Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4757-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4757-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model
Bok H. Baek
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Rizzieri Pedruzzi
Department of Sanitary and Environmental Engineering, Federal
University of Minas Gerais, Belo Horizonte, Brazil
Minwoo Park
Department of Technology Fusion Engineering, College of Engineering,
Konkuk University, Seoul, Republic of Korea
Chi-Tsan Wang
Center for Spatial Information Science and Systems, George Mason
University, Fairfax, VA, USA
Younha Kim
Energy, Climate, and Environment Program, International Institute for
Applied Systems Analysis, Laxenburg, Austria
Chul-Han Song
School of Earth and Environmental Engineering, Gwangju Institute
Science and Technology, Gwangju, Republic of Korea
Jung-Hun Woo
CORRESPONDING AUTHOR
Department of Technology Fusion Engineering, College of Engineering,
Konkuk University, Seoul, Republic of Korea
Civil and Environmental Engineering, College of Engineering, Konkuk
University, Seoul, Republic of Korea
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Earth Syst. Sci. Data, 15, 5261–5279, https://doi.org/10.5194/essd-15-5261-2023, https://doi.org/10.5194/essd-15-5261-2023, 2023
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Hazardous air pollutant (HAP) human exposure studies usually rely on local measurements or dispersion model methods, but those methods are limited under spatial and temporal conditions. We processed the US EPA emission data to simulate the hourly HAP emission patterns and applied the chemical transport model to simulate the HAP concentrations. The modeled HAP results exhibit good agreement (R is 0.75 and NMB is −5.6 %) with observational data.
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To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Wendell W. Walters, Madeline Karod, Emma Willcocks, Bok H. Baek, Danielle E. Blum, and Meredith G. Hastings
Atmos. Chem. Phys., 22, 13431–13448, https://doi.org/10.5194/acp-22-13431-2022, https://doi.org/10.5194/acp-22-13431-2022, 2022
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Atmospheric ammonia and its products are a significant source of urban haze and nitrogen deposition. We have investigated the seasonal source contributions to a mid-sized city in the northeastern US megalopolis utilizing geospatial statistical analysis and novel isotopic constraints, which indicate that vehicle emissions were significant components of the urban-reduced nitrogen budget. Reducing vehicle ammonia emissions should be considered to improve ecosystems and human health.
Uma Shankar, Donald McKenzie, Jeffrey P. Prestemon, Bok Haeng Baek, Mohammed Omary, Dongmei Yang, Aijun Xiu, Kevin Talgo, and William Vizuete
Atmos. Chem. Phys., 19, 15157–15181, https://doi.org/10.5194/acp-19-15157-2019, https://doi.org/10.5194/acp-19-15157-2019, 2019
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We evaluate two wildfire emissions estimates for the southeastern US, based on projected annual areas burned in 2011–2060, against a benchmark wildfire inventory in air quality (AQ) simulations for 2010 and AQ network observations. Our emissions estimates compare well with the benchmark but all three simulations have large biases compared to observations. We find our methods suitable to assess current and future wildfire AQ impacts but also identify areas for AQ model improvements.
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This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We incoporated each HONO process into the current CMAQ modeling framework to enhance the accuracy of HONO mixing ratios predictions. These results expand our understanding of HONO photochemistry and identify crucial sources of HONO that impact the total HONO budget in Seoul, South Korea. Through this investigation, we contribute to resolving discrepancies in understading chemical transport models, with implications for better air quality mangement and environmental protection in the region.
