Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-715-2022
© Author(s) 2022. 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-15-715-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
27 Jan 2022
Model description paper |
| 27 Jan 2022
| 27 Jan 2022
EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model
Julian F. Quinting
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany
Christian M. Grams
Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany
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Julian F. Quinting, Christian M. Grams, Edmund Kar-Man Chang, Stephan Pfahl, and Heini Wernli
Weather Clim. Dynam., 5, 65–85, https://doi.org/10.5194/wcd-5-65-2024, https://doi.org/10.5194/wcd-5-65-2024, 2024
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Research in the last few decades has revealed that rapidly ascending airstreams in extratropical cyclones have an important effect on the evolution of downstream weather and predictability. In this study, we show that the occurrence of these airstreams over the North Pacific is modulated by tropical convection. Depending on the modulation, known atmospheric circulation patterns evolve quite differently, which may affect extended-range predictions in the Atlantic–European region.
Axel Seifert, Vanessa Bachmann, Florian Filipitsch, Jochen Förstner, Christian M. Grams, Gholam Ali Hoshyaripour, Julian Quinting, Anika Rohde, Heike Vogel, Annette Wagner, and Bernhard Vogel
Atmos. Chem. Phys., 23, 6409–6430, https://doi.org/10.5194/acp-23-6409-2023, https://doi.org/10.5194/acp-23-6409-2023, 2023
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We investigate how mineral dust can lead to the formation of cirrus clouds. Dusty cirrus clouds lead to a reduction in solar radiation at the surface and, hence, a reduced photovoltaic power generation. Current weather prediction systems are not able to predict this interaction between mineral dust and cirrus clouds. We have developed a new physical description of the formation of dusty cirrus clouds. Overall we can show a considerable improvement in the forecast quality of clouds and radiation.
Patrick Ludwig, Florian Ehmele, Mário J. Franca, Susanna Mohr, Alberto Caldas-Alvarez, James E. Daniell, Uwe Ehret, Hendrik Feldmann, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Michael Kunz, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci., 23, 1287–1311, https://doi.org/10.5194/nhess-23-1287-2023, https://doi.org/10.5194/nhess-23-1287-2023, 2023
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Heavy precipitation in July 2021 led to widespread floods in western Germany and neighboring countries. The event was among the five heaviest precipitation events of the past 70 years in Germany, and the river discharges exceeded by far the statistical 100-year return values. Simulations of the event under future climate conditions revealed a strong and non-linear effect on flood peaks: for +2 K global warming, an 18 % increase in rainfall led to a 39 % increase of the flood peak in the Ahr river.
Susanna Mohr, Uwe Ehret, Michael Kunz, Patrick Ludwig, Alberto Caldas-Alvarez, James E. Daniell, Florian Ehmele, Hendrik Feldmann, Mário J. Franca, Christian Gattke, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Marc Scheibel, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci., 23, 525–551, https://doi.org/10.5194/nhess-23-525-2023, https://doi.org/10.5194/nhess-23-525-2023, 2023
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The flood event in July 2021 was one of the most severe disasters in Europe in the last half century. The objective of this two-part study is a multi-disciplinary assessment that examines the complex process interactions in different compartments, from meteorology to hydrological conditions to hydro-morphological processes to impacts on assets and environment. In addition, we address the question of what measures are possible to generate added value to early response management.
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.
Assaf Hochman, Sebastian Scher, Julian Quinting, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Dynam., 12, 133–149, https://doi.org/10.5194/esd-12-133-2021, https://doi.org/10.5194/esd-12-133-2021, 2021
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Skillful forecasts of extreme weather events have a major socioeconomic relevance. Here, we compare two approaches to diagnose the predictability of eastern Mediterranean heat waves: one based on recent developments in dynamical systems theory and one leveraging numerical ensemble weather forecasts. We conclude that the former can be a useful and cost-efficient complement to conventional numerical forecasts for understanding the dynamics of eastern Mediterranean heat waves.
