Articles | Volume 13, issue 8
https://doi.org/10.5194/gmd-13-3489-2020
© Author(s) 2020. 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-13-3489-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
05 Aug 2020
Model evaluation paper |
| 05 Aug 2020
| 05 Aug 2020
Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2)
Arman Pouyaei
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, TX, USA
Yunsoo Choi
CORRESPONDING AUTHOR
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, TX, USA
Jia Jung
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, TX, USA
Bavand Sadeghi
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, TX, USA
Chul Han Song
School of Earth Science and Environmental Engineering, Gwangju Institute
of Science and Technology (GIST), Gwangju, South Korea
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Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-565, https://doi.org/10.5194/acp-2021-565, 2021
Revised manuscript not accepted
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The most significant contributions of VOCs over the Houston Ship Channel came from alkanes. Light alkanes were dominant sources in both seasons. We explored the photochemical reaction of organic compounds and studied their contributions to ozone formation. Ethylene and propylene have the highest. Through weighted trajectory, VOCs at Lynchburg Ferry site was influenced by petrochemical sectors of Baytown and Galveston Bay refineries and industrial facilities of the Bayport industrial district.
Kiyeon Kim, Kyung Man Han, Chul Han Song, Hyojun Lee, Ross Beardsley, Jinhyeok Yu, Greg Yarwood, Bonyoung Koo, Jasper Madalipay, Jung-Hun Woo, and Seogju Cho
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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.
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Atmos. Meas. Tech., 16, 3039–3057, https://doi.org/10.5194/amt-16-3039-2023, https://doi.org/10.5194/amt-16-3039-2023, 2023
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In response to the recent release of new geostationary platform-derived observational data generated by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study utilized the GEMS data fusion product and its proxy data in adjusting aerosol precursor emissions over East Asia. The use of spatiotemporally more complete observation references in updating the emissions resulted in more promising model performances in estimating aerosol loadings in East Asia.
Bok H. Baek, Rizzieri Pedruzzi, Minwoo Park, Chi-Tsan Wang, Younha Kim, Chul-Han Song, and Jung-Hun Woo
Geosci. Model Dev., 15, 4757–4781, https://doi.org/10.5194/gmd-15-4757-2022, https://doi.org/10.5194/gmd-15-4757-2022, 2022
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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.
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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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Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-565, https://doi.org/10.5194/acp-2021-565, 2021
Revised manuscript not accepted
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The most significant contributions of VOCs over the Houston Ship Channel came from alkanes. Light alkanes were dominant sources in both seasons. We explored the photochemical reaction of organic compounds and studied their contributions to ozone formation. Ethylene and propylene have the highest. Through weighted trajectory, VOCs at Lynchburg Ferry site was influenced by petrochemical sectors of Baytown and Galveston Bay refineries and industrial facilities of the Bayport industrial district.
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Geosci. Model Dev., 13, 6237–6251, https://doi.org/10.5194/gmd-13-6237-2020, https://doi.org/10.5194/gmd-13-6237-2020, 2020
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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
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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.
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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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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Wonbae Jeon, Yunsoo Choi, Peter Percell, Amir Hossein Souri, Chang-Keun Song, Soon-Tae Kim, and Jhoon Kim
Geosci. Model Dev., 9, 3671–3684, https://doi.org/10.5194/gmd-9-3671-2016, https://doi.org/10.5194/gmd-9-3671-2016, 2016
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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
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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.
S. Choi, J. Joiner, Y. Choi, B. N. Duncan, A. Vasilkov, N. Krotkov, and E. Bucsela
Atmos. Chem. Phys., 14, 10565–10588, https://doi.org/10.5194/acp-14-10565-2014, https://doi.org/10.5194/acp-14-10565-2014, 2014
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
Y. Choi
Atmos. Chem. Phys., 14, 675–690, https://doi.org/10.5194/acp-14-675-2014, https://doi.org/10.5194/acp-14-675-2014, 2014
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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 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.
