Articles | Volume 16, issue 24
https://doi.org/10.5194/gmd-16-7223-2023
© 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-16-7223-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
14 Dec 2023
Model experiment description paper |
| 14 Dec 2023
| 14 Dec 2023
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Liangke Huang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
Shengwei Lan
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
Ge Zhu
CORRESPONDING AUTHOR
College of Surveying and Geo-informatics, Tongji University, Shanghai, 200092, China
Fade Chen
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
Junyu Li
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
Lilong Liu
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, 541006, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin, 541006, China
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Leon Geers, Ruud Janssen, Gudrun Thorkelsdottir, Jordi Vilà-Guerau de Arellano, and Martijn Schaap
EGUsphere, https://doi.org/10.5194/egusphere-2025-426, https://doi.org/10.5194/egusphere-2025-426, 2025
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High-resolution data on reactive nitrogen deposition are needed to inform cost-effective policies. Here, we describe the implementation of a dry deposition module into a large eddy simulation code. With this model, we are able to represent the turbulent exchange of tracers at the hectometer resolution. The model calculates the dispersion and deposition of NOx and NH3 in great spatial detail, clearly showing the influence of local land use patterns.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Kasper Skjold Tølløse and Jens Havskov Sørensen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-173, https://doi.org/10.5194/gmd-2024-173, 2025
Preprint under review for GMD
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In this study, we improve the short-scale dispersion modelling capabilities of the Danish Emergency Response Model of the Atmosphere (DERMA) by developing and implementing a new hybrid particle-puff description of turbulent diffusion, as well as updating a few other parameterizations in the model. The new model is evaluated against data from three different tracer gas experiments, and the promising results are an important first step towards using DERMA also for short-range dispersion modelling.
Cited articles
Black, H. D.: An easily implemented algorithm for the tropospheric range correction, J. Geophys. Res., 83, 1825–1828, https://doi.org/10.1029/JB083iB04p01825, 1978.
Böhm, J., Heinkelmann, R., and Schuh, H.: Short note: A global model of pressure and temperature for geodetic applications, J. Geodesy, 81, 679–683, https://doi.org/10.1007/s00190-007-0135-3, 2007.
Böhm, J., Möller, G., Schindelegger, M., Pain, G., and Weber, R.: Development of an improved blind model for slant delays in the troposphere (GPT2w), GPS Solut., 19, 433–441, https://doi.org/10.1007/s10291-014-0403-7, 2015.
Bonafoni, S., Biondi, R., Brenot, H., and Anthes, R.: Radio occultation and ground-based GNSS products for observing, understanding and predicting extreme events: A review, Atmos. Res., 230, 104624, https://doi.org/10.1016/j.atmosres.2019.104624, 2019.
Chen, B. Y., Yu, W. K., Wang, W., Zhang, Z. T., and Dai, W. J.: A Global Assessment of Precipitable Water Vapor Derived From GNSS Zenith Tropospheric Delays With ERA5, NCEP FNL, and NCEP GFS Products, Earth and Space Science, 8, e2021EA001796, https://doi.org/10.1029/2021EA001796, 2021.
Chen, P., Ma, Y., Liu, H., and Zheng, N.: A new global tropospheric delay model considering the spatiotemporal variation characteristics of ZTD with altitude coefficient, Earth and Space Science, 7, e2019EA000888, https://doi.org/10.1029/2019EA000888, 2020.
Chen, S., Gan, T. Y., Tan, X. Z., Shao, D. G., and Zhu, J. Q.: Assessment of CFSR, ERA-Interim, JRA-55, MERRA-2, NCEP-2 reanalysis data for drought analysis over China, Clim. Dynam., 53, 737–757, https://doi.org/10.1007/s00382-018-04611-1, 2019.
Ding, J. S. and Chen, J. P.: Assessment of empirical troposphere model GPT3 based on NGL's global troposphere products, Sensors, 20, 3631, https://doi.org/10.3390/s20133631, 2020.