Susanna Mohr, Jannik Wilhelm, Jan Wandel, Michael Kunz, Raphael Portmann, Heinz Jürgen Punge, Manuel Schmidberger, Julian F. Quinting, and Christian M. Grams
Weather Clim. Dynam., 1, 325–348, https://doi.org/10.5194/wcd-1-325-2020, https://doi.org/10.5194/wcd-1-325-2020, 2020
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We investigated an exceptional thunderstorm episode in 2018, in which atmospheric blocking provided large-scale environmental conditions favouring convection. Furthermore, blocking was accompanied by a high cut-off frequency on its upstream side, which together with filaments of high PV provided the mesoscale setting for deep moist convection. The exceptional persistence of low stability combined with weak wind speed in the mid-troposphere over more than 3 weeks has never been observed before.
Hendrik Andersen, Jan Cermak, Julia Fuchs, Peter Knippertz, Marco Gaetani, Julian Quinting, Sebastian Sippel, and Roland Vogt
Atmos. Chem. Phys., 20, 3415–3438, https://doi.org/10.5194/acp-20-3415-2020, https://doi.org/10.5194/acp-20-3415-2020, 2020
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Fog and low clouds (FLCs) are an essential but poorly understood element of Namib regional climate. Here, a satellite-based data set of FLCs in central Namib, reanalysis data, and back trajectories are used to systematically analyze conditions when FLCs occur. Synoptic-scale mechanisms are identified that influence the formation of FLCs and the onshore advection of marine boundary-layer air masses. The findings lead to a new conceptual model of mechanisms that drive FLC variability in the Namib.
Karmen Babić, Norbert Kalthoff, Bianca Adler, Julian F. Quinting, Fabienne Lohou, Cheikh Dione, and Marie Lothon
Atmos. Chem. Phys., 19, 13489–13506, https://doi.org/10.5194/acp-19-13489-2019, https://doi.org/10.5194/acp-19-13489-2019, 2019
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This study investigates differences in atmospheric conditions between nights with and without low-level stratus clouds (LLCs) over southern West Africa. We use high-quality observations collected during 2016 summer monsoon season and the ERA5 reanalysis data set. Our results show that the formation of LLCs depends on the interplay between the onset time and strength of the nocturnal low-level jet, horizontal cold-air advection, and the overall moisture level in the whole region.
Joshua Dorrington, Marta Wenta, Federico Grazzini, Linus Magnusson, Frederic Vitart, and Christian Grams
EGUsphere, https://doi.org/10.5194/egusphere-2024-415, https://doi.org/10.5194/egusphere-2024-415, 2024
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Extreme rainfall is the leading weather-related source of damages in Europe, but is still difficult to predict on long time scales. A recent example of this were the devastating floods in the Italian region of Emiglia Romagna in May 2023. We present perspectives based on large-scale dynamical information that allow us to better understand and predict such events.
Marta Wenta, Christian M. Grams, Lukas Papritz, and Marc Federer
Weather Clim. Dynam., 5, 181–209, https://doi.org/10.5194/wcd-5-181-2024, https://doi.org/10.5194/wcd-5-181-2024, 2024
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Our study links air–sea interactions over the Gulf Stream to an atmospheric block in February 2019. We found that over 23 % of air masses that were lifted into the block by cyclones interacted with the Gulf Stream. As cyclones pass over the Gulf Stream, they cause intense surface evaporation events, preconditioning the environment for the development of cyclones. This implies that air–sea interactions over the Gulf Stream affect the large-scale dynamics in the North Atlantic–European region.
Julian F. Quinting, Christian M. Grams, Edmund Kar-Man Chang, Stephan Pfahl, and Heini Wernli
Weather Clim. Dynam., 5, 65–85, https://doi.org/10.5194/wcd-5-65-2024, https://doi.org/10.5194/wcd-5-65-2024, 2024
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Research in the last few decades has revealed that rapidly ascending airstreams in extratropical cyclones have an important effect on the evolution of downstream weather and predictability. In this study, we show that the occurrence of these airstreams over the North Pacific is modulated by tropical convection. Depending on the modulation, known atmospheric circulation patterns evolve quite differently, which may affect extended-range predictions in the Atlantic–European region.
