Articles | Volume 26, issue 2
https://doi.org/10.5194/acp-26-1415-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-1415-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jan 2026
Research article |
| 28 Jan 2026
| 28 Jan 2026
Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability
Yufei Chu
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, 11790, USA
NOAA/AOML/Hurricane Research Division, Miami, 33149, USA
Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, 33149, USA
Environmental Science and Technologies Department, Brookhaven National Laboratory, Upton, 11793, USA
Lulin Xue
National Science Foundation National Center for Atmospheric Research, Boulder, 80307, USA
Weiwei Li
National Science Foundation National Center for Atmospheric Research, Boulder, 80307, USA
Hyeyum Hailey Shin
National Science Foundation National Center for Atmospheric Research, Boulder, 80307, USA
Jun A. Zhang
NOAA/AOML/Hurricane Research Division, Miami, 33149, USA
Cooperative Institute for Marine and Atmospheric Studies, University of Miami, Miami, 33149, USA
Hanqing Guo
Department of Electrical and Computer Engineering, University of Hawaii at Manoa, Honolulu, 96822, USA
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, 11790, USA
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Kang Yang, Zhien Wang, and Min Deng
Atmos. Chem. Phys., 26, 13319–13339, https://doi.org/10.5194/acp-26-13319-2026, https://doi.org/10.5194/acp-26-13319-2026, 2026
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Mixed-phase clouds, which contain both ice crystals and supercooled liquid droplets, strongly influence Earth's radiation budget but remain difficult to represent in climate models. Using global satellite observations, we show that they vary greatly by cloud type, with distinct spatial distributions, vertical structures, and seasonal and surface contrasts. These results provide new observational constraints for improving model representation of mixed-phase clouds.
Timothy W. Juliano, Florian Tornow, Ann M. Fridlind, Andrew S. Ackerman, Gregory S. Elsaesser, Bart Geerts, Christian P. Lackner, David Painemal, Israel Silber, Mikhail Ovchinnikov, Gunilla Svensson, Michael Tjernström, Peng Wu, Alejandro Baró Pérez, Peter Bogenschutz, Dmitry Chechin, Kamal Kant Chandrakar, Jan Chylik, Andrey Debolskiy, Rostislav Fadeev, Anu Gupta, Luisa Ickes, Michail Karalis, Martin Köhler, Branko Kosović, Peter Kuma, Weiwei Li, Evgeny Mortikov, Hugh Morrison, Roel A. J. Neggers, Anna Possner, Tomi Raatikainen, Lea Raillard, Sami Romakkaniemi, Niklas Schnierstein, Shin-ichiro Shima, Nikita Silin, Mikhail Tolstykh, Étienne Vignon, Lulin Xue, Meng Zhang, and Xue Zheng
Atmos. Chem. Phys., 26, 13267–13302, https://doi.org/10.5194/acp-26-13267-2026, https://doi.org/10.5194/acp-26-13267-2026, 2026
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Models struggle to capture cloud and precipitation processes and their radiative effects in marine cold-air outbreaks. We use a quasi-Lagrangian framework to compare large-eddy simulation (LES) and single-column model (SCM) output with field and satellite observations. With fixed droplet and ice numbers, LES and SCM agree in liquid-only tests. In mixed-phase conditions, LES plausibly capture cloud thinning and breakup, while SCMs largely remain overcast and thereby miss cloud radiative effects.