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.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
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Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
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Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
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Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
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Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024, https://doi.org/10.5194/gmd-17-1111-2024, 2024
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This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024, https://doi.org/10.5194/gmd-17-1091-2024, 2024
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With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024, https://doi.org/10.5194/gmd-17-815-2024, 2024
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GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
Piotr Zmijewski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 17, 759–780, https://doi.org/10.5194/gmd-17-759-2024, https://doi.org/10.5194/gmd-17-759-2024, 2024
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In computer simulations of clouds it is necessary to model the myriad of droplets that constitute a cloud. A popular method for this is to use so-called super-droplets (SDs), each representing many real droplets. It has remained a challenge to model collisions of SDs. We study how precipitation in a cumulus cloud depends on the number of SDs. Surprisingly, we do not find convergence in mean precipitation even for numbers of SDs much larger than typically used in simulations.
Roya Ghahreman, Wanmin Gong, Paul A. Makar, Alexandru Lupu, Amanda Cole, Kulbir Banwait, Colin Lee, and Ayodeji Akingunola
Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, https://doi.org/10.5194/gmd-17-685-2024, 2024
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The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura
Geosci. Model Dev., 17, 651–684, https://doi.org/10.5194/gmd-17-651-2024, https://doi.org/10.5194/gmd-17-651-2024, 2024
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Global climate models with coarse grid sizes include uncertainties about the processes in aerosol–cloud–precipitation interactions. To reduce these uncertainties, here we performed numerical simulations using a new version of our global aerosol transport model with a finer grid size over a longer period than in our previous study. As a result, we found that the cloud microphysics module influences the aerosol distributions through both aerosol wet deposition and aerosol–cloud interactions.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
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In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Haiqin Li, Georg A. Grell, Ravan Ahmadov, Li Zhang, Shan Sun, Jordan Schnell, and Ning Wang
Geosci. Model Dev., 17, 607–619, https://doi.org/10.5194/gmd-17-607-2024, https://doi.org/10.5194/gmd-17-607-2024, 2024
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We developed a simple and realistic method to provide aerosol emissions for aerosol-aware microphysics in a numerical weather forecast model. The cloud-radiation differences between the experimental (EXP) and control (CTL) experiments responded to the aerosol differences. The strong positive precipitation biases over North America and Europe from the CTL run were significantly reduced in the EXP run. This study shows that a realistic representation of aerosol emissions should be considered.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
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The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Jiachen Liu, Eric Chen, and Shannon L. Capps
Geosci. Model Dev., 17, 567–585, https://doi.org/10.5194/gmd-17-567-2024, https://doi.org/10.5194/gmd-17-567-2024, 2024
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Air pollution harms human life and ecosystems, but its sources are complex. Scientists and policy makers use air pollution models to advance knowledge and inform control strategies. We implemented a recently developed numeral system to relate any set of model inputs, like pollutant emissions from a given activity, to all model outputs, like concentrations of pollutants harming human health. This approach will be straightforward to update when scientists discover new processes in the atmosphere.
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024, https://doi.org/10.5194/gmd-17-399-2024, 2024
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Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
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A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024, https://doi.org/10.5194/gmd-17-381-2024, 2024
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Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024, https://doi.org/10.5194/gmd-17-321-2024, 2024
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Turbulence-resolving flow models have strict performance requirements, as simulations often run for weeks using hundreds of processes. Many flow scenarios also require the flexibility to modify physical and numerical models for problem-specific requirements. With a new code written in Julia we hope to make such adaptations easier without compromising on performance. In this paper we discuss the modeling approach and present validation and performance results.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-234, https://doi.org/10.5194/gmd-2023-234, 2024
Revised manuscript accepted for GMD
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air quality-challenged arid/semi-arid region in the US. The unique characteristics of semi-arid/arid regions, e.g., intense heat, minimal moisture, 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.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
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In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
EGUsphere, https://doi.org/10.5194/egusphere-2023-2740, https://doi.org/10.5194/egusphere-2023-2740, 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 emission processing from continental to street scale.
Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-226, https://doi.org/10.5194/gmd-2023-226, 2024
Revised manuscript accepted for GMD
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Reanalysis data have been widely used as an initial condition for the daily forecast of the atmosphere or boundary conditions in regional models, for the study of climate change, and as proxies to complement insufficient in situ measurements. This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2023-2962, https://doi.org/10.5194/egusphere-2023-2962, 2024
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There are relatively limited researches on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPU, have distinct advantages in energy efficiency and scalability. In this study, the air quality modeling system can run stably on MIPS CPU platform, and the experiment results verify the stability of scientific computing on the platform. The work provides a technical foundation for the scientific application based on MIPS CPU platforms.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
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This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
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We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
David R. Shaw, Toby J. Carter, Helen L. Davies, Ellen Harding-Smith, Elliott C. Crocker, Georgia Beel, Zixu Wang, and Nicola Carslaw
Geosci. Model Dev., 16, 7411–7431, https://doi.org/10.5194/gmd-16-7411-2023, https://doi.org/10.5194/gmd-16-7411-2023, 2023
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Exposure to air pollution is one of the greatest risks to human health, and it is indoors, where we spend upwards of 90 % of our time, that our exposure is greatest. The INdoor CHEMical model in Python (INCHEM-Py) is a new, community-led box model that tracks the evolution and fate of atmospheric chemical pollutants indoors. We have shown the processes simulated by INCHEM-Py, its ability to model experimental data and how it may be used to develop further understanding of indoor air chemistry.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
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Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
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Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-209, https://doi.org/10.5194/gmd-2023-209, 2023
Revised manuscript accepted for GMD
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This study is about the modelling of the atmospheric composition in Europe and during the summer 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impact of two modelling processes able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Rohith Muraleedharan Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-202, https://doi.org/10.5194/gmd-2023-202, 2023
Revised manuscript accepted for GMD
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Global Navigation Satellite Systems provide moisture observations through its densely distributed ground station network. In this research, we assimilated a new type of observation called tropospheric gradient observations, which was never incorporated into a weather model. Here, we have developed a forward operator for gradient observations and performed impact studies. Promising improvements were observed in the humidity fields of the model in the assimilation study.
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu
Geosci. Model Dev., 16, 7223–7235, https://doi.org/10.5194/gmd-16-7223-2023, https://doi.org/10.5194/gmd-16-7223-2023, 2023
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The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
Cited articles
Al-Saadi, J., Carmichael, G., Crawford, J., Emmons, L., Kim, S., Song,
C.-K., Chang, L.-S., Lee, G., Kim, J., and Park, R.: KORUS-AQ: An
International Cooperative Air Quality Field Study in Korea (2016),
available at: https://espo.nasa.gov/korus-aq/content/KORUS-AQ (last access: June 2020), 2016.
Bertschi, I. T. and Jaffe, D. A.: Long-range transport of ozone, carbon
monoxide, and aerosols to the NE Pacific troposphere during the summer of
2003: Observations of smoke plumes from Asian boreal fires, J. Geophys. Res.-Atmos., 110, 1–14, https://doi.org/10.1029/2004JD005135, 2005.
Byun, D. and Schere, K. L.: Review of the governing equations, computational
algorithms, and other components of the models-3 Community Multiscale Air
Quality (CMAQ) modeling system, Appl. Mech. Rev., 59, 51–76,
https://doi.org/10.1115/1.2128636, 2006.
Carroll, M., Ocko, I. B., McNeal, F., Weremijewicz, J., Hogg, A. J., Opoku,
N., Bertman, S. B., Neil, L., Fortner, E., Thornberry, T., Town, M. S., Yip,
G., and Yageman, L.: An Assessment of Forest Pollutant Exposure Using Back
Trajectories, Anthropogenic Emissions, and Ambient Ozone and Carbon Monoxide
Measurements, American Geophysical Union Fall Meeting, San Fransisco, CA, USA,15–19 December 2008, Abstr. ID A41H-0227, 2008.
Carslaw, D. C. and Ropkins, K.: openair - An R package for air quality data
analysis, Environ. Model. Softw., 27–28, 52–61,
https://doi.org/10.1016/j.envsoft.2011.09.008, 2012.
