A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations-a case study of Xuzhou, China
A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations-a case study of Xuzhou, China
复制标题
大气混合比观测估算CO表面通量的区域资料同化系统——以徐州为例
DOI：
10.1007/s11356-019-04246-7
复制
发表时间：
2019
影响因子：
5.8
通讯作者：
Li Yanpeng
中科院分区：
文献类型：
--
作者：
Lu Lijiang;Chen Baozhang;Guo Lifeng;Zhang Huifang;Li Yanpeng
一氧化碳（CO）排放清单数据对于空气质量控制至关重要。然而，排放清单是劳动密集型和耗时的，并且通常具有很大的不确定性。在本研究中，我们开发了一个新的区域数据同化系统（TracersTracker），用于使用基于适当正交分解（POD）的四维变分（4D-VAR）数据同化方法从连续混合比观测估计地面CO排放通量（POD-4DVar）和耦合区域模式（天气研究和预报模型（WRF）与模型-3社区多尺度空气质量（CMAQ）模型）。应用该系统估算了中国徐州市的CO排放量。利用2016年1月和7月21个监测塔的连续逐小时地面CO混合比观测数据进行了实验。该系统的实验结果进行了检查，并与连续的表面CO观测（先验排放）进行比较。研究发现，反演的CO排放通量高于先验排放通量，主要分布在城市和工业区，1月（冬季）比先验排放通量高104%，7月（夏季）比先验排放通量高44%。
Carbon monoxide (CO) emission inventory data are crucial for air quality control. However, the emission inventories are labor-intensive and time-consuming and generally have large uncertainties. In this study, we developed a new regional data assimilation system (TracersTracker) for estimating the surface CO emission flux from continuous mixing ratio observations using the proper orthogonal decomposition (POD)-based four-dimensional variational (4D-VAR) data assimilation method (POD-4DVar) and a coupled regional model (Weather Research and Forecasting model (WRF) with the Models-3 Community Multi-scale Air Quality (CMAQ) model). This system was applied to estimate CO emissions in Xuzhou city, China. An experiment was conducted with the continuous hourly surface CO mixing ratio observations from 21 monitoring towers in January and July of 2016. The experimental results of the system were examined and compared with the continuous surface CO observations (a priori emission). We found that the retrieved CO emission fluxes were higher than the a priori emission and were mainly distributed in urban and industrial areas, which were 104% higher in January (winter) and 44% higher in July (summer).