Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-1909-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1909-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 Apr 2023
Development and technical paper |
| 06 Apr 2023
| 06 Apr 2023
Improving the representation of shallow cumulus convection with the simplified-higher-order-closure–mass-flux (SHOC+MF v1.0) approach
Maria J. Chinita
CORRESPONDING AUTHOR
Joint Institute for Regional Earth System Science and Engineering, University of California Los Angeles,
Los Angeles, California, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Mikael Witte
Joint Institute for Regional Earth System Science and Engineering, University of California Los Angeles,
Los Angeles, California, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Department of Meteorology, Naval Postgraduate School, Monterey, California, USA
Marcin J. Kurowski
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Joao Teixeira
Joint Institute for Regional Earth System Science and Engineering, University of California Los Angeles,
Los Angeles, California, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Kay Suselj
Joint Institute for Regional Earth System Science and Engineering, University of California Los Angeles,
Los Angeles, California, USA
Running Tide Technologies, Inc., Portland, Maine, USA
Georgios Matheou
Department of Mechanical Engineering, University of Connecticut, Storrs, Connecticut, USA
Peter Bogenschutz
Lawrence Livermore National Laboratory, Livermore, California, USA
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Peter A. Bogenschutz, Shuaiqi Tang, Peter M. Caldwell, Shaocheng Xie, Wuyin Lin, and Yao-Sheng Chen
Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, https://doi.org/10.5194/gmd-13-4443-2020, 2020
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Siraput Jongaramrungruang, Christian Frankenberg, Georgios Matheou, Andrew K. Thorpe, David R. Thompson, Le Kuai, and Riley M. Duren
Atmos. Meas. Tech., 12, 6667–6681, https://doi.org/10.5194/amt-12-6667-2019, https://doi.org/10.5194/amt-12-6667-2019, 2019
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Christoph Heinze, Veronika Eyring, Pierre Friedlingstein, Colin Jones, Yves Balkanski, William Collins, Thierry Fichefet, Shuang Gao, Alex Hall, Detelina Ivanova, Wolfgang Knorr, Reto Knutti, Alexander Löw, Michael Ponater, Martin G. Schultz, Michael Schulz, Pier Siebesma, Joao Teixeira, George Tselioudis, and Martin Vancoppenolle
Earth Syst. Dynam., 10, 379–452, https://doi.org/10.5194/esd-10-379-2019, https://doi.org/10.5194/esd-10-379-2019, 2019
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Earth system models for producing climate projections under given forcings include additional processes and feedbacks that traditional physical climate models do not consider. We present an overview of climate feedbacks for key Earth system components and discuss the evaluation of these feedbacks. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research.
Luis F. Millán, Matthew D. Lebsock, and Joao Teixeira
Atmos. Chem. Phys., 19, 8491–8502, https://doi.org/10.5194/acp-19-8491-2019, https://doi.org/10.5194/acp-19-8491-2019, 2019
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The synergy of the collocated Advanced Microwave Scanning Radiometer (AMSR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global estimates of marine boundary layer water vapor. AMSR provides the total column water vapor, while MODIS provides the water vapor above the cloud layers. The difference between the two gives the vapor between the surface and the cloud top, which may be interpreted as the boundary layer water vapor.
Dejian Fu, Susan S. Kulawik, Kazuyuki Miyazaki, Kevin W. Bowman, John R. Worden, Annmarie Eldering, Nathaniel J. Livesey, Joao Teixeira, Fredrick W. Irion, Robert L. Herman, Gregory B. Osterman, Xiong Liu, Pieternel F. Levelt, Anne M. Thompson, and Ming Luo
Atmos. Meas. Tech., 11, 5587–5605, https://doi.org/10.5194/amt-11-5587-2018, https://doi.org/10.5194/amt-11-5587-2018, 2018
Jesse Dorrestijn, Brian H. Kahn, João Teixeira, and Fredrick W. Irion
Atmos. Meas. Tech., 11, 2717–2733, https://doi.org/10.5194/amt-11-2717-2018, https://doi.org/10.5194/amt-11-2717-2018, 2018
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Atmospheric Infrared Sounder (AIRS) satellite observations are used to quantify the scale-dependent variance of temperature and water vapor in the atmosphere. The scale dependence is much more variable than previously thought, using a new methodology based on individual satellite swaths. A break in the scale dependence is found to vary from less than 100 to greater than 1000 km. These new variance scaling results are of high importance for improving climate GCM subgrid parameterizations.
