Articles | Volume 12, issue 8
https://doi.org/10.5194/gmd-12-3687-2019
© Author(s) 2019. 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-12-3687-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Aug 2019
Model description paper |
| 22 Aug 2019
| 22 Aug 2019
An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission
Hamza Kouichi
LMEE, Université d'Evry Val-d'Essonne, 40 Rue du Pelvoux, 91020 Courcouronnes, France
Pierre Ngae
LMEE, Université d'Evry Val-d'Essonne, 40 Rue du Pelvoux, 91020 Courcouronnes, France
LMEE, Université d'Evry Val-d'Essonne, 40 Rue du Pelvoux, 91020 Courcouronnes, France
Amir-Ali Feiz
LMEE, Université d'Evry Val-d'Essonne, 40 Rue du Pelvoux, 91020 Courcouronnes, France
Nadir Bekka
LMEE, Université d'Evry Val-d'Essonne, 40 Rue du Pelvoux, 91020 Courcouronnes, France
LSA, Université Saad Dahlab-Blida, 09130 Blida, Algeria
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Pramod Kumar, Grégoire Broquet, Didier Hauglustaine, Maureen Beaudor, Lieven Clarisse, Martin Van Damme, Pierre Coheur, Anne Cozic, Bo Zheng, Beatriz Revilla Romero, Antony Delavois, and Philippe Ciais
EGUsphere, https://doi.org/10.5194/egusphere-2025-162, https://doi.org/10.5194/egusphere-2025-162, 2025
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Global maps of the NH3 emissions over 2019–2022 are derived using IASI NH3 spaceborne observations, the LMDZ-INCA chemistry-transport model at 1.27°×2.5° resolution and mass balance approach. The average global NH3 emissions over the period are ~98 Tg NH3 yr-1, which is significantly higher than three reference inventories. The analysis provides confidence in the seasonal variability and regional budgets, and provides new insights into NH3 emissions at global and regional scales.
Adil Shah, Olivier Laurent, Pramod Kumar, Grégoire Broquet, Loïc Loigerot, Timothé Depelchin, Mathis Lozano, Camille Yver Kwok, Carole Philippon, Clément Romand, Elisa Allegrini, Matthieu Trombetti, and Philippe Ciais
EGUsphere, https://doi.org/10.5194/egusphere-2024-4089, https://doi.org/10.5194/egusphere-2024-4089, 2025
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Acetylene mole fraction measurements from the Picarro G2203 were characterised. Abnormal sensor behaviour was observed in wet conditions. A dry linear calibration was derived, although this was invalid below 1.2 ppb due to unstable measurements. The instrument was used to measure acetylene as a methane tracer at a landfill site. Fluxes were derived from fourteen downwind transects using both raw and calibrated acetylene mole fraction, with raw measurements resulting in 8 % flux underestimation.
Rodrigo Rivera-Martinez, Pramod Kumar, Olivier Laurent, Gregoire Broquet, Christopher Caldow, Ford Cropley, Diego Santaren, Adil Shah, Cécile Mallet, Michel Ramonet, Leonard Rivier, Catherine Juery, Olivier Duclaux, Caroline Bouchet, Elisa Allegrini, Hervé Utard, and Philippe Ciais
Atmos. Meas. Tech., 17, 4257–4290, https://doi.org/10.5194/amt-17-4257-2024, https://doi.org/10.5194/amt-17-4257-2024, 2024
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We explore the use of metal oxide semiconductors (MOSs) as a low-cost alternative for detecting and measuring CH4 emissions from industrial facilities. MOSs were exposed to several controlled releases to test their accuracy in detecting and quantifying emissions. Two reconstruction models were compared, and emission estimates were computed using a Gaussian dispersion model. Findings show that MOSs can provide accurate emission estimates with a 25 % emission rate error and a 9.5 m location error.
