We propose a 3-year project enabling the use of NASA Earth-Sun System results in an existing air quality forecast decision support system (AQF-DSS). We will improve operational ozone and haze forecasts by assimilating retrieved tropospheric-NO2 from AURA/OMI and (recently upgraded) aerosol optical depth (AOD) from MODIS. After benchmarking the improvements, we will disseminate the improved forecasts to end users nationally on a sustained basis. The end-user/ decision-makers include multiple state/local air quality forecast agencies, the DOE, the National Park Service, the VISTAS regional planning organization, and over 200 broadcast meteorologists and the general public. The AQF-DSS is runs operationally at Baron Advanced Meteorological Systems (BAMS) using an ensemble modeling system that includes CMAQ and MAQSIP-RT driven by MM5 or WRF, along with the SMOKE emissions models. A data-assimilation module will be prototyped, verified, and validated enabling the new satellite data to be ingested into the AQF-DSS. We will utilize surface PM2.5 and ASOS data to augment the satellite data and improve the vertical resolution of the retrievals. We will upgrade coupled emissions and land-surface model science by adopting the NASA Land- Information System (LIS) to provide improved relative humidity forecasts and reconcile LU/LC representations with biogenic emissions, consistent with recent GEOSS expert panel co-convened by the project PI. The Visibility Improvement State and Tribal Association of the Southeast (VISTAS) RPO will provide model data from its 2002-annual CONUS CMAQ-aerosol visibility simulation for data-assimilation algorithm evaluation. Real-time access to surface data and data processing will be performed through the DataFed system, maintained at Washington University/St. Louis. The project will validate and benchmark the improvements gained through adoption of the NASA ESS results, will share datasets with USGEO and other relevant NASA partners, and will sustain the improved system indefinitely. BAMS has assembled an outstanding team of scientists and engineers led by PI John N. McHenry - who pioneered operational-commercial air quality forecasting in the US (McHenry et al., 2004; McHenry and Dabberdt, 2005). Co-I’s include Jay Herman and Christina Hsu at NASA, and Rudy Husar at WU/CAPITA/Lantern Corporation, experts in satellite remote-sensing and aerosol-radiation physics respectively. Key BAMS team members include Carlie Coats and Jeff Vukovich, both of whom have worked with McHenry for years. Christina Hsu developed the “Deep Blue” retrieval algorithm, enabling significant improvements in MODIS AOD retrieval. Montse Fuentes (Consultant) is one of this country’s leading experts in spatial data analysis, and Mike Abraczinskas (Collaborator) represents VISTAS. The planned improvements will affect both ozone and haze forecasting through: (1) assimilation of NO2 data; (2) assimilation of AOD data; (3) improved surface relative humidity; (4) reconciliation of land-use information; and (5) improved methods for assimilating real-time fire information. We expect anywhere between a 10-25% improvement in baseline categorical and discrete measures for ozone and perhaps more than that for haze. Project results are expected to make a substantial contribution to NASA’s stated objectives for this solicitation fully consistent with NASA’s Air Quality management Roadmap. The improved AQF-DSS will provide end-users/decision-makers with increased confidence in mitigating episodic and regional air quality episodes year-round and nationally. Their improved decisions, in turn, will enable reductions in harmful exposures and foster investment in better policy. This will yield socioeconomic savings through improved health while encouraging more effective strategies for long-term sustainable reductions in mean levels of criteria pollutants.