Peter A. Bogenschutz, Andrew Gettelman, Cecile Hannay, Vincent E. Larson, Richard B. Neale, Cheryl Craig, and Chih-Chieh Chen
Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, https://doi.org/10.5194/gmd-11-235-2018, 2018
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This paper compares results of developmental versions of a widely used climate model. The simulations only differ in the choice of how to model the sub-grid-scale physics in the atmospheric model. This work is novel because it is the first time that a particular physics option has been tested in a fully coupled climate model. Here, we demonstrate that this physics option has the ability to produce credible coupled climate simulations, with improved metrics in certain fields.
Brian H. Kahn, Georgios Matheou, Qing Yue, Thomas Fauchez, Eric J. Fetzer, Matthew Lebsock, João Martins, Mathias M. Schreier, Kentaroh Suzuki, and João Teixeira
Atmos. Chem. Phys., 17, 9451–9468, https://doi.org/10.5194/acp-17-9451-2017, https://doi.org/10.5194/acp-17-9451-2017, 2017
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The global-scale patterns of subtropical marine boundary layer clouds are investigated with coincident NASA A-train satellite and reanalysis data. This study is novel in that all data are used at the finest spatial and temporal resolution possible. Our results are consistent with surface-based data and suggest that the combination of satellite and reanalysis data sets have potential to add to the global context of our understanding of the subtropical cumulus-dominated marine boundary layer.
K. Thayer-Calder, A. Gettelman, C. Craig, S. Goldhaber, P. A. Bogenschutz, C.-C. Chen, H. Morrison, J. Höft, E. Raut, B. M. Griffin, J. K. Weber, V. E. Larson, M. C. Wyant, M. Wang, Z. Guo, and S. J. Ghan
Geosci. Model Dev., 8, 3801–3821, https://doi.org/10.5194/gmd-8-3801-2015, https://doi.org/10.5194/gmd-8-3801-2015, 2015
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This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that is implemented in CAM v5.3. We show mean climate and tropical variability results from global simulations. The model has a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. We also show estimation of computational expense and sensitivity to number of subcolumns.
M. K. Witte, P. Y. Chuang, and G. Feingold
Atmos. Chem. Phys., 14, 6729–6738, https://doi.org/10.5194/acp-14-6729-2014, https://doi.org/10.5194/acp-14-6729-2014, 2014
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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
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev., 18, 4855–4876, https://doi.org/10.5194/gmd-18-4855-2025, https://doi.org/10.5194/gmd-18-4855-2025, 2025
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In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled aerosol–meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the China Meteorological Administration Global Forecasting System (CMA-GFS) operational global numerical weather model. The results show that assimilating BC (black carbon) observations can generate analysis increments not only for BC but also for atmospheric variables.
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev., 18, 4667–4684, https://doi.org/10.5194/gmd-18-4667-2025, https://doi.org/10.5194/gmd-18-4667-2025, 2025
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We develop the data-driven method of dynamic mode decomposition for producing a robust and stable surrogate reduced-order model of atmospheric chemistry dynamics. The model is computationally efficient, provides interpretable patterns of activity, and produces uncertainty quantification metrics. It is ideal for the forecasting of atmospheric chemistry in a computationally tractable manner.
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Geosci. Model Dev., 18, 4625–4641, https://doi.org/10.5194/gmd-18-4625-2025, https://doi.org/10.5194/gmd-18-4625-2025, 2025
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This study evaluates various machine learning and statistical methods for interpolating turbulent heat flux data over the Tibetan Plateau. The Transformer model showed the best performance, leading to the development of the Transformer_CNN model, which combines global and local attention mechanisms. Results show that Transformer_CNN outperforms the other models and was successfully applied to interpolate heat flux data from 2007 to 2016.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025, https://doi.org/10.5194/gmd-18-4571-2025, 2025
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We introduce a new simulation platform based on the Dutch Atmospheric Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in turbulent environments at a hectometer resolution. This model incorporates both anthropogenic emission inventories and online ecosystem fluxes. Simulation results for the main urban area in the Netherlands demonstrate the strong potential of DALES to improve CO2 emission modeling and to support mitigation strategies.