Chen, T. F., Chang, K. H., and Tsai, C. Y.: Modeling direct and indirect
effect of long range transport on atmospheric PM2.5 levels, Atmos.
Environ., 89, 1–9, https://doi.org/10.1016/j.atmosenv.2014.01.065, 2014.
Chock, D. P., Sun, P., and Winkler, S. L.: Trajectory-grid: An accurate
sign-preserving advection-diffusion approach for air quality modeling,
Atmos. Environ., 30, 857–868, https://doi.org/10.1016/1352-2310(95)00332-0, 1996.
Chock, D. P., Whalen, M. J., Winkler, S. L., and Sun, P.: Implementing the
trajectory-grid transport algorithm in an air quality model, Atmos.
Environ., 39, 4015–4023, https://doi.org/10.1016/j.atmosenv.2005.03.037, 2005.
Choi, J., Park, R. J., Lee, H. M., Lee, S., Jo, D. S., Jeong, J. I., Henze,
D. K., Woo, J. H., Ban, S. J., Lee, M. Do, Lim, C. S., Park, M. K., Shin, H.
J., Cho, S., Peterson, D., and Song, C. K.: Impacts of local vs.
trans-boundary emissions from different sectors on PM2.5 exposure in South
Korea during the KORUS-AQ campaign, Atmos. Environ., 203, 196–205,
https://doi.org/10.1016/j.atmosenv.2019.02.008, 2019.
Choi, S. H., Ghim, Y. S., Chang, Y. S., and Jung, K.: Behavior of particulate
matter during high concentration episodes in Seoul, Environ. Sci. Pollut.
Res., 21, 5972–5982, https://doi.org/10.1007/s11356-014-2555-y, 2014.
Chuang, M. T., Fu, J. S., Jang, C. J., Chan, C. C., Ni, P. C., and Lee, C.
Te: Simulation of long-range transport aerosols from the Asian Continent to
Taiwan by a Southward Asian high-pressure system, Sci. Total Environ.,
406, 168–179, https://doi.org/10.1016/j.scitotenv.2008.07.003, 2008.
Chuang, M. T., Lee, C. Te and Hsu, H. C.: Quantifying PM2.5 from long-range
transport and local pollution in Taiwan during winter monsoon: An efficient
estimation method, J. Environ. Manage., 227, 10–22,
https://doi.org/10.1016/j.jenvman.2018.08.066, 2018.
Cristofanelli, P., Bonasoni, P., Carboni, G., Calzolari, F., Casarola, L.,
Zauli Sajani, S., and Santaguida, R.: Anomalous high ozone concentrations
recorded at a high mountain station in Italy in summer 2003, Atmos.
Environ., 41, 1383–1394, https://doi.org/10.1016/j.atmosenv.2006.10.017, 2007.
Döös, K., Jönsson, B., and Kjellsson, J.: Evaluation of oceanic and atmospheric trajectory schemes in the TRACMASS trajectory model v6.0, Geosci. Model Dev., 10, 1733–1749, https://doi.org/10.5194/gmd-10-1733-2017, 2017.
Draxler, R. R.: An overview of the HYSPLIT_4 modelling system
for trajectories, dispersion and deposition, Aust. Meteorol. Mag., 47,
295–308, 1998.
Eslami, E., Salman, A. K., Choi, Y., Sayeed, A., and Lops, Y.: A data
ensemble approach for real-time air quality forecasting using extremely
randomized trees and deep neural networks, Neural Comput. Appl., 32, 7563–7579,
https://doi.org/10.1007/s00521-019-04287-6, 2019.
Gratz, L. E., Jaffe, D. A., and Hee, J. R.: Causes of increasing ozone and
decreasing carbon monoxide in springtime at the Mt. Bachelor Observatory
from 2004 to 2013, Atmos. Environ., 109, 323–330,
https://doi.org/10.1016/j.atmosenv.2014.05.076, 2015.
