Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-851-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-851-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Cell tracking of convective rainfall: sensitivity of climate-change signal to tracking algorithm and cell definition (Cell-TAO v1.0)
Edmund P. Meredith
CORRESPONDING AUTHOR
Institut für Meteorologie, Freie Universität Berlin, Berlin, Germany
Uwe Ulbrich
Institut für Meteorologie, Freie Universität Berlin, Berlin, Germany
Henning W. Rust
Institut für Meteorologie, Freie Universität Berlin, Berlin, Germany
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Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
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In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
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Nat. Hazards Earth Syst. Sci., 25, 2331–2350, https://doi.org/10.5194/nhess-25-2331-2025, https://doi.org/10.5194/nhess-25-2331-2025, 2025
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Nat. Hazards Earth Syst. Sci., 25, 2179–2196, https://doi.org/10.5194/nhess-25-2179-2025, https://doi.org/10.5194/nhess-25-2179-2025, 2025
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Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025, https://doi.org/10.5194/hess-29-1637-2025, 2025
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Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.
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Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Madlen Peter, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 24, 1261–1285, https://doi.org/10.5194/nhess-24-1261-2024, https://doi.org/10.5194/nhess-24-1261-2024, 2024
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The paper introduces a statistical modeling approach describing daily extreme precipitation in Germany more accurately by including changes within the year and between the years simultaneously. The changing seasonality over years is regionally divergent and mainly weak. However, some regions stand out with a more pronounced linear rise of summer intensities, indicating a possible climate change signal. Improved modeling of extreme precipitation is beneficial for risk assessment and adaptation.
Yan Li, Bo Huang, and Henning W. Rust
Hydrol. Earth Syst. Sci., 28, 321–339, https://doi.org/10.5194/hess-28-321-2024, https://doi.org/10.5194/hess-28-321-2024, 2024
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The inconsistent changes in temperature and precipitation induced by forest cover change are very likely to affect drought condition. We use a set of statistical models to explore the relationship between forest cover change and drought change in different timescales and climate zones. We find that the influence of forest cover on droughts varies under different precipitation and temperature quantiles. Forest cover also could modulate the impacts of precipitation and temperature on drought.
Katrin M. Nissen, Martina Wilde, Thomas M. Kreuzer, Annika Wohlers, Bodo Damm, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 23, 2737–2748, https://doi.org/10.5194/nhess-23-2737-2023, https://doi.org/10.5194/nhess-23-2737-2023, 2023
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The effect of climate change on rockfall probability in the German low mountain regions is investigated in observations and in 23 different climate scenario simulations. Under a pessimistic greenhouse gas scenario, the simulations suggest a decrease in rockfall probability. This reduction is mainly caused by a decrease in the number of freeze–thaw cycles due to higher atmospheric temperatures.
Johannes Riebold, Andy Richling, Uwe Ulbrich, Henning Rust, Tido Semmler, and Dörthe Handorf
Weather Clim. Dynam., 4, 663–682, https://doi.org/10.5194/wcd-4-663-2023, https://doi.org/10.5194/wcd-4-663-2023, 2023
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Arctic sea ice loss might impact the atmospheric circulation outside the Arctic and therefore extremes over mid-latitudes. Here, we analyze model experiments to initially assess the influence of sea ice loss on occurrence frequencies of large-scale circulation patterns. Some of these detected circulation changes can be linked to changes in occurrences of European temperature extremes. Compared to future global temperature increases, the sea-ice-related impacts are however of secondary relevance.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Katrin M. Nissen, Stefan Rupp, Thomas M. Kreuzer, Björn Guse, Bodo Damm, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 22, 2117–2130, https://doi.org/10.5194/nhess-22-2117-2022, https://doi.org/10.5194/nhess-22-2117-2022, 2022
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A statistical model is introduced which quantifies the influence of individual potential triggering factors and their interactions on rockfall probability in central Europe. The most important factor is daily precipitation, which is most effective if sub-surface moisture levels are high. Freeze–thaw cycles in the preceding days can further increase the rockfall hazard. The model can be applied to climate simulations in order to investigate the effect of climate change on rockfall probability.
Robert Polzin, Annette Müller, Henning Rust, Peter Névir, and Péter Koltai
Nonlin. Processes Geophys., 29, 37–52, https://doi.org/10.5194/npg-29-37-2022, https://doi.org/10.5194/npg-29-37-2022, 2022
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In this study, a recent algorithmic framework called Direct Bayesian Model Reduction (DBMR) is applied which provides a scalable probability-preserving identification of reduced models directly from data. The stochastic method is tested in a meteorological application towards a model reduction to latent states of smaller scale convective activity conditioned on large-scale atmospheric flow.