Meng Li, Junichi Kurokawa, Qiang Zhang, Jung-Hun Woo, Tazuko Morikawa, Satoru Chatani, Zifeng Lu, Yu Song, Guannan Geng, Hanwen Hu, Jinseok Kim, Owen R. Cooper, and Brian C. McDonald
Atmos. Chem. Phys., 24, 3925–3952, https://doi.org/10.5194/acp-24-3925-2024, https://doi.org/10.5194/acp-24-3925-2024, 2024
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In this work, we developed MIXv2, a mosaic Asian emission inventory for 2010–2017. With high spatial (0.1°) and monthly temporal resolution, MIXv2 integrates anthropogenic and open biomass burning emissions across seven sectors following a mosaic methodology. It provides CO2 emissions data alongside nine key pollutants and three chemical mechanisms. Our publicly accessible gridded monthly emissions data can facilitate long-term atmospheric and climate model analyses.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
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Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Chi-Tsan Wang, Bok H. Baek, William Vizuete, Lawrence S. Engel, Jia Xing, Jaime Green, Marc Serre, Richard Strott, Jared Bowden, and Jung-Hun Woo
Earth Syst. Sci. Data, 15, 5261–5279, https://doi.org/10.5194/essd-15-5261-2023, https://doi.org/10.5194/essd-15-5261-2023, 2023
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Hazardous air pollutant (HAP) human exposure studies usually rely on local measurements or dispersion model methods, but those methods are limited under spatial and temporal conditions. We processed the US EPA emission data to simulate the hourly HAP emission patterns and applied the chemical transport model to simulate the HAP concentrations. The modeled HAP results exhibit good agreement (R is 0.75 and NMB is −5.6 %) with observational data.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
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To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Shixian Zhai, Daniel J. Jacob, Drew C. Pendergrass, Nadia K. Colombi, Viral Shah, Laura Hyesung Yang, Qiang Zhang, Shuxiao Wang, Hwajin Kim, Yele Sun, Jin-Soo Choi, Jin-Soo Park, Gan Luo, Fangqun Yu, Jung-Hun Woo, Younha Kim, Jack E. Dibb, Taehyoung Lee, Jin-Seok Han, Bruce E. Anderson, Ke Li, and Hong Liao
Atmos. Chem. Phys., 23, 4271–4281, https://doi.org/10.5194/acp-23-4271-2023, https://doi.org/10.5194/acp-23-4271-2023, 2023
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Anthropogenic fugitive dust in East Asia not only causes severe coarse particulate matter air pollution problems, but also affects fine particulate nitrate. Due to emission control efforts, coarse PM decreased steadily. We find that the decrease of coarse PM is a major driver for a lack of decrease of fine particulate nitrate, as it allows more nitric acid to form fine particulate nitrate. The continuing decrease of coarse PM requires more stringent ammonia and nitrogen oxides emission controls.
Wendell W. Walters, Madeline Karod, Emma Willcocks, Bok H. Baek, Danielle E. Blum, and Meredith G. Hastings
Atmos. Chem. Phys., 22, 13431–13448, https://doi.org/10.5194/acp-22-13431-2022, https://doi.org/10.5194/acp-22-13431-2022, 2022
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Atmospheric ammonia and its products are a significant source of urban haze and nitrogen deposition. We have investigated the seasonal source contributions to a mid-sized city in the northeastern US megalopolis utilizing geospatial statistical analysis and novel isotopic constraints, which indicate that vehicle emissions were significant components of the urban-reduced nitrogen budget. Reducing vehicle ammonia emissions should be considered to improve ecosystems and human health.
Katherine R. Travis, James H. Crawford, Gao Chen, Carolyn E. Jordan, Benjamin A. Nault, Hwajin Kim, Jose L. Jimenez, Pedro Campuzano-Jost, Jack E. Dibb, Jung-Hun Woo, Younha Kim, Shixian Zhai, Xuan Wang, Erin E. McDuffie, Gan Luo, Fangqun Yu, Saewung Kim, Isobel J. Simpson, Donald R. Blake, Limseok Chang, and Michelle J. Kim
Atmos. Chem. Phys., 22, 7933–7958, https://doi.org/10.5194/acp-22-7933-2022, https://doi.org/10.5194/acp-22-7933-2022, 2022
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The 2016 Korea–United States Air Quality (KORUS-AQ) field campaign provided a unique set of observations to improve our understanding of PM2.5 pollution in South Korea. Models typically have errors in simulating PM2.5 in this region, which is of concern for the development of control measures. We use KORUS-AQ observations to improve our understanding of the mechanisms driving PM2.5 and the implications of model errors for determining PM2.5 that is attributable to local or foreign sources.
Soon-Young Park, Uzzal Kumar Dash, Jinhyeok Yu, Keiya Yumimoto, Itsushi Uno, and Chul Han Song
Geosci. Model Dev., 15, 2773–2790, https://doi.org/10.5194/gmd-15-2773-2022, https://doi.org/10.5194/gmd-15-2773-2022, 2022
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An EnKF was applied to CMAQ for assimilating ground PM2.5 observations from China and South Korea. The EnKF performed better than that without assimilation and even superior to 3D-Var. The reduced MBs in 24 h predictions were 48 % and 27 % by improving ICs and BCs, respectively.
Shixian Zhai, Daniel J. Jacob, Jared F. Brewer, Ke Li, Jonathan M. Moch, Jhoon Kim, Seoyoung Lee, Hyunkwang Lim, Hyun Chul Lee, Su Keun Kuk, Rokjin J. Park, Jaein I. Jeong, Xuan Wang, Pengfei Liu, Gan Luo, Fangqun Yu, Jun Meng, Randall V. Martin, Katherine R. Travis, Johnathan W. Hair, Bruce E. Anderson, Jack E. Dibb, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jung-Hun Woo, Younha Kim, Qiang Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 16775–16791, https://doi.org/10.5194/acp-21-16775-2021, https://doi.org/10.5194/acp-21-16775-2021, 2021
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Geostationary satellite aerosol optical depth (AOD) has tremendous potential for monitoring surface fine particulate matter (PM2.5). Our study explored the physical relationship between AOD and PM2.5 by integrating data from surface networks, aircraft, and satellites with the GEOS-Chem chemical transport model. We quantitatively showed that accurate simulation of aerosol size distributions, boundary layer depths, relative humidity, coarse particles, and diurnal variations in PM2.5 are essential.