Gupta, P., Verma, S., Bhatla, R., Chandel, S. A., Singh, J., and Payra, S.: Validation of surface temperature derived from MERRA-2 Reanalysis against IMD gridded data set over India, Earth and Space Science, 7, e2019EA000910, https://doi.org/10.1029/2019EA000910, 2020.
Hopfield, H. S.: Two-Quartic tropospheric refractivity profile for correcting satellite data, J. Geophys. Res., 74, 4487–4499, https://doi.org/10.1029/JC074i018p04487, 1969.
Huang, L. K., Jiang, W. P., Liu, L. L., Chen, H., and Ye, S. R.: A new global grid model for the determination of atmospheric weighted mean temperature in GPS precipitable water vapor, J. Geodesy, 93, 159–176, https://doi.org/10.1007/s00190-018-1148-9, 2019.
Huang, L. K., Guo, L. J., Liu, L. L., Chen, H., Chen, J., and Xie, S. F.: Evaluation of the ZWD/ZTD values derived from MERRA-2 global reanalysis products using GNSS observations and radiosonde data, Sensors, 20, 6440, https://doi.org/10.3390/s20226440, 2020.
Huang, L. K., Zhu, G., Liu, L. L., Chen, H., and Jiang, W. P.: A global grid model for the correction of the vertical zenith total delay based on a sliding window algorithm, GPS Solut., 25, 98, https://doi.org/10.1007/s10291-021-01138-7, 2021.
Huang, L. K., Wang, X., Xiong, S., Li, J. Y., Liu, L. L., Mo, Z. X., Fu, B. L., and He, H. C.: High-precision GNSS PWV retrieval using dense GNSS sites and in-situ meteorological observations for the evaluation of MERRA-2 and ERA5 reanalysis products over China, Atmos. Res., 276, 106247, https://doi.org/10.1016/j.atmosres.2022.106247, 2022.
Huang, L., Zhu, G,, Peng, H., Liu, L., Ren, C., and Jiang, W.: An improved global grid model for calibrating zenith tropospheric delay for GNSS applications, GPS Solut., 27, 17, https://doi.org/10.1007/s10291-022-01354-9, 2023a.
Huang, L., Lan, S., Zhu, G., Chen, F., Li, J., and Liu, L.: A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes, Zenodo [data set], https://doi.org/10.5281/zenodo.8206173, 2023b.
Krueger, E., Schüler, T., Hein, G., and Martellucci, A.: Galileo tropospheric correction approaches developed within GSTB-V1, in: Proceedings of ENC-GNSS 2004, Rotterdam, the Netherlands, 16–19 May 2004, https://www.researchgate.net/publication/228730717_Galileo_Tropospheric_Correction_Approaches_Developed_within_GSTB-V1 (last access: 20 June 2023), 2004.
Lagler, K., Schindelegger, M., Böhm, J., Krásná, H., and Nilsson, T.: GPT2: empirical slant delay model for radio space geodetic techniques, Geophys. Res. Lett., 40, 1069–1073, https://doi.org/10.1002/grl.50288, 2013.
Landskron, D. and Böhm, J.: VMF3/GPT3: Refined discrete and empirical troposphere mapping functions, J. Geodesy, 92, 349–360, https://doi.org/10.1007/s00190-017-1066-2, 2018.
Leandro, R., Santos, M., and Langley, R.: UNB neutral atmosphere models: development and performance, in: Proceedings of the ION NTM 2006 Monterey, California USA 18–20 January 2006, 564–573, https://doi.org/10.1007/s10291-007-0077-5, 2006.
Leandro, R., Langley, R., and Santos, M.: UNB3m_ pack: A neutral atmosphere delay package for radiometric space techniques, GPS Solut., 12, 65–70, 2008.
Li, H., Zhu, G., Kang, Q., and Wang, H.: A global zenith tropospheric delay model with ERA5 and GNSS-based ZTD difference correction, GPS Solut., 27, 154, https://doi.org/10.1007/s10291-023-01503-8, 2023.
