Articles | Volume 6, issue 5
https://doi.org/10.5194/gmd-6-1813-2013
© Author(s) 2013. 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-6-1813-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
29 Oct 2013
Model description paper |
| 29 Oct 2013
The Subgrid Importance Latin Hypercube Sampler (SILHS): a multivariate subcolumn generator
V. E. Larson
Department of Mathematical Sciences, University of Wisconsin – Milwaukee, Milwaukee, WI, USA
D. P. Schanen
Department of Mathematical Sciences, University of Wisconsin – Milwaukee, Milwaukee, WI, USA
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Hui Wan, Shixuan Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, and Huiping Yan
Geosci. Model Dev., 14, 1921–1948, https://doi.org/10.5194/gmd-14-1921-2021, https://doi.org/10.5194/gmd-14-1921-2021, 2021
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Brian M. Griffin and Vincent E. Larson
Geosci. Model Dev., 9, 2031–2053, https://doi.org/10.5194/gmd-9-2031-2016, https://doi.org/10.5194/gmd-9-2031-2016, 2016
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A multivariate probability density function (PDF) can be used to represent the subgrid (below grid-box size) variability of atmospheric fields. The PDF was previously extended to include hydrometeor fields, such as rain water mixing ratio. Now, the PDF of hydrometeor fields is altered to account for precipitating and precipitation-less regions of the subgrid domain. Accounting for these regions allowed the hydrometeor PDF to produce an improved match to results from large-eddy simulations.
E. K. Raut and V. E. Larson
Geosci. Model Dev., 9, 413–429, https://doi.org/10.5194/gmd-9-413-2016, https://doi.org/10.5194/gmd-9-413-2016, 2016
Short summary
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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
Short summary
Short summary
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.
R. L. Storer, B. M. Griffin, J. Höft, J. K. Weber, E. Raut, V. E. Larson, M. Wang, and P. J. Rasch
Geosci. Model Dev., 8, 1–19, https://doi.org/10.5194/gmd-8-1-2015, https://doi.org/10.5194/gmd-8-1-2015, 2015
Short summary
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Representing clouds in climate models is a challenging problem. It is particularly difficult to represent deep convective clouds and, historically, deep convective parameterization is separate from the representation of other cloud types. Here we use a single-column cloud model to simulate three deep convective cases, and two shallow cloud cases. The results look reasonable, demonstrating that it may be possible to use one parameterization within a climate model for all cloud types.
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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.
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.
Cited articles
Barker, H.: Representing cloud overlap with an effective decorrelation length: A}n assessment using {CloudSat and CALIPSO data, J. Geophys. Res., 113, D24205, https://doi.org/10.1029/2008JD010391, 2008.
Barker, H. W., Pincus, R., and Morcrette, J.-J.: The M}onte Carlo Independent Column Approximation: Application within large-scale models, in: Proceedings of the {GCSS workshop, Kananaskis, Alberta, Canada, Amer. Meteor. Soc., 2002.
Barker, H. W., Cole, J. N. S., Morcrette, J.-J., Pincus, R., Räisänen, P., von Salzen, K., and Vaillancourt, P. A.: The Monte Carlo Independent Column Approximation: An Assessment using Several Global Atmospheric Models, Q. J. Roy. Meteor. Soc., 134, 1463–1478, 2008.
Bergman, J. W. and Rasch, P. J.: Parameterizing vertically coherent cloud distributions, J. Atmos. Sci., 59, 2165–2182, 2002.
Berner, J., Doblas-Reyes, F. J., Palmer, T. N., Shutts, G., and Weisheimer, A.: Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model, Phil. Trans. R. Soc. A, 366, 2561–2579, https://doi.org/10.1098/rsta.2008.0033, 2008.
Berner, J., Jung, T., and Palmer, T. N.: Systematic model error: the impact of increased horizontal resolution versus improved stochastic and deterministic parameterizations., J. Climate, 25, 4946–4962, https://doi.org/10.1175/JCLI-D-11-00297.1, 2012.
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., and Schanen, D. P.: Higher-Order Turbulence Closure and Its Impact on Climate Simulations in the Community Atmosphere Model, J. Climate, https://doi.org/10.1175/JCLI-D-13-00075.1, 2013.
