1. Development and Preliminary Results of Image Processing Tools for Meteorology and Air Quality Modeling Limei Ran Center for Environmental Modeling for Policy Development Institute for the Environment, UNC-Chapel Hill Jonathan Pleim and Robert Gilliam Atmospheric Modeling and Analysis Division USEPA/ORD/NERL, RTP, NC

2. Outline of the Presentation Spatial Allocator (SA) – Raster Tools GOES, OMI, and MODIS Tools WRF and CMAQ Outputs, 08/2006 Conclusions and Future Work Acknowledgements

3. Raster tools in Spatial Allocator (SA) The SA Raster tools are developed to compute image/satellite data on model grids The tools can process: 2001 30m NLCD and 1km MODIS land cover data GOES satellite data (new) OMI Level 2G and 3 data (new) MODIS Level 2 cloud product (under development) SA Web site: http://www.ie.unc.edu/cempd/projects/mims/spatial/ Plan to release new SA: 11/2009

4. Model Grid Satellite Data Computation Three steps in computing model grid satellite data 1. Create model grid shapeFile or raster file 2. Rasterize model grids 3. Compute model grid satellite value: average or total 3b.Project grid cells to Sat. data projection 3a. Project sat. points to grid cells 3a 3b

5. GOES Satellite Data GOES satellite data from National Space Science and Technology Center (NSSTC) at Huntsville, AL http://satdas.nsstc.nasa.gov/index.html GOES data contain two types of hourly data: –GOES Imager 4km Cloud Albedo, Insolation, Surface Albedo, Cloud Top Pressure, Infrared Temperature –GOES Sounder 10km Cloud Top Pressure, Skin Temperature, Total Precipitable Water –Data are in GRIB format Two script files –Compute GOES data on model grids and create a WRF- ready NetCDF file (1 hour time step) –Convert NetCDF file into WRF data assimilation format

6. GOES Imager Surface Albedo GOES Data East US 12km Domain (%)

7. GOES Sounder Skin Temperature GOES Data East US 12km Domain

8. OMI Level 2G and 3 Product Tool Aura OMI daily global data from NASA GIOVANNI web site: http://disc.sci.gsfc.nasa.gov/giovanni/overview/inst ances_atmospheric.html –Level 3: ozone, aerosol, and cloud (0.25 or 1 degree resolution) –Level 2G: ozone, aerosol, SO 2 and NO 2 (0.125 or 0.25 degree resolution) –Data are in HDF4 format One script file to compute grid OMI data

9. OMI L2G NO 2 Tropospheric Column Density 10^15 molec/cm^2

10. MODIS L2 Cloud Products Each MODIS L2 cloud product file (one granule) contains: –39 cloud variables in 5X5km or 1X1km arrays (at nadir) –five-minute time interval data for area ~1354X2030km –Data are in HDF4 format http://modis-atmos.gsfc.nasa.gov/MOD06_L2/index.html http://ladsweb.nascom.nasa.gov/data/search.html Two tools: –Compute model grid one cloud variable from one MODIS L2 cloud product file –Compute model grid multiple cloud variables from given period’s MODIS L2 cloud product files (under development)

11. NLCD and GLCC Evergreen Forest East US 12km DomainTexas 4km Domain Houston 1km Domain

12. US Eastern 12km Grid Domain: small difference GLCC

13. Difference in NH 4 aerosol concentration error for August 2006 NLCD MAE – GLCC MAE

14. Houston 4km Comparisons NLCD versus USGS Example of how the more finely resolved NLCD landuse represents areas along complex coastlines more accurately. The NLCD simulation represents diurnal temperature better than the 1-km base USGS. Landmask to the right is more refined when NLCD is used. NLCDUSGS

15. 2-m Temperature Time Series Average of all sites around Houston Mean absolute error (MAE) for NLCD simulation is 0.69 K and the USGS is 0.83 K. Mean BIAS is near zero for NLCD and -0.18 for USGS. Index of Agreement (IOA) is 0.94 versus 0.91.

16. 2-m Temperature and 10-m Winds Aug 5, 2006 at 21 UT (4 PM LT) NLCD LU data  Fine scale effects of urban heat island  Bay breeze from Galveston Bay Houston area 1-km grid resolution WRF model simulation

17. Conclusions WRF runs: –Preliminary comparisons show small differences at 12-km grid resolution –Improved results from NLCD runs at 4-km and 1-km grid resolutions, particularly near coastlines CMAQ runs: –Largest difference is in the bi-directional NH 3 surface flux because it is closely related to landuse, especially crops.

18. Future Work Run CMAQ for 4-km and 1-km Texas domains Compare results with WRF MODIS landuse data Use BELD4 from 2001 NLCD and MODIS land cover, FIA, and NASS for biogenic emission Develop tools to process and analyze A-Train satellite products (MODIS Aqua, Aura, PARASOL, CALIPSO, and CloudSAT) for aerosol and cloud interaction in air quality and regional climate chemistry research

19. Acknowledgements This project is funded by: – US EPA under the Contract No. EP-W-095-023, “Operation of the Center for Community Air Quality Modeling and Analysis”. –NASA ROSE Grants: UAH and NNX08AL28G We gratefully acknowledge the support of: –Arastoo Pour Biazar and Bill Crosson from the National Space Science and Technology Center at UA Huntsville, AL –Aijun Xiu, Uma Shankar from IE, UNC-Chapel Hill –William Benjey, Ellen Cooter, and Wyat Appel from US EPA