Articles | Volume 9, issue 1
https://doi.org/10.5194/gmd-9-111-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-111-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
19 Jan 2016
Model description paper |
| 19 Jan 2016
Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods
Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, USA
S. M. Kreidenweis
Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO, USA
P. J. Ziemann
Department of Chemistry and Biochemistry, Colorado University, Boulder, CO, USA
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M. D. Petters and S. M. Kreidenweis
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Andre Schaum, Kelvin Bates, Kyung-Eun Min, Faith Myers, Emmaline Longnecker, Manjula Canagaratna, Mitchell Alton, and Paul Ziemann
Aerosol Research Discuss., https://doi.org/10.5194/ar-2025-23, https://doi.org/10.5194/ar-2025-23, 2025
Preprint under review for AR
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Organic aerosols consist of complex chemical mixtures that are challenging to characterize using chemical ionization mass spectrometry alone. This study presents a method for coupling liquid chromatography and chemical ionization mass spectrometry for offline analysis of organic aerosols. Evaluation of the method using standards and laboratory-generated and field-collected organic aerosols showed that it can provide detailed characterization of environmentally relevant mixtures.
Kevin R. Barry, Thomas C. J. Hill, Sonia M. Kreidenweis, Paul J. DeMott, Yutaka Tobo, and Jessie M. Creamean
EGUsphere, https://doi.org/10.5194/egusphere-2025-128, https://doi.org/10.5194/egusphere-2025-128, 2025
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The Arctic is changing rapidly, and we sought to better understand how their clouds may change in the future through quantifying the natural cloud seeding particles over a year and uncover what they are made of. We wanted to determine their likely sources through concurrent DNA sequencing of airborne bacteria and fungi and found a persistent mixture of local and longer-range sources at all times.
Paul J. DeMott, Jessica A. Mirrielees, Sarah Suda Petters, Daniel J. Cziczo, Markus D. Petters, Heinz G. Bingemer, Thomas C. J. Hill, Karl Froyd, Sarvesh Garimella, A. Gannet Hallar, Ezra J. T. Levin, Ian B. McCubbin, Anne E. Perring, Christopher N. Rapp, Thea Schiebel, Jann Schrod, Kaitlyn J. Suski, Daniel Weber, Martin J. Wolf, Maria Zawadowicz, Jake Zenker, Ottmar Möhler, and Sarah D. Brooks
Atmos. Meas. Tech., 18, 639–672, https://doi.org/10.5194/amt-18-639-2025, https://doi.org/10.5194/amt-18-639-2025, 2025
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The Fifth International Ice Nucleation Workshop Phase 3 (FIN-03) compared the ambient atmospheric performance of ice-nucleating particle (INP) measuring systems and explored general methods for discerning atmospheric INP compositions. Mirroring laboratory results, INP concentrations agreed within 5–10 factors. Measurements of total aerosol properties and investigations of INP compositions supported a dominant role of soil and plant organic aerosol elements as INPs during the study.
Tommaso Galeazzo, Bernard Aumont, Marie Camredon, Richard Valorso, Yong B. Lim, Paul J. Ziemann, and Manabu Shiraiwa
Atmos. Chem. Phys., 24, 5549–5565, https://doi.org/10.5194/acp-24-5549-2024, https://doi.org/10.5194/acp-24-5549-2024, 2024
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Melinda K. Schueneman, Douglas A. Day, Dongwook Kim, Pedro Campuzano-Jost, Seonsik Yun, Marla P. DeVault, Anna C. Ziola, Paul J. Ziemann, and Jose L. Jimenez
Aerosol Research, 2, 59–76, https://doi.org/10.5194/ar-2-59-2024, https://doi.org/10.5194/ar-2-59-2024, 2024
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Our study presents a novel method for quantifying mass spectrometer responses to molecular species in organic aerosols. Traditional calibrations often fail for complex mixtures like secondary organic aerosol. We combined chromatography with statistical component analysis to improve separation and quantification, achieving promising agreement with direct calibration. Our findings offer a new approach to assess aerosol composition, especially beneficial for complex mixtures.
Melissa A. Morris, Demetrios Pagonis, Douglas A. Day, Joost A. de Gouw, Paul J. Ziemann, and Jose L. Jimenez
Atmos. Meas. Tech., 17, 1545–1559, https://doi.org/10.5194/amt-17-1545-2024, https://doi.org/10.5194/amt-17-1545-2024, 2024
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Polymer absorption of volatile organic compounds (VOCs) is important to characterize for atmospheric sampling setups (as interactions cause sampling delays) and indoor air quality. Here we test different polymer materials and quantify their absorptive capacities through modeling. We found the main polymers in carpets to be highly absorptive, acting as large reservoirs for indoor pollution. We also demonstrated how polymer tubes can be used as a low-cost gas separation technique.
Ryan J. Patnaude, Kathryn A. Moore, Russell J. Perkins, Thomas C. J. Hill, Paul J. DeMott, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 24, 911–928, https://doi.org/10.5194/acp-24-911-2024, https://doi.org/10.5194/acp-24-911-2024, 2024
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Markus D. Petters, Tyas Pujiastuti, Ajmal Rasheeda Satheesh, Sabin Kasparoglu, Bethany Sutherland, and Nicholas Meskhidze
Atmos. Chem. Phys., 24, 745–762, https://doi.org/10.5194/acp-24-745-2024, https://doi.org/10.5194/acp-24-745-2024, 2024
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This work introduces a new method that uses remote sensing techniques to obtain surface number emissions of particles with a diameter greater than 500 nm. The technique was applied to study particle emissions at an urban site near Houston, TX, USA. The emissions followed a diurnal pattern and peaked near noon local time. The daily averaged emissions correlated with wind speed. The source is likely due to wind-driven erosion of material situated on asphalted and other hard surfaces.
