Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4617-2023
© 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-16-4617-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
17 Aug 2023
Methods for assessment of models |
| 17 Aug 2023
| 17 Aug 2023
Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)
Christoph Neuhauser
CORRESPONDING AUTHOR
School of Computation, Information and Technology, Technical University of Munich, Garching bei München, Germany
Maicon Hieronymus
Institute of Computer Science, Johannes Gutenberg University, Mainz, Germany
Michael Kern
Advanced Micro Devices GmbH, Dornach bei München, Germany
Marc Rautenhaus
Visual Data Analysis Group, Regional Computing Centre, University of Hamburg, Hamburg, Germany
Annika Oertel
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Rüdiger Westermann
School of Computation, Information and Technology, Technical University of Munich, Garching bei München, Germany
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Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
EGUsphere, https://doi.org/10.5194/egusphere-2024-753, https://doi.org/10.5194/egusphere-2024-753, 2024
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Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5x to 150x) without compromising the data's scientific value. We developed a user-friendly tool called 'enstools-compression' that makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Alexander Lojko, Andrew Charles Winters, Annika Oertel, Christiane Jablonowski, and Ashley Elizabeth Payne
EGUsphere, https://doi.org/10.5194/egusphere-2024-382, https://doi.org/10.5194/egusphere-2024-382, 2024
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Recent studies show that convective storms can produce anticyclonically rotating vortices (~10 km) referred to as negative potential vorticity (NPV), which can elongate to larger scales (~1000 km). Our composite analysis shows that elongated NPV frequently occurs along the Western North Atlantic tropopause where they are observed to accelerate jet stream winds and influence its evolution. This may impinge on aviation turbulence and weather forecasting despite its small-scale origin.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-218, https://doi.org/10.5194/gmd-2023-218, 2023
Revised manuscript accepted for GMD
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This study presents a method for identifying and tracking 3-D potential vorticity structures within African Easterly Waves (AEWs). Each identified structure is characterized by descriptors, including its position and orientation in 3-D, which have been are validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, https://doi.org/10.5194/gmd-16-4427-2023, 2023
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We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Annika Oertel, Annette K. Miltenberger, Christian M. Grams, and Corinna Hoose
Atmos. Chem. Phys., 23, 8553–8581, https://doi.org/10.5194/acp-23-8553-2023, https://doi.org/10.5194/acp-23-8553-2023, 2023
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Warm conveyor belts (WCBs) are cloud- and precipitation-producing airstreams in extratropical cyclones that are important for the large-scale flow and cloud radiative forcing. We analyze cloud formation processes during WCB ascent in a two-moment microphysics scheme. Quantification of individual diabatic heating rates shows the importance of condensation, vapor deposition, rain evaporation, melting, and cloud-top radiative cooling for total heating and WCB-related potential vorticity structure.
Andreas Alexander Beckert, Lea Eisenstein, Annika Oertel, Timothy Hewson, George C. Craig, and Marc Rautenhaus
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2022-36, https://doi.org/10.5194/wcd-2022-36, 2022
Preprint withdrawn
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This study revises and extends a previously presented 3-D objective front detection method and demonstrates its benefits to analyse weather dynamics in numerical simulation data. Based on two case studies of extratropical cyclones, we demonstrate the evaluation of conceptual models from dynamic meteorology, illustrate the benefits of our interactive analysis approach by comparing fronts in data with different model resolutions, and study the impact of convection on fronts.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Roderick van der Linden, Michael Maier-Gerber, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 15, 4447–4468, https://doi.org/10.5194/gmd-15-4447-2022, https://doi.org/10.5194/gmd-15-4447-2022, 2022
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Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics. For example, anomalies in the PV field near the tropopause are linked to extreme weather events. In this study, an objective strategy to identify these anomalies is presented and evaluated. As a novel concept, it can be applied to three-dimensional (3-D) data sets. Supported by 3-D visualizations, we illustrate advantages of this new analysis over existing studies along a case study.