Halliday, H. S., DiGangi, J. P., Choi, Y., Diskin, G. S., Pusede, S. E.,
Rana, M., Nowak, J. B., Knote, C., Ren, X., He, H., Dickerson, R. R., and Li,
Z.: Using Short-Term CO/CO2 Ratios to Assess Air Mass Differences over the
Korean Peninsula during KORUS-AQ , J. Geophys. Res.-Atmos., 124, 1–22,
https://doi.org/10.1029/2018jd029697, 2019.
Heald, C. C., Jacob, D. J., Fiore, A. M., Emmons, L. K., Gille, J. C.,
Deeter, M. N., Warner, J., Edwards, D. P., Crawford, J. H., Hamlin, A. J.,
Sachse, G. W., Browell, E. V., Avery, M. A., Vay, S. A., Westberg, D. J.,
Blake, D. R., Singh, H. B., Sandholm, S. T., Talbot, R. W., and Fuelberg, H.
E.: Asian outflow and trans-Pacific transport of carbon monoxide and ozone
pollution: An integrated satellite, aircraft, and model perspective, J.
Geophys. Res.-Atmos., 108, 4804, https://doi.org/10.1029/2003jd003507, 2003.
Hu, Y. and Talat Odman, M.: A comparison of mass conservation methods for
air quality models, Atmos. Environ., 42, 8322–8330,
https://doi.org/10.1016/j.atmosenv.2008.07.042, 2008.
Jeon, W., Choi, Y., Percell, P., Souri, A. H., Song, C.-K., Kim, S.-T., and Kim, J.: Computationally efficient air quality forecasting tool: implementation of STOPS v1.5 model into CMAQ v5.0.2 for a prediction of Asian dust, Geosci. Model Dev., 9, 3671–3684, https://doi.org/10.5194/gmd-9-3671-2016, 2016.
Jung, J., Souri, A. H., Wong, D. C., Lee, S., Jeon, W., Kim, J., and Choi,
Y.: The Impact of the Direct Effect of Aerosols on Meteorology and Air
Quality Using Aerosol Optical Depth Assimilation During the KORUS-AQ
Campaign, J. Geophys. Res.-Atmos., 124, 8303–8319,
https://doi.org/10.1029/2019jd030641, 2019.
Kain, J. S.: The Kain–Fritsch convective parameterization: An update, J.
Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
KORUS-AQ: An International Cooperative Air Quality Field Study in Korea, https://doi.org/10.5067/Suborbital/KORUSAQ/DATA01, 2020.
Kruse, S., Gerdes, A., Kath, N. J., and Herzschuh, U.: Implementing spatially explicit wind-driven seed and pollen dispersal in the individual-based larch simulation model: LAVESI-WIND 1.0, Geosci. Model Dev., 11, 4451–4467, https://doi.org/10.5194/gmd-11-4451-2018, 2018.
Lee, S., Ho, C. H., and Choi, Y. S.: High-PM10 concentration episodes in
Seoul, Korea: Background sources and related meteorological conditions,
Atmos. Environ., 45, 7240–7247, https://doi.org/10.1016/j.atmosenv.2011.08.071,
2011.
Lee, S., Ho, C. H., Lee, Y. G., Choi, H. J., and Song, C. K.: Influence of
transboundary air pollutants from China on the high-PM10 episode in Seoul,
Korea for the period October 16–20, 2008, Atmos. Environ., 77, 430–439,
https://doi.org/10.1016/j.atmosenv.2013.05.006, 2013.
Lee, S., Kim, J., Choi, M., Hong, J., Lim, H., Eck, T. F., Holben, B. N.,
Ahn, J. Y., Kim, J., and Koo, J. H.: Analysis of long-range transboundary
transport (LRTT) effect on Korean aerosol pollution during the KORUS-AQ
campaign, Atmos. Environ., 204, 53–67, https://doi.org/10.1016/j.atmosenv.2019.02.020, 2019.
Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, https://doi.org/10.5194/acp-17-935-2017, 2017.