Pramod Kumar, Christopher Caldow, Grégoire Broquet, Adil Shah, Olivier Laurent, Camille Yver-Kwok, Sebastien Ars, Sara Defratyka, Susan Warao Gichuki, Luc Lienhardt, Mathis Lozano, Jean-Daniel Paris, Felix Vogel, Caroline Bouchet, Elisa Allegrini, Robert Kelly, Catherine Juery, and Philippe Ciais
Atmos. Meas. Tech., 17, 1229–1250, https://doi.org/10.5194/amt-17-1229-2024, https://doi.org/10.5194/amt-17-1229-2024, 2024
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This study presents a series of mobile measurement campaigns to monitor the CH4 emissions from an active landfill. These measurements are processed using a Gaussian plume model and atmospheric inversion techniques to quantify the landfill CH4 emissions. The methane emission estimates range between ~0.4 and ~7 t CH4 per day, and their variations are analyzed. The robustness of the estimates is assessed depending on the distance of the measurements from the potential sources in the landfill.
Ioannis Cheliotis, Thomas Lauvaux, Jinghui Lian, Theodoros Christoudias, George Georgiou, Alba Badia, Frédéric Chevallier, Pramod Kumar, Yathin Kudupaje, Ruixue Lei, and Philippe Ciais
EGUsphere, https://doi.org/10.5194/egusphere-2023-2487, https://doi.org/10.5194/egusphere-2023-2487, 2023
Preprint withdrawn
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A consistent estimation of CO2 emissions is complicated due to the scarcity of CO2 observations. In this study, we showcase the potential to improve the CO2 emissions estimations from the NO2 concentrations based on the NO2-to-CO2 ratio, which should be constant for a source co-emitting NO2 and CO2, by comparing satellite observations with atmospheric chemistry and transport model simulations for NO2 and CO2. Furthermore, we demonstrate the significance of the chemistry in NO2 simulations.
Pramod Kumar, Grégoire Broquet, Camille Yver-Kwok, Olivier Laurent, Susan Gichuki, Christopher Caldow, Ford Cropley, Thomas Lauvaux, Michel Ramonet, Guillaume Berthe, Frédéric Martin, Olivier Duclaux, Catherine Juery, Caroline Bouchet, and Philippe Ciais
Atmos. Meas. Tech., 14, 5987–6003, https://doi.org/10.5194/amt-14-5987-2021, https://doi.org/10.5194/amt-14-5987-2021, 2021
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This study presents a simple atmospheric inversion modeling framework for the localization and quantification of unknown CH4 and CO2 emissions from point sources based on near-surface mobile concentration measurements and a Gaussian plume dispersion model. It is applied for the estimate of a series of brief controlled releases of CH4 and CO2 with a wide range of rates during the TOTAL TADI-2018 experiment. Results indicate a ~10 %–40 % average error on the estimate of the release rates.
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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.
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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.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
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We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS 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 e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
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. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
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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-km 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 improved representation of clouds and visibility.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Revised manuscript accepted for GMD
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Wonbae Bang, Jacob Carlin, Kwonil Kim, Alexander Ryzhkov, Guosheng Liu, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-179, https://doi.org/10.5194/gmd-2024-179, 2024
Revised manuscript accepted for GMD
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Microphysics model-based diagnosis such as the spectral bin model (SBM) recently has 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 have relatively higher accuracy about snow and wetsnow events whereas lower accuracy about rain event. When microphysics scheme in the SBM was optimized for the corresponding region, accuracy about rain events was improved.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-194, https://doi.org/10.5194/gmd-2024-194, 2024
Revised manuscript accepted for GMD
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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.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Cited articles
Abida, R.: Static and mobile networks design for atmospheric accidental
releases monitoring, Theses, Ecole des Ponts ParisTech, available at:
https://pastel.archives-ouvertes.fr/pastel-00638050 (last access: July 2016), 2010. a
Abida, R. and Bocquet, M.: Targeting of observations for accidental atmospheric
release monitoring, Atmos. Environ., 43, 6312–6327,
https://doi.org/10.1016/j.atmosenv.2009.09.029,
2009. a
Abida, R., Bocquet, M., Vercauteren, N., and Isnard, O.: Design of a monitoring network over France in case of a radiological accidental release, Atmos. Environ., 42, 5205–5219,
https://doi.org/10.1016/j.atmosenv.2008.02.065,
2008. a, b
Beljaars, A. and Holtslag, A.: Flux parameterization over land surfaces for
atmospheric models, J. Appl. Meteorol., 30, 327–341,
https://doi.org/10.1175/1520-0450(1991)030<0327:FPOLSF>2.0.CO;2,
1991. a
Bocquet, M.: Reconstruction of an atmospheric tracer source using the principle of maximum entropy. I: Theory, Q. J. Roy.