Julian F. Quinting, Christian M. Grams, Annika Oertel, and Moritz Pickl
Geosci. Model Dev., 15, 731–744, https://doi.org/10.5194/gmd-15-731-2022, https://doi.org/10.5194/gmd-15-731-2022, 2022
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This study applies novel artificial-intelligence-based models that allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart, which requires data at higher spatiotemporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process understanding of biases in weather prediction models.
Marcel Meyer, Iuliia Polkova, Kameswar Rao Modali, Laura Schaffer, Johanna Baehr, Stephan Olbrich, and Marc Rautenhaus
Weather Clim. Dynam., 2, 867–891, https://doi.org/10.5194/wcd-2-867-2021, https://doi.org/10.5194/wcd-2-867-2021, 2021
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Novel techniques from computer science are used to study extreme weather events. Inspired by the interactive 3-D visual analysis of the recently released ERA5 reanalysis data, we improve commonly used metrics for measuring polar winter storms and outbreaks of cold air. The software (Met.3D) that we have extended and applied as part of this study is freely available and can be used generically for 3-D visualization of a broad variety of atmospheric processes in weather and climate data.
Annika Oertel, Michael Sprenger, Hanna Joos, Maxi Boettcher, Heike Konow, Martin Hagen, and Heini Wernli
Weather Clim. Dynam., 2, 89–110, https://doi.org/10.5194/wcd-2-89-2021, https://doi.org/10.5194/wcd-2-89-2021, 2021
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Convection embedded in the stratiform cloud band of strongly ascending airstreams in extratropical cyclones (so-called warm conveyor belts) can influence not only surface precipitation but also the
upper-tropospheric potential vorticity (PV) and waveguide. The comparison of intense vs. moderate embedded convection shows that its strength alone is not a reliable measure for upper-tropospheric PV modification. Instead, characteristics of the ambient flow co-determine its dynamical significance.
Josué Gehring, Annika Oertel, Étienne Vignon, Nicolas Jullien, Nikola Besic, and Alexis Berne
Atmos. Chem. Phys., 20, 7373–7392, https://doi.org/10.5194/acp-20-7373-2020, https://doi.org/10.5194/acp-20-7373-2020, 2020
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In this study, we analyse how large-scale meteorological conditions influenced the local enhancement of snowfall during an intense precipitation event in Korea. We used atmospheric models, weather radars and snowflake images. We found out that a rising airstream in the warm sector of the low pressure system associated to this event influenced the evolution of snowfall. This study highlights the importance of interactions between large and local scales in this intense precipitation event.
Annika Oertel, Maxi Boettcher, Hanna Joos, Michael Sprenger, and Heini Wernli
Weather Clim. Dynam., 1, 127–153, https://doi.org/10.5194/wcd-1-127-2020, https://doi.org/10.5194/wcd-1-127-2020, 2020
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Warm conveyor belts (WCBs) are important, mainly stratiform cloud forming airstreams in extratropical cyclones that can include embedded convection. This WCB case study systematically compares the characteristics of convective vs. slantwise ascent of the WCB. We find that embedded convection leads to regions of significantly stronger precipitation. Moreover, it strongly modifies the potential vorticity distribution in the lower and upper troposphere, where its also influences the waveguide.
M. Rautenhaus, M. Kern, A. Schäfler, and R. Westermann
Geosci. Model Dev., 8, 2329–2353, https://doi.org/10.5194/gmd-8-2329-2015, https://doi.org/10.5194/gmd-8-2329-2015, 2015
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This article presents "Met.3D", a new open-source tool for the interactive 3D visualization of numerical ensemble weather predictions. Met.3D builds a bridge from proven 2D visualization methods commonly used in meteorology to 3D visualization and implements approaches to using the ensemble to allow the user to assess forecast uncertainty. The article is the first part of a two-paper study discussing how 3D and ensemble visualization can be used in a meaningful way suited to weather forecasting.