Arman Pouyaei, Yunsoo Choi, Jia Jung, Bavand Sadeghi, and Chul Han Song
Geosci. Model Dev., 13, 3489–3505, https://doi.org/10.5194/gmd-13-3489-2020, https://doi.org/10.5194/gmd-13-3489-2020, 2020
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This paper introduces a novel Lagrangian model (Concentration Trajectory of Air pollution with an Integrated Lagrangian model, C-TRAIL) for showing the source and receptor areas by following polluted air masses. To investigate the concentrations and trajectories of air masses simultaneously, we use the trajectory-grid (TG) Lagrangian advection model. The TG model follows the concentrations of representative air
packetsof species along trajectories determined by the wind field.
Sojin Lee, Chul Han Song, Kyung Man Han, Daven K. Henze, Kyunghwa Lee, Jinhyeok Yu, Jung-Hun Woo, Jia Jung, Yunsoo Choi, Pablo E. Saide, and Gregory R. Carmichael
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-116, https://doi.org/10.5194/gmd-2020-116, 2020
Revised manuscript not accepted
Kyunghwa Lee, Jinhyeok Yu, Sojin Lee, Mieun Park, Hun Hong, Soon Young Park, Myungje Choi, Jhoon Kim, Younha Kim, Jung-Hun Woo, Sang-Woo Kim, and Chul H. Song
Geosci. Model Dev., 13, 1055–1073, https://doi.org/10.5194/gmd-13-1055-2020, https://doi.org/10.5194/gmd-13-1055-2020, 2020
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For the purpose of providing reliable and robust air quality predictions, an operational air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2) by preparing the initial conditions for model simulations via data assimilation using satellite- and ground-based observations. The performance of the developed air quality prediction system was evaluated using ground in situ data during the KORUS-AQ campaign period.
Uma Shankar, Donald McKenzie, Jeffrey P. Prestemon, Bok Haeng Baek, Mohammed Omary, Dongmei Yang, Aijun Xiu, Kevin Talgo, and William Vizuete
Atmos. Chem. Phys., 19, 15157–15181, https://doi.org/10.5194/acp-19-15157-2019, https://doi.org/10.5194/acp-19-15157-2019, 2019
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We evaluate two wildfire emissions estimates for the southeastern US, based on projected annual areas burned in 2011–2060, against a benchmark wildfire inventory in air quality (AQ) simulations for 2010 and AQ network observations. Our emissions estimates compare well with the benchmark but all three simulations have large biases compared to observations. We find our methods suitable to assess current and future wildfire AQ impacts but also identify areas for AQ model improvements.
Chi-Tsan Wang, Christine Wiedinmyer, Kirsti Ashworth, Peter C. Harley, John Ortega, Quazi Z. Rasool, and William Vizuete
Atmos. Chem. Phys., 19, 13973–13987, https://doi.org/10.5194/acp-19-13973-2019, https://doi.org/10.5194/acp-19-13973-2019, 2019
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The legal commercialization of cannabis has created a new and almost unregulated industry. Here we present the first inventory of volatile organic compound emissions from cannabis cultivation facilities (CCFs) for Colorado. When applied within a regulatory air quality model to predict regional ozone impacts, our inventory results in net ozone formation near CCFs with the largest increases in Denver County. However, our inventory is highly uncertain and we identify future critical data needs.
Jie Li, Tatsuya Nagashima, Lei Kong, Baozhu Ge, Kazuyo Yamaji, Joshua S. Fu, Xuemei Wang, Qi Fan, Syuichi Itahashi, Hyo-Jung Lee, Cheol-Hee Kim, Chuan-Yao Lin, Meigen Zhang, Zhining Tao, Mizuo Kajino, Hong Liao, Meng Li, Jung-Hun Woo, Jun-ichi Kurokawa, Zhe Wang, Qizhong Wu, Hajime Akimoto, Gregory R. Carmichael, and Zifa Wang
Atmos. Chem. Phys., 19, 12993–13015, https://doi.org/10.5194/acp-19-12993-2019, https://doi.org/10.5194/acp-19-12993-2019, 2019
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This study evaluated and intercompared 14 CTMs with ozone observations in East Asia, within the framework of the Model Inter-Comparison Study for ASIA Phase III (MICS-Asia III). Potential causes of the discrepancies between model results and observation were investigated by assessing the planetary boundary layer heights, emission fluxes, dry deposition, chemistry and vertical transport among models. Finally, a multi-model estimate of pollution distributions was provided.
