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Advanced Land Surface Processes in the Coupled WRF/CMAQ with MODIS Input
Ran, L., R. Gilliam, David-C Wong, H. Foroutan, Jon Pleim, G. Pouliot, W. Appel, D. Kang, S. Roselle, B. Eder, AND E. Cooter. Advanced Land Surface Processes in the Coupled WRF/CMAQ with MODIS Input. 16th Annual CMAS Conference, Chapel Hill, North Carolina, October 23 - 26, 2017.
Land surface modeling (LSM) is important in WRF/CMAQ for simulating the exchange of heat, moisture, momentum, trace atmospheric chemicals, and windblown dust between the land surface and the atmosphere.? Vegetation and soil treatments are crucial in LSM for surface energy budgets and water and carbon cycles.? Vegetation is a source and sink of many atmospheric pollutants and precursor chemicals such as O3 and volatile organic compounds while soil properties directly influence windblown dust emissions.? There are recent improvements to the vegetation and soil processes and ozone dry deposition on the bare soil in the offline WRF/CMAQ modeling system through the use of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation product.? This presentation highlights the use of MODIS vegetation, improved soil treatment, and a simple irrigation scheme in a coupled-WRF/CMAQ which is updated with the recent improvements. We will demonstrate the improvements in surface meteorology, O3, and PM2.5 simulations using the updated coupled-WRF/CMAQ.? Results from simulations covering continental U.S. with 12 km grids for April and August 2016 and 2017 will be presented. ?In addition, simulation results for May and June 2010 covering California with 4 km grids during the CalNex campaign period will be demonstrated with the irrigation option. ?Distinct improvements in dust emissions estimation are observed with reduced PM2.5 high bias during dust outbreaks. Surface O3 estimation is reduced in areas with dominant bare land, and surface O3 simulation is improved in areas with better vegetation representation from MODIS during the green up season.? The simple irrigation scheme improves 2-m temperature and mixing ratio results in irrigation-dominant crop lands.? The influence on air quality will be evaluated against the CalNex campaign observations.
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Record Details:Record Type: DOCUMENT (PRESENTATION/SLIDE)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY
COMPUTATIONAL EXPOSURE DIVISION