M. Rautenhaus, C. M. Grams, A. Schäfler, and R. Westermann
Geosci. Model Dev., 8, 2355–2377, https://doi.org/10.5194/gmd-8-2355-2015, https://doi.org/10.5194/gmd-8-2355-2015, 2015
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This article presents the application of interactive 3D visualization of ensemble
weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. A method to predict 3D probabilities of the spatial occurrence of WCBs is developed and integrated into the 3D visualization tool "Met.3D", introduced in the first part of this two-paper study. A case study demonstrates the use of 3D and uncertainty visualization for weather forecasting.
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Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
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Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
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Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
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Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
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Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
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An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
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Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
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In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
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The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
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In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
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The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
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In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
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Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
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This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
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PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
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Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
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Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
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We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
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The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
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We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
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A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
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Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
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Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
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Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
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Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024, https://doi.org/10.5194/gmd-17-1111-2024, 2024
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This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024, https://doi.org/10.5194/gmd-17-1091-2024, 2024
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With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024, https://doi.org/10.5194/gmd-17-815-2024, 2024
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GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
Piotr Zmijewski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 17, 759–780, https://doi.org/10.5194/gmd-17-759-2024, https://doi.org/10.5194/gmd-17-759-2024, 2024
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In computer simulations of clouds it is necessary to model the myriad of droplets that constitute a cloud. A popular method for this is to use so-called super-droplets (SDs), each representing many real droplets. It has remained a challenge to model collisions of SDs. We study how precipitation in a cumulus cloud depends on the number of SDs. Surprisingly, we do not find convergence in mean precipitation even for numbers of SDs much larger than typically used in simulations.
Roya Ghahreman, Wanmin Gong, Paul A. Makar, Alexandru Lupu, Amanda Cole, Kulbir Banwait, Colin Lee, and Ayodeji Akingunola
Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, https://doi.org/10.5194/gmd-17-685-2024, 2024
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The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura
Geosci. Model Dev., 17, 651–684, https://doi.org/10.5194/gmd-17-651-2024, https://doi.org/10.5194/gmd-17-651-2024, 2024
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Global climate models with coarse grid sizes include uncertainties about the processes in aerosol–cloud–precipitation interactions. To reduce these uncertainties, here we performed numerical simulations using a new version of our global aerosol transport model with a finer grid size over a longer period than in our previous study. As a result, we found that the cloud microphysics module influences the aerosol distributions through both aerosol wet deposition and aerosol–cloud interactions.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
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In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Haiqin Li, Georg A. Grell, Ravan Ahmadov, Li Zhang, Shan Sun, Jordan Schnell, and Ning Wang
Geosci. Model Dev., 17, 607–619, https://doi.org/10.5194/gmd-17-607-2024, https://doi.org/10.5194/gmd-17-607-2024, 2024
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We developed a simple and realistic method to provide aerosol emissions for aerosol-aware microphysics in a numerical weather forecast model. The cloud-radiation differences between the experimental (EXP) and control (CTL) experiments responded to the aerosol differences. The strong positive precipitation biases over North America and Europe from the CTL run were significantly reduced in the EXP run. This study shows that a realistic representation of aerosol emissions should be considered.
Nathan Patrick Arnold
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-245, https://doi.org/10.5194/gmd-2023-245, 2024
Revised manuscript accepted for GMD
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Earth System Models often represent the land surface at smaller scales than the atmosphere, but surface-atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
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The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Jiachen Liu, Eric Chen, and Shannon L. Capps
Geosci. Model Dev., 17, 567–585, https://doi.org/10.5194/gmd-17-567-2024, https://doi.org/10.5194/gmd-17-567-2024, 2024
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Air pollution harms human life and ecosystems, but its sources are complex. Scientists and policy makers use air pollution models to advance knowledge and inform control strategies. We implemented a recently developed numeral system to relate any set of model inputs, like pollutant emissions from a given activity, to all model outputs, like concentrations of pollutants harming human health. This approach will be straightforward to update when scientists discover new processes in the atmosphere.
Gerrit Kuhlmann, Erik F. M. Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2023-2936, https://doi.org/10.5194/egusphere-2023-2936, 2024
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We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter Notebooks included in the library.