Moritz Deinhard and Christian M. Grams
EGUsphere, https://doi.org/10.5194/egusphere-2023-1938, https://doi.org/10.5194/egusphere-2023-1938, 2023
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Stochastic perturbations are a frequently used technique to represent model uncertainties in numerical weather prediction. While such schemes are beneficial for the forecast skill, they can also change the mean state of the model. We focus on how different schemes modulate rapidly ascending air streams, such as warm conveyor belts, and whether the changes to these weather systems are projected onto larger scales. We thereby provide a process chain how perturbations affect the model climate.
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.
Axel Seifert, Vanessa Bachmann, Florian Filipitsch, Jochen Förstner, Christian M. Grams, Gholam Ali Hoshyaripour, Julian Quinting, Anika Rohde, Heike Vogel, Annette Wagner, and Bernhard Vogel
Atmos. Chem. Phys., 23, 6409–6430, https://doi.org/10.5194/acp-23-6409-2023, https://doi.org/10.5194/acp-23-6409-2023, 2023
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We investigate how mineral dust can lead to the formation of cirrus clouds. Dusty cirrus clouds lead to a reduction in solar radiation at the surface and, hence, a reduced photovoltaic power generation. Current weather prediction systems are not able to predict this interaction between mineral dust and cirrus clouds. We have developed a new physical description of the formation of dusty cirrus clouds. Overall we can show a considerable improvement in the forecast quality of clouds and radiation.
Seraphine Hauser, Franziska Teubler, Michael Riemer, Peter Knippertz, and Christian M. Grams
Weather Clim. Dynam., 4, 399–425, https://doi.org/10.5194/wcd-4-399-2023, https://doi.org/10.5194/wcd-4-399-2023, 2023
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Blocking describes a flow configuration in the midlatitudes where stationary high-pressure systems block the propagation of weather systems. This study combines three individual perspectives that capture the dynamics and importance of various processes in the formation of a major blocking in 2016 from a weather regime perspective. In future work, this framework will enable a holistic view of the dynamics and the role of moist processes in different life cycle stages of blocked weather regimes.
Patrick Ludwig, Florian Ehmele, Mário J. Franca, Susanna Mohr, Alberto Caldas-Alvarez, James E. Daniell, Uwe Ehret, Hendrik Feldmann, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Michael Kunz, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci., 23, 1287–1311, https://doi.org/10.5194/nhess-23-1287-2023, https://doi.org/10.5194/nhess-23-1287-2023, 2023
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Heavy precipitation in July 2021 led to widespread floods in western Germany and neighboring countries. The event was among the five heaviest precipitation events of the past 70 years in Germany, and the river discharges exceeded by far the statistical 100-year return values. Simulations of the event under future climate conditions revealed a strong and non-linear effect on flood peaks: for +2 K global warming, an 18 % increase in rainfall led to a 39 % increase of the flood peak in the Ahr river.
Franziska Teubler, Michael Riemer, Christopher Polster, Christian M. Grams, Seraphine Hauser, and Volkmar Wirth
Weather Clim. Dynam., 4, 265–285, https://doi.org/10.5194/wcd-4-265-2023, https://doi.org/10.5194/wcd-4-265-2023, 2023
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Weather regimes govern an important part of the sub-seasonal variability of the mid-latitude circulation. The year-round dynamics of blocked regimes in the Atlantic European region are investigated in over 40 years of data. We show that the dynamics between the regimes are on average very similar. Within the regimes, the main variability – starting from the characteristics of dynamical processes alone – dominates and transcends the variability in season and types of transitions.
Susanna Mohr, Uwe Ehret, Michael Kunz, Patrick Ludwig, Alberto Caldas-Alvarez, James E. Daniell, Florian Ehmele, Hendrik Feldmann, Mário J. Franca, Christian Gattke, Marie Hundhausen, Peter Knippertz, Katharina Küpfer, Bernhard Mühr, Joaquim G. Pinto, Julian Quinting, Andreas M. Schäfer, Marc Scheibel, Frank Seidel, and Christina Wisotzky
Nat. Hazards Earth Syst. Sci., 23, 525–551, https://doi.org/10.5194/nhess-23-525-2023, https://doi.org/10.5194/nhess-23-525-2023, 2023
Short summary
Short summary
The flood event in July 2021 was one of the most severe disasters in Europe in the last half century. The objective of this two-part study is a multi-disciplinary assessment that examines the complex process interactions in different compartments, from meteorology to hydrological conditions to hydro-morphological processes to impacts on assets and environment. In addition, we address the question of what measures are possible to generate added value to early response management.