Kevin R. Barry, Thomas C. J. Hill, Marina Nieto-Caballero, Thomas A. Douglas, Sonia M. Kreidenweis, Paul J. DeMott, and Jessie M. Creamean
Atmos. Chem. Phys., 23, 15783–15793, https://doi.org/10.5194/acp-23-15783-2023, https://doi.org/10.5194/acp-23-15783-2023, 2023
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Ice-nucleating particles (INPs) are important for the climate due to their influence on cloud properties. To understand potential land-based sources of them in the Arctic, we carried out a survey near the northernmost point of Alaska, a landscape connected to the permafrost (thermokarst). Permafrost contained high concentrations of INPs, with the largest values near the coast. The thermokarst lakes were found to emit INPs, and the water contained elevated concentrations.
Nicole A. June, Anna L. Hodshire, Elizabeth B. Wiggins, Edward L. Winstead, Claire E. Robinson, K. Lee Thornhill, Kevin J. Sanchez, Richard H. Moore, Demetrios Pagonis, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Matthew M. Coggon, Jonathan M. Dean-Day, T. Paul Bui, Jeff Peischl, Robert J. Yokelson, Matthew J. Alvarado, Sonia M. Kreidenweis, Shantanu H. Jathar, and Jeffrey R. Pierce
Atmos. Chem. Phys., 22, 12803–12825, https://doi.org/10.5194/acp-22-12803-2022, https://doi.org/10.5194/acp-22-12803-2022, 2022
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The evolution of organic aerosol composition and size is uncertain due to variability within and between smoke plumes. We examine the impact of plume concentration on smoke evolution from smoke plumes sampled by the NASA DC-8 during FIREX-AQ. We find that observed organic aerosol and size distribution changes are correlated to plume aerosol mass concentrations. Additionally, coagulation explains the majority of the observed growth.
Sabin Kasparoglu, Mohammad Maksimul Islam, Nicholas Meskhidze, and Markus D. Petters
Atmos. Meas. Tech., 15, 5007–5018, https://doi.org/10.5194/amt-15-5007-2022, https://doi.org/10.5194/amt-15-5007-2022, 2022
Short summary
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A modified version of a Handix Scientific printed optical particle spectrometer is introduced. The paper presents characterization experiments, including concentration, size, and time responses. Integration of an external multichannel analyzer card removes counting limitations of the original instrument. It is shown that the high-resolution light-scattering amplitude data can be used to sense particle-phase transitions.
Russell J. Perkins, Peter J. Marinescu, Ezra J. T. Levin, Don R. Collins, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 22, 6197–6215, https://doi.org/10.5194/acp-22-6197-2022, https://doi.org/10.5194/acp-22-6197-2022, 2022
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We used 5 years (2009–2013) of aerosol and cloud condensation nuclei (CCN) data from a total of seven instruments housed at the Southern Great Plains site, which were merged into a quality-controlled, continuous dataset of CCN spectra at ~45 min resolution. The data cover all seasons, are representative of a rural, agricultural mid-continental site, and are useful for model initialization and validation. Our analysis of this dataset focuses on seasonal and hourly variability.
Markus D. Petters
Atmos. Meas. Tech., 14, 7909–7928, https://doi.org/10.5194/amt-14-7909-2021, https://doi.org/10.5194/amt-14-7909-2021, 2021
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Inverse methods infer physical properties from a measured instrument response. Measurement noise often interferes with the inversion. This work presents a general, domain-independent, accessible, and computationally efficient software implementation of a common class of statistical inversion methods. In addition, a new method to invert data from humidified tandem differential mobility analyzers is introduced. Results show that the approach is suitable for inversion of large-scale datasets.
Anna L. Hodshire, Emily Ramnarine, Ali Akherati, Matthew L. Alvarado, Delphine K. Farmer, Shantanu H. Jathar, Sonia M. Kreidenweis, Chantelle R. Lonsdale, Timothy B. Onasch, Stephen R. Springston, Jian Wang, Yang Wang, Lawrence I. Kleinman, Arthur J. Sedlacek III, and Jeffrey R. Pierce
Atmos. Chem. Phys., 21, 6839–6855, https://doi.org/10.5194/acp-21-6839-2021, https://doi.org/10.5194/acp-21-6839-2021, 2021
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Biomass burning emits particles and vapors that can impact both health and climate. Here, we investigate the role of dilution in the evolution of aerosol size and composition in observed US wildfire smoke plumes. Centers of plumes dilute more slowly than edges. We see differences in concentrations and composition between the centers and edges both in the first measurement and in subsequent measurements. Our findings support the hypothesis that plume dilution influences smoke aging.
Sabin Kasparoglu, Ying Li, Manabu Shiraiwa, and Markus D. Petters
Atmos. Chem. Phys., 21, 1127–1141, https://doi.org/10.5194/acp-21-1127-2021, https://doi.org/10.5194/acp-21-1127-2021, 2021
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Viscosity is important because it determines the lifetime, impact, and fate of particulate matter. We collected new data to rigorously test a framework that is used to constrain the phase state in global simulations. We find that the framework is accurate as long as appropriate compound specific inputs are available.
Megan S. Claflin, Demetrios Pagonis, Zachary Finewax, Anne V. Handschy, Douglas A. Day, Wyatt L. Brown, John T. Jayne, Douglas R. Worsnop, Jose L. Jimenez, Paul J. Ziemann, Joost de Gouw, and Brian M. Lerner
Atmos. Meas. Tech., 14, 133–152, https://doi.org/10.5194/amt-14-133-2021, https://doi.org/10.5194/amt-14-133-2021, 2021
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We have developed a field-deployable gas chromatograph with thermal desorption preconcentration and detector switching between two high-resolution mass spectrometers for in situ measurements of volatile organic compounds (VOCs). This system combines chromatography with both proton transfer and electron ionization to offer fast time response and continuous molecular speciation. This technique was applied during the 2018 ATHLETIC campaign to characterize VOC emissions in an indoor environment.