Li, Q. Z., Yuan, L. G., Chen, P., and Jiang, Z. S.: Global grid-based Tm model with vertical adjustment for GNSS precipitable water retrieval, GPS Solut., 24, 73, https://doi.org/10.1007/s10291-020-00988-x, 2020.
Li, W., Yuan, Y. B., Ou, J. K., and He, Y. J.: IGGtrop_SH and IGGtrop_rH: two improved empirical tropospheric delay models based on vertical reduction functions, IEEE T. Geosci. Remote, 56, 5276–5288, https://doi.org/10.1109/TGRS.2018.2812850, 2018.
Li, X. X., Huang, J X., Li, X., Lyu, H. B., Wang, B., Xiong, Y., and Xie, W. L.: Multi-constellation GNSS PPP instantaneous ambiguity resolution with precise atmospheric corrections augmentation, GPS Solut., 25, 107, https://doi.org/10.1007/s10291-021-01123-0, 2021.
Nafisi, V., Urquhart, L., Santos, M., Cannon, M. E., and Work, D. B.: Comparison of ray-tracing packages for troposphere delays, IEEE T. Geosci. Remote Sens., 50, 469–480, https://doi.org/10.1109/TGRS.2011.2160952, 2012.
Penna, N., Dodson, A., and Chen, W.: Assessment of EGNOS tropospheric correction model, J. Navigation, 54, 37–55, https://doi.org/10.1017/S0373463300001107, 2001.
Prado, A., Vieira, T., and Fernandes, M. J.: Assessment of SIRGAS-CON tropospheric products using ERA5 and IGS, Journal of Geodetic Science, 12, 195–210, https://doi.org/10.1515/jogs-2022-0144, 2022.
Randles, C. A., Sliva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., and Flynn, C.: The MERRA-2 aerosol reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation, J. Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017.
Saastamoinen, J.: Contributions to the theory of atmospheric refraction, B. Géod., 105, 279–298, https://doi.org/10.1007/BF02521844, 1972.
Schüler, T.: The TropGrid2 standard tropospheric correction model, GPS Solut., 18, 123–131, https://doi.org/10.1007/s10291-013-0316-x, 2014.
Shangguan, M., Cheng, X., Pan, X., Dang, M., Wu, L., and Xie, Z.: Assessments of global tropospheric delay retrieval from reanalysis based on GNSS data, Chinese Journal of Geophysics, 66, 939–950, https://doi.org/10.6038/cjg2022Q0023, 2023 (in Chinese).
Sun, Y. L., Yang, F., Liu, M. J., Li, Z., Gong, X., and Wang, Y. Y.: Evaluation of the weighted mean temperature over China using multiple reanalysis data and radiosonde, Atmos. Res., 285, 106664, https://doi.org/10.1016/j.atmosres.2023.106664, 2023.
Sun, Z. Y., Zhang, B., and Yao, Y. B.: An ERA5-based model for estimating tropospheric delay and weighted mean temperature over China with improved spatiotemporal resolutions, Earth and Space Science, 6, 1926–1941, https://doi.org/10.1029/2019EA000701, 2019.
Tang, Y. X., Liu, L. L., and Yao, C. L.: Empirical model for mean temperature and assessment of precipitable water vapor derived from GPS, Geodesy and Geodynamics, 4, 51–56, https://doi.org/10.3724/SP.J.1246.2013.04051, 2013.
Thayer, G. D.: An improved equation for the radio refractive index of air, Radio Sci., 9, 803–807, https://doi.org/10.1029/RS009i010p00803, 1974.
Yang, F., Guo, J., Zhang, C., Li, Y., and Li, J.: A Regional Zenith Tropospheric Delay (ZTD) Model Based on GPT3 and ANN, Remote Sensing, 13, 838, https://doi.org/10.3390/rs13050838, 2021.
Yao, Y., Zhu, S., and Yue, S.: A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere, J. Geodesy, 86, 1125–1135, https://doi.org/10.1007/s00190-012-0568-1, 2012.