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
Short summary
Short summary
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.
Assaf Hochman, Sebastian Scher, Julian Quinting, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Dynam., 12, 133–149, https://doi.org/10.5194/esd-12-133-2021, https://doi.org/10.5194/esd-12-133-2021, 2021
Short summary
Short summary
Skillful forecasts of extreme weather events have a major socioeconomic relevance. Here, we compare two approaches to diagnose the predictability of eastern Mediterranean heat waves: one based on recent developments in dynamical systems theory and one leveraging numerical ensemble weather forecasts. We conclude that the former can be a useful and cost-efficient complement to conventional numerical forecasts for understanding the dynamics of eastern Mediterranean heat waves.
Daniela I. V. Domeisen, Christian M. Grams, and Lukas Papritz
Weather Clim. Dynam., 1, 373–388, https://doi.org/10.5194/wcd-1-373-2020, https://doi.org/10.5194/wcd-1-373-2020, 2020
Short summary
Short summary
We cannot currently predict the weather over Europe beyond 2 weeks. The stratosphere provides a promising opportunity to go beyond that limit by providing a change in probability of certain weather regimes at the surface. However, not all stratospheric extreme events are followed by the same surface weather evolution. We show that this weather evolution is related to the tropospheric weather regime around the onset of the stratospheric extreme event for many stratospheric events.
Susanna Mohr, Jannik Wilhelm, Jan Wandel, Michael Kunz, Raphael Portmann, Heinz Jürgen Punge, Manuel Schmidberger, Julian F. Quinting, and Christian M. Grams
Weather Clim. Dynam., 1, 325–348, https://doi.org/10.5194/wcd-1-325-2020, https://doi.org/10.5194/wcd-1-325-2020, 2020
Short summary
Short summary
We investigated an exceptional thunderstorm episode in 2018, in which atmospheric blocking provided large-scale environmental conditions favouring convection. Furthermore, blocking was accompanied by a high cut-off frequency on its upstream side, which together with filaments of high PV provided the mesoscale setting for deep moist convection. The exceptional persistence of low stability combined with weak wind speed in the mid-troposphere over more than 3 weeks has never been observed before.
Hendrik Andersen, Jan Cermak, Julia Fuchs, Peter Knippertz, Marco Gaetani, Julian Quinting, Sebastian Sippel, and Roland Vogt
Atmos. Chem. Phys., 20, 3415–3438, https://doi.org/10.5194/acp-20-3415-2020, https://doi.org/10.5194/acp-20-3415-2020, 2020
Short summary
Short summary
Fog and low clouds (FLCs) are an essential but poorly understood element of Namib regional climate. Here, a satellite-based data set of FLCs in central Namib, reanalysis data, and back trajectories are used to systematically analyze conditions when FLCs occur. Synoptic-scale mechanisms are identified that influence the formation of FLCs and the onshore advection of marine boundary-layer air masses. The findings lead to a new conceptual model of mechanisms that drive FLC variability in the Namib.
Karmen Babić, Norbert Kalthoff, Bianca Adler, Julian F. Quinting, Fabienne Lohou, Cheikh Dione, and Marie Lothon
Atmos. Chem. Phys., 19, 13489–13506, https://doi.org/10.5194/acp-19-13489-2019, https://doi.org/10.5194/acp-19-13489-2019, 2019
Short summary
Short summary
This study investigates differences in atmospheric conditions between nights with and without low-level stratus clouds (LLCs) over southern West Africa. We use high-quality observations collected during 2016 summer monsoon season and the ERA5 reanalysis data set. Our results show that the formation of LLCs depends on the interplay between the onset time and strength of the nocturnal low-level jet, horizontal cold-air advection, and the overall moisture level in the whole region.
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
Short summary
Short summary
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.
C. M. Grams, H. Binder, S. Pfahl, N. Piaget, and H. Wernli
Nat. Hazards Earth Syst. Sci., 14, 1691–1702, https://doi.org/10.5194/nhess-14-1691-2014, https://doi.org/10.5194/nhess-14-1691-2014, 2014
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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.