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
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Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025, https://doi.org/10.5194/gmd-18-4417-2025, 2025
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A numerical model that simulates the measurement processes behind the ground-based radars used to detect volcanic ash clouds is introduced. Using weather radars to detect volcanic clouds is not ideal, as fine ash particles are smaller than raindrops and remain undetected. We evaluate the performance of weather radars to study ash clouds and to identify optimal frequencies that balance the trade-off between a higher return signal and the higher path attenuation that comes at these higher frequencies.
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025, https://doi.org/10.5194/gmd-18-4433-2025, 2025
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Photochemical models describe how the composition of the atmosphere changes due to chemical reactions, transport, and other processes. These models are useful for studying the composition of the Earth's and other planets' atmospheres. Understanding the results of these models can be difficult. Here, we build on previous work to develop open-source code that can identify the reaction chains (pathways) that produce the results of these models, facilitating the understanding of these results.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025, https://doi.org/10.5194/gmd-18-4353-2025, 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, this model is valuable for airglow research and astronomical observatories.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025, https://doi.org/10.5194/gmd-18-4335-2025, 2025
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We introduce a new version of WAM2layers (Water Accounting Model – 2 layers), a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data had become a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent, reliable, and easier to maintain.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
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We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
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A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
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SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025, https://doi.org/10.5194/gmd-18-3921-2025, 2025
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It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
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Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
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.
Yuming Jin, Britton B. Stephens, Matthew C. Long, Naveen Chandra, Frédéric Chevallier, Joram J. D. Hooghiem, Ingrid T. Luijkx, Shamil Maksyutov, Eric J. Morgan, Yosuke Niwa, Prabir K. Patra, Christian Rödenbeck, and Jesse Vance
EGUsphere, https://doi.org/10.5194/egusphere-2025-1736, https://doi.org/10.5194/egusphere-2025-1736, 2025
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We carry out a comprehensive atmospheric transport model (ATM) intercomparison project. This project aims to evaluate errors in ATMs and three air-sea O2 exchange products by comparing model simulations with observations collected from surface stations, ships, and aircraft. We also present a model evaluation framework to independently quantify transport-related and flux-related biases that contribute to model-observation discrepancies in atmospheric tracer distributions.
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.
Joseph Mouallem, Kun Gao, Brandon G. Reichl, Lauren Chilutti, Lucas Harris, Rusty Benson, Niki Zadeh, Jing Chen, Jan-Huey Chen, and Cheng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1690, https://doi.org/10.5194/egusphere-2025-1690, 2025
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We introduce a new high-resolution model that couple the atmosphere and ocean to better simulate extreme weather events. It combines GFDL’s advanced atmospheric and ocean models with a powerful coupling system that allows robust and efficient two-way interactions. Simulations show the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model is a key step toward improving extreme weather forecast.
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.
Travis Hahn, Hershel Weiner, Calvin Brooks, Jie Xi Li, Siddhant Gupta, and Dié Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1328, https://doi.org/10.5194/egusphere-2025-1328, 2025
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Understanding how clouds evolve is important for improving weather predictions, but existing tools for tracking cloud changes are complex and difficult to compare. To address this, we developed the Community Cloud Model Evaluation Toolkit (CoCoMET) that makes it easier to analyze clouds in both models and observations. By simplifying data processing, standardizing results, and introducing new analysis features, CoCoMET helps researchers better evaluate cloud behavior and improve models.
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
Short summary
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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.
Cited articles
Beljaars, A., Balsamo, G., Bechtold, P., Bozzo, A., Forbes, R., Hogan, R.
J., Köhler, M., Morcrette, J. J., Tompkins, A. M., Viterbo, P., and
Wedi, N.: The Numerics of Physical Parametrization in the ECMWF Model,
Front. Earth Sci., 6, 1–18, https://doi.org/10.3389/feart.2018.00137, 2018.
