Collaborative Research: A Computational Framework for Assessing the Observation Impact in Air Quality Forecasting

合作研究：评估空气质量预测观测影响的计算框架

• 批准号: 0915047
• 负责人: Adrian Sandu
• 金额: $ 41.86万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915047&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Framework; Assessing

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This research develops the algorithmic and computational framework needed for a judicious assessment of the observation impact in air quality modeling. Novel algorithms in the framework of model-constrained optimization will allow to account for the data location in the time-space domain, observation type, instrument type, and data interaction in the presence of multiple observing systems. Specifically, the research is focused on: development of high-order adjoint-based observation impact techniques consistent to four-dimensional variational data assimilation schemes; assessment of forecast sensitivity to observation error variances and estimation of the forecast impact of uncertainties in the specification of the input error statistics; development of efficient computational approaches to allow for practical implementations of the observation impact algorithms; validation of the novel techniques through observing system experiments and assessment of the potential impact of new observing systems.The ability to accurately represent the distribution of atmospheric pollutants in relation to various anthropogenic activities is essential for chemical weather forecasting to protect the population, for answering science questions related to the future of our planet, and for designing sound environmental policies. An accurate representation of the chemical composition of the atmosphere requires a close integration of models and observations through data assimilation.Data assimilation is the process by which model predictions utilize measurements to produce an optimal representation of the state of the atmosphere. As more observations are becoming available and new measurement networks are being planned, it is of critical importance to develop the capabilities to best utilize the data, to better manage the sensing resources, and to design more effective field experiments and networks to support atmospheric chemistry and air quality studies. This research develops the computational tools required to optimize the information provided by the existing observing systems and to provide guidance for future improvements to the observational network and instruments design. The new developments will also help the design process of new field experiments and of new chemical monitoring networks.

该奖项是根据2009年的《美国回收与再投资法》（公法111-5）资助的。这项研究开发了对空气质量建模中观察影响的明智评估所需的算法和计算框架。模型受限优化框架中的新算法将允许在存在多个观测系统的情况下，在时间空间域，观察类型，仪器类型和数据相互作用中考虑数据位置。具体而言，研究的重点是：基于高阶的观察影响技术的开发与四维变分数据同化方案一致；评估对观察误差方差的预测敏感性以及对输入误差统计规范中不确定性的预测影响的估计；开发有效的计算方法，以实现观察影响算法的实际实施；通过观察系统实验和评估新观测系统的潜在影响来验证新技术。准确表示与各种人为活动有关的大气污染物的分布的能力对于化学天气预测以保护人群，对于保护与我们的星球的未来以及设计环境环境策略有关的化学天气预测至关重要。大气化学成分的准确表示需要通过数据同化的模型和观察的密切整合。数据同化是模型预测利用测量来产生大气状态的最佳表示的过程。随着越来越多的观察结果并计划了新的测量网络，开发能力以最好地利用数据，更好地管理感应资源，并设计更有效的现场实验和网络以支持大气化学和空气质量研究至关重要。本研究开发了优化现有观测系统提供的信息所需的计算工具，并为观察网络和工具设计的未来改进提供指导。新的发展还将帮助新的现场实验和新化学监测网络的设计过程。