Steven Albers, Stephen M. Saleeby, Sonia Kreidenweis, Qijing Bian, Peng Xian, Zoltan Toth, Ravan Ahmadov, Eric James, and Steven D. Miller
Atmos. Meas. Tech., 13, 3235–3261, https://doi.org/10.5194/amt-13-3235-2020, https://doi.org/10.5194/amt-13-3235-2020, 2020
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A fast 3D visible-light forward operator is used to realistically visualize, validate, and potentially assimilate ground- and space-based camera and satellite imagery with NWP models. Three-dimensional fields of hydrometeors, aerosols, and 2D land surface variables are considered in the generation of radiance fields and RGB imagery from a variety of vantage points.
Peter J. Marinescu, Ezra J. T. Levin, Don Collins, Sonia M. Kreidenweis, and Susan C. van den Heever
Atmos. Chem. Phys., 19, 11985–12006, https://doi.org/10.5194/acp-19-11985-2019, https://doi.org/10.5194/acp-19-11985-2019, 2019
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We characterized and provided fits for the seasonal aerosol size distributions (7 nm–14 µm diameter) at a North American, long–term surface site (SGP), which can be applied to models. Key cycles on timescales of several hours to weeks were also assessed using power spectra for various aerosol size ranges. One key finding is the consistent presence of diurnal cycles in the smallest particles in each season, providing insights into the formation and roles of new particle formation at SGP.
Steven D. Miller, Louie D. Grasso, Qijing Bian, Sonia M. Kreidenweis, Jack F. Dostalek, Jeremy E. Solbrig, Jennifer Bukowski, Susan C. van den Heever, Yi Wang, Xiaoguang Xu, Jun Wang, Annette L. Walker, Ting-Chi Wu, Milija Zupanski, Christine Chiu, and Jeffrey S. Reid
Atmos. Meas. Tech., 12, 5101–5118, https://doi.org/10.5194/amt-12-5101-2019, https://doi.org/10.5194/amt-12-5101-2019, 2019
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Satellite–based detection of lofted mineral via infrared–window channels, well established in the literature, faces significant challenges in the presence of atmospheric moisture. Here, we consider a case featuring the juxtaposition of two dust plumes embedded within dry and moist air masses. The case is considered from the vantage points of numerical modeling, multi–sensor observations, and radiative transfer theory arriving at a new method for mitigating the water vapor masking effect.
Stephen M. Saleeby, Susan C. van den Heever, Jennie Bukowski, Annette L. Walker, Jeremy E. Solbrig, Samuel A. Atwood, Qijing Bian, Sonia M. Kreidenweis, Yi Wang, Jun Wang, and Steven D. Miller
Atmos. Chem. Phys., 19, 10279–10301, https://doi.org/10.5194/acp-19-10279-2019, https://doi.org/10.5194/acp-19-10279-2019, 2019
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This study seeks to understand how intense dust storms impact the heating and cooling of the land surface and atmosphere. Dust storms that are intense enough to substantially impact visibility can also alter how much sunlight reaches the surface during the day and how much heat is trapped in the atmosphere at night. These radiation changes can impact the temperature of the atmosphere and impact the weather in the vicinity.
Benjamin L. Deming, Demetrios Pagonis, Xiaoxi Liu, Douglas A. Day, Ranajit Talukdar, Jordan E. Krechmer, Joost A. de Gouw, Jose L. Jimenez, and Paul J. Ziemann
Atmos. Meas. Tech., 12, 3453–3461, https://doi.org/10.5194/amt-12-3453-2019, https://doi.org/10.5194/amt-12-3453-2019, 2019
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Losses or measurement delays of gas-phase compounds sampled through tubing are important to atmospheric science. Here we characterize 14 tubing materials by measuring the effects on step changes in organic compound concentration. We find that polymeric tubings exhibit absorptive partitioning behaviour while glass and metal tubings show adsorptive partitioning. Adsorptive materials impart complex humidity, concentration, and VOC–VOC interaction dependencies that absorptive tubings do not.
Xiaoxi Liu, Benjamin Deming, Demetrios Pagonis, Douglas A. Day, Brett B. Palm, Ranajit Talukdar, James M. Roberts, Patrick R. Veres, Jordan E. Krechmer, Joel A. Thornton, Joost A. de Gouw, Paul J. Ziemann, and Jose L. Jimenez
Atmos. Meas. Tech., 12, 3137–3149, https://doi.org/10.5194/amt-12-3137-2019, https://doi.org/10.5194/amt-12-3137-2019, 2019
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Delays or losses of gases in sampling tubing and instrumental surfaces due to surface interactions can lead to inaccurate quantification. By sampling with several chemical ionization mass spectrometers and six tubing materials, we quantify delays of semivolatile organic compounds and small polar gases. Delay times generally increase with decreasing volatility or increasing polarity and also depend on materials. The method and results will inform inlet material selection and instrumental design.
Samuel A. Atwood, Sonia M. Kreidenweis, Paul J. DeMott, Markus D. Petters, Gavin C. Cornwell, Andrew C. Martin, and Kathryn A. Moore
Atmos. Chem. Phys., 19, 6931–6947, https://doi.org/10.5194/acp-19-6931-2019, https://doi.org/10.5194/acp-19-6931-2019, 2019
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This paper presents measurements of aerosol particles at a coastal location. The particles were classified into distinct aerosol types using both microphysical measurements and meteorological information, allowing rapid changes between the aerosol types to be reliably identified. These particles can alter cloud and precipitation processes, and inclusion of the differences between types can improve atmospheric models and remote sensing retrievals in littoral zones.