Bony, S. and Emanuel, K. A.: A parameterization of the cloudiness associated with cumulus convection; Evaluation using TOGA COARE data, J. Atmos. Sci., 58, 3158–3183, 2001.
Buizza, R., Miller, M., and Palmer, T. N.: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System, Q. J. Roy. Meteor. Soc., 125, 2887–2908, 1999.
Cahalan, R. F., Ridgway, W., Wiscombe, W. J., Bell, T. L., and Snider, J. K.: The albedo of fractal stratocumulus clouds, J. Atmos. Sci., 51, 2434–2455, 1994.
Cheng, A. and Xu, K.-M.: A PDF-Based Microphysics Parameterization for Simulation of Drizzling Boundary Layer Clouds, J. Atmos. Sci., 66, 2317–2334, 2009.
Garvey, P. R.: Probability methods for cost uncertainty analysis, Marcel Dekker, 2000.
Gentle, J. E.: Random Number Generation and Monte Carlo Methods, Springer, 2nd edn., 2003.
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, 2002.
Griffin, B. M. and Larson, V. E.: Analytic upscaling of local microphysics parameterizations, Part II: Simulations, Q. J. Roy. Meteorol. Soc., 139, 58–69, 2013.
Hill, P. G., Manners, J., and Petch, J. C.: Reducing noise associated with the Monte Carlo Independent Column Approximation for weather forecasting models, Q. J. Roy. Meteorol. Soc., 137, 219–228, 2011.
Hogan, R. J. and Illingworth, A. J.: Deriving cloud overlap statistics from radar, Q. J. Roy. Meteorol. Soc., 126, 2903–2909, 2000.
Johnson, M. E.: Multivariate Statistical Simulation, John Wiley and Sons, 1987.
Khairoutdinov, M. and Kogan, Y.: A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus, Mon. Weather Rev., 128, 229–243, 2000.
Larson, V. E.: From cloud overlap to PDF overlap, Q. J. Roy. Meteorol. Soc., 133, 1877–1891, https://doi.org/10.1002/qj.165, 2007.
Larson, V. E. and Golaz, J.-C.: Using probability density functions to derive consistent closure relationships among higher-order moments, Mon. Weather Rev., 133, 1023–1042, 2005.
Larson, V. E. and Griffin, B. M.: Analytic upscaling of local microphysics parameterizations, Part I: Derivation, Q. J. Roy. Meteorol. Soc., 139, 46–57, 2013.
Larson, V. E., Wood, R., Field, P. R., Golaz, J.-C., Vonder Haar, T. H., and Cotton, W. R.: Systematic biases in the microphysics and thermodynamics of numerical models that ignore subgrid-scale variability, J. Atmos. Sci., 58, 1117–1128, 2001.
Larson, V. E., Golaz, J.-C., Jiang, H., and Cotton, W. R.: Supplying Local Microphysics Parameterizations with Information about Subgrid Variability: Latin Hypercube Sampling, J. Atmos. Sci., 62, 4010–4026, 2005.
Larson, V. E., Nielsen, B. J., Fan, J., and Ovchinnikov, M.: Parameterizing correlations between hydrometeors in mixed-phase Arctic clouds, J. Geophys. Res., 116, D00T02, https://doi.org/10.1029/2010JD015570, 2011.
Larson, V. E., Schanen, D. P., Wang, M., Ovchinnikov, M., and Ghan, S.: PDF parameterization of boundary layer clouds in models with horizontal grid spacings from 2 to 16 km, Mon. Weather Rev., 140, 285–306, 2012.
Lewellen, W. S. and Yoh, S.: Binormal model of ensemble partial cloudiness, J. Atmos. Sci., 50, 1228–1237, 1993.
Lin, J. W. B. and Neelin, J. D.: Influence of a stochastic moist convective parameterization on tropical climate variability, Geophys. Res. Lett., 27, 3691–3694, 2000.
Matsumoto, M. and Nishimura, T.: Mersenne Twister: A 623-dimensionally equidistributed uniform pseudo-random number generator, ACM Trans. Model. Comput. Simul., 8, 3–30, 1998.
McKay, M. D., Beckman, R. J., and Conover, W. J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21, 239–245, 1979.
Mellor, G. L.: The Gaussian cloud model relations, J. Atmos. Sci., 34, 356–358, 1977.
