Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2247-2024
© Author(s) 2024. 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-17-2247-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
19 Mar 2024
Model description paper |
| 19 Mar 2024
| 19 Mar 2024
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Daniela I. V. Domeisen
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
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Monika Feldmann, Daniela I. V. Domeisen, and Olivia Martius
Weather Clim. Dynam., 6, 1089–1106, https://doi.org/10.5194/wcd-6-1089-2025, https://doi.org/10.5194/wcd-6-1089-2025, 2025
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Severe thunderstorm outbreaks are a source of major damage across Europe. Using historical data, we analysed the large-scale weather patterns leading to these outbreaks in eight different regions. Three types of regions emerge: those limited by temperature, those limited by saturation, and those overall favourable for thunderstorms, consistent with their associated weather patterns and the general climate. These findings help explain regional differences and provide a basis for future forecast improvements.
Wolfgang Wicker, Emmanuele Russo, and Daniela I. V. Domeisen
Weather Clim. Dynam., 6, 965–979, https://doi.org/10.5194/wcd-6-965-2025, https://doi.org/10.5194/wcd-6-965-2025, 2025
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Heatwaves are becoming more frequent, but the contribution by atmospheric circulation changes is unclear. Experiments with an idealized model that simulates atmospheric dynamcis, but excludes clouds, radiation, and moisture, show how a poleward storm track shift increases the eastward phase speed of Rossby waves and reduces mid-latitude heatwave frequency. A comparison with real data for the Southern Hemisphere is attempted.
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).
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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.
Lou Brett, Christopher J. White, Daniela I. V. Domeisen, Bart van den Hurk, Philip Ward, and Jakob Zscheischler
Nat. Hazards Earth Syst. Sci., 25, 2591–2611, https://doi.org/10.5194/nhess-25-2591-2025, https://doi.org/10.5194/nhess-25-2591-2025, 2025
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Compound events, where multiple weather or climate hazards occur together, pose significant risks to both society and the environment. These events, like simultaneous wind and rain, can have more severe impacts than single hazards. Our review of compound event research from 2012–2022 reveals a rise in studies, especially on events that occur concurrently, hot and dry events, and compounding flooding. The review also highlights opportunities for research in the coming years.
Bastien François, Khalil Teber, Lou Brett, Richard Leeding, Luis Gimeno-Sotelo, Daniela I. V. Domeisen, Laura Suarez-Gutierrez, and Emanuele Bevacqua
Earth Syst. Dynam., 16, 1029–1051, https://doi.org/10.5194/esd-16-1029-2025, https://doi.org/10.5194/esd-16-1029-2025, 2025
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Spatially compounding wind and precipitation (CWP) extremes can lead to severe impacts on society. We find that concurrent climate variability modes favor the occurrence of such wintertime spatially compounding events in the Northern Hemisphere and can even amplify the number of regions and population exposed. Our analysis highlights the importance of considering the interplay between variability modes to improve risk management of such spatially compounding events.
Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Y. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Daniela I. V. Domeisen, Javier García-Serrano, Neil P. Hindley, Martin Jucker, Hera Kim, Robert W. Lee, Simon H. Lee, Marisol Osman, Froila M. Palmeiro, Inna Polichtchouk, Jian Rao, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
Weather Clim. Dynam., 6, 171–195, https://doi.org/10.5194/wcd-6-171-2025, https://doi.org/10.5194/wcd-6-171-2025, 2025
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Variability in the extratropical stratosphere and troposphere is coupled, and because of the longer timescales characteristic of the stratosphere, this allows for a window of opportunity for surface prediction. This paper assesses whether models used for operational prediction capture these coupling processes accurately. We find that most processes are too weak; however downward coupling from the lower stratosphere to the near surface is too strong.
Pauline Rivoire, Sonia Dupuis, Antoine Guisan, Pascal Vittoz, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3482, https://doi.org/10.5194/egusphere-2024-3482, 2024
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Our study investigates the conditions in temperature, precipitation, humidity, and soil moisture leading to the browning of the European forests in summer. Using a Random Forest model and satellite measurement of vegetation greenness, we identify key conditions that predict forest damage. We conclude that hot and dry conditions in spring and summer are adverse conditions, in particular for broad-leaved trees. The hydro-meteorological conditions during the preceding year can also have an impact.
Rachel W.-Y. Wu, Gabriel Chiodo, Inna Polichtchouk, and Daniela I. V. Domeisen
Atmos. Chem. Phys., 24, 12259–12275, https://doi.org/10.5194/acp-24-12259-2024, https://doi.org/10.5194/acp-24-12259-2024, 2024
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Strong variations in the strength of the stratospheric polar vortex can profoundly affect surface weather extremes; therefore, accurately predicting the stratosphere can improve surface weather forecasts. The research reveals how uncertainty in the stratosphere is linked to the troposphere. The findings suggest that refining models to better represent the identified sources and impact regions in the troposphere is likely to improve the prediction of the stratosphere and its surface impacts.
Michael Schutte, Daniela I. V. Domeisen, and Jacopo Riboldi
Weather Clim. Dynam., 5, 733–752, https://doi.org/10.5194/wcd-5-733-2024, https://doi.org/10.5194/wcd-5-733-2024, 2024
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The winter circulation in the stratosphere, a layer of the Earth’s atmosphere between 10 and 50 km height, is tightly linked to the circulation in the lower atmosphere determining our daily weather. This interconnection happens in the form of waves propagating in and between these two layers. Here, we use space–time spectral analysis to show that disruptions and enhancements of the stratospheric circulation modify the shape and propagation of waves in both layers.