Patrick C. Taylor, Armin Sorooshian, Rei Ueyama, Sebastian Schmidt, Ihab Abboud, Quincy Allison, Kevin Barry, Sebastian Becker, Holly A. Bender, Joseph R. Bennett, James B. Blair, Niklas Bohn, Linette Boisvert, Matthew D. Brown, Roelof Bruintjes, Anthony Bucholtz, Megan Buzanowicz, Brian Cairns, Filippo Calì Quaglia, Eduard Chemyakin, Bo Chen, Gao Chen, Hong Chen, Yu-Wen Chen, Zezhen Cheng, Swarup China, Dan Chirica, Yonghoon Choi, Peter Colarco, Brian Collister, Ewan Crosbie, Maurice J. Cross, Janet Daniels, Paul DeMott, Joshua P. DiGangi, Alcide Giorgio di Sarra, Glenn S. Diskin, Erica K. Dolinar, Eva-Lou Edwards, Samuel Ephraim, Nikolaos Evangeliou, Romanos Foskinis, Francesca Gallo, Lan Gao, José Luis Gómez-Amo, Daisy Gonzalez, Christine Groot Zwaaftink, Pawan Gupta, Ivan Heckman, Michael Hendrickson, Miguel Ricardo A. Hilario, Ken Hirata, Michelle Hofton, Andrew L. Holen, Ulas Im, Alia L. Khan, Ralph Kahn, Alexei V. Korolev, Sonia Kreidenweis, Thomas Krumpen, Nathan Kurtz, Leslie Lait, Bradley Lamkin, Jack Landy, Nurun Nahar Lata, Paul Lawson, Samuel LeBlanc, Sean Leavor, Jing Li, Thorsten Markus, Hal Maring, Andreas H. Massling, Camille Mavis, Flynn McGinnity, Kerry Meyer, Gabriel Mojica, Richard H. Moore, Parker Morris, Giovanni Muscari, Vikas Nataraja, Amin R. Nehrir, Athanasios Nenes, Edward P. Nowottnick, Matteo Ottaviani, Chelsea Parker, Ryan Patnaude, Michael Perez, Russell J. Perkins, Colten Peterson, Alek Petty, Stevie Phothisane, Chris Polashenski, Kerri A. Pratt, Kayla M. Preisler, John Prytherch, David Rabine, Jens Redemann, Ju-Mee Ryoo, Joseph S. Schlosser, Vanessa Selimovic, Michael A. Shook, Morgan Silverman, Henrik Skov, Alexander Smirnov, Cassidy Soloff, Amy Solomon, Snorre Stamnes, Azusa Takeishi, David R. Thompson, K. Lee Thornhill, Rachel Tilling, Michael Tjernström, Monica Tosco, Pedro C. Valdelomar, David Van Gilst, Jian Wang, Zhien Wang, Andrzej Wasilewski, Manfred Wendisch, Brent Wilder, Edward L. Winstead, Albert Wu, Peng Xian, Lauren M. Zamora, Jiaoshi Zhang, Lei Zhang, Lu Zhang, Luke Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-241, https://doi.org/10.5194/essd-2026-241, 2026
Preprint under review for ESSD
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This article summarizes the data sets collected during the Arctic Radiation Cloud aerosol Sea ice Interaction eXperiment (ARCSIX), NASA's most comprehensive Arctic field campaign to date. The overarching goal was to quantify the contributions of surface, clouds, aerosol particles, and precipitation to summer sea ice melt. This paper describes the ARCSIX implementation including the flight strategy, instruments, complementary data sets, access, and usage details.
Sisi Chen, Lulin Xue, Michelle Harrold, Sarah Tessendorf, Jamie Wolff, Nick Dawson, and Darcy Jacobson
EGUsphere, https://doi.org/10.5194/egusphere-2026-3289, https://doi.org/10.5194/egusphere-2026-3289, 2026
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Glaciogenic seeding is used in winter mountains to enhance snowpack and address water scarcity. Liquid propane seeding, a type of glaciogenic seeding, generates ice in relatively warm conditions where silver iodide is less effective, yet no models exist to simulate its impacts. We built a liquid propane seeding module in a weather model and tested it over idealized mountains. Simulations show snow enhancement is strongest where clouds are rich in supercooled liquid and rain formation is weak.
Min Deng, Scott Giangrande, Adam K. Theisen, Karen Johnson, Iosif A. Lindenmaier, Timothy G. Wendler, Jennifer Comstock, Marquette Rocque, Zeen Zhu, and Alyssa Matthews
EGUsphere, https://doi.org/10.5194/egusphere-2026-3304, https://doi.org/10.5194/egusphere-2026-3304, 2026
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Cloud radars are used to measure air motion, clouds, and precipitation, but small pointing errors can reduce their accuracy. We developed a method to detect these errors using routine weather balloon observations. Tests with several radar systems in different environments showed that the method can identify small but important pointing biases, helping improve the accuracy and long-term consistency of weather and climate observations.
Samuel Ephraim, Paquita Zuidema, Aaron Bansemer, Lintong Cai, Markus Petters, Elise Rosky, Jefferson R. Snider, Zhien Wang, Sarah Woods, Kevin Barry, Theresa Campos, Owen Cruikshank, Sabine Eckhardt, Nikolaos Evangeliou, Romanos Foskinis, Jeffrey R. French, Bart Geerts, Coltin Grasmick, Silvia Henning, Varun Kumar, Andreas Massling, Camille Mavis, Greg M. McFarquhar, Athanasios Nenes, Gunnar Noer, Ryan Patnaude, Russell Perkins, Lise Lotte Sorensen, Henrik Skov, Tyler Tatro, Florian Tornow, and Lu Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2026-2940, https://doi.org/10.5194/egusphere-2026-2940, 2026
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The processes that establish how mixed-phase closed-cell clouds transition to more open cellular structures are poorly known. First-of-its kind aircraft observations document such a transition in the presence of anomalously high aerosol concentrations over the Nordic Seas at cloud temperatures < -15 °C. The reduces the drop size, discouraging riming. Eventually, ice precipitation produces surface cold pools that drive the convective transition, despite strong counteracting surface fluxes.