Meteor. Soc., 131, 2191–2208, https://doi.org/10.1256/qj.04.67, 2005. a
Cohn, H. and Fielding, M.: Simulated Annealing: Searching for an Optimal
Temperature Schedule, SIAM J. Optimiz., 9, 779–802,
https://doi.org/10.1137/S1052623497329683, 1999. a, b
Efthimiou, G. C., Kovalets, I. V., Venetsanos, A., Andronopoulos, S.,
Argyropoulos, C. D., and Kakosimos, K.: An optimized inverse modelling method
for determining the location and strength of a point source releasing
airborne material in urban environment, Atmos. Environ., 170, 118–129, https://doi.org/10.1016/j.atmosenv.2017.09.034,
2017. a, b
Gamerman, D. and Lopes, H. F.: Markov chain Monte Carlo: stochastic simulation
for Bayesian inference, CRC Press, New York, USA, 2006. a
Gryning, S.-E., Batchvarova, E., Brummer, B., Jorgensen, H., and Larsen, S.: On the extension of the wind profile over homogeneous terrain beyond the surface boundary layer, Bound.-Lay. Meteorol., 124, 251–268,
https://doi.org/10.1007/s10546-007-9166-9, 2007. a
Haupt, S. E., Young, G. S., and Allen, C. T.: Validation of a
receptor-dispersion model coupled with a genetic algorithm using synthetic
data, J. Appl. Meteorol. Clim., 45, 476–490,
https://doi.org/10.1175/JAM2359.1,
2006. a
Hourdin, F. and Talagrand, O.: Eulerian backtracking of atmospheric tracers.
I: Adjoint derivation and parametrization of subgrid-scale transport,
Q. J. Roy. Meteor. Soc., 132, 567–583,
https://doi.org/10.1256/qj.03.198.A, 2006. a
Hutchinson, M., Oh, H., and Chen, W.-H.: A review of source term estimation
methods for atmospheric dispersion events using static or mobile sensors,
Inform. Fusion, 36, 130–148,
https://doi.org/10.1016/j.inffus.2016.11.010,
2017. a
Issartel, J.-P.: Emergence of a tracer source from air concentration measurements, a new strategy for linear assimilation, Atmos. Chem. Phys., 5, 249–273, https://doi.org/10.5194/acp-5-249-2005, 2005. a, b, c
Issartel, J.-P., Sharan, M., and Singh, S. K.: Identification of a Point of
Release by Use of Optimally Weighted Least Squares, Pure Appl.
