Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaign
Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaign
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DOI：
10.5194/acp-18-11007-2018
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发表时间：
2018-08
影响因子：
6.3
通讯作者：
W. Tang;A. Arellano;J. Digangi;Yonghoon Choi;G. Diskin;A. Agustí-Panareda;M. Parrington;S. Massart;B. Gaubert;Youngjae Lee;Danbi Kim;Jinsang Jung;Jinkyu Hong;Je-Woo Hong;Y. Kanaya;Mindo Lee;R. Stauffer;A. Thompson;J. Flynn;J. Woo
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文献类型：
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作者：
W. Tang;A. Arellano;J. Digangi;Yonghoon Choi;G. Diskin;A. Agustí-Panareda;M. Parrington;S. Massart;B. Gaubert;Youngjae Lee;Danbi Kim;Jinsang Jung;Jinkyu Hong;Je-Woo Hong;Y. Kanaya;Mindo Lee;R. Stauffer;A. Thompson;J. Flynn;J. Woo
抽象。由于人为燃烧对健康和环境的重大影响，特别是在城市到区域的范围内，因此必须对人为燃烧进行准确和一致的监测。在这里，我们评估了哥白尼大气监测服务（CAMS）全球预测系统的性能，在2016年5月至6月的韩国-美国空气质量（KORUS-AQ）实地研究期间，使用飞机，地面站点和船舶的测量结果。我们的评估侧重于CAMS CO和CO2分析以及两个更高分辨率的预测（16和9公里的水平分辨率），以评估其在预测东亚地区燃烧特征的能力。我们的研究结果表明，CAMS CO2的平均偏差对空气中的CO2测量2.2，0.7和0.3 ppmv的16和9公里的CO2预测和分析，分别略有高估。在16公里的预测CO2的平均偏差为正似乎是一致的垂直剖面的测量。相比之下，我们发现CAMS CO的估计有适度的低估，与空气CO测量值相比，总体偏差为-19.2（16 km），-16.7（9 km）和-20.7 ppbv（分析）。这种负的CO平均偏差主要出现在750 hPa以下的所有三个预报/分析配置。尽管有这些偏见，CAMS表现出显着的协议与观测到的CO与CO2的增强比在首尔大都市区和西（黄）海，东亚外流在研究期间进行了采样。与西海（dCO/dCO 2 =28 ppbv ppmv−1）相比，在首尔（dCO/dCO 2 =9 ppbv ppmv −1）观察到更有效的燃烧。这种“燃烧特征对比”与这两个区域的以往研究结果一致。CAMS捕捉到了这种增强率的差异（首尔：8-12 ppbv ppmv-1，西海：1030 ppbv ppmv-1），而与预报/分析配置无关。CAMS CO偏差与CO2偏差的相关性在这两个区域相对较高（首尔：0.64-0.90，西海：0.80），这表明CAMS捕获的对比度可能是由CAMS中使用的人为排放比率主导的。然而，CAMS表现出较差的性能，在捕捉本地到城市的CO和CO2的变化。沿着在朝鲜半岛地面站点的测量，CAMS在早晨样本中产生了太高的CO和CO2浓度，其垂直梯度（CO2为0.4 ppmv hPa−1，CO为3.5 ppbv hPa−1）比观测值（CO2为0.25 ppmv hPa−1，CO为1.7 ppbv hPa−1）更陡，表明模式中边界层混合较弱。最后，我们发现CO分析的组合（即，改进的初始条件）和使用更精细的分辨率（9公里对16公里）通常会产生更好的预报。
Abstract. Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea-United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO2 analyses as well as two higher-resolution forecasts (16 and 9 km horizontal resolution) to assess their capability in predicting combustion signatures over east Asia. Our results show a slight overestimation of CAMS CO2 with a mean bias against airborne CO2 measurements of 2.2, 0.7, and 0.3 ppmv for 16 and 9 km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the 16 km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of −19.2 (16 km), −16.7 (9 km), and −20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS shows a remarkable agreement with observed enhancement ratios of CO with CO2 over the Seoul metropolitan area and over the West (Yellow) Sea, where east Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul (dCO/dCO2=9 ppbv ppmv−1) compared to the West Sea (dCO/dCO2=28 ppbv ppmv−1). This “combustion signature contrast” is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8–12 ppbv ppmv−1, the West Sea: ∼30 ppbv ppmv−1) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO2 bias is relatively high over these two regions (Seoul: 0.64–0.90, the West Sea: ∼0.80) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO2 variability. Along with measurements at ground sites over the Korean Peninsula, CAMS produces too high CO and CO2 concentrations at the surface with steeper vertical gradients (∼0.4 ppmv hPa−1 for CO2 and 3.5 ppbv hPa−1 for CO) in the morning samples than observed (∼0.25 ppmv hPa−1 for CO2 and 1.7 ppbv hPa−1 for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9 km vs. 16 km) generally produces better forecasts.