Fan Mei, Jian Wang, Israel Silber, Nurun Nahar Lata, Gregory W. Vandergrift, Jing Li, Bo Chen, Sarah Brooks, Michael P. Jensen, Min Deng, Damao Zhang, Darielle Dexheimer, Beat Schmid, Zezhen Cheng, and Swarup China
EGUsphere, https://doi.org/10.5194/egusphere-2026-2245, https://doi.org/10.5194/egusphere-2026-2245, 2026
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Tethered balloon measurements from 149 flights during DOE ARM TRACER over Houston characterize vertical aerosol and CCN structure. Back-trajectory clustering identifies three air mass types—marine, mixed, and urban—with distinct profiles shaped by boundary-layer depth and coastal circulations. A case study shows mesoscale advection simultaneously transforms thermodynamic and aerosol conditions, underscoring the need to constrain meteorology before attributing cloud changes to aerosol forcing.
Tamanna Subba, Michael P. Jensen, Min Deng, Scott E. Giangrande, Mark C. Harvey, Ashish Singh, Die Wang, Maria Zawadowicz, and Chongai Kuang
Atmos. Chem. Phys., 26, 2853–2879, https://doi.org/10.5194/acp-26-2853-2026, https://doi.org/10.5194/acp-26-2853-2026, 2026
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Using TRacking Aerosol Convection Interactions Experiment field campaign observations and model simulations, we studied summertime sea-breeze events in southern Texas. When sea-breeze fronts moved inland, they mixed marine and continental air, changing aerosol concentrations by up to a factor of two as far as 50 km inland. The sea breeze also reduced the number of particles that can form cloud droplets, highlighting the connection between coastal meteorology and aerosol-cloud interactions.
Timothy W. Juliano, Florian Tornow, Ann M. Fridlind, Andrew S. Ackerman, Gregory S. Elsaesser, Bart Geerts, Christian P. Lackner, David Painemal, Israel Silber, Mikhail Ovchinnikov, Gunilla Svensson, Michael Tjernström, Peng Wu, Alejandro Baró Pérez, Peter Bogenschutz, Dmitry Chechin, Kamal Kant Chandrakar, Jan Chylik, Andrey Debolskiy, Rostislav Fadeev, Anu Gupta, Luisa Ickes, Michail Karalis, Martin Köhler, Branko Kosović, Peter Kuma, Weiwei Li, Evgeny Mortikov, Hugh Morrison, Roel A. J. Neggers, Anna Possner, Tomi Raatikainen, Sami Romakkaniemi, Niklas Schnierstein, Shin-ichiro Shima, Nikita Silin, Mikhail Tolstykh, Lulin Xue, Meng Zhang, and Xue Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-6217, https://doi.org/10.5194/egusphere-2025-6217, 2026
Preprint archived
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Models struggle to capture cloud and precipitation processes and their radiative effects in marine cold-air outbreaks. We use a quasi-Lagrangian framework to compare large-eddy simulation (LES) and single-column model (SCM) output with field and satellite observations. With fixed droplet and ice numbers, LES and SCM agree in liquid-only tests. In mixed-phase conditions, LES plausibly capture cloud thinning and breakup, while SCMs largely remain overcast and thereby miss cloud radiative effects.
Meilian Chen, Xiaoqin Jing, Jiaojiao Li, Jing Yang, Xiaobo Dong, Bart Geerts, Yan Yin, Baojun Chen, Lulin Xue, Mengyu Huang, Ping Tian, and Shaofeng Hua
Atmos. Chem. Phys., 25, 7581–7596, https://doi.org/10.5194/acp-25-7581-2025, https://doi.org/10.5194/acp-25-7581-2025, 2025
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Several recent studies have reported complete cloud glaciation induced by airborne-based glaciogenic cloud seeding over plains. Since turbulence is an important factor to maintain clouds in a mixed phase, it is hypothesized that turbulence may have an impact on the seeding effect. This hypothesis is evident in the present study, which shows that turbulence can accelerate the impact of airborne glaciogenic seeding of stratiform clouds.