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.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
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Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
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Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-209, https://doi.org/10.5194/gmd-2023-209, 2023
Revised manuscript accepted for GMD
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This study is about the modelling of the atmospheric composition in Europe and during the summer 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impact of two modelling processes able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Rohith Muraleedharan Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-202, https://doi.org/10.5194/gmd-2023-202, 2023
Revised manuscript accepted for GMD
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Global Navigation Satellite Systems provide moisture observations through its densely distributed ground station network. In this research, we assimilated a new type of observation called tropospheric gradient observations, which was never incorporated into a weather model. Here, we have developed a forward operator for gradient observations and performed impact studies. Promising improvements were observed in the humidity fields of the model in the assimilation study.
Cited articles
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. a, b
Baumgart, M., Riemer, M., Wirth, V., Teubler, F., and Lang, S. T.: Potential vorticity dynamics of Forecast errors: A quantitative case study, Mon. Weather Rev., 146, 1405–1425, https://doi.org/10.1175/MWR-D-17-0196.1, 2018. a
Berman, J. D. and Torn, R. D.: The impact of initial condition and warm conveyor belt forecast uncertainty on variability in the downstream waveguide in an ECWMF case study, Mon. Weather Rev., 147, 4071–4089, https://doi.org/10.1175/MWR-D-18-0333.1, 2019. a
Binder, H., Boettcher, M., Joos, H., and Wernli, H.: The role of warm conveyor belts for the intensification of extratropical cyclones in Northern Hemisphere winter, J. Atmos. Sci., 73, 3997–4020, https://doi.org/10.1175/JAS-D-15-0302.1, 2016. a, b
Bosart, L. F., Moore, B. J., Cordeira, J. M., Archambault, H. M., Bosart, L. F., Moore, B. J., Cordeira, J. M., and Archambault, H. M.: Interactions of North Pacific tropical, midlatitude, and polar disturbances resulting in linked extreme weather events over North America in October 2007, Mon. Weather Rev., 145, 1245–1273, https://doi.org/10.1175/MWR-D-16-0230.1, 2017. a
Bowman, K. P., Lin, J. C., Stohl, A., Draxler, R., Konopka, P., Andrews, A., and Brunner, D.: Input data requirements for Lagrangian trajectory models, B. Am. Meteorol. Soc., 94, 1051–1058, https://doi.org/10.1175/BAMS-D-12-00076.1, 2013. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Browning, K. A. and Roberts, N. M.: Structure of a frontal cyclone, Q. J. Roy. Meteor. Soc., 120, 1535–1557, https://doi.org/10.1002/qj.49712052006, 1994. a
Bröcker, J. and Smith, L. A.: Increasing the Reliability of Reliability Diagrams, Weather Forecast., 22, 651–661, https://doi.org/10.1175/WAF993.1, https://journals.ametsoc.org/view/journals/wefo/22/3/waf993_1.xml, 2007. a, b
Carlson, T. N.: Airflow through midlatitude cyclones and the comma cloud pattern., Mon. Weather Rev., 108, 1498–1509, https://doi.org/10.1175/1520-0493(1980)108<1498:ATMCAT>2.0.CO;2, 1980. a
Dacre, H. F., Martínez-Alvarado, O., and Mbengue, C. O.: Linking atmospheric rivers and warm conveyor belt airflows, J. Hydrometeorol., 20, 1183–1196, https://doi.org/10.1175/JHM-D-18-0175.1, 2019. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Eckhardt, S., Stohl, A., Wernli, H., James, P., Forster, C., and Spichtinger, N.: A 15 year climatology of warm conveyor belts, J. Climate, 17, 218–237, https://doi.org/10.1175/1520-0442(2004)017<0218:AYCOWC>2.0.CO;2, 2004. a, b