Naruki Hiranuma, Kouji Adachi, David M. Bell, Franco Belosi, Hassan Beydoun, Bhaskar Bhaduri, Heinz Bingemer, Carsten Budke, Hans-Christian Clemen, Franz Conen, Kimberly M. Cory, Joachim Curtius, Paul J. DeMott, Oliver Eppers, Sarah Grawe, Susan Hartmann, Nadine Hoffmann, Kristina Höhler, Evelyn Jantsch, Alexei Kiselev, Thomas Koop, Gourihar Kulkarni, Amelie Mayer, Masataka Murakami, Benjamin J. Murray, Alessia Nicosia, Markus D. Petters, Matteo Piazza, Michael Polen, Naama Reicher, Yinon Rudich, Atsushi Saito, Gianni Santachiara, Thea Schiebel, Gregg P. Schill, Johannes Schneider, Lior Segev, Emiliano Stopelli, Ryan C. Sullivan, Kaitlyn Suski, Miklós Szakáll, Takuya Tajiri, Hans Taylor, Yutaka Tobo, Romy Ullrich, Daniel Weber, Heike Wex, Thomas F. Whale, Craig L. Whiteside, Katsuya Yamashita, Alla Zelenyuk, and Ottmar Möhler
Atmos. Chem. Phys., 19, 4823–4849, https://doi.org/10.5194/acp-19-4823-2019, https://doi.org/10.5194/acp-19-4823-2019, 2019
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A total of 20 ice nucleation measurement techniques contributed to investigate the immersion freezing behavior of cellulose particles – natural polymers. Our data showed several types of cellulose are able to nucleate ice as efficiently as some mineral dust samples and cellulose has the potential to be an important atmospheric ice-nucleating particle. Continued investigation/collaboration is necessary to obtain further insight into consistency or diversity of ice nucleation measurements.
Ankit Tandon, Nicholas E. Rothfuss, and Markus D. Petters
Atmos. Chem. Phys., 19, 3325–3339, https://doi.org/10.5194/acp-19-3325-2019, https://doi.org/10.5194/acp-19-3325-2019, 2019
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Organic compounds may form a barrier to condensation. Such barriers have been hypothesized to prevent water and other substances from mixing with salt cores. This will hinder the particles' ability to aid cloud formation of < 100 nm particles. Here we perform experiments encasing particles in plastic shells akin to water bottles. Against expectations, the plastic shell did not alter the droplet activation behavior of the encased particles. Water appears to readily permeate the plastic shell.
Yajuan Duan, Markus D. Petters, and Ana P. Barros
Atmos. Chem. Phys., 19, 1413–1437, https://doi.org/10.5194/acp-19-1413-2019, https://doi.org/10.5194/acp-19-1413-2019, 2019
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A new cloud parcel model that simulates entrainment, condensational growth, and collision–coalescence processes is presented and evaluated against airborne observations in complex terrain during IPHEx. Analysis of model simulations reveals that nonlinear interactions among turbulent dispersion, activation, and droplet growth processes modulate spectral width and explain the emergence of bimodal cloud drop spectra in aircraft measurements from different cloud regions and at different heights.
Jian Wang, John E. Shilling, Jiumeng Liu, Alla Zelenyuk, David M. Bell, Markus D. Petters, Ryan Thalman, Fan Mei, Rahul A. Zaveri, and Guangjie Zheng
Atmos. Chem. Phys., 19, 941–954, https://doi.org/10.5194/acp-19-941-2019, https://doi.org/10.5194/acp-19-941-2019, 2019
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Earlier studies showed organic hygroscopicity increases with oxidation level. Such increases have been attributed to higher water solubility for more oxidized organics. By systematically varying the water content of activating droplets, we show that for secondary organic aerosols, essentially all organics are dissolved at the point of droplet activation. Therefore, the organic hygroscopicity is not limited by solubility but is dictated mainly by the molecular weight of organic species.
Paul J. DeMott, Ottmar Möhler, Daniel J. Cziczo, Naruki Hiranuma, Markus D. Petters, Sarah S. Petters, Franco Belosi, Heinz G. Bingemer, Sarah D. Brooks, Carsten Budke, Monika Burkert-Kohn, Kristen N. Collier, Anja Danielczok, Oliver Eppers, Laura Felgitsch, Sarvesh Garimella, Hinrich Grothe, Paul Herenz, Thomas C. J. Hill, Kristina Höhler, Zamin A. Kanji, Alexei Kiselev, Thomas Koop, Thomas B. Kristensen, Konstantin Krüger, Gourihar Kulkarni, Ezra J. T. Levin, Benjamin J. Murray, Alessia Nicosia, Daniel O'Sullivan, Andreas Peckhaus, Michael J. Polen, Hannah C. Price, Naama Reicher, Daniel A. Rothenberg, Yinon Rudich, Gianni Santachiara, Thea Schiebel, Jann Schrod, Teresa M. Seifried, Frank Stratmann, Ryan C. Sullivan, Kaitlyn J. Suski, Miklós Szakáll, Hans P. Taylor, Romy Ullrich, Jesus Vergara-Temprado, Robert Wagner, Thomas F. Whale, Daniel Weber, André Welti, Theodore W. Wilson, Martin J. Wolf, and Jake Zenker
Atmos. Meas. Tech., 11, 6231–6257, https://doi.org/10.5194/amt-11-6231-2018, https://doi.org/10.5194/amt-11-6231-2018, 2018
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The ability to measure ice nucleating particles is vital to quantifying their role in affecting clouds and precipitation. Methods for measuring droplet freezing were compared while co-sampling relevant particle types. Measurement correspondence was very good for ice nucleating particles of bacterial and natural soil origin, and somewhat more disparate for those of mineral origin. Results reflect recently improved capabilities and provide direction toward addressing remaining measurement issues.
