Our ability to predict air quality and to understand chemistry-climate interactions depends on a comprehensive understanding of atmospheric composition, developed through the comparison of observations and models. This project will facilitate identifying short-comings and uncertainties in models, help assess new model developments, and identify where and what type of new observations are needed to improve our understanding of atmospheric composition and processes. This will be accomplished through the design of a modular framework that integrates diverse atmospheric chemistry observational datasets with numerical model results for the evaluation of air quality predictions. In addition, by making observational datasets more accessible, a larger community, including students, will be engaged in atmospheric composition research.

This project will design a modular framework that integrates existing and future diverse atmospheric chemistry observational datasets with chemistry model results for the evaluation of air quality and atmospheric composition. This framework, MELODIES (Model EvaLuation using Observations, DIagnostics and Experiments Software), will be developed as part of the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA). As opposed to existing model evaluation tools, this project will develop a generic, portable, and model-agnostic software. The first year of the project will fully explore existing model comparison software available in the atmospheric chemistry community and identify components that could be incorporated for this project. Community input will clarify the needs for model-observation comparisons and identify suitable datasets. The research team will also develop adaptable and usable tools to provide access to the complex atmospheric chemistry datasets (numerous compounds with complex, non-standardized names, various time and spatial sampling, different instruments and platforms), as well as routines to extract model results at appropriate time and spatial resolutions to quantitatively compare to the observations. Such tools will need to operate on a range of different models (global or regional, structured or unstructured grid), provide an interface to ingest new observational datasets in a user-friendly way and provide comprehensive User Guides. A tutorial will be given near the end of the project, targeting graduate students and postdocs, to demonstrate the completed tools. The users will be taught the process of evaluating models with a wide range of atmospheric chemistry observations, as well as illustrating ways they could contribute to the further development of MELODIES. This project is funded by the Atmospheric Chemistry program in the division of Atmospheric and Geospace Science and the Directorate for Geosciences

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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University Corporation for Atmospheric Res
United States
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