ECMWF: ERA Interim, Daily, ECMWF [data set], available at: https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/, last access:13 January 2022. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fukushima, K. and Miyake, S.: Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position, Pattern Recogn., 15, 455–469, https://doi.org/10.1016/0031-3203(82)90024-3, 1982. a
Gagne, D. J., Haupt, S. E., Nychka, D. W., and Thompson, G.: Interpretable deep learning for spatial analysis of severe hailstorms, Mon. Weather Rev., 147, 2827–2845, https://doi.org/10.1175/MWR-D-18-0316.1, 2019. a
Grams, C. M., Wernli, H., Böttcher, M., Čampa, J., Corsmeier, U., Jones, S. C., Keller, J. H., Lenz, C. J., and Wiegand, L.: The key role of diabatic processes in modifying the upper-tropospheric wave guide: A North Atlantic case-study, Q. J. Roy. Meteor. Soc., 137, 2174–2193, https://doi.org/10.1002/qj.891, 2011. a, b, c
Grams, C. M., Magnusson, L., and Madonna, E.: An atmospheric dynamics perspective on the amplification and propagation of forecast error in numerical weather prediction models: A case study, Q. J. Roy. Meteor. Soc., 144, 2577–2591, https://doi.org/10.1002/qj.3353, 2018. a
Hamill, T. M. and Kiladis, G. N.: Skill of the MJO and Northern Hemisphere blocking in GEFS medium-range reforecasts, Mon. Weather Rev., 142, 868–885, https://doi.org/10.1175/MWR-D-13-00199.1, 2014. a
Harrold, T. W.: Mechanisms influencing the distribution of precipitation within baroclinic disturbances, Q. J. Roy. Meteor. Soc., 99, 232–251, https://doi.org/10.1002/qj.49709942003, 1973. a
Ioffe, S. and Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France, 6–11 July 2015, JMLR.org, 37, 448–456, available at: http://proceedings.mlr.press/v37/ioffe15.pdf (last access: 13 January 2022), 2015. a
Kashinath, K., Mustafa, M., Albert, A., Wu, J. L., Jiang, C., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., Manepalli, A., Chirila, D., Yu, R., Walters, R., White, B., Xiao, H., Tchelepi, H. A., Marcus, P., Anandkumar, A., Hassanzadeh, P., and Prabhat: Physics-informed machine learning: Case studies for weather and climate modelling, Philos. T. Roy. Soc. A, 379, 20200093, https://doi.org/10.1098/rsta.2020.0093, 2021. a
Kingma, D. P. and Ba, J. L.: Adam: A method for stochastic optimization, 3rd International Conference on Learning Representations, in:
ICLR 2015 – 3rd International Conference on Learning Representations, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings, 1–15, available at: https://arxiv.org/pdf/1412.6980.pdf (last access: 13 January 2022), 2015. a
Kumler-Bonfanti, C., Stewart, J., Hall, D., and Govett, M.: Tropical and extratropical cyclone detection using deep learning, J. Appl. Meteorol. Clim., 59, 1971–1985, https://doi.org/10.1175/JAMC-D-20-0117.1, 2020. a
Lagerquist, R., McGovern, A. M., and Gagne, D. J.: Deep learning for spatially explicit prediction of synoptic-scale fronts, Weather Forecast., 34, 1137–1160, https://doi.org/10.1175/WAF-D-18-0183.1, 2019. a, b, c
Lamberson, W. S., Torn, R. D., Bosart, L. F., and Magnusson, L.: Diagnosis of the source and evolution of medium-range forecast errors for extratropical Cyclone Joachim, Weather Forecast., 31, 1197–1214, https://doi.org/10.1175/WAF-D-16-0026.1, 2016. a
Lebedev, V., Ivashkin, V., Rudenko, I., Ganshin, A., Molchanov, A., Ovcharenko, S., Grokhovetskiy, R., Bushmarinov, I., and Solomentsev, D.: Precipitation nowcasting with satellite imagery, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, USA, 2680–2688, https://doi.org/10.1145/3292500.3330762, 2019. a
Lu, C., Kong, Y., and Guan, Z.: A mask R-CNN model for reidentifying extratropical cyclones based on quasi-supervised thought, Sci. Rep., 10, 1–9, https://doi.org/10.1038/s41598-020-71831-z, 2020. a