Kaitlyn J. Suski, Tom C. J. Hill, Ezra J. T. Levin, Anna Miller, Paul J. DeMott, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 18, 13755–13771, https://doi.org/10.5194/acp-18-13755-2018, https://doi.org/10.5194/acp-18-13755-2018, 2018
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The harvesting of crops emits large amounts of particles into the air. These particles can form and interact with clouds to alter cloud properties and precipitation, but the magnitude of these effects is unknown. This study looked at the ability of harvest particles to form ice in clouds by sampling with an ice nucleation chamber downwind of fields being harvested. Some crops emitted large amounts of ice-nucleating particles, and harvest emissions are mixtures of organics, soil, and minerals.
Gregory P. Schill, Paul J. DeMott, Ezra J. T. Levin, and Sonia M. Kreidenweis
Atmos. Meas. Tech., 11, 3007–3020, https://doi.org/10.5194/amt-11-3007-2018, https://doi.org/10.5194/amt-11-3007-2018, 2018
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Few techniques can measure the contribution of refractory black carbon (rBC) to ice-nucleating particle (INP) concentrations. One technique uses the single particle soot photometer (SP2) as a pre-filter to an online INP counter to selectively remove rBC particles from an aerosol stream. In this work, we expand upon this technique by determining the effect of the SP2 laser on INP proxies mixed with rBC. We also bounded the SP2 conditions under which rBC is fully vaporized in the SP2 exhaust.
Qijing Bian, Badr Alharbi, Mohammed M. Shareef, Tahir Husain, Mohammad J. Pasha, Samuel A. Atwood, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 18, 3969–3985, https://doi.org/10.5194/acp-18-3969-2018, https://doi.org/10.5194/acp-18-3969-2018, 2018
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We report long-term and spatially resolved hourly measurements of organic carbon (OC) and elemental carbon (EC) concentrations in ambient particulate matter in Riyadh, Saudi Arabia. Our analysis suggests both local vehicular emissions and regional sources (e.g., oil extraction and refining) were strong influences. Our work informs the development of pollution control strategies for Riyadh.
Demetrios Pagonis, Jordan E. Krechmer, Joost de Gouw, Jose L. Jimenez, and Paul J. Ziemann
Atmos. Meas. Tech., 10, 4687–4696, https://doi.org/10.5194/amt-10-4687-2017, https://doi.org/10.5194/amt-10-4687-2017, 2017
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Laboratory studies were conducted to investigate gas-wall partitioning of atmospheric organic compounds in Teflon tubing and inside an instrument used to monitor concentrations. Rapid partitioning caused time delays in instrument response that vary with tubing length and diameter, flow rate, and compound volatility. Tubing delay times of seconds to hours were described using a model that also included effects of instrument surfaces. The results can enable better design of air sampling systems.
Paul J. DeMott, Thomas C. J. Hill, Markus D. Petters, Allan K. Bertram, Yutaka Tobo, Ryan H. Mason, Kaitlyn J. Suski, Christina S. McCluskey, Ezra J. T. Levin, Gregory P. Schill, Yvonne Boose, Anne Marie Rauker, Anna J. Miller, Jake Zaragoza, Katherine Rocci, Nicholas E. Rothfuss, Hans P. Taylor, John D. Hader, Cedric Chou, J. Alex Huffman, Ulrich Pöschl, Anthony J. Prenni, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 17, 11227–11245, https://doi.org/10.5194/acp-17-11227-2017, https://doi.org/10.5194/acp-17-11227-2017, 2017
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The consistency and complementarity of different methods for measuring the numbers of particles capable of forming ice in clouds are examined in the atmosphere. Four methods for collecting particles for later (offline) freezing studies are compared to a common instantaneous method. Results support very good agreement in many cases but also biases that require further research. Present capabilities and uncertainties for obtaining global data on these climate-relevant aerosols are thus defined.
Yajuan Duan, Markus D. Petters, and Ana P. Barros
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-396, https://doi.org/10.5194/acp-2017-396, 2017
Revised manuscript not accepted
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To investigate aerosol-cloud interactions in cumulus development, a new cloud parcel model is developed to predict the vertical structure of cloud formation at early stages and evaluated against airborne observations during the Integrated Precipitation and Hydrology Experiment over the Southern Appalachian Mountains. The findings provide new insights into determinant factors of mid-day cumulus congestus formation that can explain a large fraction of warm season rainfall in mountainous regions.
Qijing Bian, Shantanu H. Jathar, John K. Kodros, Kelley C. Barsanti, Lindsay E. Hatch, Andrew A. May, Sonia M. Kreidenweis, and Jeffrey R. Pierce
Atmos. Chem. Phys., 17, 5459–5475, https://doi.org/10.5194/acp-17-5459-2017, https://doi.org/10.5194/acp-17-5459-2017, 2017
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In this paper, we perform simulations of the evolution of biomass-burning organic aerosol in laboratory smog-chamber experiments and ambient plumes. We find that in smog-chamber experiments, vapor wall losses lead to a large reduction in the apparent secondary organic aerosol formation. In ambient plumes, fire size and meteorology regulate the plume dilution rate, primary organic aerosol evaporation rate, and secondary organic aerosol formation rate.