Hyun S. Kim, Inyoung Park, Chul H. Song, Kyunghwa Lee, Jae W. Yun, Hong K. Kim, Moongu Jeon, Jiwon Lee, and Kyung M. Han
Atmos. Chem. Phys., 19, 12935–12951, https://doi.org/10.5194/acp-19-12935-2019, https://doi.org/10.5194/acp-19-12935-2019, 2019
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In this study, a deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. In general, the accuracies of the LSTM-based predictions were superior to the 3-D CTM-based predictions. Based on this, we concluded that the LSTM-based system could be applied to daily operational PM forecasts in South Korea. We expect that similar AI systems can be applied to the predictions of other atmospheric pollutants.
Daniel L. Goldberg, Pablo E. Saide, Lok N. Lamsal, Benjamin de Foy, Zifeng Lu, Jung-Hun Woo, Younha Kim, Jinseok Kim, Meng Gao, Gregory Carmichael, and David G. Streets
Atmos. Chem. Phys., 19, 1801–1818, https://doi.org/10.5194/acp-19-1801-2019, https://doi.org/10.5194/acp-19-1801-2019, 2019
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Using satellite data, we are able to estimate the emissions of NOx (NOx=NO+NO2), a toxic group of air pollutants, in the Seoul metropolitan area. We first develop an enhanced satellite product that better observes NO2 in urban regions. Using this new product, we derive NOx emissions to be twice as large as the emissions reported by the South Korean government. The implication is that the measures taken to reduce NOx emissions in South Korea have not been as effective as regulators have thought.
Myungje Choi, Jhoon Kim, Jaehwa Lee, Mijin Kim, Young-Je Park, Brent Holben, Thomas F. Eck, Zhengqiang Li, and Chul H. Song
Atmos. Meas. Tech., 11, 385–408, https://doi.org/10.5194/amt-11-385-2018, https://doi.org/10.5194/amt-11-385-2018, 2018
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This study is a major version upgrade of the aerosol product from GOCI, the first and unique ocean color imager in geostationary earth orbit. It describes the improvement of version 2 of the GOCI Yonsei aerosol retrieval algorithm for near-real-time processing with improved accuracy from the modification of cloud masking, surface reflectance, etc. The product is validated against AERONET/SONET over East Asia with analyses of various errors features, and a pixel-level uncertainty is calculated.
Myungje Choi, Jhoon Kim, Jaehwa Lee, Mijin Kim, Young-Je Park, Ukkyo Jeong, Woogyung Kim, Hyunkee Hong, Brent Holben, Thomas F. Eck, Chul H. Song, Jae-Hyun Lim, and Chang-Keun Song
Atmos. Meas. Tech., 9, 1377–1398, https://doi.org/10.5194/amt-9-1377-2016, https://doi.org/10.5194/amt-9-1377-2016, 2016
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The Geostationary Ocean Color Imager (GOCI) is the first ocean color sensor in geostationary orbit. It enables hourly aerosol optical properties to be observed in high spatial resolution. This study presents improvements of the GOCI Yonsei Aerosol Retrieval (YAER) algorithm and its validation results using ground-based and other satellite-based observation products during DRAGON-NE Asia 2012 Campaign. Retrieval errors are also analyzed according to various factors through the validation studies.
S. Lee, C. H. Song, R. S. Park, M. E. Park, K. M. Han, J. Kim, M. Choi, Y. S. Ghim, and J.-H. Woo
Geosci. Model Dev., 9, 17–39, https://doi.org/10.5194/gmd-9-17-2016, https://doi.org/10.5194/gmd-9-17-2016, 2016
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We developed an integrated air quality modeling system using AOD data retrieved from a geostationary satellite sensor, GOCI (Geostationary Ocean Color Imager), over Northeast Asia with an application of the spatiotemporal-kriging (STK) method and conducted short-term hindcast runs using the developed system. It appears that the STK approach can greatly reduce not only the errors and biases of AOD and PM10 predictions but also the computational burden of a chemical weather forecast (CWF).
K. M. Han, S. Lee, L. S. Chang, and C. H. Song
Atmos. Chem. Phys., 15, 1913–1938, https://doi.org/10.5194/acp-15-1913-2015, https://doi.org/10.5194/acp-15-1913-2015, 2015
S. Seo, J. Kim, H. Lee, U. Jeong, W. Kim, B. N. Holben, S.-W. Kim, C. H. Song, and J. H. Lim
Atmos. Chem. Phys., 15, 319–334, https://doi.org/10.5194/acp-15-319-2015, https://doi.org/10.5194/acp-15-319-2015, 2015
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The estimation of PM10 from optical measurement of AERONET and MODIS by various empirical models was evaluated for the DRAGON-Asia campaign. The results showed the importance of boundary layer height (BLH) and effective radius (Reff) in estimating PM10. The highest correlation between the estimated and measured values was found to be 0.81 in winter due to the stagnant air mass and low BLH, while the poorest values were 0.54 in spring due to the influence of long-range transport above BLH.