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024, https://doi.org/10.5194/gmd-17-399-2024, 2024
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Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
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A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024, https://doi.org/10.5194/gmd-17-381-2024, 2024
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Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024, https://doi.org/10.5194/gmd-17-321-2024, 2024
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Turbulence-resolving flow models have strict performance requirements, as simulations often run for weeks using hundreds of processes. Many flow scenarios also require the flexibility to modify physical and numerical models for problem-specific requirements. With a new code written in Julia we hope to make such adaptations easier without compromising on performance. In this paper we discuss the modeling approach and present validation and performance results.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-234, https://doi.org/10.5194/gmd-2023-234, 2024
Revised manuscript accepted for GMD
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air quality-challenged arid/semi-arid region in the US. The unique characteristics of semi-arid/arid regions, e.g., intense heat, minimal moisture, persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
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In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
EGUsphere, https://doi.org/10.5194/egusphere-2023-2740, https://doi.org/10.5194/egusphere-2023-2740, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure facilitating further processing to allow emission processing from continental to street scale.
Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-226, https://doi.org/10.5194/gmd-2023-226, 2024
Revised manuscript accepted for GMD
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Reanalysis data have been widely used as an initial condition for the daily forecast of the atmosphere or boundary conditions in regional models, for the study of climate change, and as proxies to complement insufficient in situ measurements. This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2023-2962, https://doi.org/10.5194/egusphere-2023-2962, 2024
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There are relatively limited researches on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPU, have distinct advantages in energy efficiency and scalability. In this study, the air quality modeling system can run stably on MIPS CPU platform, and the experiment results verify the stability of scientific computing on the platform. The work provides a technical foundation for the scientific application based on MIPS CPU platforms.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
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This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
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We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
David R. Shaw, Toby J. Carter, Helen L. Davies, Ellen Harding-Smith, Elliott C. Crocker, Georgia Beel, Zixu Wang, and Nicola Carslaw
Geosci. Model Dev., 16, 7411–7431, https://doi.org/10.5194/gmd-16-7411-2023, https://doi.org/10.5194/gmd-16-7411-2023, 2023
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Exposure to air pollution is one of the greatest risks to human health, and it is indoors, where we spend upwards of 90 % of our time, that our exposure is greatest. The INdoor CHEMical model in Python (INCHEM-Py) is a new, community-led box model that tracks the evolution and fate of atmospheric chemical pollutants indoors. We have shown the processes simulated by INCHEM-Py, its ability to model experimental data and how it may be used to develop further understanding of indoor air chemistry.
Cited articles
Afzal, S., Hittawe, M., Ghani, S., Jamil, T., Knio, O., Hadwiger, M., and
Hoteit, I.: The State of the Art in Visual Analysis Approaches for Ocean and
Atmospheric Datasets, Comput. Graph. Forum, 38, 881–907,
https://doi.org/10.1111/cgf.13731, 2019. a
Bader, R., Sprenger, M., Ban, N., Radisuhli, S., Schar, C., and Günther, T.:
Extraction and Visual Analysis of Potential Vorticity Banners around
the Alps, IEEE T. Vis. Comput. Gr., 26,
259–269, https://doi.org/10.1109/TVCG.2019.2934310, 2019. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and
Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with
the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139,
3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barklie, R. H. D. and Gokhale, N. R.: The freezing of supercooled water drops, Stormy Weather Group, McGill Univ., Sci. Rep. MW-30, Part 3, 43–64, 1959. a
Baumgartner, M., Sagebaum, M., Gauger, N. R., Spichtinger, P., and Brinkmann, A.: Algorithmic differentiation for cloud schemes (IFS Cy43r3) using CoDiPack (v1.8.1), Geosci. Model Dev., 12, 5197–5212, https://doi.org/10.5194/gmd-12-5197-2019, 2019. a
Beckert, A. A., Eisenstein, L., Oertel, A., Hewson, T., Craig, G. C., and Rautenhaus, M.: The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models, Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, 2023. a
Bösiger, L., Sprenger, M., Boettcher, M., Joos, H., and Günther, T.: Integration-based extraction and visualization of jet stream cores, Geosci. Model Dev., 15, 1079–1096, https://doi.org/10.5194/gmd-15-1079-2022, 2022. a
Chen, L., Özsu, M. T., and Oria, V.: Robust and Fast Similarity Search for
Moving Object Trajectories, in: Proceedings of the 2005 ACM SIGMOD
International Conference on Management of Data, SIGMOD '05, 491–502,
Association for Computing Machinery, New York, NY, USA, June 2005,
https://doi.org/10.1145/1066157.1066213, 2005. a
Gong, X., Si, Y.-W., Fong, S., and Mohammed, S.: NSPRING:
Normalization-supported SPRING for subsequence matching on time series
streams, in: 2014 IEEE 15th International Symposium on Computational
Intelligence and Informatics (CINTI), Budapest, Hungary, November 2014, 373–378,
https://doi.org/10.1109/CINTI.2014.7028704, 2014. a
Griewank, A. and Walther, A.: Evaluating Derivatives: Principles and Techniques
of Algorithmic Differentiation, second edn., SIAM, https://doi.org/10.1137/1.9780898717761, 2008. a
Harrower, M. and Brewer, C. A.: ColorBrewer.org: An Online Tool for Selecting
Colour Schemes for Maps, Cartogr. J., 40, 27–37,
https://doi.org/10.1179/000870403235002042, 2003. a
He, J., Chen, H., Chen, Y., Tang, X., and Zou, Y.: Diverse Visualization
Techniques and Methods of Moving-Object-Trajectory Data: A Review, ISPRS
Int. J. Geo-Inf., 8, 63, https://doi.org/10.3390/ijgi8020063, 2019. a
Hieronymus, M.: wavestoweather/AD_Sensitivity_Analysis: Source for Reference
Paper, Zenodo [code], https://doi.org/10.5281/zenodo.6645540, 2022. a
Hieronymus, M. and Oertel, A.: Trajectory Data with Sensitivities to Cloud
Microphysical Parameters, Zenodo [data set], https://doi.org/10.5281/zenodo.8043592, 2023. a
Jeyaratnam, J., Booth, J. F., Naud, C. M., Luo, Z. J., and Homeyer, C. R.:
Upright Convection in Extratropical Cyclones: A Survey Using Ground‐Based
Radar Data Over the United States, Geophys. Res. Lett., 47, e2019GL086620,
https://doi.org/10.1029/2019GL086620, 2020. a, b
Joos, H. and Forbes, R. M.: Impact of different IFS microphysics on a warm
conveyor belt and the downstream flow evolution, Q. J. Roy. Meteor. Soc.,
142, 2727–2739, https://doi.org/10.1002/qj.2863, 2016. a
Kappe, C., Böttinger, M., and Leitte, H.: Topology-Based Feature Analysis
of Scalar Field Ensembles: An Application to Climate (Change) Analysis,
Comput. Graph., 104, 59–71, https://doi.org/10.1016/j.cag.2022.03.004, 2022. a
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based parameterization
of cirrus cloud formation for use in global atmospheric models, J. Geophys.
Res.-Atmos., 111, D01205, https://doi.org/10.1029/2005JD006219, 2006. a
Kern, M., Hewson, T., Sadlo, F., Westermann, R., and Rautenhaus, M.: Robust
Detection and Visualization of Jet-stream Core Lines in Atmospheric Flow,
IEEE T. Vis. Comput. Gr., 24, 893–902,
https://doi.org/10.1109/tvcg.2017.2743989, 2018. a
Kern, M., Hewson, T., Schatler, A., Westermann, R., and Rautenhaus, M.:
Interactive 3D Visual Analysis of Atmospheric Fronts, IEEE
T. Vis. Comput. Gr., 25, 1080–1090,
https://doi.org/10.1109/TVCG.2018.2864806, 2019. a
Leutbecher, M. and Palmer, T.: Ensemble Forecasting, J. Comput.
