Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model
Optimal estimation of initial concentrations and emission sources with 4D-Var for air pollution prediction in a 2D transport model
复制标题
使用 4D-Var 对初始浓度和排放源进行优化估计，用于 2D 传输模型中的空气污染预测
DOI：
10.1016/j.scitotenv.2021.145580
复制
发表时间：
2021
影响因子：
9.8
通讯作者：
Fung J C H
中科院分区：
文献类型：
--
作者：
Liu Caili;Zhang Shaoqing;Gao Yang;Wang Yuhang;Sheng Lifang;Gao Huiwang;Fung J C H
通过部署有效的观测网络来确定空气污染事件的来源是空气质量控制和预报研究中的一个重要和有趣的问题，但它非常具有挑战性。为了评估污染事件对排放源的敏感性，本文基于水平二维输运模式及其伴随模式建立了一个综合框架。以华北地区PM2.5理想污染事件为例，通过一系列最小化过程，定义了一个目标函数，对初始浓度和排放源进行了最优估计。四维变分法的结果表明，在最优的初始条件和排放源下，该模型可以成功地预报几天内的污染事件。基于灵敏度分析的最优观测网络与全网格观测系统相比，仅需三分之一的成本，但可预报性得到了很大程度的保留，而随机部署相同数量的观测点时，几乎检测不到可预报性。我们评估空气污染的可预报性，重点是在何种程度上的模拟浓度和目标的空气污染之间的均方根误差是有限的最佳观测网络。结果表明，与最佳观测网相关联的空气污染可预报性仅限于6天左右的时间尺度。这种基于灵敏度的最优观测系统具有高效率和经济性，一旦执行包括传输和化学过程的现实大气模式的伴随和变分过程，就有希望准确地预测目标区域的空气污染事件。
Attributing sources of air pollution events by deploying an efficient observational network is an important and interesting problem in air quality control and forecast studies, but it is very challenging. In order to estimate the sensitivities of pollution events to emission sources, a comprehensive framework is built based on a horizontal 2-dimensional transport model and its adjoint in solving this problem. In an analysis of an idealized air pollution event of PM2.5over the region of North China, an objective function is defined to optimally estimate the initial concentrations and emission sources through a series of minimization procedures. Results by means of the 4-dimensional variational approach show that, with the optimal initial conditions and emission sources, the model can successfully forecast the pollution event in a few days. The optimal observing network based on sensitivity analysis takes only one third of the cost but greatly retains predictability skill compared to the full-grid observing system, while nearly no predictability skill is detectable if the same number of observational sites is randomly deployed. We evaluate air pollution predictability in the point of focusing on to what degree the root mean square errors between the modeled concentration and the targeted air pollution are limited by the optimal observational network. Results show that air pollution predictability in association with the optimal observational network is limited in the time scales about 6 days. With the high efficiency and in an economic fashion, such a sensitivity-based optimal observing system holds promise for accurately predicting an air pollution event in the targeted area once the adjoint and variational procedure of a realistic atmosphere model including transport and chemical processes is performed.