Efforts to enhance greenhouse gas (GHG) emission reduction in East Asia play a pivotal role on both global and regional scales in advancing climate mitigation strategies. This study aimed to better constrain anthropogenic CO emission estimates by expanding the network of near-surface in-situ stations for CO observations across South Korea. To achieve an optimal CO network design, we conducted an Observing System Simulation Experiment (OSSE) coupled with the Stochastic Lagrangian Transport model (STILT), utilizing meteorological data from the Korean Integrated Model (KIM). Our inversion setup incorporated two CO emission datasets with a 0.1 resolution: EDGAR v6 for prior emissions and GRACED for truth emissions. A uniform model-mismatch error of 3 ppm was introduced across sites. The effectiveness of the existing five in-situ stations, termed the base network, in South Korea was evaluated to gauge their ability to constrain CO surface flux estimates. However, the findings revealed a reduction in flux uncertainty of only 29.2%, which fell short of the desired uncertainty reduction goal. In this base network, the Lotte World Tower (LWT: 37.5126°E, 127.1025°E) in Seoul and the Anmyeondo (AMY: 36.538576° N, 126.330071° E) site in Taean county stood as major contributors, with estimated reductions of 17.48% and 6.35%, respectively. Consequently, we proposed and developed an extended network, identifying seven candidate sites based on consideration of logistical factors, existing infrastructures, and proximity to the emission source regions. An incremental optimization scheme ranked their contributions, resulting in an additional 25% reduction, bringing the total to 54.13%. However, it is noteworthy that diminishing returns (ranging from 13% to less than 0.1%) were observed with an increase in station count mainly due to the possibility that adding a station earlier in the sequence might render subsequent stations redundant. Despite this, the proposed CO network successfully reduced uncertainty in emissions, narrowing the gap with the objectives of the Global Greenhouse Gas Watch (G3W).

中文翻译：

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使用贝叶斯反演与拉格朗日建模相结合，设计韩国上空额外的 CO2 现场地面观测网络

东亚加强温室气体减排的努力在全球和区域范围内推进气候减缓战略方面发挥着关键作用。这项研究旨在通过扩大韩国各地近地表二氧化碳观测站网络，更好地限制人为二氧化碳排放估算。为了实现最佳的 CO 网络设计，我们利用韩国综合模型 (KIM) 的气象数据，结合随机拉格朗日传输模型 (STILT) 进行了观测系统模拟实验 (OSSE)。我们的反演设置包含两个分辨率为 0.1 的 CO 排放数据集：用于先前排放的 EDGAR v6 和用于真实排放的 GRACED。跨站点引入了 3 ppm 的统一模型失配误差。对韩国现有的五个现场站（称为基础网络）的有效性进行了评估，以衡量它们限制二氧化碳表面通量估算的能力。然而，研究结果显示通量不确定性仅降低了 29.2%，未达到预期的不确定性降低目标。在该基础网络中，首尔的乐天世界塔（LWT：37.5126°E，127.1025°E）和泰安县的安眠岛（AMY：36.538576°N，126.330071°E）站点是主要贡献者，估计减少了 17.48分别为 % 和 6.35%。因此，我们提出并开发了一个扩展网络，根据物流因素、现有基础设施以及与排放源地区的邻近程度确定了七个候选地点。增量优化方案对他们的贡献进行了排名，导致额外减少了 25%，使总数达到 54.13%。然而，值得注意的是，随着站点数量的增加，观察到收益递减（范围从 13% 到小于 0.1%），这主要是由于在序列中较早添加站点可能会使后续站点变得冗余。尽管如此，拟议的二氧化碳网络成功地减少了排放的不确定性，缩小了与全球温室气体观察（G3W）目标的差距。