Betts, A. K.: Non-precipitating cumulus convection and its parameterization,
Q. J. Roy. Meteor. Soc., 99, 178–196,
https://doi.org/10.1002/qj.49709941915, 1973.
Blackadar, A. K.: The vertical distribution of wind and turbulent exchange
in a neutral atmosphere, J. Geophys. Res., 67, 3095–3102, 1962.
Bogenschutz, P. A. and Krueger, S. K.: A simplified PDF parameterization of
subgrid-scale clouds and turbulence for cloud-resolving models, J. Adv.
Model. Earth Sy., 5, 195–211, https://doi.org/10.1002/jame.20018, 2013.
Bogenschutz, P. A., Tang, S., Caldwell, P. M., Xie, S., Lin, W., and Chen, Y.-S.: The E3SM version 1 single-column model, Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, 2020.
Böing, S. J., Jonker, H. J. J., Siebesma, A. P., and Grabowski, W. W.:
Influence of the subcloud layer on the development of a deep convective
ensemble, J. Atmos. Sci., 69, 2682–2698,
https://doi.org/10.1175/JAS-D-11-0317.1, 2012.
Brient, F., Couvreux, F., Villefranque, N., Rio, C., and Honnert, R.:
Object-Oriented Identification of Coherent Structures in Large Eddy
Simulations: Importance of Downdrafts in Stratocumulus, Geophys. Res. Lett.,
46, 2854–2864, https://doi.org/10.1029/2018GL081499, 2019.
Brown, A. R., Cederwall, R. T., Chlond, A., Duynkerke, P. G., Golaz, J. C.,
Khairoutdinov, M., Lewellen, D. C., Lock, A. P., MacVean, M. K., Moeng, C.
H., Neggers, R. A. J., Siebesma, A. P., and Stevens, B.: Large-eddy
simulation of the diurnal cycle of shallow cumulus convection over land, Q. J. Roy. Meteor. Soc., 128, 1075–1093,
https://doi.org/10.1256/003590002320373210, 2002.
Caldwell, P. M., Terai, C. R., Hillman, B., Keen, N. D., Bogenschutz, P.,
Lin, W., Beydoun, H., Taylor, M., Bertagna, L., Bradley, A. M., Clevenger,
T. C., Donahue, A. S., Eldred, C., Foucar, J., Golaz, J. C., Guba, O.,
Jacob, R., Johnson, J., Krishna, J., Liu, W., Pressel, K., Salinger, A. G.,
Singh, B., Steyer, A., Ullrich, P., Wu, D., Yuan, X., Shpund, J., Ma, H. Y.,
and Zender, C. S.: Convection-Permitting Simulations With the E3SM Global
Atmosphere Model, J. Adv. Model. Earth Sy., 13, e2021MS002544,
https://doi.org/10.1029/2021MS002544, 2021.
Cheng, A. and Xu, K. M.: Simulation of shallow cumuli and their transition
to deep convective clouds by cloud-resolving models with different
third-order turbulence closures, Q. J. Roy. Meteor. Soc., 132, 359–382,
https://doi.org/10.1256/qj.05.29, 2006.
Cheng, A. and Xu, K. M.: Simulation of boundary-layer cumulus and
stratocumulus clouds using a cloud-resolving model with low- and third-order
turbulence closures, J. Meteorol. Soc. Japan, 86A, 67–86,
https://doi.org/10.2151/jmsj.86A.67, 2008.
Chinita, M.: Code for SHOC+MF_v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.7011628, 2022a.
Chinita, M.: Data for SHOC+MF_v1.0, Zenodo [data set], https://doi.org/10.5281/zenodo.7011652, 2022b.
Chinita, M. J., Matheou, G., and Teixeira, J.: A joint probability
density-based decomposition of turbulence in the atmospheric boundary layer,
Mon. Weather Rev., 146, 503–523, https://doi.org/10.1175/MWR-D-17-0166.1, 2018.
Chinita, M. J., Matheou, G., and Miranda, P. M. A.: Large-eddy simulation of
very stable boundary layers. Part I: Modeling methodology, Q. J. Roy. Meteor. Soc., 148, 1805–1823, https://doi.org/10.1002/qj.4279,
2022a.
