Fine-scale WRF-CMAQ Modeling for the 2013 DISCOVER-AQ Campaign in California

Robert Chad Gilliam, US EPA, Atmospheric Modeling and Analysis Division, Research Triangle Park, NC, United States, Jonathan E Pleim, Center for Environmental Measurement & Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA, Research Triangle Park, United States and Wyat Appel, Environmental Protection Agency Research Triangle Park, Research Triangle Park, NC, United States
Abstract:
Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) is an ongoing four year NASA campaign to improve remote sensing in order to better resolve the distribution of pollutants in the lower atmosphere for public health reasons. These observational campaigns are a prime opportunity to evaluate and improve weather and air quality models, in particular the finer scales, since the collected observations are not only unique (boundary layer profiles, planetary boundary layer height and LIDAR), but of high spatial density. For the first campaign in the Washington DC-Baltimore region, a number of meteorological model improvements were crucial for quality results at the finer grid scales.

The main techniques tested in the DISCOVER-AQ Washington DC-Baltimore experiment were iterative indirect soil nudging, a simple urban parameterization based on highly resolved impervious surface data, and the use of a high resolution 1 km sea surface temperature dataset. A fourth technique, first tested in a separate cold season application in the US Rocky Mountains, was the assimilation of high resolution 1 km SNOw Data Assimilation System (SNODAS) data for better snow cover representation in retrospective modeling. These methods will be leveraged using a nested 12-4-2 km WRF-CMAQ modeling platform for the 2013 DISCOVER-AQ California campaign where the 2 km domain covers the entire San Joaquin Valley (SJV), coastal areas and all of Los Angeles. The purpose is to demonstrate methods to derive high quality meteorology for retrospective air quality modeling over geographically complex areas of the Western US where current coarser resolution modeling may not be sufficient. Accurate air quality modeling is particularly important for California, which has some of the most polluted areas in the US, within the SJV. Furthermore, this work may inform modeling in other areas of the Intermountain West that are experiencing air quality issue as a result of the rapid expansion of the oil and gas extraction industry.

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