Luca G. Severino, Chahan M. Kropf, Hilla Afargan-Gerstman, Christopher Fairless, Andries Jan de Vries, Daniela I. V. Domeisen, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 24, 1555–1578, https://doi.org/10.5194/nhess-24-1555-2024, https://doi.org/10.5194/nhess-24-1555-2024, 2024
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We combine climate projections from 30 climate models with a climate risk model to project winter windstorm damages in Europe under climate change. We study the uncertainty and sensitivity factors related to the modelling of hazard, exposure and vulnerability. We emphasize high uncertainties in the damage projections, with climate models primarily driving the uncertainty. We find climate change reshapes future European windstorm risk by altering damage locations and intensity.
Hilla Afargan-Gerstman, Dominik Büeler, C. Ole Wulff, Michael Sprenger, and Daniela I. V. Domeisen
Weather Clim. Dynam., 5, 231–249, https://doi.org/10.5194/wcd-5-231-2024, https://doi.org/10.5194/wcd-5-231-2024, 2024
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The stratosphere is a layer of Earth's atmosphere found above the weather systems. Changes in the stratosphere can affect the winds and the storm tracks in the North Atlantic region for a relatively long time, lasting for several weeks and even months. We show that the stratosphere can be important for weather forecasts beyond 1 week, but more work is needed to improve the accuracy of these forecasts for 3–4 weeks.
Maria Pyrina, Wolfgang Wicker, Andries Jan de Vries, Georgios Fragkoulidis, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3088, https://doi.org/10.5194/egusphere-2023-3088, 2024
Preprint withdrawn
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We investigate the atmospheric dynamics behind heatwaves, specifically of those occurring simultaneously across regions, known as concurrent heatwaves. We find that heatwaves are strongly modulated by Rossby wave packets, being Rossby waves whose amplitude has a local maximum and decays at larger distances. High amplitude Rossby wave packets increase the occurrence probabilities of concurrent and non-concurrent heatwaves by a factor of 15 and 18, respectively, over several regions globally.
David Martin Straus, Daniela I. V. Domeisen, Sarah-Jane Lock, Franco Molteni, and Priyanka Yadav
Weather Clim. Dynam., 4, 1001–1018, https://doi.org/10.5194/wcd-4-1001-2023, https://doi.org/10.5194/wcd-4-1001-2023, 2023
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The global response to the Madden–Julian oscillation (MJO) is potentially predictable. Yet the diabatic heating is uncertain even within a particular episode of the MJO. Experiments with a global model probe the limitations imposed by this uncertainty. The large-scale tropical heating is predictable for 25 to 45 d, yet the associated Rossby wave source that links the heating to the midlatitude circulation is predictable for 15 to 20 d. This limitation has not been recognized in prior work.
Gabriel Chiodo, Marina Friedel, Svenja Seeber, Daniela Domeisen, Andrea Stenke, Timofei Sukhodolov, and Franziska Zilker
Atmos. Chem. Phys., 23, 10451–10472, https://doi.org/10.5194/acp-23-10451-2023, https://doi.org/10.5194/acp-23-10451-2023, 2023
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Stratospheric ozone protects the biosphere from harmful UV radiation. Anthropogenic activity has led to a reduction in the ozone layer in the recent past, but thanks to the implementation of the Montreal Protocol, the ozone layer is projected to recover. In this study, we show that projected future changes in Arctic ozone abundances during springtime will influence stratospheric climate and thereby actively modulate large-scale circulation changes in the Northern Hemisphere.
Jake W. Casselman, Joke F. Lübbecke, Tobias Bayr, Wenjuan Huo, Sebastian Wahl, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 471–487, https://doi.org/10.5194/wcd-4-471-2023, https://doi.org/10.5194/wcd-4-471-2023, 2023
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El Niño–Southern Oscillation (ENSO) has remote effects on the tropical North Atlantic (TNA), but the connections' nonlinearity (strength of response to an increasing ENSO signal) is not always well represented in models. Using the Community Earth System Model version 1 – Whole Atmosphere Community Climate Mode (CESM-WACCM) and the Flexible Ocean and Climate Infrastructure version 1, we find that the TNA responds linearly to extreme El Niño but nonlinearly to extreme La Niña for CESM-WACCM.
Raphaël de Fondeville, Zheng Wu, Enikő Székely, Guillaume Obozinski, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 287–307, https://doi.org/10.5194/wcd-4-287-2023, https://doi.org/10.5194/wcd-4-287-2023, 2023
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We propose a fully data-driven, interpretable, and computationally scalable framework to characterize sudden stratospheric warmings (SSWs), extract statistically significant precursors, and produce machine learning (ML) forecasts. By successfully leveraging the long-lasting impact of SSWs, the ML predictions outperform sub-seasonal numerical forecasts for lead times beyond 25 d. Post-processing numerical predictions using their ML counterparts yields a performance increase of up to 20 %.