Andrew C. Martin, Gavin C. Cornwell, Samuel A. Atwood, Kathryn A. Moore, Nicholas E. Rothfuss, Hans Taylor, Paul J. DeMott, Sonia M. Kreidenweis, Markus D. Petters, and Kimberly A. Prather
Atmos. Chem. Phys., 17, 1491–1509, https://doi.org/10.5194/acp-17-1491-2017, https://doi.org/10.5194/acp-17-1491-2017, 2017
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Anthropogenic influence on air quality, aerosol properties, and cloud activity was observed at Bodega Bay, CA, during periods when air from California's interior was transported to the coast. The sudden change in aerosol properties can impact atmospheric radiative balance and cloud formation in ways that must be accounted for in regional climate simulations.
Samuel A. Atwood, Jeffrey S. Reid, Sonia M. Kreidenweis, Donald R. Blake, Haflidi H. Jonsson, Nofel D. Lagrosas, Peng Xian, Elizabeth A. Reid, Walter R. Sessions, and James B. Simpas
Atmos. Chem. Phys., 17, 1105–1123, https://doi.org/10.5194/acp-17-1105-2017, https://doi.org/10.5194/acp-17-1105-2017, 2017
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Aerosol particles were measured by ship in remote marine regions of the South China Sea as part of the 2012 7 Southeast Asian Studies (7SEAS) experiments. As the particle populations changed throughout the experiment, the distribution of particle sizes and the amount of water that collected on them changed as well. These changes were associated with various impacts from smoke, sea salt, and pollution sources, and impact how clouds form and precipitation occurs in the region.
Jeffrey S. Reid, Peng Xian, Brent N. Holben, Edward J. Hyer, Elizabeth A. Reid, Santo V. Salinas, Jianglong Zhang, James R. Campbell, Boon Ning Chew, Robert E. Holz, Arunas P. Kuciauskas, Nofel Lagrosas, Derek J. Posselt, Charles R. Sampson, Annette L. Walker, E. Judd Welton, and Chidong Zhang
Atmos. Chem. Phys., 16, 14041–14056, https://doi.org/10.5194/acp-16-14041-2016, https://doi.org/10.5194/acp-16-14041-2016, 2016
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This paper describes aspects of the 2012 7 Southeast Asian Studies (7SEAS) operations period, the largest within the Maritime Continent. Included were an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, multiple lidars, and a Singapore supersite. Simultaneously, a ship was dispatched to the Palawan Archipelago and Sulu Sea of the Philippines for September 2012 to observe transported smoke and pollution as it entered the southwest monsoon trough.
Jeffrey S. Reid, Nofel D. Lagrosas, Haflidi H. Jonsson, Elizabeth A. Reid, Samuel A. Atwood, Thomas J. Boyd, Virendra P. Ghate, Peng Xian, Derek J. Posselt, James B. Simpas, Sherdon N. Uy, Kimo Zaiger, Donald R. Blake, Anthony Bucholtz, James R. Campbell, Boon Ning Chew, Steven S. Cliff, Brent N. Holben, Robert E. Holz, Edward J. Hyer, Sonia M. Kreidenweis, Arunas P. Kuciauskas, Simone Lolli, Min Oo, Kevin D. Perry, Santo V. Salinas, Walter R. Sessions, Alexander Smirnov, Annette L. Walker, Qing Wang, Liya Yu, Jianglong Zhang, and Yongjing Zhao
Atmos. Chem. Phys., 16, 14057–14078, https://doi.org/10.5194/acp-16-14057-2016, https://doi.org/10.5194/acp-16-14057-2016, 2016
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This paper describes aspects of the 2012 7 Southeast Asian Studies (7SEAS) operations period, the largest within the Maritime Continent. Included were an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, multiple lidars, and a Singapore supersite. Simultaneously, a ship was dispatched to the Palawan Archipelago and Sulu Sea of the Philippines for September 2012 to observe transported smoke and pollution as it entered the southwest monsoon trough.
Tom C. J. Hill, Paul J. DeMott, Yutaka Tobo, Janine Fröhlich-Nowoisky, Bruce F. Moffett, Gary D. Franc, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 16, 7195–7211, https://doi.org/10.5194/acp-16-7195-2016, https://doi.org/10.5194/acp-16-7195-2016, 2016
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Even though aerosols that trigger the freezing of cloud droplets are rare, they can modify cloud properties and seed precipitation. While soil organic matter is a rich source of ice nucleating particles (INPs), we know little about them. The most active INPs (freeze supercooled water > −12 °C) in Wyoming and Colorado soils were organic, sensitive to heat (105 °C), and possibly fungal proteins in several soils, but they were not known species of ice nucleating bacteria. Many may also be carbohydrates.
Y. S. La, M. Camredon, P. J. Ziemann, R. Valorso, A. Matsunaga, V. Lannuque, J. Lee-Taylor, A. Hodzic, S. Madronich, and B. Aumont
Atmos. Chem. Phys., 16, 1417–1431, https://doi.org/10.5194/acp-16-1417-2016, https://doi.org/10.5194/acp-16-1417-2016, 2016
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The potential impact of chamber walls on the loss of gaseous organic species and secondary organic aerosol (SOA) formation has been explored using the GECKO-A modeling tool, which explicitly represents SOA formation and gas-wall partitioning. The model was compared with 41 smog chamber experiments of SOA formation under OH oxidation of alkane and alkene serie. The organic vapor loss to the chamber walls is found to affect SOA yields as well as the composition of the gas and the particle phase.
Q. Bian, A. A. May, S. M. Kreidenweis, and J. R. Pierce
Atmos. Chem. Phys., 15, 11027–11045, https://doi.org/10.5194/acp-15-11027-2015, https://doi.org/10.5194/acp-15-11027-2015, 2015
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Losses of semi-volatile vapors to Teflon walls may contribute to significant primary particle evaporation during wood-smoke aerosol experiments. These vapor losses may also affect secondary organic aerosol formation during these experiments.