Geophys., 169, 467–482, https://doi.org/10.1007/s00024-011-0381-4, 2012. a, b, c, d
Jiang, Z., de Bruin, S., Heuvelink, G., and Twenhöfel, C.: Optimization of mobile radioactivity monitoring networks, Fifth International Symposium on Spatial Data Quality, 13–15 June 2007, Enschede, the Netherlands, available at:
http://edepot.wur.nl/24174 (last access: March 2015), 2007. a
Keats, W. A.: Bayesian inference for source determination in the atmospheric
environment, PhD thesis, http://hdl.handle.net/10012/4317 (last access: July 2016),
2009. a
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P.: Optimization by Simulated
Annealing, Science, 220, 671–680, https://doi.org/10.1126/science.220.4598.671, 1983. a, b
Ko, C.-W., Lee, J., and Queyranne, M.: An Exact Algorithm for Maximum Entropy
Sampling, Oper. Res., 43, 684–691, https://doi.org/10.1287/opre.43.4.684, 1995. a
Kouichi, H., Turbelin, G., Ngae, P., Feiz, A., Barbosa, E., and Chpoun, A.:
Optimization of sensor networks for the estimation of atmospheric pollutants
sources, WIT Trans. Ecol. Envir., 207, 11–21, 2016. a
Kouichi, H., Ngae, P., Kumar, P., Feiz, A.-A., and Bekka, N.: Matlab code for an optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission, Zenodo, https://doi.org/10.5281/zenodo.3269751, 2019. a
Kovalets, I. V., Andronopoulos, S., Venetsanos, A. G., and Bartzis, J. G.:
Identification of strength and location of stationary point source of
atmospheric pollutant in urban conditions using computational fluid dynamics
model, Math. Comput. Simulat., 82, 244–257,
https://doi.org/10.1016/j.matcom.2011.07.002,
2011. a
Kumar, P., Feiz, A.-A., Ngae, P., Singh, S. K., and Issartel, J.-P.: CFD
simulation of short-range plume dispersion from a point release in an urban
like environment, Atmos. Environ., 122, 645–656,
https://doi.org/10.1016/j.atmosenv.2015.10.027,
2015a. a, b, c, d
Kumar, P., Singh, S. K., Feiz, A.-A., and Ngae, P.: An urban scale inverse
modelling for retrieving unknown elevated emissions with building-resolving
simulations, Atmos. Environ., 140, 135–146,
https://doi.org/10.1016/j.atmosenv.2016.05.050,
2016. a, b
Ma, D., Deng, J., and Zhang, Z.: Comparison and improvements of optimization
methods for gas emission source identification, Atmos. Environ., 81,
188–198, https://doi.org/10.1016/j.atmosenv.2013.09.012,
2013. a, b
Ma, D., Tan, W., Zhang, Z., and Hu, J.: Parameter identification for continuous
point emission source based on Tikhonov regularization method coupled with
particle swarm optimization algorithm, J. Hazard. Mater., 325,
239–250, https://doi.org/10.1016/j.jhazmat.2016.11.071,
2017. a
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and
Teller, E.: Equation of State Calculations by Fast Computing Machines,
J. Chem. Phys., 21, 1087–1092, https://doi.org/10.1063/1.1699114, 1953. a
Monache, L. D., Lundquist, J. K., Kosović, B., Johannesson, G., Dyer, K. M.,
Aines, R. D., Chow, F. K., Belles, R. D., Hanley, W. G., Larsen, S. C.,
Loosmore, G. A., Nitao, J. J., Sugiyama, G. A., and Vogt, P. J.: Bayesian
Inference and Markov Chain Monte Carlo Sampling to Reconstruct a Contaminant
Source on a Continental Scale, J. Appl. Meteorol.
Clim., 47, 2600–2613, https://doi.org/10.1175/2008JAMC1766.1, 2008. a
Nourani, Y. and Andresen, B.: A comparison of simulated annealing cooling
strategies, J. Phys. A-Math. Gen., 31, 8373,
https://doi.org/10.1088/0305-4470/31/41/011, 1998. a, b, c
Penenko, V., Baklanov, A., and Tsvetova, E.: Methods of sensitivity theory and inverse modeling for estimation of source parameters, Future Gener.
Comp. Sy., 18, 661–671, https://doi.org/10.1016/S0167-739X(02)00031-6, 2002. a
Pudykiewicz, J. A.: Application of adjoint tracer transport equations for
evaluating source parameters, Atmos. Environ., 32, 3039–3050,
https://doi.org/10.1016/S1352-2310(97)00480-9,
1998. a, b
Qu, Y., Milliez, M., Musson-Genon, L., and Carissimo, B.: Micrometeorological
Modeling of Radiative and Convective Effects with a Building-Resolving Code,
J. Appl. Meteorol. Clim., 50, 1713–1724,
https://doi.org/10.1175/2011JAMC2620.1, 2011. a, b
Rao, K. S.: Source estimation methods for atmospheric dispersion, Atmos.