Chinita, M. J., Matheou, G., and Miranda, P. M. A.: Large-eddy simulation of
very stable boundary layers. Part II: Length scales and anisotropy in
stratified atmospheric turbulence, Q. J. Roy. Meteor. Soc., 148, 1824–1839,
https://doi.org/10.1002/qj.4280, 2022b.
Chung, D. and Matheou, G.: Large-eddy simulation of stratified turbulence.
Part I: A vortex-based subgrid-scale model, J. Atmos. Sci., 71, 1863–1879,
https://doi.org/10.1175/JAS-D-13-0126.1, 2014.
Chung, D., Matheou, G., and Teixeira, J.: Steady-state large-eddy
simulations to study the stratocumulus to shallow cumulus cloud transition,
J. Atmos. Sci., 69, 3264–3276, https://doi.org/10.1175/JAS-D-11-0256.1,
2012.
Cohen, Y., Lopez-Gomez, I., Jaruga, A., He, J., Kaul, C. M., and Schneider,
T.: Unified Entrainment and Detrainment Closures for Extended
Eddy-Diffusivity Mass-Flux Schemes, J. Adv. Model. Earth Sy., 12,
e2020MS002162, https://doi.org/10.1029/2020MS002162, 2020.
Couvreux, F., Hourdin, F., and Rio, C.: Resolved versus parametrized
boundary-layer plumes. Part I: A parametrization-oriented conditional
sampling in large-eddy simulations, Bound.-Lay. Meteorol., 134, 441–458,
https://doi.org/10.1007/s10546-009-9456-5, 2010.
Couvreux, F., Bazile, E., Rodier, Q., Maronga, B., Matheou, G., Chinita, M.
J., Edwards, J., van Stratum, B. J. H., van Heerwaarden, C. C., Huang, J.,
Moene, A. F., Cheng, A., Fuka, V., Basu, S., Bou-Zeid, E., Canut, G., and
Vignon, E.: Intercomparison of Large-Eddy Simulations of the Antarctic
Boundary Layer for Very Stable Stratification, Bound.-Lay. Meteorol.,
176, 369–400, https://doi.org/10.1007/s10546-020-00539-4, 2020.
Deardorff, J. W.: The Counter-Gradient Heat Flux in the Lower Atmosphere and
in the Laboratory, J. Atmos. Sci., 23, 503–506, https://doi.org/10.1175/1520-0469(1966)023<0503:tcghfi>2.0.co;2, 1966.
E3SM Project, DOE: Energy Exascale Earth System Model v2.0. (Computer Software), DOE [software], https://doi.org/10.11578/E3SM/dc.20210927.1, 2021.
Firl, G. J. and Randall, D. A.: Fitting and analyzing les using multiple
trivariate Gaussians, J. Atmos. Sci., 72, 1094–1116,
https://doi.org/10.1175/JAS-D-14-0192.1, 2015.
Fitch, A. C.: An improved double-Gaussian closure for the subgrid vertical
velocity probability distribution function, J. Atmos. Sci., 76, 285–304,
https://doi.org/10.1175/JAS-D-18-0149.1, 2019.
Golaz, J. C., Larson, V. E., and Cotton, W. R.: A PDF-based model for
boundary layer clouds. Part I: Method and model description, J. Atmos. Sci.,
59, 3540–3551, https://doi.org/10.1175/1520-0469(2002)059<3540:APBMFB>2.0.CO;2, 2002.
Golaz, J. C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M.
A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J.
G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J.,
Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W.,
Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H. Y., Lin, W.,
Lipscomb, W. H., Ma, P. L., Mahajan, S., Maltrud, M. E., Mametjanov, A.,
McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch,
P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F.,
Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J.,
Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang,
H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang,
Y., Yoon, J. H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang,
K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model
Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019.
Han, J. and Bretherton, C. S.: TKE-based moist eddy-diffusivity mass-flux
(EDMF) parameterization for vertical turbulent mixing, Weather Forecast.,
34, 869–886, https://doi.org/10.1175/WAF-D-18-0146.1, 2019.
Han, J. and Pan, H. L.: Revision of convection and vertical diffusion
schemes in the NCEP Global Forecast System, Weather Forecast., 26, 520–533,
https://doi.org/10.1175/WAF-D-10-05038.1, 2011.