Liu, Y., Xu, S., Ling, T., Xu, L., and Shen, W.: Heme oxygenase/carbon
monoxide system participates in regulating wheat seed germination under
osmotic stress involving the nitric oxide pathway, J. Plant Physiol.,
167, 1371–1379, https://doi.org/10.1016/j.jplph.2010.05.021, 2010.
Lops, Y., Choi, Y., Eslami, E., and Sayeed, A.: Real-time 7-day forecast of
pollen counts using a deep convolutional neural network, Neural Comput.
Appl., 32, 1–10, https://doi.org/10.1007/s00521-019-04665-0, 2019.
Miyazaki, K., Sekiya, T., Fu, D., Bowman, K. W., Kulawik, S. S., Sudo, K.,
Walker, T., Kanaya, Y., Takigawa, M., Ogochi, K., Eskes, H., Boersma, K. F.,
Thompson, A. M., Gaubert, B., Barre, J., and Emmons, L. K.: Balance of
Emission and Dynamical Controls on Ozone During the Korea-United States Air
Quality Campaign From Multiconstituent Satellite Data Assimilation, J.
Geophys. Res.-Atmos., 124, 387–413, https://doi.org/10.1029/2018JD028912, 2019.
National Institute of Environmental Research: available at: https://www.airkorea.or.kr/web, last access: June 2020.
Oh, H. R., Ho, C. H., Kim, J., Chen, D., Lee, S., Choi, Y. S., Chang, L. S.,
and Song, C. K.: Long-range transport of air pollutants originating in
China: A possible major cause of multi-day high-PM10 episodes during cold
season in Seoul, Korea, Atmos. Environ., 109, 23–30,
https://doi.org/10.1016/j.atmosenv.2015.03.005, 2015.
Pekney, N. J., Davidson, C. I., Zhou, L., and Hopke, P. K.: Application of
PSCF and CPF to PMF-Modeled Sources of PM2.5 in Pittsburgh, Aerosol
Sci. Technol., 40, 952–961, https://doi.org/10.1080/02786820500543324, 2006.
Petetin, H., Beekmann, M., Sciare, J., Bressi, M., Rosso, A., Sanchez, O., and Ghersi, V.: A novel model evaluation approach focusing on local and advected contributions to urban PM2.5 levels – application to Paris, France, Geosci. Model Dev., 7, 1483–1505, https://doi.org/10.5194/gmd-7-1483-2014, 2014.
Pouyaei, A.: armanpouyaei/C-TRAIL-v1.0: First release (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.3885782, 2020.
Price, H. U., Jaffe, D. A., Cooper, O. R., and Doskey, P. V.: Photochemistry,
ozone production, and dilution during long-range transport episodes from
Eurasia to the northwest United States, J. Geophys. Res.-Atmos., 109,
1–10, https://doi.org/10.1029/2003JD004400, 2004.
Pu, W., Zhao, X., Shi, X., Ma, Z., Zhang, X., and Yu, B.: Impact of
long-range transport on aerosol properties at a regional background station
in Northern China, Atmos. Res., 153, 489–499,
https://doi.org/10.1016/j.atmosres.2014.10.010, 2015.
Rößler, T., Stein, O., Heng, Y., Baumeister, P., and Hoffmann, L.: Trajectory errors of different numerical integration schemes diagnosed with the MPTRAC advection module driven by ECMWF operational analyses, Geosci. Model Dev., 11, 575–592, https://doi.org/10.5194/gmd-11-575-2018, 2018.
Sadeghi, B., Choi, Y., Yoon, S., Flynn, J., Kotsakis, A., and Lee, S.: The
characterization of fine particulate matter downwind of Houston: Using
integrated factor analysis to identify anthropogenic and natural sources,
Environ. Pollut., 262, 114345, https://doi.org/10.1016/j.envpol.2020.114345, 2020.
Salvador, P., Artíñano, B., Querol, X., and Alastuey, A.: A combined
analysis of backward trajectories and aerosol chemistry to characterise
long-range transport episodes of particulate matter: The Madrid air basin, a
case study, Sci. Total Environ., 390, 495–506,
https://doi.org/10.1016/j.scitotenv.2007.10.052, 2008.
