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

EarthCube 数据功能：音乐旋律：比较模型结果和大气化学观测结果的模块化框架

• 批准号: 2026924
• 负责人: Louisa Emmons
• 金额: $ 47万
• 依托单位: University Corporation For Atmospheric Res
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026924&HistoricalAwards=false
• 关键词: EarthCube; Data; Capabilities; MELODIES; MUSICA

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.

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，这是通过比较观测和模型而发展起来的。 该项目将有助于确定模型中的短期和不确定性，帮助评估新的模型开发，并确定需要在何处以及何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框架来实现，该框架将各种大气化学观测数据集与数值模型结果相结合，用于评价空气质量预测。 此外，通过使观测数据集更容易获得，包括学生在内的更大的社区将参与大气成分研究。该项目将设计一个模块化框架，将现有和未来的各种大气化学观测数据集与化学模型结果相结合，用于评估空气质量和大气成分。 这个框架，MELORIO（使用观测，诊断和实验软件进行模型评估），将作为化学和气溶胶多尺度基础设施（MUSICA）的一部分开发。 相对于现有的模型评估工具，该项目将开发一个通用的，可移植的，模型无关的软件。该项目的第一年将充分探索大气化学界现有的模型比较软件，并确定可纳入该项目的组成部分。 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。研究小组还将开发可适应和可用的工具，以提供对复杂大气化学数据集（具有复杂、非标准化名称的众多化合物、各种时间和空间采样、不同仪器和平台）的访问，以及以适当的时间和空间分辨率提取模型结果以与观测进行定量比较的例程。这些工具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，以方便用户的方式获取新的观测数据集，并提供全面的用户指南。在项目接近结束时，将针对研究生和博士后提供一个教程，以演示完成的工具。 将向用户讲授利用广泛的大气化学观测评估模型的过程，并说明他们如何能够为MELOVELS的进一步发展作出贡献。该项目由大气和地球空间科学部的大气化学计划和地球科学理事会资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。