Phys., 227, 3515–3539, https://doi.org/10.1016/j.jcp.2007.02.014, 2008. a
Love, A. L., Pang, A., and Kao, D. L.: Visualizing spatial multivalue data,
IEEE Computer Graphics and Applications, 25, 69–79, 2005. a
Madonna, E., Wernli, H., Joos, H., and Martius, O.: Warm Conveyor Belts in the
ERA-Interim Dataset (1979–2010). Part I: Climatology and Potential
Vorticity Evolution, J. Climate, 27, 3–26, https://doi.org/10.1175/JCLI-D-12-00720.1,
2014. a, b
Mazoyer, M., Ricard, D., Rivière, G., Delanoë, J., Arbogast, P., Vié,
B., Lac, C., Cazenave, Q., and Pelon, P.: Microphysics Impacts on the Warm
Conveyor Belt and Ridge Building of the NAWDEX IOP6 Cyclone, Mon. Weather
Rev., 149, 3961–3980, https://doi.org/10.1175/MWR-D-21-0061.1, 2021. a
Meyer, M., Polkova, I., Modali, K. R., Schaffer, L., Baehr, J., Olbrich, S., and Rautenhaus, M.: Interactive 3-D visual analysis of ERA5 data: improving diagnostic indices for marine cold air outbreaks and polar lows, Weather Clim. Dynam., 2, 867–891, https://doi.org/10.5194/wcd-2-867-2021, 2021. a
Miltenberger, A. K., Pfahl, S., and Wernli, H.: An online trajectory module (version 1.0) for the nonhydrostatic numerical weather prediction model COSMO, Geosci. Model Dev., 6, 1989–2004, https://doi.org/10.5194/gmd-6-1989-2013, 2013. a
Munzner, T.: Visualization Analysis and Design, chap. 5, 1 edn., A K Peters/CRC Press,
https://doi.org/10.1201/b17511, 2014. a
Neuhauser, C., Wang, J., Kern, M., and Westermann, R.: Efficient High-Quality
Rendering of Ribbons and Twisted Lines, in: Vision, Modeling, and
Visualization, The Eurographics Association, 135–1439,
https://doi.org/10.2312/vmv.20221213, 2022. a, b
Neuhauser, C., Hieronymus, M., Kern, M., and Met.3D Contributors:
chrismile/met.3d: 1.6.0-multivar1, Zenodo [code], https://doi.org/10.5281/zenodo.8082371, 2023. a
Nguyen, D. B., Zhang, L., Laramee, R. S., Thompson, D., Monico, R. O., and
Chen, G.: Unsteady Flow Visualization via Physics Based Pathline Exploration,
in: 2019 IEEE Visualization Conference (VIS), Vancouver, BC, Canada, October 2019, 286–290,
https://doi.org/10.1109/VISUAL.2019.8933578, 2019. a
Nguyen, D. B., Zhang, L., Laramee, R. S., Thompson, D., Monico, R. O., and
Chen, G.: Physics-based Pathline Clustering and Exploration, Comput.
Graph. Forum, 40, 22–37, https://doi.org/10.1111/cgf.14093, 2021. a
Oertel, A., Boettcher, M., Joos, H., Sprenger, M., Konow, H., Hagen, M., and
Wernli, H.: Convective activity in an extratropical cyclone and its warm
conveyor belt – a case‐study combining observations and a
convection‐permitting model simulation, Q. J. Roy. Meteor. Soc., 145,
1406–1426, https://doi.org/10.1002/qj.3500, 2019. a, b
Oertel, A., Boettcher, M., Joos, H., Sprenger, M., and Wernli, H.: Potential vorticity structure of embedded convection in a warm conveyor belt and its relevance for large-scale dynamics, Weather Clim. Dynam., 1, 127–153, https://doi.org/10.5194/wcd-1-127-2020, 2020. a, b, c, d
Oertel, A., Sprenger, M., Joos, H., Boettcher, M., Konow, H., Hagen, M., and Wernli, H.: Observations and simulation of intense convection embedded in a warm conveyor belt – how ambient vertical wind shear determines the dynamical impact, Weather Clim. Dynam., 2, 89–110, https://doi.org/10.5194/wcd-2-89-2021, 2021. a, b
Ollinaho, P., Lock, S.-J., Leutbecher, M., Bechtold, P., Beljaars, A., Bozzo,
A., Forbes, R. M., Haiden, T., Hogan, R. J., and Sandu, I.: Towards
process-level representation of model uncertainties: stochastically perturbed
parametrizations in the ECMWF ensemble, Q. J. Roy. Meteor. Soc., 143,
408–422, https://doi.org/10.1002/qj.2931, 2017. a
Orf, L., Wilhelmson, R., Lee, B., Finley, C., and Houston, A.: Evolution of a
Long-Track Violent Tornado within a Simulated Supercell, B. Am.