Maddison, J. W., Gray, S. L., Martínez-Alvarado, O., and Williams, K. D.: Upstream cyclone influence on the predictability of block onsets over the Euro-Atlantic region, Mon. Weather Rev., 147, 1277–1296, https://doi.org/10.1175/MWR-D-18-0226.1, 2019. 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, c, d
Martínez-Alvarado, O., Madonna, E., Gray, S. L., and Joos, H.: A route to systematic error in forecasts of Rossby waves, Q. J. Roy. Meteor. Soc., 142, 196–210, https://doi.org/10.1002/qj.2645, 2016. a, b
Matsuoka, D., Nakano, M., Sugiyama, D., and Uchida, S.: Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model, Progress in Earth and Planetary Science, 5, 1–16, https://doi.org/10.1186/s40645-018-0245-y, 2018. a
Matthews, B. W.: Comparison
of the predicted and observed secondary structure of T4 phage
lysozyme, BBA-Protein Struct., 405, 442–451, https://doi.org/10.1016/0005-2795(75)90109-9, 1975. a
Muszynski, G., Kashinath, K., Kurlin, V., Wehner, M., and Prabhat: Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets, Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, 2019. a
Nair, V. and Hinton, T. J.: Rectified Linear Units Improve Restricted Boltzmann Machines, in: ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel, 21–24 June 2010, Omnipress, Madison, WI, USA, 807–814, available at: https://icml.cc/Conferences/2010/papers/432.pdf (last access: 13 January 2022), 2010. a
Pegion, K., Kirtman, B. P., Becker, E., Collins, D. C., Lajoie, E., Burgman, R., Bell, R., Delsole, T., Min, D., Zhu, Y., Li, W., Sinsky, E., Guan, H., Gottschalck, J., Joseph Metzger, E., Barton, N. P., Achuthavarier, D., Marshak, J., Koster, R. D., Lin, H., Gagnon, N., Bell, M., Tippett, M. K., Robertson, A. W., Sun, S., Benjamin, S. G., Green, B. W., Bleck, R., and Kim, H.: The subseasonal experiment (SUBX), B. Am. Meteorol. Soc., 100, 2043–2060, https://doi.org/10.1175/BAMS-D-18-0270.1, 2019. a
Pomroy, H. R. and Thorpe, A. J.: The evolution and dynamical role of reduced upper-tropospheric potential vorticity in intensive observing period one of FASTEX, Mon. Weather Rev., 128, 1817–1834, https://doi.org/10.1175/1520-0493(2000)128<1817:TEADRO>2.0.CO;2, 2000. a, b
Prabhat, Kashinath, K., Mudigonda, M., Kim, S., Kapp-Schwoerer, L., Graubner, A., Karaismailoglu, E., von Kleist, L., Kurth, T., Greiner, A., Mahesh, A., Yang, K., Lewis, C., Chen, J., Lou, A., Chandran, S., Toms, B., Chapman, W., Dagon, K., Shields, C. A., O'Brien, T., Wehner, M., and Collins, W.: ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather, Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, 2021. a, b
Quinting, J.: EuLerian Identification of ascending AirStreams - ELIAS 2.0: GitLab [data set], available at: https://git.scc.kit.edu/nk2448/wcbmetric_v2, last access: 13 January 2022. a
Quinting, J. and Grams, C. M: EuLerian Identification of ascending AirStreams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models, Zenodo [code], https://doi.org/10.5281/zenodo.5154980, 2021a. a
Quinting, J. F. and Grams, C. M.: Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part I: Predictor Selection and Logistic Regression Model, J. Atmos. Sci., 78, 1465–1485, https://doi.org/10.1175/JAS-D-20-0139.1, 2021b. a, b, c, d, e, f, g, h, i, j, k, l, m
Quinting, J. F., Grams, C. M., Oertel, A., and Pickl, M.: EuLerian Identification of ascending AirStreams (ELIAS 2.0) in
numerical weather prediction and climate models –
Part 2: Model application to different datasets, Geosci. Model Dev., 15, 731–744, https://doi.org/10.5194/gmd-15-731-2022, 2022. a
Rodwell, M. J., Richardson, D. S., Parsons, D. B., and Wernli, H.: Flow-dependent reliability: A path to more skillful ensemble forecasts, B. Am. Meteorol. Soc., 99, 1015–1026, https://doi.org/10.1175/BAMS-D-17-0027.1, 2018. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by: Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., Springer International Publishing, Cham, pp. 234–241, 2015. a, b