H.-K. Kim, J.-H. Woo, R. S. Park, C. H. Song, J.-H. Kim, S.-J. Ban, and J.-H. Park
Atmos. Chem. Phys., 14, 7461–7484, https://doi.org/10.5194/acp-14-7461-2014, https://doi.org/10.5194/acp-14-7461-2014, 2014
R. S. Park, S. Lee, S.-K. Shin, and C. H. Song
Atmos. Chem. Phys., 14, 2185–2201, https://doi.org/10.5194/acp-14-2185-2014, https://doi.org/10.5194/acp-14-2185-2014, 2014
M. E. Park, C. H. Song, R. S. Park, J. Lee, J. Kim, S. Lee, J.-H. Woo, G. R. Carmichael, T. F. Eck, B. N. Holben, S.-S. Lee, C. K. Song, and Y. D. Hong
Atmos. Chem. Phys., 14, 659–674, https://doi.org/10.5194/acp-14-659-2014, https://doi.org/10.5194/acp-14-659-2014, 2014
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Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
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The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
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By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
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We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
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This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
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Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
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The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
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The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
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There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
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This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
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We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
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This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
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Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
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In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
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We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
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HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
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This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
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An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
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This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
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Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
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Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
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Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
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This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
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Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
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Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
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Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
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Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
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An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
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Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
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In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
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The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
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In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
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The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
EGUsphere, https://doi.org/10.22541/essoar.169903618.82717612/v2, https://doi.org/10.22541/essoar.169903618.82717612/v2, 2024
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Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate of the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
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In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
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Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
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This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
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PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
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Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
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Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
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We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
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The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
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We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
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A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
Cited articles
Appel, W., Chemel, C., Roselle, S., Francis, X., Hu, R.-M., Sokhi, R., Rao,
S. T., and Galmarini, S.: Examination of the Community Multiscale Air
Quality (CMAQ) model performance over the North American and European
domains, Atmos. Environ., 53, 142–155,
10.1016/j.atmosenv.2011.11.016, 2013.
Baek, B. H. and Seppanen, C., SMOKE v4.8.1 Public Release (29 January
2021). (Version SMOKEv481_Jan2021),
https://doi.org/10.5281/zenodo.4480334, 2021.
Baek, B. H., Pedruzzi, R., Wang, C.-T., and Woo, J.-H.:
bokhaeng/CARS: CARS (Comprehensive Automobile Emissions Research Simulator)
version 1.0 Public Release (CARSv1.0), Zenodo,
https://doi.org/10.5281/zenodo.5033314, 2021.
Burnett, R., Chen, H., Szyszkowicz, M., Fann, N., Hubbell, B., Pope, C. A.,
Apte, J. S., Brauer, M., Cohen, A., Weichenthal, S., Coggins, J., Di, Q.,
Brunekreef, B., Frostad, J., Lim, S. S., Kan, H., Walker, K. D., Thurston,
G. D., Hayes, R. B., Lim, C. C., Turner, M. C., Jerrett, M., Krewski, D.,
Gapstur, S. M., Diver, W. R., Ostro, B., Goldberg, D., Crouse, D. L.,
Martin, R. V., Peters, P., Pinault, L., Tjepkema, M., van Donkelaar, A.,
Villeneuve, P. J., Miller, A. B., Yin, P., Zhou, M., Wang, L., Janssen, N.
A. H., Marra, M., Atkinson, R. W., Tsang, H., Quoc Thach, T., Cannon, J. B.,
Allen, R. T., Hart, J. E., Laden, F., Cesaroni, G., Forastiere, F.,
Weinmayr, G., Jaensch, A., Nagel, G., Concin, H., and Spadaro, J. V.: Global
estimates of mortality associated with long-term exposure to outdoor fine
particulate matter, P. Natl. Acad. Sci. USA, 115,
9592, 10.1073/pnas.1803222115, 2018.
Carter, W. P. L. and Heo, G.: Development of revised SAPRC aromatics mechanisms, Atmos. Environ., 77, 404–414, https://doi.org/10.1016/j.atmosenv.2013.05.021, 2013.
Choi, D., Beardsley, M., Brzezinski, D., Koupal, J., and Warila, J.: MOVES
Sensitivity Analysis: The Impacts of Temperature and Humidity on Emissions, https://www3.epa.gov/ttn/chief/conference/ei19/session6/choi.pdf (last access: 18 May 2022), 2017.
Choi, K.-C., Lee, J.-J., Bae, C. H., Kim, C.-H., Kim, S., Chang, L.-S., Ban,
S.-J., Lee, S.-J., Kim, J., and Woo, J.-H.: Assessment of transboundary
ozone contribution toward South Korea using multiple source–receptor
modeling techniques, Atmos. Environ., 92, 118–129,
https://doi.org/10.1016/j.atmosenv.2014.03.055, 2014.
