Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaign
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 9km 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 0.3ppmv for 16 and 9km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the 16km 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 (16km), -16.7 (9km), 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 overmore »
- Authors:
-
- Univ. of Arizona, Tucson, AZ (United States)
- NASA Langley Research Center, Hampton, VA (United States)
- NASA Langley Research Center, Hampton, VA (United States); Science Systems and Applications, Inc., Hampton, VA (United States)
- European Centre for Medium-Range Weather Forecasts, Reading (United Kingdom)
- National Center for Atmospheric Research, Boulder, CO (United States)
- Korea Research Institute of Standards and Science, Daejeon (South Korea)
- Yonsei Univ., Seoul (South Korea)
- Japan Agency for Marine-Earth Science and Technology, Kanagawa (Japan)
- Univ. Space Research Assoc., Columbia, MD (United States); NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
- NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
- Univ. of Houston, Houston, TX (United States)
- Konkuk Univ., Seoul (South Korea)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1479436
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Atmospheric Chemistry and Physics (Online)
- Additional Journal Information:
- Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 18; Journal Issue: 15; Journal ID: ISSN 1680-7324
- Publisher:
- European Geosciences Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES
Citation Formats
Tang, Wenfu, Arellano, Avelino F., DiGangi, Joshua P., Choi, Yonghoon, Diskin, Glenn S., Agustí-Panareda, Anna, Parrington, Mark, Massart, Sebastien, Gaubert, Benjamin, Lee, Youngjae, Kim, Danbi, Jung, Jinsang, Hong, Jinkyu, Hong, Je -Woo, Kanaya, Yugo, Lee, Mindo, Stauffer, Ryan M., Thompson, Anne M., Flynn, James H., and Woo, Jung-Hun. Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaign. United States: N. p., 2018.
Web. doi:10.5194/acp-18-11007-2018.
Tang, Wenfu, Arellano, Avelino F., DiGangi, Joshua P., Choi, Yonghoon, Diskin, Glenn S., Agustí-Panareda, Anna, Parrington, Mark, Massart, Sebastien, Gaubert, Benjamin, Lee, Youngjae, Kim, Danbi, Jung, Jinsang, Hong, Jinkyu, Hong, Je -Woo, Kanaya, Yugo, Lee, Mindo, Stauffer, Ryan M., Thompson, Anne M., Flynn, James H., & Woo, Jung-Hun. Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaign. United States. https://doi.org/10.5194/acp-18-11007-2018
Tang, Wenfu, Arellano, Avelino F., DiGangi, Joshua P., Choi, Yonghoon, Diskin, Glenn S., Agustí-Panareda, Anna, Parrington, Mark, Massart, Sebastien, Gaubert, Benjamin, Lee, Youngjae, Kim, Danbi, Jung, Jinsang, Hong, Jinkyu, Hong, Je -Woo, Kanaya, Yugo, Lee, Mindo, Stauffer, Ryan M., Thompson, Anne M., Flynn, James H., and Woo, Jung-Hun. Tue .
"Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaign". United States. https://doi.org/10.5194/acp-18-11007-2018. https://www.osti.gov/servlets/purl/1479436.
@article{osti_1479436,
title = {Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaign},
author = {Tang, Wenfu and Arellano, Avelino F. and DiGangi, Joshua P. and Choi, Yonghoon and Diskin, Glenn S. and Agustí-Panareda, Anna and Parrington, Mark and Massart, Sebastien and Gaubert, Benjamin and Lee, Youngjae and Kim, Danbi and Jung, Jinsang and Hong, Jinkyu and Hong, Je -Woo and Kanaya, Yugo and Lee, Mindo and Stauffer, Ryan M. and Thompson, Anne M. and Flynn, James H. and Woo, Jung-Hun},
abstractNote = {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 9km 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 0.3ppmv for 16 and 9km CO2 forecasts, and analyses, respectively. The positive CO2 mean bias in the 16km 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 (16km), -16.7 (9km), 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 Combining double low line 9 ppbvppmv-1) compared to the West Sea (dCO dCO2 Combining double low line 28 ppbvppmv-1). This "combustion signature contrast" is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8-12 ppbvppmv-1, the West Sea: ~ 30 ppbvppmv-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 ppmvhPa-1 for CO2 and 3.5 ppbvhPa-1 for CO) in the morning samples than observed (~ 0.25 ppmvhPa-1 for CO2 and 1.7 ppbvhPa-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 (9km vs. 16km) generally produces better forecasts.},
doi = {10.5194/acp-18-11007-2018},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 15,
volume = 18,
place = {United States},
year = {Tue Aug 07 00:00:00 EDT 2018},
month = {Tue Aug 07 00:00:00 EDT 2018}
}
Web of Science
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