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

本研究的目的是利用统计方法提供反应速率系数的替代估计，以改进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。该项目提供的对反应速率的独立估计将为估计活性氮化学对健康和气候的影响提供重要的约束。该项目旨在通过实现以下目标来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（如nsf支持的DC3活动），其中三维模型中的氮不平衡已被显示；(2)利用基于简化模式集和观测数据集的似然计算约束气候相关环境下的速率系数和产物化学计量；(3)利用代表对流后输运的模拟气团轨迹集合，将随机物理输运替换为机械输运。随机模型、机械化学和高质量的观察将共同用于补充实验室研究，并为后续的实验室研究确定反应。

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.