Yao, Y., He, C., Zhang, B., and Xv, C.: A new global zenith tropospheric delay model GZTD, Chinese Journal of Geophysics, 56, 2218–2227, https://doi.org/10.6038/cjg20130709, 2013.
Yao, Y. B., Xu, X. Y., Xu, C. Q., Peng, W. J., and Wan, Y. Y.: GGOS tropospheric delay forecast product performance evaluation and its application in real-time PPP, J. Atmos. Sol.-Terr. Phy., 175, 1–17, https://doi.org/10.1016/j.jastp.2018.05.002, 2018.
Yao, Y. B., Xu, X. Y., Xu, C. Q., Peng, W. J., and Wan, Y. Y.: Establishment of a real-time local tropospheric fusion model, Remote Sensing, 11, 1321, https://doi.org/10.3390/rs11111321, 2019.
Zhang, H., Yuan, Y., and Li, W.: An analysis of multisource tropospheric hydrostatic delays and their implications for GPS/GLONASS PPP-based zenith tropospheric delay and height estimations, J. Geodesy, 95, 83, https://doi.org/10.1007/s00190-021-01535-3, 2021.
Zhang, H., Yuan, Y., and Li, W.: Real-time wide-area precise tropospheric corrections (WAPTCs) jointly using GNSS and NWP forecasts for China, J. Geodesy, 96, 44, https://doi.org/10.1007/s00190-022-01630-z, 2022.
Zhang, W. X., Lou, Y. D., Liu, W. X., Huang, J. F., Wang, Z. P., Zhou, Y. Z., and Zhang, H. S.: Rapid troposphere tomography using adaptive simultaneous iterative reconstruction technique, J. Geodesy, 94, 76, https://doi.org/10.1007/s00190-020-01386-4, 2020.
Zhao, Q., Yao, Y., Yao, W., and Zhang, S.: GNSS-derived PWV and comparison with radiosonde and ECMWF ERA-Interim data over mainland China, J. Atmos. Sol.-Terr. Phy., 182, 85–92, https://doi.org/10.1016/j.jastp.2018.11.004, 2019.
Zhao, Q. Z., Su, J., Li, Z. F., Yang, P. F., and Yao, Y. B.: Adaptive aerosol optical depth forecasting model using GNSS observation, IEEE T. Geosci. Remote, 60, 2454–2462, https://doi.org/10.1109/TGRS.2021.3129159, 2022.
Zhao, Q., Liu, K., Sun, T., Yao, Y., and Li, Z.: A novel regional drought monitoring method using GNSS-derived ZTD and precipitation, Remote Sens. Environ., 297, 113778, https://doi.org/10.1016/j.rse.2023.113778, 2023a.
Zhao, Q., Su, J., Xu, C., Yao, Y., Zhang, J., and Wu, J.: High-precision ZTD model of altitude-related correction, IEEE J. Sel. Top. Appl., 16, 609–621, https://doi.org/10.1109/JSTARS.2022.3228917, 2023b.
Zhou, C. C., Peng, B. B., Li, W., Zhong S. M., Ou, J. K., Chen, R. J., and Zhao, X. L: Establishment of a Site-Specific Tropospheric Model Based on Ground Meteorological Parameters over the China Region, Sensors, 17, 1722, https://doi.org/10.3390/s17081722, 2017.
Zhou, Y. Z., Lou, Y. D., Zhang, Z. Y., Zhang, W. X., and Bai, J. N.: An improved tropospheric mapping function modeling method for space geodetic techniques, J. Geodesy, 95, 98, https://doi.org/10.1007/s00190-021-01556-y, 2021.
Zhu G., Huang, L. L., Yang, Y. Z., Li, J. Y., Zhou, L., and Liu, L. L.: Refining the ERA5-based global model for vertical adjustment of zenith tropospheric delay, Satellite Navigation, 3, 27, https://doi.org/10.1186/s43020-022-00088-w, 2022.
Short summary
The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
The existing zenith tropospheric delay (ZTD) models have limitations such as using a single...
