USING NASA REMOTE SENSING DATA FOR MULTISCALE AIR QUALITY MODELS

Recent progress in NASA remote sensing data services has enabled usability, intercomparison, and analysis of NASA data generated from multiple sources. This also applies to the role NASA remote sensing data has played in the development of NASA generated climate models and assimilated data. Unfortunately, the process of locating, filtering and interpolating remote sensing data for use in model development is still a barrier to the broader use of satellite data. 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.

The various efforts to transform NASA remote sensing data to a format directly usable by regional 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.

Regional air quality modelers represent an important, and under-engaged, group of NASA Earth science data users. Thus increasing the utilization of NASA data by this community through the aforementioned efforts, would greatly increase data utilization.

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 and potential solutions that address these roadblocks. Data visualization comparisons, model ready versus model not ready data, and implementation suggestions are provided.