Morcrette, J.-J. and Fouquart, Y.: The overlapping of cloud layers in shortwave radiation parameterization, J. Atmos. Sci., 43, 321–328, 1986.
Morrison, H. and Gettelman, A.: A New Two-Moment Bulk Stratiform Cloud Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3). Part I: Description and Numerical Tests, J. Climate, 21, 3642–3659, 2008.
Norris, P. M., Oreopoulos, L., Hou, A. Y., Tao, W.-K., and Zeng, X.: Representation of 3D heterogeneous cloud fields using copulas: Theory for water clouds, Q. J. Roy. Meteorol. Soc., 134, 1843–1864, 2008.
Oreopoulos, L., Lee, D., Sud, Y. C., and Suarez, M. J.: Radiative impacts of cloud heterogeneity and overlap in an atmospheric General Circulation Model, Atmos. Chem. Phys., 12, 9097–9111, https://doi.org/10.5194/acp-12-9097-2012, 2012.
Owen, A. B.: Quasi-Monte Carlo Techniques, in: Siggraph 2003, Course 44, San Diego, CA, Association for Computing Machinery, 2003.
Palmer, T. N. and Williams, P. D.: Introduction. Stochastic physics and climate modelling, Phil. Trans. R. Soc. A, 366, 2421–2427, https://doi.org/10.1098/rsta.2008.0059, 2008.
Pincus, R., Barker, H. W., and Morcrette, J.-J.: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields, J. Geophys. Res., 108, 4376, https://doi.org/10.1029/2002JD003322, 2003.
Pincus, R., Hannay, C., Klein, S. A., Xu, K.-M., and Hemler, R.: Overlap assumptions for assumed probability distribution function cloud schemes in large-scale models, J. Geophys. Res., 110, D15S09, https://doi.org/10.1029/2004JD005100, 2005.
Pincus, R., Hemler, R., and Klein, S. A.: Using stochastically-generated subcolumns to represent cloud structure in a large-scale model, Mon. Weather Rev., 134, 3644–3656, 2006.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Numerical Recipes in C: The art of scientific computing, Cambridge University Press, 2nd Edn., 1992.
Räisänen, P. and Barker, H. W.: Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation, Q. J. Roy. Meteorol. Soc., 130, 2069–2085, 2004.
Räisänen, P., Barker, H. W., Khairoutdinov, M. F., Li, J., and Randall, D. A.: Stochastic generation of subgrid-scale cloudy columns for large-scale models, Q. J. Roy. Meteorol. Soc., 130, 2047–2067, 2004.
Räisänen, P., Barker, H. W., and Cole, J. N. S.: The Monte Carlo Independent Column Approximation's conditional random noise: Impact on simulated climate, J. Climate, 18, 4715–4730, https://doi.org/10.1175/JCLI3556.1, 2005.
Räisänen, P., Järvenoja, S., Järvinen, H., Giorgetta, M., Roeckner, E., Jylhä, K., and Ruosteenoja, K.: Tests of M}onte Carlo Independent Column Approximation in the {ECHAM5 Atmospheric GCM, J. Climate, 20, 4995–5011, https://doi.org/10.1175/JCLI4290.1, 2007.
Räisänen, P., Järvenoja, S., and Järvinen, H.: Noise due to the M}onte Carlo independent-column approximation: short-term and long-term impacts in {ECHAM5, Q. J. Roy. Meteorol. Soc., 134, 481–495, 2008.
Rauber, R. M., Rauber, R. M., Stevens, B., Ochs, H. T., Knight, C., Albrecht, B. A., Blyth, A. M., Fairall, C. W., Jensen, J. B., Lasher-Trapp, S. G., Mayol-Bracero, O. L., Vali, G., Anderson, J. R., Baker, B. A., Bandy, A. R., Burnet, E., Brenguier, J.-L., Brewer, W. A., Brown, P. R. A., Chuang, P., Cotton, W. R., Girolamo, L. D., Geerts, B., Gerber, H., Göke, S., Gomes, L., Heikes, B. G., Hudson, J. G., Kollias, P., Lawson, R. P., Krueger, S. K., Lenschow, D. H., Nuijens, L., O'Sullivan, D. W., Rilling, R. A., Rogers, D. C., Siebesma, A. P., Snodgrass, E., Stith, J. L., Thornton, D. C., Tucker, S., Twohy, C. H., , Zuidema, P., Sperber, K. R., and Waliser, D. E.: Rain in Shallow Cumulus Over the Ocean: T}he {RICO Campaign, Bull. Amer. Meteor. Soc., 88, 1912–1928, 2007.