Han, J., Witek, M. L., Teixeira, J., Sun, R., Pan, H. L., Fletcher, J. K.,
and Bretherton, C. S.: Implementation in the NCEP GFS of a hybrid
eddy-diffusivity mass-flux (EDMF) boundary layer parameterization with
dissipative heating and modified stable boundary layer mixing, Weather
Forecast., 31, 341–352, https://doi.org/10.1175/WAF-D-15-0053.1, 2016.
Holtslag, A. A. M. and Boville, B. A.: Local versus nonlocal boundary-layer
diffusion in a global climate model, J. Climate, 6, 1825–1842,
https://doi.org/10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2, 1993.
Holtslag, A. A. M. and Moeng, C.-H.: Eddy diffusivity and countergradient
transport in the convective atmospheric boundary layer, J. Atmos. Sci., 48, 1690–1698,
https://doi.org/10.1175/1520-0469(1991)048<1690:EDACTI>2.0.CO;2, 1991.
Kohler, M., Ahlgrimm, M., and Beljaars, A.: Unified treatment of dry
convective and stratocumulus-topped boundary layers in the ECMWF model, Q. J. Roy. Meteor. Soc., 137, 43–57, https://doi.org/10.1002/qj.713, 2011.
Kurowski, M. J., Suselj, K., and Grabowski, W. W.: Is Shallow Convection
Sensitive to Environmental Heterogeneities?, Geophys. Res. Lett., 46,
1785–1793, https://doi.org/10.1029/2018GL080847, 2019a.
Kurowski, M. J., Thrastarson, H. T., Suselj, K., and Teixeira, J.: Towards
unifying the planetary boundary layer and shallow convection in CAM5 with
the eddy-diffusivity/mass-flux approach, Atmosphere, 10, 484,
https://doi.org/10.3390/atmos10090484, 2019b.
Lamaakel, O. and Matheou, G.: Galilean invariance of shallow cumulus
convection large-eddy simulations, J. Comput. Phys., 427, 110012,
https://doi.org/10.1016/j.jcp.2020.110012, 2021.
Matheou, G.: Turbulence Structure in a Stratocumulus Cloud, Atmosphere, 9, 392,
https://doi.org/10.3390/atmos9100392, 2018.
Matheou, G. and Chung, D.: Large-eddy simulation of stratified turbulence.
Part II: Application of the stretched-vortex model to the atmospheric
boundary layer, J. Atmos. Sci., 71, 4439–4460,
https://doi.org/10.1175/JAS-D-13-0306.1, 2014.
Matheou, G. and Teixeira, J.: Sensitivity to physical and numerical aspects
of large-eddy simulation of stratocumulus, Mon. Weather Rev., 147,
2621–2639, https://doi.org/10.1175/MWR-D-18-0294.1, 2019.
Matheou, G., Chung, D., and Teixeira, J.: On the synergy between numerics
and subgrid scale modeling in LES of stratified flows: Grid convergence of a
stratocumulus-topped boundary layer., in: Eighth Int. Symp. on Stratified Flows, Vol. 1, San Diego,
CA, NASA Jet Propulsion Laboratory, http://hdl.handle.net/2014/46171 (last access: 20 March 2023), 2016.
Morinishi, Y., Lund, T. S., Vasilyev, O. V, and Moin, P.: Fully Conservative
Higher Order Finite Difference Schemes for Incompressible Flow, J. Comput.
Phys., 143, 90–124, https://doi.org/10.1006/jcph.1998.5962,
1998.
Neggers, R. A. J.: A dual mass flux framework for boundary layer convection.
Part II: Clouds, J. Atmos. Sci., 66, 1489–1506,
https://doi.org/10.1175/2008JAS2636.1, 2009.
Olson, J. B., Kenyon, J. S., Angevine, W. A., Brown, J. M., Pagowski, M.,
and Sušelj, K.: A Description of the MYNN-EDMF Scheme and the Coupling
to Other Components in WRF–ARW, NOAA Technical Memorandum OAR GSD-61,
https://doi.org/10.25923/n9wm-be49, 2019.
