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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Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
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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.
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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.
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.
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.
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.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
EGUsphere, https://doi.org/10.5194/egusphere-2024-3321, https://doi.org/10.5194/egusphere-2024-3321, 2024
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The effectiveness of assimilation system and its sensitivity to ensemble member size and length of assimilation window have been investigated. This study advances our understanding about the selection of basic parameters in the four-dimension local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate matter polluted environment.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
EGUsphere, https://doi.org/10.5194/egusphere-2024-2884, https://doi.org/10.5194/egusphere-2024-2884, 2024
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Assimilating Ground-based microwave radiometers' observations into numerical weather prediction models holds significant promise for enhancing forecast accuracy. Radiative transfer models (RTM) are crucial for direct data assimilation. We propose a new RTM capable of simulating brightness temperatures observed by GMRs and their Jacobians. Several improvements are introduced to achieve higher accuracy.The RTM align with RTTOV-gb well and can achieve smaller STD in water vapor absorption channels.
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.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Bjarke Tobias Eisensøe Olsen, Andrea Noemi Hahmann, Nicolás González Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
EGUsphere, https://doi.org/10.5194/egusphere-2024-3123, https://doi.org/10.5194/egusphere-2024-3123, 2024
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Low-level jets (LLJs) are strong winds in the lower atmosphere, important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely-used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
EGUsphere, https://doi.org/10.5194/egusphere-2024-2676, https://doi.org/10.5194/egusphere-2024-2676, 2024
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This work focuses on the prediction of aerosol concentration values at 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 of networks architectures and information fusion methods allows the extraction of knowledge on the most efficient methods in the context of this study.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
EGUsphere, https://doi.org/10.5194/egusphere-2024-2815, https://doi.org/10.5194/egusphere-2024-2815, 2024
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate that 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 and consists well with radiative model calculations and can be applied to atmospheric models with speed requirements.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
EGUsphere, https://doi.org/10.5194/egusphere-2024-2879, https://doi.org/10.5194/egusphere-2024-2879, 2024
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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 successful results , positioning the code for future use on exascale supercomputers.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1898, https://doi.org/10.5194/egusphere-2024-1898, 2024
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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.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
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...
