changes in meteorological fields. Performance statistics of the modeled surface meteorological variables from the two scenarios were similar. At a specific time and location, the differences of model surface wind and temperature can be big. Using different land cover data had a much larger and systematic influence on the modeled dust concentrations. The modeled near-ground dust concentration distribution using MODIS land cover matched with the satellite observed dust clouds and reduced visibility distributions better than those results when using OWE land cover. Comparisons of modeled and observed surface PM2.5 time series showed that MODIS land cover improved model results over OWE land cover. This was confirmed by performance statistics for modeled PM2.5 concentrations. The agreement index of modeled surface PM2.5 concentration increased from 0.12 to 0.58. Comparisons of modeled PM2.5 fields against
2223
observed ones also showed improved model performance by using MODIS land cover data to replace OWE land cover data. This demonstrates that, since land cover data determine dust source locations, the updates and spatial resolution of land cover data sets in desert dust modeling play an important role in predicting wind-blown dust particle spatial and temporal distributions and dust particle concentrations. Although land cover data sets attempt to represent what is in the real world, and they will never be 100% accurate, data like MODIS land cover from NASA Earth Observing System can give more upto-date and higher resolution land cover representation. In addition, studies and observations show that desert sand dune regions could be covered by ephemeral vegetations with occasional precipitation. Grazers could change grassland and shrubland to bare sand areas. These can happen in a relatively short time. There seems to be no better measure than using satellite remote sensing to resolve these changes. With careful testing of land cover classification methodologies, such as supervised, unsupervised, combinations of supervised and unsupervised methods (Friedl et al., 2002) to ensure maximum resolution of dust source areas, timely update of land cover data based on satellite remote sensing should help improve wind-blown desert dust modeling and forecasting. Acknowledgments This work is funded by NASA under an Earth Science Research, Education, and Applications Solutions Network (REASoN) project (CA#NNS04AA19A). MODIS land cover data are provided by Dr. Karl Benedict at the Earth Data Analysis Center of the University of New Mexico. We thank Professor Jim Koermer of Plymouth State University and the US EPA for providing meteorological and air quality observational data. References Alfaro, S.C., Gaudichet, A., Gomes, L., Maille, M., 1997. Modeling the size distribution of a soil aerosol produced by sandblasting. Journal of Geophysical Research 102 (D10), 11239–11249. Arakawa, A., Lamb, V.R., 1977. Computational design of the basic dynamical processes of the UCLA general circulation model. Methods of Computational Physics 17, 173–265. Chen, F., Dudhia, J., 2001. Coupling an advanced land-surface/ hydrology model with the Penn State/NCAR MM5 modeling
ARTICLE IN PRESS 2224
D. Yin et al. / Atmospheric Environment 41 (2007) 2214–2224
system. Part I: model implementation and sensitivity. Monthly Weather Review 129, 569–585. Freund, Y., Shapire, R.E., 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139. Friedl, M.A., McIver, D., Brodley, C.E., 2002. Integration of domain knowledge in the form of ancillary map data into supervised classification of remotely sensed data. In: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), vol. II, pp. 1038–1040. Georgi, F., 1986. A particle dry-deposition parameterization scheme for use in tracer transport models. Journal of Geophysical Research 91, 9794–9806. Hodges, J.C.F., Friedl, M.A., Strahler, A.H., 2001. The MODIS global land cover product: new data sets for global land surface parameterization. In: Proceedings of the Global Change Open Science Conference, Amsterdam, 2001. Kaiser, J., 2005. Mounting evidence indicts fine-particle pollution. Science 307, 1858–1861. Janjic, Z.I., 1984. Non-linear advection schemes and energy cascade on semi-staggered grids. Monthly Weather Review 118, 1234–1245. Janjic, Z.I., 1994. The step-mountain coordinate model: further developments of the convection, viscous sublayer and
turbulence closure schemes. Monthly Weather Review 122, 927–945. Mesinger, F., Janjic, Z.I., Nickovic, S., Gavrilov, D., Deaven, D.G., 1988. The step-mountain coordinate: model description and performance for cases of Apline lee cyclogenesis and for a case of an Appalachian redevelopment. Monthly Weather Review 116, 1493–1518. Nickovic, S., Kallos, G., Papadopoulos, A., Kakaliagou, O., 2001. A model for prediction of desert dust cycle in the atmosphere. Journal of Geophysical Research 106 (D16), 18113–18129. Olson, J.S., 1992. World Ecosystems (WE1.4). Digital Raster Data on a 10-minute Cartesian Orthonormal Geodetic 1082 2160 grid. In: Global Ecosystems Database, Version 2.0. National Geophysical Data Center, Boulder, CO. Quinlan, J.R., 1993. Programs for Machine Learning. Morgan Kauffman, Los Altos, CA. Shao, Y., Raupach, M.R., Findlater, P.A., 1993. Effect of saltation bombardment on the entrainment of dust by wind. Journal of Geophysical Research 98, 12719–12726. Steyn, D.G., McKendry, I.G., 1988. Quantitative and qualitative evaluation of a three-dimensional mesoscale numerical model simulation of a sea breeze in complex terrain. Monthly Weather Review 116, 1914–1926.