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep,
K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V.,
Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y.,
Martin, R., Morawska, L., Pope, C. A., III, Shin, H., Straif, K., Shaddick,
G., Thomas, M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J.
L., and Forouzanfar, M. H.: Estimates and 25-year trends of the global
burden of disease attributable to ambient air pollution: an analysis of data
from the Global Burden of Diseases Study 2015, Lancet, 389, 1907–1918,
https://doi.org/10.1016/S0140-6736(17)30505-6, 2017.
Denier van der Gon, H. A. C., Gerlofs-Nijland, M. E., Gehrig, R.,
Gustafsson, M., Janssen, N., Harrison, R. M., Hulskotte, J.,
Johansson, C., Jozwicka, M., Keuken, M., Krijgsheld, K.,
Ntziachristos, L., Riediker, M., and Cassee, F. R.: The Policy
Relevance of Wear Emissions from Road Transport, Now and in the Future – An
International Workshop Report and Consensus Statement, J. Air
Waste Manage., 63, 136–149, https://doi.org/10.1080/10962247.2012.741055, 2013.
Dennis, R., Fox, T., Fuentes, M., Gilliland, A., Hanna, S., Hogrefe, C.,
Irwin, J., Rao, S. T., Scheffe, R., Schere, K., Steyn, D., and Venkatram,
A.: A framework for evaluating regional-scale numerical photochemical
modeling systems, Environ. Fluid. Mech., 10, 471–489,
https://doi.org/10.1007/s10652-009-9163-2, 2010.
EEA: EMEP/EEO air pollutant emission inventory guidebook 2019,
Passenger cars, light commercial trucks, heavy-duty vehicles including buses and motor cycles, 1.A.3.b.i-iv, European Enviromnent Agency, 2019.
Fallahshorshani, M., André, M., Bonhomme, C., and Seigneur, C.: Coupling
Traffic, Pollutant Emission, Air and Water Quality Models: Technical Review
and Perspectives, Proced. Soc. Behav., 48, 1794–1804,
https://doi.org/10.1016/j.sbspro.2012.06.1154, 2012.
Fulvio, A., Cassee, F. R., Denier van der Gon, H. A. C.,
Gehrig, R., Gustafsson, M., Hafner, W., Harrison, R. M.,
Jozwicka, M., Kelly, F. J., Moreno, T., Prevot, A. S. H., Schaap, M.,
Sunyer, J., and Querol, X.: Urban air quality:The challenge of traffic
non-exhaust emissions, J. Hazard. Mater., 275, 31–36,
https://doi.org/10.1016/j.jhazmat.2014.04.053, 2014.
Guevara, M., Tena, C., Porquet, M., Jorba, O., and Pérez García-Pando, C.: HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 1: global and regional module, Geosci. Model Dev., 12, 1885–1907, https://doi.org/10.5194/gmd-12-1885-2019, 2019.
Hogrefe, C., Rao, S. T., Kasibhatla, P., Hao, W., Sistla, G., Mathur, R.,
and McHenry, J.: Evaluating the performance of regional-scale photochemical
modeling systems: Part II – ozone predictions, Atmos. Environ., 35,
4175–4188, https://doi.org/10.1016/S1352-2310(01)00183-2,
2001a.
Hogrefe, C., Rao, S. T., Kasibhatla, P., Kallos, G., Tremback, C. J., Hao,
W., Olerud, D., Xiu, A., McHenry, J., and Alapaty, K.: Evaluating the
performance of regional-scale photochemical modeling systems: Part
I – meteorological predictions, Atmos. Environ., 35, 4159–4174,
https://doi.org/10.1016/S1352-2310(01)00182-0, 2001b.
Ibarra-Espinosa, S., Ynoue, R., O'Sullivan, S., Pebesma, E., Andrade, M. D. F., and Osses, M.: VEIN v0.2.2: an R package for bottom–up vehicular emissions inventories, Geosci. Model Dev., 11, 2209–2229, https://doi.org/10.5194/gmd-11-2209-2018, 2018.
IEMA: Inventário de Emissões Atmosféricas do Transporte
Rodoviário de Passageiros no Município de São Paulo,
http://emissoes.energiaeambiente.org.br, last access: 1 May 2017.
Kaewunruen, S., Sussman, J. M., and Matsumoto, A.: Grand Challenges in
Transportation and Transit Systems, Frontiers in Built Environment, 2, 1–5,
https://doi.org/10.3389/fbuil.2016.00004, 2016.