A. A. May, T. Lee, G. R. McMeeking, S. Akagi, A. P. Sullivan, S. Urbanski, R. J. Yokelson, and S. M. Kreidenweis
Atmos. Chem. Phys., 15, 6323–6335, https://doi.org/10.5194/acp-15-6323-2015, https://doi.org/10.5194/acp-15-6323-2015, 2015
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Smoke plumes from some prescribed fires in the southeastern United States were sampled via aircraft to observe changes in organic aerosol (OA) with atmospheric transport. These plumes underwent rapid mixing, and, hence, substantial dilution with background air occurred. Dilution-driven evaporation appears to be the primary driver of OA transformations within the sampled plumes rather than photochemistry.
N. Hiranuma, S. Augustin-Bauditz, H. Bingemer, C. Budke, J. Curtius, A. Danielczok, K. Diehl, K. Dreischmeier, M. Ebert, F. Frank, N. Hoffmann, K. Kandler, A. Kiselev, T. Koop, T. Leisner, O. Möhler, B. Nillius, A. Peckhaus, D. Rose, S. Weinbruch, H. Wex, Y. Boose, P. J. DeMott, J. D. Hader, T. C. J. Hill, Z. A. Kanji, G. Kulkarni, E. J. T. Levin, C. S. McCluskey, M. Murakami, B. J. Murray, D. Niedermeier, M. D. Petters, D. O'Sullivan, A. Saito, G. P. Schill, T. Tajiri, M. A. Tolbert, A. Welti, T. F. Whale, T. P. Wright, and K. Yamashita
Atmos. Chem. Phys., 15, 2489–2518, https://doi.org/10.5194/acp-15-2489-2015, https://doi.org/10.5194/acp-15-2489-2015, 2015
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Seventeen ice nucleation measurement techniques contributed to investigate the immersion freezing behavior of illite NX. All data showed a similar temperature trend, but the measured ice nucleation activity was on average smaller for the wet suspended samples and higher for the dry-dispersed aerosol samples at high temperatures. A continued investigation and collaboration is necessary to obtain further insights into consistency or diversity of ice nucleation measurements.
M. I. Schurman, T. Lee, Y. Sun, B. A. Schichtel, S. M. Kreidenweis, and J. L. Collett Jr.
Atmos. Chem. Phys., 15, 737–752, https://doi.org/10.5194/acp-15-737-2015, https://doi.org/10.5194/acp-15-737-2015, 2015
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Atmospheric particles can contribute to environmental degradation. An aerosol mass spectrometer was used with positive matrix factorization to explore submicron particle sources in Rocky Mountain National Park, finding that ammonium (3.9%), nitrate (4.3%), sulfate (16.6%), and two types of oxidized organic aerosol (66.9% total) are transported on upslope winds from the urban Front Range, while local campfires contribute 8.4% of mass.
P. J. DeMott, A. J. Prenni, G. R. McMeeking, R. C. Sullivan, M. D. Petters, Y. Tobo, M. Niemand, O. Möhler, J. R. Snider, Z. Wang, and S. M. Kreidenweis
Atmos. Chem. Phys., 15, 393–409, https://doi.org/10.5194/acp-15-393-2015, https://doi.org/10.5194/acp-15-393-2015, 2015
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Laboratory and field data are used together to develop an empirical relation between the concentrations of mineral dust particles at sizes above 0.5 microns, approximated as a single compositional type, and ice nucleating particle concentrations measured versus temperature. This should be useful in global modeling of ice cloud formation. The utility of laboratory data for parameterization development is reinforced, and the need for careful interpretation of ice nucleation data is emphasized.
T. K. V. Nguyen, M. D. Petters, S. R. Suda, H. Guo, R. J. Weber, and A. G. Carlton
Atmos. Chem. Phys., 14, 10911–10930, https://doi.org/10.5194/acp-14-10911-2014, https://doi.org/10.5194/acp-14-10911-2014, 2014
A. P. Sullivan, A. A. May, T. Lee, G. R. McMeeking, S. M. Kreidenweis, S. K. Akagi, R. J. Yokelson, S. P. Urbanski, and J. L. Collett Jr.