Richtmyer, R. and Morton, K.: Difference methods for initial value
problems, 2nd edn., Wiley-Interscience, New York, 405 pp., 1967.
Rio, C. and Hourdin, F.: A thermal plume model for the convective boundary
layer: Representation of cumulus clouds, J. Atmos. Sci., 65, 407–425,
https://doi.org/10.1175/2007JAS2256.1, 2008.
Romps, D. M. and Kuang, Z.: Nature versus nurture in shallow convection, J.
Atmos. Sci., 67, 1655–1666, https://doi.org/10.1175/2009JAS3307.1, 2010.
Schneider, T., Teixeira, J., Bretherton, C. S., Brient, F., Pressel, K. G.,
Schär, C., and Siebesma, A. P.: Climate goals and computing the future
of clouds, Nat. Clim. Change, 7, 3–5, https://doi.org/10.1038/nclimate3190,
2017.
Sherwood, S. C., Bony, S., and Dufresne, J. L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42,
https://doi.org/10.1038/nature12829, 2014.
Siebesma, A., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J.,
Duynkerke, P. G., Jiang, H., Khairoutdinov, M., Lewellen, D., Moeng, C. H.,
Sanchez, E., Stevens, B., and Stevens, D. E.: A large eddy simulation
intercomparison study of shallow cumulus convection, J. Atmos. Sci., 60,
1201–1219, https://doi.org/10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2, 2003.
Siebesma, A. P. and Cuijpers, J. W. M.: Evaluation of parametric assumptions
for shallow cumulus convection, J. Atmos. Sci., 52, 650–666,
https://doi.org/10.1175/1520-0469(1995)052<0650:EOPAFS>2.0.CO;2, 1995.
Siebesma, A. P., Soares, P. M. M., and Teixeira, J.: A combined
eddy-diffusivity mass-flux approach for the convective boundary layer, J.
Atmos. Sci., 64, 1230–1248, https://doi.org/10.1175/JAS3888.1, 2007.
Siebesma, P. and Teixeira, J.: An advection-diffusion scheme for the
convective boundary layer: Description and 1D-results, in: Proc. 14th Symp. on Boundary Layers and Turbulence, Aspen, CO,
Amer. Meteor. Soc., 133–136, 2000.
Soares, P. M. M., Miranda, P. M. A., Siebesma, A. P., and Teixeira, J.: An
eddy-diffusivity/mass-flux parametrization for dry and shallow cumulus
convection, Q. J. Roy. Meteor. Soc., 130, 3365–3383,
https://doi.org/10.1256/qj.03.223, 2004.
Stevens, B.: Quasi-steady analysis of a PBL model with an Eddy-diffusivity
profile and nonlocal fluxes, Mon. Weather Rev., 128, 824–836,
https://doi.org/10.1175/1520-0493(2000)128<0824:QSAOAP>2.0.CO;2, 2000.
Suselj, K., Teixeira, J., and Chung, D.: A unified model for moist
convective boundary layers based on a stochastic eddy-diffusivity/mass-flux
parameterization, J. Atmos. Sci., 70, 1929–1953,
https://doi.org/10.1175/JAS-D-12-0106.1, 2013.
Suselj, K., Hogan, T. F., and Teixeira, J.: Implementation of a stochastic
eddy-diffusivity/mass-flux parameterization into the Navy Global
environmental model, Weather Forecast., 29, 1374–1390,
https://doi.org/10.1175/WAF-D-14-00043.1, 2014.
Suselj, K., Kurowski, M. J., and Teixeira, J.: A unified
eddy-diffusivity/mass-flux approach for modeling atmospheric convection, J.
Atmos. Sci., 76, 2505–2537, https://doi.org/10.1175/JAS-D-18-0239.1, 2019a.
Suselj, K., Kurowski, M. J., and Teixeira, J.: On the factors controlling
the development of shallow convection in eddy-diffusivity/mass-flux models,
J. Atmos. Sci., 76, 433–456, https://doi.org/10.1175/JAS-D-18-0121.1,
2019b.