Environ., 41, 6964–6973,
https://doi.org/10.1016/j.atmosenv.2007.04.064,
2007. a
Saunier, O., Bocquet, M., Mathieu, A., and Isnard, O.: Model reduction via
principal component truncation for the optimal design of atmospheric
monitoring networks, Atmos. Environ., 43, 4940–4950,
https://doi.org/10.1016/j.atmosenv.2009.07.011,
2009. a
Seibert, P.: Inverse Modelling with a Lagrangian Particle Disperion Model:
Application to Point Releases Over Limited Time Intervals,
Springer US, Boston, MA, USA, 381–389, https://doi.org/10.1007/0-306-47460-3_38, 2001. a
Seibert, P. and Frank, A.: Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode, Atmos. Chem. Phys., 4, 51–63, https://doi.org/10.5194/acp-4-51-2004, 2004. a
Sharan, M., Singh, S. K., and Issartel, J.-P.: Least Square Data Assimilation
for Identification of the Point Source Emissions, Pure Appl.
Geophys., 169, 483–497, https://doi.org/10.1007/s00024-011-0382-3, 2012. a, b, c, d
Siarry, P.: Simulated Annealing, Springer International Publishing, Cham, 19–50, https://doi.org/10.1007/978-3-319-45403-0_2, 2016. a
Wang, B., Chen, B., and Zhao, J.: The real-time estimation of hazardous gas
dispersion by the integration of gas detectors, neural network and gas
dispersion models, J. Hazard. Mater., 300, 433–442,
https://doi.org/10.1016/j.jhazmat.2015.07.028,
2015. a
Yang, Y., Gu, M., Chen, S., and Jin, X.: New inflow boundary conditions for
modelling the neutral equilibrium atmospheric boundary layer in computational
wind engineering, J. Wind Eng. Ind. Aerod.,
97, 88–95, https://doi.org/10.1016/j.jweia.2008.12.001,
2009. a
Yee, E. and Biltoft, C.: Concentration Fluctuation Measurements in a Plume
Dispersing Through a Regular Array of Obstacles, Bound.-Lay. Meteorol.,
111, 363–415, https://doi.org/10.1023/B:BOUN.0000016496.83909.ee,
2004. a, b, c
Yee, E., Hoffman, I., and Ungar, K.: Bayesian Inference for Source
Reconstruction: A Real-World Application, International Scholarly Research
Notices, 2014, 1–12, https://doi.org/10.1155/2014/507634, 2014. a
Zhang, X., Raskob, W., Landman, C., Trybushnyi, D., and Li, Y.: Sequential
multi-nuclide emission rate estimation method based on gamma dose rate
measurement for nuclear emergency management, J. Hazard. Mater.,
325, 288–300, https://doi.org/10.1016/j.jhazmat.2016.10.072,
2017. a
Zhang, X. L., Su, G. F., Yuan, H. Y., Chen, J. G., and Huang, Q. Y.: Modified
ensemble Kalman filter for nuclear accident atmospheric dispersion:
Prediction improved and source estimated, J. Hazard. Mater.,
280, 143–155, https://doi.org/10.1016/j.jhazmat.2014.07.064,
2014.
a, b
Zhang, X. L., Su, G. F., Chen, J. G., Raskob, W., Yuan, H. Y., and Huang,
Q. Y.: Iterative ensemble Kalman filter for atmospheric dispersion in nuclear
accidents: An application to Kincaid tracer experiment, J. Hazard. Mater., 297, 329–339, https://doi.org/10.1016/j.jhazmat.2015.05.035, 2015. a, b
Short summary
The retrieval of a hazardous source (from a leak, terrorist attack, etc.) is an important operational issue for local authorities and health professionals in terms of security and defense. The estimation of the source parameters must therefore be precise. To ensure this, an established monitoring network must be optimal. This study presents a methodology for the optimization of a monitoring network of sensors in an urban-like environment with a view to estimating an unknown emission source.
The retrieval of a hazardous source (from a leak, terrorist attack, etc.) is an important...
