Making NASA Remote Sensing Data Directly Usable in Multiscale Air Quality Models

Air quality modelers represent an important group of NASA Earth science data users. Recent progress in NASA remote sensing data services has enabled usability, intercomparison, and analysis of NASA data generated from multiple sources for a wide variety of science research and application areas. This also applies to the role NASA remote sensing data has played in the development of NASA generated climate models and assimilated data. The process of locating, filtering and interpolating remote sensing data for use in air quality model development is, however, still a barrier to the broader use of satellite data by air quality modelers and model users. In particular, the Community Multiscale Air Quality (CMAQ) modeling community, which includes over 5000 registered users in 90 countries, currently have no easy way to obtain quality assured remote sensing data and convert it into CMAQ model-compatible grid projections and data formats. Incompatibilities between remote sensing data and model required data formats have also hindered remote sensing and model intercomparisons and validation The various efforts to transform NASA remote sensing data to a format directly usable by air quality modelers have been identified and analyzed. They include: - Spatial re-projection - Generate regional model gridding. - Format conversion - Convert to CMAQ model format. - Quality filtering - Apply quality control flags. - Model ready output - Provide desired spatial/temporal requirements, format, and projection. This presentation describes NASA data that are of interest to air quality modelers, description of roadblocks that air quality modelers face in using this data, and present potential solutions that address these roadblocks. Data visualization comparisons, quality filtering examples, and implementation suggestions are provided.