Sarwar, G., Simon, H., Bhave, P., and Yarwood, G.: Examining the impact of heterogeneous nitryl chloride production on air quality across the United States, Atmos. Chem. Phys., 12, 6455–6473, https://doi.org/10.5194/acp-12-6455-2012, 2012.
Sayeed, A., Choi, Y., Eslami, E., Lops, Y., Roy, A., and Jung, J.: Using a
deep convolutional neural network to predict 2017 ozone concentrations, 24
hours in advance, Neural Networks, 121, 396–408,
https://doi.org/10.1016/j.neunet.2019.09.033, 2020.
Souri, A. H., Choi, Y., Li, X., Kotsakis, A., and Jiang, X.: A 15-year
climatology of wind pattern impacts on surface ozone in Houston, Texas,
Atmos. Res., 174–175, 124–134, https://doi.org/10.1016/j.atmosres.2016.02.007, 2016.
Stenke, A., Dameris, M., Grewe, V., and Garny, H.: Implications of Lagrangian transport for simulations with a coupled chemistry-climate model, Atmos. Chem. Phys., 9, 5489–5504, https://doi.org/10.5194/acp-9-5489-2009, 2009.
Stohl, A.: Trajectory statistics – A new method to establish source-receptor
relationships of air pollutants and its application to the transport of
particulate sulfate in Europe, Atmos. Environ., 30, 579–587,
https://doi.org/10.1016/1352-2310(95)00314-2, 1996.
Stohl, A.: Computation, accuracy and applications of trajectories – a review
and bibliography, Dev. Environm. Sci., 1, 615–654,
https://doi.org/10.1016/S1474-8177(02)80024-9, 2002.
Stohl, A. and Seibert, P.: Accuracy of trajectories as determined from the
conservation of meteorological tracers, Q. J. Roy. Meteor. Soc., 124,
1465–1484, https://doi.org/10.1002/qj.49712454907, 1998.
US EPA Office of Research and Development: CMAQ (Version 5.2), Zenodo, https://doi.org/10.5281/zenodo.1167892, 2017.
Vay, S. A., Choi, Y., Vadrevu, K. P., Blake, D. R., Tyler, S. C., Wisthaler,
A., Hecobian, A., Kondo, Y., Diskin, G. S., Sachse, G. W., Woo, J. H.,
Weinheimer, A. J., Burkhart, J. F., Stohl, A., and Wennberg, P. O.: Patterns
of CO2 and radiocarbon
across high northern latitudes during International Polar Year 2008, J.
Geophys. Res.-Atmos., 116, 1–22, https://doi.org/10.1029/2011JD015643, 2011.
Wang, F., Chen, D. S., Cheng, S. Y., Li, J. B., Li, M. J., and Ren, Z. H.:
Identification of regional atmospheric PM10 transport pathways using
HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis, Environ. Model.
Softw., 25, 927–934, https://doi.org/10.1016/j.envsoft.2010.02.004, 2010.
Weiss-Penzias, P., Jaffe, D. A., Jaeglé, L., and Liang, Q.: Influence of
long-range-transported pollution on the annual and diurnal cycles of carbon
monoxide and ozone at Cheeka Peak Observatory, J. Geophys. Res.-Atmos.,
109, 1–15, https://doi.org/10.1029/2004JD004505, 2004.
Xu, S., Warner, N., Bohlin-Nizzetto, P., Durham, J., and McNett, D.:
Long-range transport potential and atmospheric persistence of cyclic
volatile methylsiloxanes based on global measurements, Chemosphere, 228,
460–468, https://doi.org/10.1016/j.chemosphere.2019.04.130, 2019.
Zhang, Q., Xue, D., Liu, X., Gong, X., and Gao, H.: Process analysis of PM2.5 pollution events in a coastal city of China using CMAQ, J. Environ. Sci.
(China), 79, 225–238, https://doi.org/10.1016/j.jes.2018.09.007, 2019.
Short summary
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.
This paper introduces a novel Lagrangian model (Concentration Trajectory of Air pollution with...