Atmos. Chem. Phys., 14, 10535–10545, https://doi.org/10.5194/acp-14-10535-2014, https://doi.org/10.5194/acp-14-10535-2014, 2014
C. E. Stockwell, R. J. Yokelson, S. M. Kreidenweis, A. L. Robinson, P. J. DeMott, R. C. Sullivan, J. Reardon, K. C. Ryan, D. W. T. Griffith, and L. Stevens
Atmos. Chem. Phys., 14, 9727–9754, https://doi.org/10.5194/acp-14-9727-2014, https://doi.org/10.5194/acp-14-9727-2014, 2014
Y. Tobo, P. J. DeMott, T. C. J. Hill, A. J. Prenni, N. G. Swoboda-Colberg, G. D. Franc, and S. M. Kreidenweis
Atmos. Chem. Phys., 14, 8521–8531, https://doi.org/10.5194/acp-14-8521-2014, https://doi.org/10.5194/acp-14-8521-2014, 2014
S. Nakao, S. R. Suda, M. Camp, M. D. Petters, and S. M. Kreidenweis
Atmos. Meas. Tech., 7, 2227–2241, https://doi.org/10.5194/amt-7-2227-2014, https://doi.org/10.5194/amt-7-2227-2014, 2014
J. Ortega, A. Turnipseed, A. B. Guenther, T. G. Karl, D. A. Day, D. Gochis, J. A. Huffman, A. J. Prenni, E. J. T. Levin, S. M. Kreidenweis, P. J. DeMott, Y. Tobo, E. G. Patton, A. Hodzic, Y. Y. Cui, P. C. Harley, R. S. Hornbrook, E. C. Apel, R. K. Monson, A. S. D. Eller, J. P. Greenberg, M. C. Barth, P. Campuzano-Jost, B. B. Palm, J. L. Jimenez, A. C. Aiken, M. K. Dubey, C. Geron, J. Offenberg, M. G. Ryan, P. J. Fornwalt, S. C. Pryor, F. N. Keutsch, J. P. DiGangi, A. W. H. Chan, A. H. Goldstein, G. M. Wolfe, S. Kim, L. Kaser, R. Schnitzhofer, A. Hansel, C. A. Cantrell, R. L. Mauldin, and J. N. Smith
Atmos. Chem. Phys., 14, 6345–6367, https://doi.org/10.5194/acp-14-6345-2014, https://doi.org/10.5194/acp-14-6345-2014, 2014
J. D. Hader, T. P. Wright, and M. D. Petters
Atmos. Chem. Phys., 14, 5433–5449, https://doi.org/10.5194/acp-14-5433-2014, https://doi.org/10.5194/acp-14-5433-2014, 2014
E. J. T. Levin, A. J. Prenni, B. B. Palm, D. A. Day, P. Campuzano-Jost, P. M. Winkler, S. M. Kreidenweis, P. J. DeMott, J. L. Jimenez, and J. N. Smith
Atmos. Chem. Phys., 14, 2657–2667, https://doi.org/10.5194/acp-14-2657-2014, https://doi.org/10.5194/acp-14-2657-2014, 2014
C. L. Loza, J. S. Craven, L. D. Yee, M. M. Coggon, R. H. Schwantes, M. Shiraiwa, X. Zhang, K. A. Schilling, N. L. Ng, M. R. Canagaratna, P. J. Ziemann, R. C. Flagan, and J. H. Seinfeld
Atmos. Chem. Phys., 14, 1423–1439, https://doi.org/10.5194/acp-14-1423-2014, https://doi.org/10.5194/acp-14-1423-2014, 2014
L. D. Yee, J. S. Craven, C. L. Loza, K. A. Schilling, N. L. Ng, M. R. Canagaratna, P. J. Ziemann, R. C. Flagan, and J. H. Seinfeld
Atmos. Chem. Phys., 13, 11121–11140, https://doi.org/10.5194/acp-13-11121-2013, https://doi.org/10.5194/acp-13-11121-2013, 2013
J. A. Huffman, A. J. Prenni, P. J. DeMott, C. Pöhlker, R. H. Mason, N. H. Robinson, J. Fröhlich-Nowoisky, Y. Tobo, V. R. Després, E. Garcia, D. J. Gochis, E. Harris, I. Müller-Germann, C. Ruzene, B. Schmer, B. Sinha, D. A. Day, M. O. Andreae, J. L. Jimenez, M. Gallagher, S. M. Kreidenweis, A. K. Bertram, and U. Pöschl
Atmos. Chem. Phys., 13, 6151–6164, https://doi.org/10.5194/acp-13-6151-2013, https://doi.org/10.5194/acp-13-6151-2013, 2013
S. K. Akagi, R. J. Yokelson, I. R. Burling, S. Meinardi, I. Simpson, D. R. Blake, G. R. McMeeking, A. Sullivan, T. Lee, S. Kreidenweis, S. Urbanski, J. Reardon, D. W. T. Griffith, T. J. Johnson, and D. R. Weise
Atmos. Chem. Phys., 13, 1141–1165, https://doi.org/10.5194/acp-13-1141-2013, https://doi.org/10.5194/acp-13-1141-2013, 2013
M. D. Petters and S. M. Kreidenweis
Atmos. Chem. Phys., 13, 1081–1091, https://doi.org/10.5194/acp-13-1081-2013, https://doi.org/10.5194/acp-13-1081-2013, 2013
J. S. Craven, L. D. Yee, N. L. Ng, M. R. Canagaratna, C. L. Loza, K. A. Schilling, R. L. N. Yatavelli, J. A. Thornton, P. J. Ziemann, R. C. Flagan, and J. H. Seinfeld
Atmos. Chem. Phys., 12, 11795–11817, https://doi.org/10.5194/acp-12-11795-2012, https://doi.org/10.5194/acp-12-11795-2012, 2012
Related subject area
Atmospheric sciences
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models
Mitigating Hail Overforecasting in the 2-Moment Milbrandt-Yau Microphysics Scheme (v2.25.2_beta_04) in WRF (v4.5.1) by Incorporating the Graupel Spongy Wet Growth Process (MY2_GSWG v1.0)
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Comprehensive evaluation of iAMAS (v1.0) in simulating Antarctic meteorological fields with observations and reanalysis
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
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Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
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This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
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This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
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In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-229, https://doi.org/10.5194/gmd-2024-229, 2025
Revised manuscript accepted for GMD
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This study presents the first comprehensive evaluation of unstructured meshes using the iAMAS model over Antarctica, encompassing both surface and upper-level meteorological fields. Comparison with ERA5 and observational data reveals that the iAMAS model performs well in simulating the Antarctic atmosphere; iAMAS demonstrates comparable, and in some cases superior, performance in simulating temperature and wind speed in East Antarctica when compared to ERA5.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
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Short summary
Organic particles suspended in air serve as nucleation seeds for droplets in atmospheric clouds. Over time their chemical composition changes towards more functionalized compounds. This work presents a model that can predict an organic compounds' ability promote the nucleation of cloud drops from its functional group composition. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote droplet nucleation. Methylene and nitrate moieties inhibit droplet nucleation.
Organic particles suspended in air serve as nucleation seeds for droplets in atmospheric clouds....