Wolfgang Wicker, Inna Polichtchouk, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 81–93, https://doi.org/10.5194/wcd-4-81-2023, https://doi.org/10.5194/wcd-4-81-2023, 2023
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Sudden stratospheric warmings are extreme weather events where the winter polar stratosphere warms by about 25 K. An improved representation of small-scale gravity waves in sub-seasonal prediction models can reduce forecast errors since their impact on the large-scale circulation is predictable multiple weeks ahead. After a sudden stratospheric warming, vertically propagating gravity waves break at a lower altitude than usual, which strengthens the long-lasting positive temperature anomalies.
Marina Friedel, Gabriel Chiodo, Andrea Stenke, Daniela I. V. Domeisen, and Thomas Peter
Atmos. Chem. Phys., 22, 13997–14017, https://doi.org/10.5194/acp-22-13997-2022, https://doi.org/10.5194/acp-22-13997-2022, 2022
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In spring, winds the Arctic stratosphere change direction – an event called final stratospheric warming (FSW). Here, we examine whether the interannual variability in Arctic stratospheric ozone impacts the timing of the FSW. We find that Arctic ozone shifts the FSW to earlier and later dates in years with high and low ozone via the absorption of UV light. The modulation of the FSW by ozone has consequences for surface climate in ozone-rich years, which may result in better seasonal predictions.
Nora Bergner, Marina Friedel, Daniela I. V. Domeisen, Darryn Waugh, and Gabriel Chiodo
Atmos. Chem. Phys., 22, 13915–13934, https://doi.org/10.5194/acp-22-13915-2022, https://doi.org/10.5194/acp-22-13915-2022, 2022
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Polar vortex extremes, particularly situations with an unusually weak cyclonic circulation in the stratosphere, can influence the surface climate in the spring–summer time in the Southern Hemisphere. Using chemistry-climate models and observations, we evaluate the robustness of the surface impacts. While models capture the general surface response, they do not show the observed climate patterns in midlatitude regions, which we trace back to biases in the models' circulations.
Jake W. Casselman, Bernat Jiménez-Esteve, and Daniela I. V. Domeisen
Weather Clim. Dynam., 3, 1077–1096, https://doi.org/10.5194/wcd-3-1077-2022, https://doi.org/10.5194/wcd-3-1077-2022, 2022
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Using an atmospheric general circulation model, we analyze how the tropical North Atlantic influences the El Niño–Southern Oscillation connection towards the North Atlantic European region. We also focus on the lesser-known boreal spring and summer response following an El Niño–Southern Oscillation event. Our results show that altered tropical Atlantic sea surface temperatures may cause different responses over the Caribbean region, consequently influencing the North Atlantic European region.
Zachary D. Lawrence, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Amy H. Butler, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Daniela I. V. Domeisen, Etienne Dunn-Sigouin, Javier García-Serrano, Chaim I. Garfinkel, Neil P. Hindley, Liwei Jia, Martin Jucker, Alexey Y. Karpechko, Hera Kim, Andrea L. Lang, Simon H. Lee, Pu Lin, Marisol Osman, Froila M. Palmeiro, Judith Perlwitz, Inna Polichtchouk, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Irene Erner, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
Weather Clim. Dynam., 3, 977–1001, https://doi.org/10.5194/wcd-3-977-2022, https://doi.org/10.5194/wcd-3-977-2022, 2022
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Forecast models that are used to predict weather often struggle to represent the Earth’s stratosphere. This may impact their ability to predict surface weather weeks in advance, on subseasonal-to-seasonal (S2S) timescales. We use data from many S2S forecast systems to characterize and compare the stratospheric biases present in such forecast models. These models have many similar stratospheric biases, but they tend to be worse in systems with low model tops located within the stratosphere.
Rachel Wai-Ying Wu, Zheng Wu, and Daniela I.V. Domeisen
Weather Clim. Dynam., 3, 755–776, https://doi.org/10.5194/wcd-3-755-2022, https://doi.org/10.5194/wcd-3-755-2022, 2022
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Accurate predictions of the stratospheric polar vortex can enhance surface weather predictability. Stratospheric events themselves are less predictable, with strong inter-event differences. We assess the predictability of stratospheric acceleration and deceleration events in a sub-seasonal prediction system, finding that the predictability of events is largely dependent on event magnitude, while extreme drivers of deceleration events are not fully represented in the model.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
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This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Chen Schwartz, Chaim I. Garfinkel, Priyanka Yadav, Wen Chen, and Daniela I. V. Domeisen
Weather Clim. Dynam., 3, 679–692, https://doi.org/10.5194/wcd-3-679-2022, https://doi.org/10.5194/wcd-3-679-2022, 2022
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Eleven operational forecast models that run on subseasonal timescales (up to 2 months) are examined to assess errors in their simulated large-scale stationary waves in the Northern Hemisphere winter. We found that models with a more finely resolved stratosphere generally do better in simulating the waves in both the stratosphere (10–50 km) and troposphere below. Moreover, a connection exists between errors in simulated time-mean convection in tropical regions and errors in the simulated waves.