Sisi Chen, Lulin Xue, Sarah A. Tessendorf, Thomas Chubb, Andrew Peace, Suzanne Kenyon, Johanna Speirs, Jamie Wolff, and Bill Petzke
Atmos. Chem. Phys., 25, 6703–6724, https://doi.org/10.5194/acp-25-6703-2025, https://doi.org/10.5194/acp-25-6703-2025, 2025
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This study aims to investigate how cloud seeding affects snowfall in Australia's Snowy Mountains. By running simulations with different setups, we found that seeding impact varies greatly with weather conditions. Seeding increased snow in stable weather but sometimes reduced it in stormy weather. This helps us to better understand when seeding works best to boost water supplies.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Jing Yang, Jiaojiao Li, Meilian Chen, Xiaoqin Jing, Yan Yin, Bart Geerts, Zhien Wang, Yubao Liu, Baojun Chen, Shaofeng Hua, Hao Hu, Xiaobo Dong, Ping Tian, Qian Chen, and Yang Gao
Atmos. Chem. Phys., 24, 13833–13848, https://doi.org/10.5194/acp-24-13833-2024, https://doi.org/10.5194/acp-24-13833-2024, 2024
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Detecting unambiguous signatures is vital for examining cloud-seeding impacts, but often, seeding signatures are immersed in natural variability. In this study, reflectivity changes induced by glaciogenic seeding using different AgI concentrations are investigated under various conditions, and a method is developed to estimate the AgI concentration needed to detect unambiguous seeding signatures. The results aid in operational seeding-based decision-making regarding the amount of AgI dispersed.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Jingting Huang, S. Marcela Loría-Salazar, Min Deng, Jaehwa Lee, and Heather A. Holmes
Atmos. Chem. Phys., 24, 3673–3698, https://doi.org/10.5194/acp-24-3673-2024, https://doi.org/10.5194/acp-24-3673-2024, 2024
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Increased wildfire intensity has resulted in taller wildfire smoke plumes. We investigate the vertical structure of wildfire smoke plumes using aircraft lidar data and establish two effective smoke plume height metrics. Four novel satellite-based plume height products are evaluated for wildfires in the western US. Our results provide guidance on the strengths and limitations of these satellite products and set the stage for improved plume rise estimates by leveraging satellite products.
Sisi Chen, Lulin Xue, Sarah Tessendorf, Kyoko Ikeda, Courtney Weeks, Roy Rasmussen, Melvin Kunkel, Derek Blestrud, Shaun Parkinson, Melinda Meadows, and Nick Dawson
Atmos. Chem. Phys., 23, 5217–5231, https://doi.org/10.5194/acp-23-5217-2023, https://doi.org/10.5194/acp-23-5217-2023, 2023
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The possible mechanism of effective ice growth in the cloud-top generating cells in winter orographic clouds is explored using a newly developed ultra-high-resolution cloud microphysics model. Simulations demonstrate that a high availability of moisture and liquid water is critical for producing large ice particles. Fluctuations in temperature and moisture down to millimeter scales due to cloud turbulence can substantially affect the growth history of the individual cloud particles.
Yabin Gou, Haonan Chen, Hong Zhu, and Lulin Xue
Atmos. Chem. Phys., 23, 2439–2463, https://doi.org/10.5194/acp-23-2439-2023, https://doi.org/10.5194/acp-23-2439-2023, 2023
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This article investigates the complex precipitation microphysics associated with super typhoon Lekima using a host of in situ and remote sensing observations, including rain gauge and disdrometer data, as well as polarimetric radar observations. The impacts of precipitation microphysics on multi-source data consistency and radar precipitation estimation are quantified. It is concluded that the dynamical precipitation microphysical processes must be considered in radar precipitation estimation.
Istvan Geresdi, Lulin Xue, Sisi Chen, Youssef Wehbe, Roelof Bruintjes, Jared A. Lee, Roy M. Rasmussen, Wojciech W. Grabowski, Noemi Sarkadi, and Sarah A. Tessendorf
Atmos. Chem. Phys., 21, 16143–16159, https://doi.org/10.5194/acp-21-16143-2021, https://doi.org/10.5194/acp-21-16143-2021, 2021
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By releasing soluble aerosols into the convective clouds, cloud seeding potentially enhances rainfall. The seeding impacts are hard to quantify with observations only. Numerical models that represent the detailed physics of aerosols, cloud and rain formation are used to investigate the seeding impacts on rain enhancement under different natural aerosol backgrounds and using different seeding materials. Our results indicate that seeding may enhance rainfall under certain conditions.
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Short summary
We developed a new machine learning approach to estimate the height of the mixing layer in the lower atmosphere, which is important for predicting weather and air quality. By using daily temperature and heat patterns, the model learns how the atmosphere changes throughout the day. It gives accurate results across different locations and seasons, helping improve future climate and weather forecasts through better understanding of surface–atmosphere interactions.
We developed a new machine learning approach to estimate the height of the mixing layer in the...
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