Shonk, J. K. P. and Hogan, R. J.: Tripleclouds: A}n efficient method for representing horizontal cloud inhomogeneity in {1D radiation schemes by using three regions at each height, J. Climate, 21, 2352–2370, 2008.
Shonk, J. K. P., Hogan, R. J., Mace, G. G., and Edwards, J. M.: Effect of improving representation of horizontal and vertical cloud structure on the Earth's global radiation budget, Part I: review and parametrisation, Q. J. Roy. Meteorol. Soc., 136, 1191–1204, 2010.
Shonk, J. K. P., Hogan, R. J., and Manners, J.: Impact of improved representation of horizontal and vertical cloud structure in a climate model, Clim. Dynam., 38, 2365–2376, 2012.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., and Powers, J. G.: A description of the A}dvanced Research {WRF Version 2., Tech. Report NCAR TN-468 STR, Natl. Cent. for Atmos. Res., Boulder, CO, 2005.
Smith, R. N. B.: A scheme for predicting layer clouds and their water content in a general circulation model, Q. J. Roy. Meteor. Soc., 116, 435–460, 1990.
Sommeria, G. and Deardorff, J. W.: Subgrid-scale condensation in models of nonprecipitating clouds, J. Atmos. Sci., 34, 344–355, 1977.
Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist, B., Brenguier, J.-L., Bretherton, C. S., Burnet, F., Campos, T., Chai, S., Faloona, I., Friesen, D., Haimov, S., Laursen, K., Lilly, D. K., Loehrer, S. M., Malinowski, S. P., Morley, B., Petters, M. D., Rogers, D. C., Russell, L., Savic-Jovcic, V., Snider, J. R., Straub, D., Szumowski, M. J., Takagi, H., Thornton, D. C., Tschudi, M., Twohy, C., Wetzel, M., and V}an Zanten, M. C.: Dynamics and chemistry of marine stratocumulus – {DYCOMS-II, Bull. Amer. Meteor. Soc., 84, 579–593, 2003.
Teixeira, J. and Reynolds, C. A.: Stochastic Nature of Physical Parameterizations in Ensemble Prediction: A Stochastic Convection Approach, Mon. Weather Rev., 136, 483–496, 2008.
Tompkins, A. M.: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover, J. Atmos. Sci., 59, 1917–1942, 2002.
vanZanten, M., Stevens, B., Nuijens, L., Siebesma, A., Ackerman, A., Burnet, F., Cheng, A., Couvreux, F., Jiang, H., Khairoutdinov, M., Kogan, Y., Lewellen, D., Mechem, D., Nakamura, K., Noda, A., Shipway, B., Slawinska, J., Wang, S., and Wyszogrodzki, A.: Controls on precipitation and cloudiness in simulations of trade-wind cumulus as observed during RICO, J. Adv. Model. Earth Syst., 3, M06001, https://doi.org/10.1029/2011MS000056, 2011.
Weber, T. and Quaas, J.: Incorporating the subgrid-scale variability of clouds in the autoconversion parameterization using a PDF-scheme, J. Adv. Model. Earth Syst., 4, M11003, https://doi.org/10.1029/2012MS000156, 2012.
Wyant, M. C., Bretherton, C. S., Chlond, A., Griffin, B. M., Kitagawa, H., Lappen, C.-L., Larson, V. E., Lock, A., Park, S., de Roode, S. R., Uchida, J., Zhao, M., and Ackerman, A. S.: A Single-Column-Model Intercomparison of a Heavily Drizzling Stratocumulus Topped Boundary Layer, J. Geophys. Res., 112, D24204, https://doi.org/10.1029/2007JD008536, 2007.
Zhang, J., Lohmann, U., and Lin, B.: A new statistically based autoconversion rate parameterization for use in large-scale models, J. Geophys. Res., 107, 4750, https://doi.org/10.1029/2001JD001484, 2002.