Meteorol. Soc., 98, 45–68, https://doi.org/10.1175/bams-d-15-00073.1, 2016. a
Pfahl, S., Madonna, E., Boettcher, M., Joos, H., and Wernli, H.: Warm Conveyor
Belts in the ERA-Interim Dataset (1979–2010). Part II: Moisture Origin
and Relevance for Precipitation, J. Climate, 27, 27–40, https://doi.org/10.1175/JCLI-D-13-00223.1,
2014. a
Phillips, V. T. J., DeMott, P. J., and Andronache, C.: An Empirical
Parameterization of Heterogeneous Ice Nucleation for Multiple Chemical
Species of Aerosol, J. Atmos. Sci, 65, 2757–2783,
https://doi.org/10.1175/2007JAS2546.1, 2008. a
Pickl, M., Lang, S. T. K., Leutbecher, M., and Grams, C. M.: The effect of
stochastically perturbed parametrisation tendencies (SPPT) on rapidly
ascending air streams, Q. J. Roy. Meteor. Soc., 148, 1242–1261,
https://doi.org/10.1002/qj.4257, 2022. a
Potter, K., Kniss, J., Riesenfeld, R., and Johnson, C. R.: Visualizing Summary
Statistics and Uncertainty, Comput. Graph. Forum, 29, 823–832,
https://doi.org/10.1111/j.1467-8659.2009.01677.x, 2010. a
Rao, R. and Card, S. K.: The Table Lens: Merging Graphical and Symbolic
Representations in an Interactive Focus + Context Visualization for Tabular
Information, in: Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, CHI '94, Association for Computing
Machinery, New York, NY, USA, Boston, Massachusetts, USA, April 1994, 318–322, https://doi.org/10.1145/191666.191776, 1994. a
Rautenhaus, M., Grams, C. M., Schäfler, A., and Westermann, R.: Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns, Geosci. Model Dev., 8, 2355–2377, https://doi.org/10.5194/gmd-8-2355-2015, 2015a. a, b, c
Rautenhaus, M., Kern, M., Schäfler, A., and Westermann, R.: Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0), Geosci. Model Dev., 8, 2329–2353, https://doi.org/10.5194/gmd-8-2329-2015, 2015b. a
Rautenhaus, M., Bottinger, M., Siemen, S., Hoffman, R., Kirby, R. M.,
Mirzargar, M., Röber, N., and Westermann, R.: Visualization in
Meteorology – A Survey of Techniques and Tools for Data
Analysis Tasks, IEEE Trans. Visual. Comput. Graphics, 24, 3268–3296,
https://doi.org/10.1109/tvcg.2017.2779501, 2018. a, b
Russig, B., Graß, D., Dachselt, R., and Gumhold, S.: On-Tube Attribute
Visualization for Multivariate Trajectory Data, IEEE T.