Kim, B.-U., Bae, C., Kim, H. C., Kim, E., and Kim, S.: Spatially and
chemically resolved source apportionment analysis: Case study of high
particulate matter event, Atmos. Environ., 162, 55–70,
https://doi.org/10.1016/j.atmosenv.2017.05.006, 2017a.
Kim, H. C., Kim, E., Bae, C., Cho, J. H., Kim, B.-U., and Kim, S.: Regional contributions to particulate matter concentration in the Seoul metropolitan area, South Korea: seasonal variation and sensitivity to meteorology and emissions inventory, Atmos. Chem. Phys., 17, 10315–10332, https://doi.org/10.5194/acp-17-10315-2017, 2017b.
Kim, H. C., Kim, S., Kim, B.-U., Jin, C.-S., Hong, S., Park, R., Son, S.-W.,
Bae, C., Bae, M., Song, C.-K., and Stein, A.: Recent increase of surface
particulate matter concentrations in the Seoul Metropolitan Area, Korea,
Sci. Rep.-UK, 7, 4710, https://doi.org/10.1038/s41598-017-05092-8, 2017c.
Lee, D., Lee, Y.-M., Jang, K.-W., Yoo, C., Kang, K.-H., Lee, J.-H., Jung,
S.-W., Park, J.-M., Lee, S.-B., Han, J.-S., Hong, J.-H., and Lee, S.-J.:
Korean National Emissions Inventory System and 2007 Air Pollutant Emissions,
Asian J. Atmos. Environ., 5–4, 278–291, 2011a.
Lee, D.-G., Lee, Y.-M., Jang, K.-W., Yoo, C., Kang, K.-H., Lee, J.-H., Jung,
S.-W., Park, J.-M., Lee, S.-B., Han, J.-S., Hong, J.-H., and Lee, S.-J.:
Korean National Emissions Inventory System and 2007 Air Pollutant Emissions,
Asian J. Atmos. Environ., 5, 278–291, https://doi.org/10.5572/ajae.2011.5.4.278,
2011b.
Lejri, D., Can, A., Schiper, N., and Leclercq, L.: Accounting for traffic
speed dynamics when calculating COPERT and PHEM pollutant emissions at the
urban scale, Transport. Res. D Tr. E., 63,
588–603, https://doi.org/10.1016/j.trd.2018.06.023, 2018.
Li, F., Zhuang, J., Cheng, X., Li, M., Wang, J., and Yan, Z.: Investigation
and Prediction of Heavy-Duty Diesel Passenger Bus Emissions in Hainan Using
a COPERT Model, Atmosphere, 10, 106, https://doi.org/10.3390/atmos10030106, 2019.
Liu, Y. and Sander, S. P.: Rate Constant for the OH + CO Reaction at Low
Temperatures, J. Phys. Chem. A, 119, 10060–10066,
https://doi.org/10.1021/acs.jpca.5b07220, 2015.
Luo, H., Astitha, M., Hogrefe, C., Mathur, R., and Rao, S. T.: A new method
for assessing the efficacy of emission control strategies, Atmos. Environ., 199, 233–243, https://doi.org/10.1016/j.atmosenv.2018.11.010, 2019.
Lv, W., Hu, Y., Li, E., Liu, H., Pan, H., Ji, S., Hayat, T., Alsaedi, A.,
and Ahmad, B.: Evaluation of vehicle emission in Yunnan province from 2003
to 2015, J. Clean Prod., 207, 814–825,
https://doi.org/10.1016/j.jclepro.2018.09.227, 2019.
Moussiopoulos, N., Vlachokostas, C., Tsilingiridis, G., Douros, I.,
Hourdakis, E., Naneris, C., and Sidiropoulos, C.: Air quality status in
Greater Thessaloniki Area and the emission reductions needed for attaining
the EU air quality legislation, Sci. Total Environ., 407, 1268–1285,
https://doi.org/10.1016/j.scitotenv.2008.10.034, 2009.
Nagpure, A. S., Gurjar, B. R., Kumar, V., and Kumar, P.: Estimation of
exhaust and non-exhaust gaseous, particulate matter and air toxics emissions
from on-road vehicles in Delhi, Atmos. Environ., 127, 118–124,
https://doi.org/10.1016/j.atmosenv.2015.12.026, 2016.
NIER: Study on Air Pollutant Emission Estimation Method in Transportation
section(II) 11-1480523-003573-01, National Archives of Korea,
https://www.archives.go.kr/next/manager/publishmentSubscriptionDetail.do?prt_seq=114054&page=1554&prt_arc_title=&prt_pub_kikwan=&prt_no (last access: 18 May 2022), 2018.
Ntziachristos, L. and Samaras, Z.: Speed-dependent representative emission
factors for catalyst passenger cars and influencing parameters, Atmos. Environ., 34, 4611–4619, https://doi.org/10.1016/S1352-2310(00)00180-1, 2000.