Adam A. Scaife, Mark P. Baldwin, Amy H. Butler, Andrew J. Charlton-Perez, Daniela I. V. Domeisen, Chaim I. Garfinkel, Steven C. Hardiman, Peter Haynes, Alexey Yu Karpechko, Eun-Pa Lim, Shunsuke Noguchi, Judith Perlwitz, Lorenzo Polvani, Jadwiga H. Richter, John Scinocca, Michael Sigmond, Theodore G. Shepherd, Seok-Woo Son, and David W. J. Thompson
Atmos. Chem. Phys., 22, 2601–2623, https://doi.org/10.5194/acp-22-2601-2022, https://doi.org/10.5194/acp-22-2601-2022, 2022
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Great progress has been made in computer modelling and simulation of the whole climate system, including the stratosphere. Since the late 20th century we also gained a much clearer understanding of how the stratosphere interacts with the lower atmosphere. The latest generation of numerical prediction systems now explicitly represents the stratosphere and its interaction with surface climate, and here we review its role in long-range predictions and projections from weeks to decades ahead.
Zheng Wu, Bernat Jiménez-Esteve, Raphaël de Fondeville, Enikő Székely, Guillaume Obozinski, William T. Ball, and Daniela I. V. Domeisen
Weather Clim. Dynam., 2, 841–865, https://doi.org/10.5194/wcd-2-841-2021, https://doi.org/10.5194/wcd-2-841-2021, 2021
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We use an advanced statistical approach to investigate the dynamics of the development of sudden stratospheric warming (SSW) events in the winter Northern Hemisphere. We identify distinct signals that are representative of these events and their event type at lead times beyond currently predictable lead times. The results can be viewed as a promising step towards improving the predictability of SSWs in the future by using more advanced statistical methods in operational forecasting systems.
Amy H. Butler and Daniela I. V. Domeisen
Weather Clim. Dynam., 2, 453–474, https://doi.org/10.5194/wcd-2-453-2021, https://doi.org/10.5194/wcd-2-453-2021, 2021
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We classify by wave geometry the stratospheric polar vortex during the final warming that occurs every spring in both hemispheres due to a combination of radiative and dynamical processes. We show that the shape of the vortex, as well as the timing of the seasonal transition, is linked to total column ozone prior to and surface weather following the final warming. These results have implications for prediction and our understanding of stratosphere–troposphere coupling processes in springtime.
Cited articles
Anaconda, Inc.: Anaconda Software Distribution., Anaconda, Inc., https://www.anaconda.com (last access: 13 March 2024), 2016. a
Bengston, L., Botzet, M., and Esch, M.: Hurricane-type vortices in a general circulation model, Tellus A, 47, 175–196, https://doi.org/10.1034/j.1600-0870.1995.t01-1-00003.x, 1995. a
Beucler, T., Ebert-Uphoff, I., Michael, S. R., Pritchard, M., and Gentine, P.: Machine Learning for Clouds and Climate, invited Chapter for the AGU Geophysical Monograph Series “Clouds and Climate”, https://doi.org/10.1002/essoar.10506925.1, 2021. a
Brown, J. R., Lengaigne, M., Lintner, B. R., Widlansky, M. J., van der Wiel, K., Dutheil, C., Linsley, B. K., Matthews, A. J., and Renwick, J.: South Pacific Convergence Zone dynamics, variability and impacts in a changing climate, Nat. Rev. Earth Environ., 1, 530–543, https://doi.org/10.1038/s43017-020-0078-2, 2020. a, b
Camargo, S. J. and Zebiak, S. E.: Improving the Detection and Tracking of Tropical Cyclones in Atmospheric General Circulation Models, Weather Forecast., 17, 1152–1162, https://doi.org/10.1175/1520-0434(2002)017<1152:ITDATO>2.0.CO;2, 2002. a
Carvalho, L. M. V., Jones, C., and Liebmann, B.: Extreme Precipitation Events in Southeastern South America and Large-Scale Convective Patterns in the South Atlantic Convergence Zone, J. Climate, 15, 2377–2394, https://doi.org/10.1175/1520-0442(2002)015<2377:EPEISS>2.0.CO;2, 2002. a
Carvalho, L. M. V., Jones, C., and Liebmann, B.: The South Atlantic Convergence Zone: Intensity, Form, Persistence, and Relationships with Intraseasonal to Interannual Activity and Extreme Rainfall, J. Climate, 17, 88–108, https://doi.org/10.1175/1520-0442(2004)017<0088:TSACZI>2.0.CO;2, 2004. a, b, c
Chauvin, F., Royer, J.-F., and Déqué, M.: Response of hurricane-type vortices to global warming as simulated by ARPEGE-Climat at high resolution, Clim. Dynam., 27, 377–399, https://doi.org/10.1007/s00382-006-0135-7, 2006. a
Cilli, R., Monaco, A., Amoroso, N., Tateo, A., Tangaro, S., and Bellotti, R.: Machine Learning for Cloud Detection of Globally Distributed Sentinel-2 Images, Remote Sens., 12, 2355, https://doi.org/10.3390/rs12152355, 2020. a
Cook, K. H.: The South Indian Convergence Zone and Interannual Rainfall Variability over Southern Africa, J. Climate, 13, 3789–3804, https://doi.org/10.1175/1520-0442(2000)013<3789:TSICZA>2.0.CO;2, 2000. a, b, c, d
Dettinger, M. D., Ralph, F. M., Das, T., Neiman, P. J., and Cayan, D. R.: Atmospheric Rivers, Floods and the Water Resources of California, Water, 3, 445–478, https://doi.org/10.3390/w3020445, 2011. a
de Vries, A. J.: A global climatological perspective on the importance of Rossby wave breaking and intense moisture transport for extreme precipitation events, Weather Clim. Dynam., 2, 129–161, https://doi.org/10.5194/wcd-2-129-2021, 2021. a
de Vries, A. J., Ouwersloot, H. G., Feldstein, S. B., Riemer, M., El Kenawy, A. M., McCabe, M. F., and Lelieveld, J.: Identification of Tropical-Extratropical Interactions and Extreme Precipitation Events in the Middle East Based On Potential Vorticity and Moisture Transport, J. Geophys. Res.-Atmos., 123, 861–881, https://doi.org/10.1002/2017JD027587, 2018. a
Dowdy, A. J., Qi, L., Jones, D., Ramsay, H., Fawcett, R., and Kuleshov, Y.: Tropical Cyclone Climatology of the South Pacific Ocean and Its Relationship to El Niño–Southern Oscillation, J. Climate, 25, 6108–6122, https://doi.org/10.1175/JCLI-D-11-00647.1, 2012. a
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze Jr., R. A., Li, J., Hardin, J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective System Database Using Satellite-Derived Cloud Tops, Surface Precipitation, and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021. a
Feng, Z., Hardin, J., Barnes, H. C., Li, J., Leung, L. R., Varble, A., and Zhang, Z.: PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis, Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, 2023. a, b
Fiolleau, T. and Roca, R.: An Algorithm for the Detection and Tracking of Tropical Mesoscale Convective Systems Using Infrared Images From Geostationary Satellite, IEEE T. Geosci. Remote, 51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762, 2013. a, b
Guan, B., Waliser, D. E., and Ralph, F. M.: Global Application of the Atmospheric River Scale, J. Geophys. Res.-Atmos., 128, e2022JD037180, https://doi.org/10.1029/2022JD037180, 2023. a
Harrison, M. S. J.: A generalized classification of South African summer rain-bearing synoptic systems, J. Climatol., 4, 547–560, https://doi.org/10.1002/joc.3370040510, 1984. a
Hart, N. C. G., Reason, C. J. C., and Fauchereau, N.: Building a Tropical–Extratropical Cloud Band Metbot, Mon. Weather Rev., 140, 4005–4016, https://doi.org/10.1175/MWR-D-12-00127.1, 2012. a, b, c, d
Hart, N. C. G., Reason, C. J. C., and Fauchereau, N.: Cloud bands over southern Africa: seasonality, contribution to rainfall variability and modulation by the MJO, Clim. Dynam., 41, 1199–1212, https://doi.org/10.1007/s00382-012-1589-4, 2013. a
Hart, N. C. G., Washington, R., and Reason, C. J. C.: On the Likelihood of Tropical–Extratropical Cloud Bands in the South Indian Convergence Zone during ENSO Events, J. Climate, 31, 2797–2817, https://doi.org/10.1175/JCLI-D-17-0221.1, 2018. a, b, c
Hartmann, D. L., Moy, L. A., and Fu, Q.: Tropical Convection and the Energy Balance at the Top of the Atmosphere, J. Climate, 14, 4495–4511, https://doi.org/10.1175/1520-0442(2001)014<4495:TCATEB>2.0.CO;2, 2001. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Holloway, C. E. and Woolnough, S. J.: The sensitivity of convective aggregation to diabatic processes in idealized radiative-convective equilibrium simulations, J. Adv. Model. Earth Sy., 8, 166–195, https://doi.org/10.1002/2015MS000511, 2016. a
Houze, R. A.: Stratiform Precipitation in Regions of Convection: A Meteorological Paradox?, B. Am. Meteorol. Soc., 78, 2179–2196, https://doi.org/10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2, 1997. a
Houze Jr., R. A.: Mesoscale convective systems, Rev. Geophys., 42, 4, https://doi.org/10.1029/2004RG000150, 2004. a, b
Houze Jr., R. A., Rasmussen, K. L., Zuluaga, M. D., and Brodzik, S. R.: The variable nature of convection in the tropics and subtropics: A legacy of 16 years of the Tropical Rainfall Measuring Mission satellite, Rev. Geophys., 53, 994–1021, https://doi.org/10.1002/2015RG000488, 2015. a
Huaman, L., Schumacher, C., and Kiladis, G. N.: Eastward-Propagating Disturbances in the Tropical Pacific, Mon. Weather Rev., 148, 3713–3728, https://doi.org/10.1175/MWR-D-20-0029.1, 2020. a
Huang, X., Hu, C., Huang, X., Chu, Y., Tseng, Y.-H., Zhang, G. J., and Lin, Y.: A long-term tropical mesoscale convective systems dataset based on a novel objective automatic tracking algorithm, Clim. Dynam., 51, 3145–3159, https://doi.org/10.1007/s00382-018-4071-0, 2018. a
Hudson, H. R.: On the Relationship Between Horizontal Moisture Convergence and Convective Cloud Formation, J. Appl. Meteorol. Climatol., 10, 755–762, https://doi.org/10.1175/1520-0450(1971)010<0755:OTRBHM>2.0.CO;2, 1971. a
Huffman, G., Behrangi, A., Bolvin, D., and Nelkin, E. (Eds.): GPCP Version 3.1 Satellite-Gauge (SG) Combined Precipitation Data Set, Greenbelt, Maryland, USA, NASA GES DISC [data set], https://doi.org/10.5067/DBVUO4KQHXTK, 2020. a, b
Huffman, G. J., Behrangi, A., D. Bolvin, T., and Nelkin, E. J.: GPCP Version 3.2 Satellite-Gauge (SG) Combined Precipitation Data Set, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/MEASURES/GPCP/DATA304, 2022.
Jones, C. and Carvalho, L. M. V.: Active and Break Phases in the South American Monsoon System, J. Climate, 15, 905–914, https://doi.org/10.1175/1520-0442(2002)015<0905:AABPIT>2.0.CO;2, 2002. a
Kiladis, G. N. and Weickmann, K. M.: Extratropical forcing of tropical Pacific convection during northern winter, Mon. Weather Rev., 120, 1924–1938, https://doi.org/10.1175/1520-0493(1992)120<1924:EFOTPC>2.0.CO;2, 1992. a
Kiladis, G. N., von Storch, H., and van Loon, H.: Origin of the South Pacific Convergence Zone, J. Climate, 2, 1185–1195, 1989. a
Knippertz, P.: Tropical–Extratropical Interactions Associated with an Atlantic Tropical Plume and Subtropical Jet Streak, Mon. Weather Rev., 133, 2759–2776, https://doi.org/10.1175/MWR2999.1, 2005. a, b
Knippertz, P.: Tropical–extratropical interactions related to upper-level troughs at low latitudes, Dynamics of Atmospheres and Oceans, current Contributions to Understanding the General Circulation of the Atmosphere, 43, 36–62, https://doi.org/10.1016/j.dynatmoce.2006.06.003, 2007. a
Kodama, Y.: Large-Scale Common Features of Subtropical Precipitation Zones (the Baiu Frontal Zone, the SPCZ, and the SACZ) Part I: Characteristics of Subtropical Frontal Zones, J. Meteorol. Soc. Jpn. Ser. II, 70, 813–836, https://doi.org/10.2151/jmsj1965.70.4_813, 1992. a, b
Kummerow, C. D., Ringerud, S., Crook, J., Randel, D., and Berg, W.: An Observationally Generated A Priori Database for Microwave Rainfall Retrievals, J. Atmos. Ocean. Tech., 28, 113–130, https://doi.org/10.1175/2010JTECHA1468.1, 2011. a
Laing, A. G. and Michael Fritsch, J.: The global population of mesoscale convective complexes, Q. J. Roy. Meteor. Soc., 123, 389–405, https://doi.org/10.1002/qj.49712353807, 1997. a
Lenters, J. D. and Cook, K. H.: On the Origin of the Bolivian High and Related Circulation Features of the South American Climate, J. Atmos. Sci., 54, 656–678, https://doi.org/10.1175/1520-0469(1997)054<0656:OTOOTB>2.0.CO;2, 1997. a
Li, Y., Deng, Y., Yang, S., and Zhang, H.: Multi-scale temporospatial variability of the East Asian Meiyu-Baiu fronts: characterization with a suite of new objective indices, Clim. Dynam., 51, 1659–1670, https://doi.org/10.1007/s00382-017-3975-4, 2018. a
Liebmann, B., Kiladis, G. N., Marengo, J., Ambrizzi, T., and Glick, J. D.: Submonthly Convective Variability over South America and the South Atlantic Convergence Zone, J. Climate, 12, 1877–1891, https://doi.org/10.1175/1520-0442(1999)012<1877:SCVOSA>2.0.CO;2, 1999. a, b
Limbach, S., Schömer, E., and Wernli, H.: Detection, tracking and event localization of jet stream features in 4-D atmospheric data, Geosci. Model Dev., 5, 457–470, https://doi.org/10.5194/gmd-5-457-2012, 2012. a, b
Liu, C., Liao, X., Yang, Y., Feng, X., Allan, R. P., Cao, N., Long, J., and Xu, J.: Observed variability of intertropical convergence zone over 1998–2018, Environ. Res. Lett., 15, 104011, https://doi.org/10.1088/1748-9326/aba033, 2020. a
Massie, S., Gettelman, A., Randel, W., and Baumgardner, D.: Distribution of tropical cirrus in relation to convection, J. Geophys. Res.-Atmos., 107, AAC 19-1–AAC 19-16, https://doi.org/10.1029/2001JD001293, 2002. a, b, c
Matthews, A. J.: A multiscale framework for the origin and variability of the South Pacific Convergence Zone, Q. J. Roy. Meteor. Soc., 138, 1165–1178, https://doi.org/10.1002/qj.1870, 2012. a, b
Matthews, A. J., Hoskins, B. J., Slingo, J. M., and Blackburn, M.: Development of convection along the SPCZ within a Madden-Julian oscillation, Q. J. Roy. Meteor. Soc., 122, 669–688, https://doi.org/10.1002/qj.49712253106, 1996. a
Neiman, P. J., Ralph, F. M., Wick, G. A., Lundquist, J. D., and Dettinger, M. D.: Meteorological Characteristics and Overland Precipitation Impacts of Atmospheric Rivers Affecting the West Coast of North America Based on Eight Years of SSM/I Satellite Observations, J. Hydrometeorol., 9, 22–47, https://doi.org/10.1175/2007JHM855.1, 2008. a
Nesbitt, S. W., Cifelli, R., and Rutledge, S. A.: Storm Morphology and Rainfall Characteristics of TRMM Precipitation Features, Mon. Weather Rev., 134, 2702–2721, https://doi.org/10.1175/MWR3200.1, 2006. a
Ninomiya, K.: Similarities and Differences among the South Indian Ocean Convergence Zone, North American Convergence Zone, and Other Subtropical Convergence Zones Simulated Using an AGCM, J. Meteorol. Soc. Jpn. Ser. II, 86, 141–165, https://doi.org/10.2151/jmsj.86.141, 2008. a
Nugent, J. M., Turbeville, S. M., Bretherton, C. S., Blossey, P. N., and Ackerman, T. P.: Tropical Cirrus in Global Storm-Resolving Models: 1. Role of Deep Convection, Earth Space Sci., 9, e2021EA001965, https://doi.org/10.1029/2021EA001965, 2022. a
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms, IEEE T. Syst. Man Cyb., 9, 62–66, https://doi.org/10.1109/TSMC.1979.4310076, 1979. a
Oueslati, B. and Bellon, G.: Convective Entrainment and Large-Scale Organization of Tropical Precipitation: Sensitivity of the CNRM-CM5 Hierarchy of Models, J. Climate, 26, 2931–2946, https://doi.org/10.1175/JCLI-D-12-00314.1, 2013. a
Paegle, J. N., Byerle, L. A., and Mo, K. C.: Intraseasonal Modulation of South American Summer Precipitation, Mon. Weather Rev., 128, 837–850, https://doi.org/10.1175/1520-0493(2000)128<0837:IMOSAS>2.0.CO;2, 2000. a
Pohl, B., Richard, Y., and Fauchereau, N.: Influence of the Madden–Julian Oscillation on Southern African Summer Rainfall, J. Climate, 20, 4227–4242, https://doi.org/10.1175/JCLI4231.1, 2007. a
Post, F. H., Vrolijk, B., Hauser, H., Laramee, R. S., and Doleisch, H.: The State of the Art in Flow Visualisation: Feature Extraction and Tracking, Computer Graphics Forum, 22, 775–792, https://doi.org/10.1111/j.1467-8659.2003.00723.x, 2003. a
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
Rickenbach, T. M. and Rutledge, S. A.: Convection in TOGA COARE: Horizontal Scale, Morphology, and Rainfall Production, J. Atmos. Sci., 55, 2715–2729, 1998. a
Roca, R. and Ramanathan, V.: Scale Dependence of Monsoonal Convective Systems over the Indian Ocean, J. Climate, 13, 1286–1298, https://doi.org/10.1175/1520-0442(2000)013<1286:SDOMCS>2.0.CO;2, 2000. a
Roca, R., Bergès, J.-C., Brogniez, H., Capderou, M., Chambon, P., Chomette, O., Cloché, S., Fiolleau, T., Jobard, I., Lémond, J., Ly, M., Picon, L., Raberanto, P., Szantai, A., and Viollier, M.: On the water and energy cycles in the Tropics, Comptes Rendus Geoscience, 342, 390–402, https://doi.org/10.1016/j.crte.2010.01.003, 2010. a
Roca, R., Aublanc, J., Chambon, P., Fiolleau, T., and Viltard, N.: Robust Observational Quantification of the Contribution of Mesoscale Convective Systems to Rainfall in the Tropics, J. Climate, 27, 4952–4958, https://doi.org/10.1175/JCLI-D-13-00628.1, 2014. a, b
Rosa, E. B., Pezzi, L. P., Quadro, M. F. L. d., and Brunsell, N.: Automated Detection Algorithm for SACZ, Oceanic SACZ, and Their Climatological Features, Front. Environ. Sci., 8, https://doi.org/10.3389/fenvs.2020.00018, 2020. a, b, c
Sassen, K., Wang, Z., and Liu, D.: Cirrus clouds and deep convection in the tropics: Insights from CALIPSO and CloudSat, J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2009JD011916, 2009. a
Schoeberl, M. R., Jensen, E. J., Pfister, L., Ueyama, R., Wang, T., Selkirk, H., Avery, M., Thornberry, T., and Dessler, A. E.: Water Vapor, Clouds, and Saturation in the Tropical Tropopause Layer, J. Geophys. Res.-Atmos., 124, 3984–4003, https://doi.org/10.1029/2018JD029849, 2019. a
Schumacher, C. and Houze, R. A.: Stratiform Rain in the Tropics as Seen by the TRMM Precipitation Radar, J. Climate, 16, 1739–1756, https://doi.org/10.1175/1520-0442(2003)016<1739:SRITTA>2.0.CO;2, 2003. a
Shannon, G., Lutjeharms, L., and Nelson, J.: Causative mechanisms for intra-annual and interannual variability in the marine environment around southern Africa, S. Afr. J. Sci., 86, 356, https://hdl.handle.net/10520/AJA00382353_4501, 1990. a
Sokol, A. B. and Hartmann, D. L.: Tropical Anvil Clouds: Radiative Driving Toward a Preferred State, J. Geophys. Res.-Atmos., 125, e2020JD033107, https://doi.org/10.1029/2020JD033107, 2020. a
Streten, N. A.: Some Characteristics of Satellite-Observed Bands Of Persistent Cloudiness Over the Southern Hemisphere, Mon. Weather Rev., 101, 486–495, https://doi.org/10.1175/1520-0493(1973)101<0486:SCOSBO>2.3.CO;2, 1973. a, b
Su, W., Mao, J., Ji, F., and Qin, Y.: Outgoing longwave radiation and cloud radiative forcing of the Tibetan Plateau, J. Geophys. Res.-Atmos., 105, 14863–14872, https://doi.org/10.1029/2000JD900201, 2000. a
Sugi, M., Noda, A., and Sato, N.: Influence of the Global Warming on Tropical Cyclone Climatology: An Experiment with the JMA Global Model, J. Meteorol. Soc. Jpn. Ser. II, 80, 249–272, https://doi.org/10.2151/jmsj.80.249, 2002. a
Takahashi, K. and Battisti, D. S.: Processes Controlling the Mean Tropical Pacific Precipitation Pattern. Part II: The SPCZ and the Southeast Pacific Dry Zone, J. Climate, 20, 5696–5706, https://doi.org/10.1175/2007JCLI1656.1, 2007. a
The Pip Development Team: Python Package Index – PyPI, version 21.2, https://pip.pypa.io (last access: 13 March 2024), 2021. a
Tsuji, H., Takayabu, Y. N., Shibuya, R., Kamahori, H., and Yokoyama, C.: The Role of Free-Tropospheric Moisture Convergence for Summertime Heavy Rainfall in Western Japan, Geophys. Res. Lett., 48, e2021GL095030, https://doi.org/10.1029/2021GL095030, 2021. a
Ulbrich, U., Leckebusch, G. C., and Pinto, J. G.: Extra-tropical cyclones in the present and future climate: a review, Theor. Appl. Climatol., 96, 117–131, https://doi.org/10.1007/s00704-008-0083-8, 2009. a
Vincent, D. G.: The South Pacific Convergence Zone (SPCZ): A Review, Mon. Weather Rev., 122, 1949–1970, https://doi.org/10.1175/1520-0493(1994)122<1949:TSPCZA>2.0.CO;2, 1994. a, b, c, d
Vincent, E., Lengaigne, M., Menkes, C., Jourdain, N., Marchesiello, P., and Madec, G.: Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis, Clim. Dynam., 36, 1881–1896, https://doi.org/10.1007/s00382-009-0716-3, 2011. a
Vincent, E. M., Lengaigne, M., Menkes, C. E., Jourdain, N. C., Marchesiello, P., and Madec, G.: Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis, Clim. Dynam., 36, 1881–1896, https://doi.org/10.1007/s00382-009-0716-3, 2011. a
Waliser, D. and Jiang, X.: TROPICAL METEOROLOGY AND CLIMATE | Intertropical Convergence Zone, in: Encyclopedia of Atmospheric Sciences (Second Edition), edited by: North, G. R., Pyle, J., and Zhang, F., 121–131, Academic Press, Oxford, 2nd edn., ISBN 978-0-12-382225-3, https://doi.org/10.1016/B978-0-12-382225-3.00417-5, 2015. a
Waliser, D. E., Graham, N. E., and Gautier, C.: Comparison of the Highly Reflective Cloud and Outgoing Longwave Radiation Datasets for Use in Estimating Tropical Deep Convection, J. Climate, 6, 331–353, https://doi.org/10.1175/1520-0442(1993)006<0331:COTHRC>2.0.CO;2, 1993. a
Williams, M. and Houze, R. A.: Satellite-Observed Characteristics of Winter Monsoon Cloud Clusters, Mon. Weather Rev., 115, 505–519, https://doi.org/10.1175/1520-0493(1987)115<0505:SOCOWM>2.0.CO;2, 1987. a
Wright, J. S., Sun, X., Konopka, P., Krüger, K., Legras, B., Molod, A. M., Tegtmeier, S., Zhang, G. J., and Zhao, X.: Differences in tropical high clouds among reanalyses: origins and radiative impacts, Atmos. Chem. Phys., 20, 8989–9030, https://doi.org/10.5194/acp-20-8989-2020, 2020. a
Yen, J.-C., Chang, F.-J., and Chang, S.: A new criterion for automatic multilevel thresholding, IEEE T. Image Process., 4, 370–378, https://doi.org/10.1109/83.366472, 1995. a
Yuan, J. and Houze, R. A.: Global Variability of Mesoscale Convective System Anvil Structure from A-Train Satellite Data, J. Climate, 23, 5864–5888, https://doi.org/10.1175/2010JCLI3671.1, 2010. a
Zhang, K., Randel, W. J., and Fu, R.: Relationships between outgoing longwave radiation and diabatic heating in reanalyses., Clim. Dynam., 49, 2911–2929, https://doi.org/10.1007/s00382-016-3501-0, 2017. a
Zilli, M. T. and Hart, N. C. G.: Rossby Wave Dynamics over South America Explored with Automatic Tropical–Extratropical Cloud Band Identification Framework, J. Climate, 34, 8125–8144, https://doi.org/10.1175/JCLI-D-21-0020.1, 2021. a, b, c
Zucker, S. W.: Region growing: Childhood and adolescence, Comp. Graph., 5, 382–399, https://doi.org/10.1016/S0146-664X(76)80014-7, 1976. a
Short summary
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.
This paper introduces a new method for detecting atmospheric cloud bands to identify long...
