Air Quality Modeling of TexAQS-II Episodes with Data Assimilation
| Project Period: | 06/01/2008 - 08/31/2009 |
| Total Budget: | $390,000 |
| Sub-Contractors: | University of Houston
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This is an omnibus modeling project to synthesize key observational findings of the 2006 Texas Air Quality Study-II (TexAQS-II) experiment. The proposed research will enhance air quality modeling capabilities through meteorological and chemical data assimilation. The objectives of the research are: (1) to improve model inputs affecting air pollution chemistry using satellite-derived quantities, such as cloudiness and sea-surface temperature, in addition to various surface and upper meteorological observations and updated emissions inventories; (2) to synthesize and analyze meteorological and chemical measurements from CAMS, Moody Tower, Baylor aircraft, NOAA aircraft, and other in-situ and remote-sensing measurements through model sensitivity studies; (3) to assess uncertainties in model inputs, in particular NOx, CO, and HRVOC emissions and model boundary conditions through 4-DVar chemical data assimilation; (4) to provide continued ozone forecasting for the Houston and Dallas ozone nonattainment areas; and (5) to implement a surface HONO chemistry module in CMAQ.
Retrospective meteorological simulations can be improved by the utilization of accurate and high-resolution databases such as land-use/land-cover and subsoil classification data, and the remaining biases can be considerably reduced through data assimilation of satellite observations (clouds and SST) together with MADIS and CAMS surface and upper air observations. Application of CMAQ/4DVar using comprehensive chemistry measurement datasets with consistent meteorological inputs will reduce uncertainties in the emissions inputs and initial and boundary conditions for different meteorological conditions. Continued forecast modeling of Houston and Dallas air quality for 2008/2009 will help to verify findings of the TexAQS studies, enhance conceptual models, and assist other TERC-supported field studies.
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