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.
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
Related authors
Edgar Dolores-Tesillos, Olivia Martius, and Julian Quinting
Weather Clim. Dynam., 6, 471–487, https://doi.org/10.5194/wcd-6-471-2025, https://doi.org/10.5194/wcd-6-471-2025, 2025
Short summary
Short summary
An accurate representation of synoptic weather systems in climate models is required to estimate their societal and economic impacts under climate warming. Current climate models poorly represent the frequency of atmospheric blocking. Few studies have analysed the role of moist processes as a source of the bias of blocks. Here, we implement ELIAS2.0, a deep-learning tool, to validate the representation of moist processes in CMIP6 models and their link to the Euro-Atlantic blocking biases.
Alexandre Mass, Hendrik Andersen, Jan Cermak, Paola Formenti, Eva Pauli, and Julian Quinting
Atmos. Chem. Phys., 25, 491–510, https://doi.org/10.5194/acp-25-491-2025, https://doi.org/10.5194/acp-25-491-2025, 2025
Short summary
Short summary
This study investigates the interaction between smoke aerosols and fog and low clouds (FLCs) in the Namib Desert between June and October. Here, a satellite-based dataset of FLCs, reanalysis data and machine learning are used to systematically analyze FLC persistence under different aerosol loadings. Aerosol plumes are shown to modify local thermodynamics, which increase FLC persistence. But fully disentangling aerosol effects from meteorological ones remains a challenge.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
Annie Y.-Y. Chang, Shaun Harrigan, Maria-Helena Ramos, Massimiliano Zappa, Christian M. Grams, Daniela I. V. Domeisen, and Konrad Bogner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3411, https://doi.org/10.5194/egusphere-2025-3411, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study presents a machine learning-aided hybrid forecasting framework to improve early warnings of low flows in the European Alps. It combines weather regime information, streamflow observations, and model simulations (EFAS). Even using only weather regime data improves predictions over climatology, while integrating different data sources yields the best result, emphasizing the value of integrating diverse data sources.
Edgar Dolores-Tesillos, Olivia Martius, and Julian Quinting
Weather Clim. Dynam., 6, 471–487, https://doi.org/10.5194/wcd-6-471-2025, https://doi.org/10.5194/wcd-6-471-2025, 2025
Short summary
Short summary
An accurate representation of synoptic weather systems in climate models is required to estimate their societal and economic impacts under climate warming. Current climate models poorly represent the frequency of atmospheric blocking. Few studies have analysed the role of moist processes as a source of the bias of blocks. Here, we implement ELIAS2.0, a deep-learning tool, to validate the representation of moist processes in CMIP6 models and their link to the Euro-Atlantic blocking biases.
Marc Federer, Lukas Papritz, Michael Sprenger, and Christian M. Grams
Weather Clim. Dynam., 6, 211–230, https://doi.org/10.5194/wcd-6-211-2025, https://doi.org/10.5194/wcd-6-211-2025, 2025
Short summary
Short summary
Although extratropical cyclones in the North Atlantic are among the most impactful midlatitude weather systems, their intensification is not entirely understood. Here, we explore how individual cyclones convert available potential energy (APE) into kinetic energy and relate these conversions to the synoptic development of the cyclones. By combining potential vorticity thinking with a local APE framework, we offer a novel perspective on established concepts in dynamic meteorology.
Alexandre Mass, Hendrik Andersen, Jan Cermak, Paola Formenti, Eva Pauli, and Julian Quinting
Atmos. Chem. Phys., 25, 491–510, https://doi.org/10.5194/acp-25-491-2025, https://doi.org/10.5194/acp-25-491-2025, 2025
Short summary
Short summary
This study investigates the interaction between smoke aerosols and fog and low clouds (FLCs) in the Namib Desert between June and October. Here, a satellite-based dataset of FLCs, reanalysis data and machine learning are used to systematically analyze FLC persistence under different aerosol loadings. Aerosol plumes are shown to modify local thermodynamics, which increase FLC persistence. But fully disentangling aerosol effects from meteorological ones remains a challenge.
Svenja Christ, Marta Wenta, Christian M. Grams, and Annika Oertel
Weather Clim. Dynam., 6, 17–42, https://doi.org/10.5194/wcd-6-17-2025, https://doi.org/10.5194/wcd-6-17-2025, 2025
Short summary
Short summary
The detailed representation of sea surface temperature (SST) in numerical models is important for the prediction of atmospheric blocking in the North Atlantic. Yet the underlying physical processes are not fully understood. Using SST sensitivity experiments for a case study, we identify a physical pathway through which SST in the Gulf Stream region is linked to the downstream upper-level flow evolution in the North Atlantic.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
Short summary
Short summary
Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Joshua Dorrington, Marta Wenta, Federico Grazzini, Linus Magnusson, Frederic Vitart, and Christian M. Grams
Nat. Hazards Earth Syst. Sci., 24, 2995–3012, https://doi.org/10.5194/nhess-24-2995-2024, https://doi.org/10.5194/nhess-24-2995-2024, 2024
Short summary
Short summary
Extreme rainfall is the leading weather-related source of damages in Europe, but it is still difficult to predict on long timescales. A recent example of this was the devastating floods in the Italian region of Emiglia Romagna in May 2023. We present perspectives based on large-scale dynamical information that allows us to better understand and predict such events.
Moritz Deinhard and Christian M. Grams
Weather Clim. Dynam., 5, 927–942, https://doi.org/10.5194/wcd-5-927-2024, https://doi.org/10.5194/wcd-5-927-2024, 2024
Short summary
Short summary
Stochastic perturbations are an established 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 analyse how different schemes modulate rapidly ascending airstreams and whether the changes to such weather systems are projected onto larger scales. We thereby provide a process-oriented perspective on how perturbations affect the model climate.
Luise J. Fischer, David N. Bresch, Dominik Büeler, Christian M. Grams, Matthias Röthlisberger, and Heini Wernli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1253, https://doi.org/10.5194/egusphere-2024-1253, 2024
Short summary
Short summary
Atmospheric flows over the North Atlantic can be meaningfully classified into weather regimes, and climate simulations suggest that the regime frequencies might change in the future. We provide a quantitative framework that helps assessing whether these regime frequency changes are relevant for understanding climate change signals in precipitation. At least in our example application, this is not the case, i.e., regime frequency changes explain little of the projected precipitation changes.
Seraphine Hauser, Franziska Teubler, Michael Riemer, Peter Knippertz, and Christian M. Grams
Weather Clim. Dynam., 5, 633–658, https://doi.org/10.5194/wcd-5-633-2024, https://doi.org/10.5194/wcd-5-633-2024, 2024
Short summary
Short summary
Blocking over Greenland has substantial impacts on the weather and climate in mid- and high latitudes. This study applies a quasi-Lagrangian thinking on the dynamics of Greenland blocking and reveals two pathways of anticyclonic anomalies linked to the block. Moist processes were found to play a dominant role in the formation and maintenance of blocking. This emphasizes the necessity of the correct representation of moist processes in weather and climate models to realistically depict blocking.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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...