Ntziachristos, L., Gkatzoflias, D., Kouridis, C., and Samaras, Z.: COPERT: A European Road Transport Emission Inventory Model, in: Information Technologies in Environmental Engineering, Environmental Science and Engineering, edited by: Athanasiadis, I. N., Rizzoli, A. E., Mitkas, P. A., and Gómez, J. M., Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-88351-7_37, 2009.
Pedruzzi, R., Baek, B. H., and Wang, C.-T.: CARS, https://github.com/CMASCenter/CARS, last access: 1 May 2020.
Perugu, H., Ramirez, L., and DaMassa, J.: Incorporating temperature effects
in California's on-road emission gridding process for air quality model
inputs, Environ. Pollut., 239, 1–12, https://doi.org/10.1016/j.envpol.2018.03.094, 2018.
Pfister, G., Wang, C.-t., Barth, M., Flocke, F., Vizuete, W., and Walters,
S.: Chemical Characteristics and Ozone Production in the Northern Colorado
Front Range, J. Geophys. Res.-Atmos., 124, 13397–13419, https://doi.org/10.1029/2019jd030544, 2019.
Pinto, J. A., Kumar, P., Alonso, M. F., Andreão, W. L., Pedruzzi, R.,
dos Santos, F. S., Moreira, D. M., and Albuquerque, T. T. D. A.: Traffic
data in air quality modeling: A review of key variables, improvements in
results, open problems and challenges in current research, Atmos.
Pollut. Res., 11, 454–468, https://doi.org/10.1016/j.apr.2019.11.018, 2020.
Rao, S. T., Galmarini, S., and Puckett, K.: Air Quality Model Evaluation
International Initiative (AQMEII): Advancing the State of the Science in
Regional Photochemical Modeling and Its Applications,
B.
Am. Meteorol. Soc., 92, 23–30, https://doi.org/10.1175/2010BAMS3069.1, 2011.
Rodriguez-Rey, D., Guevara, M., Linares, M. P.,
Casanovas, J., Salmerón, J., Soret, A., Jorba, O., Tena, C., and Pérez
García-Pando, C.: A coupled macroscopic traffic and pollutant emission
modelling system for Barcelona, Transport. Res. D Tr. E., 92, 102725,
https://doi.org/10.1016/j.trd.2021.102725, 2021.
Rinke, M. and Zetzsch, C.: Rate Constants for the Reactions of OH Radicals
with Aromatics: Benzene, Phenol, Aniline, and 1,2,4-Trichlorobenzene,
Berichte der Bunsengesellschaft für physikalische Chemie, 88, 55–62,
https://doi.org/10.1002/bbpc.19840880114, 1984.
Russell, A. and Dennis, R.: NARSTO critical review of photochemical models
and modeling, Atmos. Environ., 34, 2283–2324,
https://doi.org/10.1016/S1352-2310(99)00468-9, 2000.
Sallis, P., Bull, F., Burdett, P., Frank, P., Griffiths, P., Giles-Corti,
P., and Stevenson, M.: Use of science to guide city planning policy and
practice: How to achieve healthy and sustainable future cities, Lancet,
388, 2936–2947, https://doi.org/10.1016/S0140-6736(16)30068-X, 2016.
Smit, R., Kingston, P., Neale, D. W., Brown, M. K., Verran, B., and Nolan,
T.: Monitoring on-road air quality and measuring vehicle emissions with
remote sensing in an urban area, Atmos. Environ., 218, 116978,
https://doi.org/10.1016/j.atmosenv.2019.116978, 2019.
Sun, W., Duan, N., Yao, R., Huang, J., and Hu, F.: Intelligent in-vehicle
air quality management: a smart mobility application dealing with air
pollution in the traffic, https://www.semanticscholar.org/paper/Intelligent-in-vehicle-air-quality-management-:-a-Sun-Duan/2ca548319ebb8a6aee7adb02ddf341aed6d6107f, last access: 19 May 2022, 2016.
Tominaga, Y. and Stathopoulos, T.: Ten questions concerning modeling of
near-field pollutant dispersion in the built environment, Build. Environ.,
105, 390–402, https://doi.org/10.1016/j.buildenv.2016.06.027,
2016.
Van, R. G. and Drake, F.: Python 3 reference manual, Scotts Valley, CA, CreateSpace, 10, 1593511, 2009.
Yarwood, G. and Jung, J.: Development, Evaluation and Testing of Version 6 of the Carbon Bond Chemical Mechanism (CB6), the 9th Annual CMAS Conference, Chapel Hill, NC, 11–13 October 2010, Chapel Hill, https://www.cmascenter.org/conference/2010/abstracts/emery_updates_carbon_2010.pdf, (last access: 19 May 2022), 2010.
Short summary
The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road-link-level network information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced inventory for policymakers, stakeholders, and the air quality modeling community.
The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile...