Improving Nitrogen Predictions by Updating Tropospheric Kinetics (INPUT-K)

通过更新对流层动力学改进氮预测 (INPUT-K)

• 批准号: 1636730
• 负责人: Myoseon Jang
• 金额: $ 16.32万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636730&HistoricalAwards=false
• 关键词: Improving; Nitrogen; Predictions; Updating; Tropospheric

The objective of this research is to provide alternative estimates of reaction rate coefficients using statistical methods to improve model predictions of atmospheric nitrogen chemistry. This is a demonstration project that will make direct use of field observations to guide further investment in laboratory experiments and reveal mechanisms that can lead to modeling biases. The independent estimates of reaction rates provided by this project will provide an important constraint on estimating the health and climate impacts of reactive nitrogen chemistry.This project aims to improve model simulations of reactive nitrogen by achieving the following objectives: (1) Apply a previously successful inference modeling framework to a more recent observational campaign (such as the NSF-supported DC3 campaign) where nitrogen imbalances in 3-D models have been shown; (2) Constrain rate coefficients and product stoichiometry in climate relevant environments using likelihood calculations based on reduced model ensembles and observational datasets; and (3) Replace stochastic physical transport with mechanistic transport using an ensemble of simulated air parcel trajectories that are representative of post-convection transport. Together, stochastic modeling, mechanistic chemistry, and high quality observations will be used to supplement laboratory studies and identify reactions for subsequent laboratory investigation.

本研究的目的是提供替代估计的反应速率系数，使用统计方法，以提高大气氮化学的模型预测。这是一个示范项目，将直接利用实地观察来指导对实验室实验的进一步投资，并揭示可能导致建模偏差的机制。本项目提供的反应速率的独立估算值将为估算活性氮化学对健康和气候的影响提供重要的制约因素，本项目旨在通过实现以下目标改进活性氮的模型模拟：（1）将先前成功的推理建模框架应用于最近的观测活动（2）利用基于简化模式集合和观测数据集的似然性计算，在与气候相关的环境中约束速率系数和产物化学计量;以及（3）用机械输送代替随机物理输送，机械输送使用代表对流后输送的模拟空气团轨迹的集合。同时，随机建模，机械化学和高质量的观察将用于补充实验室研究，并确定后续实验室研究的反应。