Sánchez, C., Methven, J., Gray, S., and Cullen, M.: Linking Rapid Forecast Error Growth to Diabatic Processes, Q. J. Roy. Meteor. Soc., 146, 3548–3569, https://doi.org/10.1002/qj.3861, 2020. a
Schäfler, A., Boettcher, M., Grams, C. M., Rautenhaus, M., Sodemann, H., and Wernli, H.: Planning aircraft measurements within a warm conveyor belt, Weather, 69, 161–166, https://doi.org/10.1002/wea.2245, 2014. a
Schubert, S., Neubert, P., Poschmann, J., and Pretzel, P.: Circular convolutional neural networks for panoramic images and laser data, in: 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 9–12 June 2019, IEEE, 653–660, https://doi.org/10.1109/IVS.2019.8813862, 2019. a
Shi, B., Bai, S., Zhou, Z., and Bai, X.: DeepPano: Deep Panoramic Representation for 3-D Shape Recognition, IEEE Signal Proc. Let., 22, 2339–2343, https://doi.org/10.1109/LSP.2015.2480802, 2015. a
Silverman, V., Nahum, S., and Raveh-Rubin, S.: Predicting origins of coherent air mass trajectories using a neural network–the case of dry intrusions, Meteorol. Appl., 28, 1–18, https://doi.org/10.1002/met.1986, 2021. a, b
Sprenger, M. and Wernli, H.: The LAGRANTO Lagrangian analysis tool – version 2.0, Geosci. Model Dev., 8, 2569–2586, https://doi.org/10.5194/gmd-8-2569-2015, 2015.
a
Sprenger, M., Fragkoulidis, G., Binder, H., Croci-Maspoli, M., Graf, P., Grams, C. M., Knippertz, P., Madonna, E., Schemm, S., Škerlak, B., and Wernli, H.: Global climatologies of Eulerian and Lagrangian flow features based on ERA-Interim, B. Am. Meteorol. Soc., 98, 1739–1748, https://doi.org/10.1175/BAMS-D-15-00299.1, 2017. a
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014. a
Stohl, A., Haimberger, L., Scheele, M. P., and Wernli, H.: An intercomparison of results from three trajectory models, Meteorol. Appl., 8, 127–135, https://doi.org/10.1017/S1350482701002018, 2001. a
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C., Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H. S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D., Woolnough, S., Wu, T., Won, D. J., Xiao, H., Zaripov, R., and Zhang, L.: The subseasonal to seasonal (S2S) prediction project database, B. Am. Meteorol. Soc., 98, 163–173, https://doi.org/10.1175/BAMS-D-16-0017.1, 2017. a, b, c, d
Wandel, J., Quinting, J. F., and Grams, C. M.: Toward a Systematic Evaluation of Warm Conveyor Belts in Numerical Weather Prediction and Climate Models. Part II: Verification of Operational Reforecasts, J. Atmos. Sci., 78, 3965–3982, https://doi.org/10.1175/JAS-D-20-0385.1, 2021. 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/10.1256/smsqj.54210, 1997. a
Wernli, H. and Davies, H. C.: A Lagrangian-based analysis of extratropical cyclones. I: The method and some applications, Q. J. Roy. Meteor. Soc., 123, 467–489, https://doi.org/10.1256/smsqj.53810, 1997. a, b, c
Wernli, H. and Schwierz, C.: Surface cyclones in the ERA-40 dataset (1958–2001). Part I: Novel identification method and global climatology, J. Atmos. Sci., 63, 2486–2507, https://doi.org/10.1175/JAS3766.1, 2006. a
Weyn, J. A., Durran, D. R., and Caruana, R.: Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere, J. Adv. Model. Earth Sy., 12, e2020MS002109, https://doi.org/10.1029/2020MS002109, 2020. a
Short summary
Physical processes in weather systems importantly affect the midlatitude large-scale circulation. This study introduces an artificial-intelligence-based framework which allows the identification of an important weather system – the so-called warm conveyor belt (WCB) – at comparably low computational costs and from data at low spatial and temporal resolution. The framework thus newly enables the systematic investigation of WCBs in large data sets such as climate model projections.
Physical processes in weather systems importantly affect the midlatitude large-scale...