Vis. Comput. Gr., 29, 1288–1298,
https://doi.org/10.1109/TVCG.2022.3209400, 2023. a, b
Sadlo, F., Peikert, R., and Sick, M.: Visualization Tools for Vorticity
Transport Analysis in Incompressible Flow, IEEE T. Vis. Comput. Gr., 12, 949–956, https://doi.org/10.1109/TVCG.2006.199, 2006. a
Sagebaum, M., Albring, T., and Gauger, N. R.: High-Performance Derivative
Computations using CoDiPack, ACM Trans. Math. Softw., 45, 38,
https://doi.org/10.1145/3356900, 2019. a
Sakurai, Y., Faloutsos, C., and Yamamuro, M.: Stream Monitoring under the Time
Warping Distance, in: 2007 IEEE 23rd International Conference on Data
Engineering, Istanbul, Turkey, April 2007, 1046–1055, https://doi.org/10.1109/ICDE.2007.368963, 2007. a
Sanyal, J., Zhang, S., Dyer, J., Mercer, A., Amburn, P., and Moorhead, R.:
Noodles: A tool for visualization of numerical weather model ensemble
uncertainty, IEEE T. Vis. Comput. Gr., 16,
1421–1430, 2010. a
Schäfler, A., Craig, G., Wernli, H., Arbogast, P., Doyle, J. D.,
McTaggart-Cowan, R., Methven, J., Rivière, G., Ament, F., Boettcher, M.,
Bramberger, M., Cazenave, Q., Cotton, R., Crewell, S., Delanoë, J.,
Dörnbrack, A., Ehrlich, A., Ewald, F., Fix, A., Grams, C. M., Gray, S. L.,
Grob, H., Groß, S., Hagen, M., Harvey, B., Hirsch, L., Jacob, M., Kölling,
T., Konow, H., Lemmerz, C., Lux, O., Magnusson, L., Mayer, B., Mech, M.,
Moore, R., Pelon, J., Quinting, J., Rahm, S., Rapp, M., Rautenhaus, M.,
Reitebuch, O., Reynolds, C. A., Sodemann, H., Spengler, T., Vaughan, G.,
Wendisch, M., Wirth, M., Witschas, B., Wolf, K., and Zinner, T.: The North
Atlantic Waveguide and Downstream Impact Experiment, B. Am. Meteorol.
Soc., 99, 1607–1637, https://doi.org/10.1175/BAMS-D-17-0003.1, 2018.
a, b
Seifert, A.: On the Parameterization of Evaporation of Raindrops as Simulated
by a One-Dimensional Rainshaft Model, J. Atmos. Sci, 65, 3608–3619,
https://doi.org/10.1175/2008JAS2586.1, 2008. a, b, c
Steinarsson, S.: Downsampling Time Series for Visual Representation, Master's
thesis, University of Iceland, https://skemman.is/bitstream/1946/15343/3/SS_MSthesis.pdf (last access: 9 August 2023), 2013. a
Stevens, S. S.: Psychophysics: Introduction to Its Perceptual, Neural, and
Social Prospects, p. 103, 104, 115, Wiley, ISBN 9780471824374, 1975. a
Stoll, C., Gumhold, S., and Seidel, H.-P.: Visualization with stylized
line primitives, in: VIS 05. IEEE Visualization, Minneapolis, MN, USA, October 2005, 2005, 695–702,
https://doi.org/10.1109/VISUAL.2005.1532859, 2005. a
Toyoda, M. and Sakurai, Y.: Subsequence Matching in Data Streams, NTT Technical
Review, 11, 208–208, 2013. a
Wang, J., Hazarika, S., Li, C., and Shen, H.: Visualization and Visual Analysis
of Ensemble Data: A Survey, IEEE T. Vis. Comput.
Gr., 25, 2853–2872, https://doi.org/10.1109/TVCG.2018.2853721, 2018. a
Wernli, H.: A lagrangian-based analysis of extratropical cyclones. II: A
detailed case-study, Q. J. Roy. Meteor. Soc., 123, 1677–1706,
https://doi.org/10.1002/qj.49712354211, 1997. a, b
Yoshizumi, A., Coffer, M. M., Collins, E. L., Gaines, M. D., Gao, X., Jones,
K., McGregor, I. R., McQuillan, K. A., Perin, V., Tomkins, L. M., Worm, T.,
and Tateosian, L.: A Review of Geospatial Content in IEEE Visualization
Publications, in: 2020 IEEE Visualization Conference (VIS), 51–55, Salt Lake City, UT, USA, October 2020,
https://doi.org/10.1109/VIS47514.2020.00017, 2020. a
Short summary
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes,...