Suselj, K., Teixeira, J., Kurowski, M. J., and Molod, A.: Improving the
representation of subtropical boundary layer clouds in the NASA GEOS model
with the eddy-diffusivity/mass-flux parameterization, Mon. Weather Rev.,
149, 793–809, https://doi.org/10.1175/MWR-D-20-0183.1, 2021.
Takahashi, H., Luo, Z. J., and Stephens, G.: Revisiting the Entrainment
Relationship of Convective Plumes: A Perspective From Global Observations,
Geophys. Res. Lett., 48, e2020GL092349, https://doi.org/10.1029/2020GL092349, 2021.
Tan, Z., Kaul, C. M., Pressel, K. G., Cohen, Y., Schneider, T., and
Teixeira, J.: An Extended Eddy-Diffusivity Mass-Flux Scheme for Unified
Representation of Subgrid-Scale Turbulence and Convection, J. Adv. Model. Earth Sy., 10, 770–800,
https://doi.org/10.1002/2017MS001162, 2018.
Teixeira, J. and Cheinet, S.: A simple mixing length formulation for the
eddy-diffusivity parameterization of dry convection, Bound.-Lay. Meteorol., 110, 435–453,
https://doi.org/10.1023/B:BOUN.0000007230.96303.0d, 2004.
Teixeira, J. and Siebesma, P.: A mass flux/K-diffusion approach to the
parameterization of the convective boundary layer: Global model results,
in: Proc. 14th Symp. on Boundary Layers and Turbulence, Aspen, CO,
Amer. Meteor. Soc., 231–234, 2000.
Teixeira, J., Ferreira, J. P., Miranda, P. M. A., Haack, T., Doyle, J.,
Siebsema, A. P., and Salgado, R.: A new mixing-length formulation for the
parameterization of dry convection: Implementation and evaluation in
mesoscale model, Mon. Weather Rev., 132, 2698–2707,
https://doi.org/10.1175/MWR2808.1, 2004.
Teixeira, J., Stevens, B., Bretherton, C. S., Cederwall, R., Doyle, J. D.,
Golaz, J. C., Holtslag, A. A. M., Klein, S. A., Lundquist, J. K., Randall,
D. A., Siebesma, A. P., and Soares, P. M. M.: Parameterization of the
atmospheric boundary layer: A view from just above the inversion, B. Am.
Meteorol. Soc., 89, 453–458, https://doi.org/10.1175/BAMS-89-4-453, 2008.
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization
in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800, 1989.
Witek, M. L., Teixeira, J., and Matheou, G.: An Integrated TKE-based eddy
diffusivity/mass flux boundary layer closure for the dry convective boundary
layer, J. Atmos. Sci., 68, 1526–1540,
https://doi.org/10.1175/2011JAS3548.1, 2011.
Witte, M. K., Herrington, A., Teixeira, J., Kurowski, M., Chinita, M. J.,
Storer, R. L., Suselj, K., Matheou, G., and Bacmeister, J.: Augmenting the
double-Gaussian representation of atmospheric turbulence and convection via
a coupled stochastic multi-plume mass flux scheme, Mon. Weather Rev., 150, 2339–2355,
https://doi.org/10.1175/MWR-D-21-0215.1, 2022.
Wu, E., Yang, H., Kleissl, J., Suselj, K., Kurowski, M. J., and Teixeira,
J.: On the parameterization of convective downdrafts for marine
stratocumulus clouds, Mon. Weather Rev., 148, 1931–1950,
https://doi.org/10.1175/MWR-D-19-0292.1, 2020.
Yoshimura, H., Mizuta, R., and Murakami, H.: A spectral cumulus
parameterization scheme interpolating between two convective updrafts with
semi-lagrangian calculation of transport by compensatory subsidence, Mon.
Weather Rev., 143, 597–621, https://doi.org/10.1175/MWR-D-14-00068.1, 2015.
Short summary
Low clouds are one of the largest sources of uncertainty in climate prediction. In this paper, we introduce the first version of the unified turbulence and shallow convection parameterization named SHOC+MF developed to improve the representation of shallow cumulus clouds in the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). Here, we also show promising preliminary results in a single-column model framework for two benchmark cases of shallow cumulus convection.
Low clouds are one of the largest sources of uncertainty in climate prediction. In this paper,...
