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Recent Advances in WRF Modeling for Air Quality Applications
Citation:
Pleim, Jon, L. Ran, AND R. Gilliam. Recent Advances in WRF Modeling for Air Quality Applications. 17th Annual WRF User's Workshop, Boulder, CO, June 27 - July 01, 2016.
Impact/Purpose:
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.
Description:
The USEPA uses WRF in conjunction with the Community Multiscale Air Quality (CMAQ) for air quality regulation and research. Over the years we have added physics options and geophysical datasets to the WRF system to enhance model capabilities especially for extended retrospective model simulations. In particular, we have added the PX-LSM and the ACM2 PBL model. This presentation will describe recent advances in these physics models that were included in WRFv3.7 and WRFv3.8. We will also present recent modeling research that has not yet been included in the WRF release including assimilation of MODIS derived vegetation characteristics for use in the PX LSM for better representation of evapotranspiration. Leaf area index (LAI) and vegetation fraction (VF) are derived from MODIS 8-day composite retrievals at 1 km resolution and regridded to the WRF 12 km grid resolution CONUS domain. The VF is approximated by the fraction of absorbed photosynthetically active radiation (FPAR) and LAI is computed from MODIS LAI/FPAR so that it represent the LAI of the vegetation fraction of the grid cell. Including the MODIS derived LAI and VF provides a much more realistic representation of vegetation, especially in the western US where vegetation is much sparser than the default representation in the PX LSM. WRF runs for a full year with and without the MODIS vegetation demonstrate the effects of the better spatial and seasonal variations from the satellite data on meteorology. CMAQ runs from both MODIS and Base WRF runs for three months, selected to represent different seasons, demonstrate the effects of MODIS vegetation on modeled air quality.
