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Impact of High Resolution Land-Use Data in Meteorology and Air Quality Modeling Systems
Citation:
Ran, L., J. E. PLEIM, AND R. C. GILLIAM. Impact of High Resolution Land-Use Data in Meteorology and Air Quality Modeling Systems. Chapter 1, Douw G. Steyn and S. Trivikrama Rao (ed.), Air Pollution Modeling and its Applications XX. Springer Netherlands, , Netherlands, C(section 1.1):3-7, (2010).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Accurate land use information is important in meteorology for land surface exchanges, in emission modeling for emission spatial allocation, and in air quality modeling for chemical surface fluxes. Currently, meteorology, emission, and air quality models often use outdated USGS Global Land Cover Characterization (GLCC) 30-second (around 1km) land cover data. With the release of the 2001 National Land Cover Data (NLCD) products at 30m cell resolution for the United Stales and 2001 NASA Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data at 1km cell resolution for the globe, meteorology and air quality modelers want to use these more current and accurate land cover data sets. In the Spatial Allocator, C++ programs were developed with the Geospatial Data Abstraction Library (GDAL) to compute modeling domain gridded land cover information based on input image data of the 2001 NLCD and MODIS land cover data. The programs output gridded fractional coverage of each land category for use in the Weather Research and Forecast (WRF) and Community Multiscale Air Quality (CMAQ) models. The land use data are used to specify vegetation and surface related parameters that are needed in land surface models (LSM) and dry deposition models. We have incorporated the gridded 2001 NLCD and MODIS land cover data in the WRF and CMAQ modeling for the CONUS and east US 12 km domains. Preliminary WRF results show slight improvement and CMAQ runs show largest difference in the bi-directional NH3 surface flux. We believe that these new land cover data should have more effects on both meteorological and air quality model simulations for higher resolution modeling.