EarthCube Data Capabilities: MELODIES for MUSICA: A modular framework to compare model results and observations of atmospheric chemistry

我们预测空气质量和了解化学-气候相互作用的能力取决于对大气成分的全面了解，这是通过对观测和模型的比较而发展起来的。该项目将有助于识别模式的缺点和不确定性，帮助评估新模式的发展，并确定需要在哪里和哪种类型的新观测来提高我们对大气成分和过程的理解。这将通过设计一个模块化框架来完成，该框架将各种大气化学观测数据集与用于评估空气质量预测的数值模式结果集成在一起。此外，通过使观测数据集更容易获得，包括学生在内的更大群体将参与大气成分研究。该项目将设计一个模块化框架，将现有和未来不同的大气化学观测数据集与化学模型结果整合起来，用于评估空气质量和大气成分。这个框架，MELODIES（使用观测、诊断和实验软件的模型评估），将作为化学和气溶胶多尺度基础设施（MUSICA）的一部分开发。与现有的模型评估工具相反，这个项目将开发一个通用的、可移植的、与模型无关的软件。该项目的第一年将充分探索大气化学领域现有的模型比较软件，并确定可以纳入该项目的组件。社区的投入将澄清模型观测比较的需要，并确定合适的数据集。研究小组还将开发适应性强和可用的工具，以提供访问复杂的大气化学数据集（许多具有复杂、非标准化名称的化合物，不同的时间和空间采样，不同的仪器和平台），以及在适当的时间和空间分辨率下提取模型结果的常规程序，以便与观测进行定量比较。这些工具将需要在一系列不同的模型（全球或区域、结构化或非结构化网格）上运行，提供一个界面，以用户友好的方式获取新的观测数据集，并提供全面的用户指南。在项目即将结束时，将会有一个针对研究生和博士后的教程来演示完成的工具。用户将学习使用广泛的大气化学观测来评估模型的过程，并说明他们可以为MELODIES的进一步发展做出贡献的方式。该项目由大气与地球空间科学部大气化学项目和地球科学理事会资助。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行了评估，认为值得支持。

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 GeosciencesThis 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.