Noelia Otero, Oscar E. Jurado, Tim Butler, and Henning W. Rust
Atmos. Chem. Phys., 22, 1905–1919, https://doi.org/10.5194/acp-22-1905-2022, https://doi.org/10.5194/acp-22-1905-2022, 2022
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Surface ozone and temperature are strongly dependent and their extremes might be exacerbated by underlying climatological drivers, such as atmospheric blocking. Using an observational data set, we measure the dependence structure between ozone and temperature under the influence of atmospheric blocking. Blocks enhanced the probability of occurrence of compound ozone and temperature extremes over northwestern and central Europe, leading to greater health risks.
Felix S. Fauer, Jana Ulrich, Oscar E. Jurado, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6479–6494, https://doi.org/10.5194/hess-25-6479-2021, https://doi.org/10.5194/hess-25-6479-2021, 2021
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Extreme rainfall events are modeled in this study for different timescales. A new parameterization of the dependence between extreme values and their timescale enables our model to estimate extremes on very short (1 min) and long (5 d) timescales simultaneously. We compare different approaches of modeling this dependence and find that our new model improves performance for timescales between 2 h and 2 d without affecting model performance on other timescales.
Jana Ulrich, Felix S. Fauer, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, https://doi.org/10.5194/hess-25-6133-2021, 2021
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The characteristics of extreme precipitation on different timescales as well as in different seasons are relevant information, e.g., for designing hydrological structures or managing water supplies. Therefore, our aim is to describe these characteristics simultaneously within one model. We find similar characteristics for short extreme precipitation at all considered stations in Germany but pronounced regional differences with respect to the seasonality of long-lasting extreme events.
Carola Detring, Annette Müller, Lisa Schielicke, Peter Névir, and Henning W. Rust
Weather Clim. Dynam., 2, 927–952, https://doi.org/10.5194/wcd-2-927-2021, https://doi.org/10.5194/wcd-2-927-2021, 2021
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Stationary, long-lasting blocked weather patterns can lead to extreme conditions. Within this study the temporal evolution of the occurrence probability is analyzed, and the onset, decay and transition probabilities of blocking within the past 30 years are modeled. Using Markov models combined with logistic regression, we found large changes in summer, where the probability of transitions to so-called Omega blocks increases strongly, while the unblocked state becomes less probable.
Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich
Geosci. Model Dev., 14, 4335–4355, https://doi.org/10.5194/gmd-14-4335-2021, https://doi.org/10.5194/gmd-14-4335-2021, 2021
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Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To ensure the applicability of these probabilistic predictions, inherent systematic errors of the prediction system must be adjusted. Since it is not clear which statistical model is optimal for this purpose, we propose a recalibration strategy with a systematic model selection based on non-homogeneous boosting for identifying the most relevant features for both ensemble mean and ensemble spread.
Nico Becker, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 20, 2857–2871, https://doi.org/10.5194/nhess-20-2857-2020, https://doi.org/10.5194/nhess-20-2857-2020, 2020
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A set of models is developed to forecast hourly probabilities of weather-related road accidents in Germany at the spatial scale of administrative districts. Model verification shows that using precipitation and temperature data leads to the best accident forecasts. Based on weather forecast data we show that skilful predictions of accident probabilities of up to 21 h ahead are possible. The models can be used to issue impact-based warnings, which are relevant for road users and authorities.
Cited articles
Amengual, A., Borga, M., Ravazzani, G., and Crema, S.: The role of storm
movement in controlling flash flood response: An analysis of the 28 September
2012 extreme event in Murcia, southeastern Spain, J.
Hydrometeorol., 22, 2379–2392, 2021. a
Armon, M., Marra, F., Enzel, Y., Rostkier-Edelstein, D., Garfinkel, C. I.,
Adam, O., Dayan, U., and Morin, E.: Reduced Rainfall in Future Heavy
Precipitation Events Related to Contracted Rain Area Despite Increased Rain
Rate, Earth's Future, 10, e2021EF002397, https://doi.org/10.1029/2021EF002397, 2022. a
Baur, F., Keil, C., and Craig, G. C.: Soil moisture–precipitation coupling
over Central Europe: Interactions between surface anomalies at different
scales and the dynamical implication, Q. J. Roy.
Meteor. Soc., 144, 2863–2875, https://doi.org/10.1002/qj.3415, 2018. a
Bennett, L., Melchers, B., and Proppe, B.: Curta: A General-purpose High-Performance Computer at ZEDAT, Freie Universität Berlin, https://doi.org/10.17169/refubium-26754, 2020. a
Brisson, E., Demuzere, M., and van Lipzig, N. P.: Modelling strategies for
performing convection-permitting climate simulations, Meteorol. Z., 25,
149–163, https://doi.org/10.1127/metz/2015/0598, 2016a. a
Brisson, E., Van Weverberg, K., Demuzere, M., Devis, A., Saeed, S., Stengel,
M., and van Lipzig, N. P.: How well can a convection-permitting climate model
reproduce decadal statistics of precipitation, temperature and cloud
characteristics?, Clim. Dynam., 47, 3043–3061,
https://doi.org/10.1007/s00382-016-3012-z, 2016b. a
Brisson, E., Brendel, C., Herzog, S., and Ahrens, B.: Lagrangian evaluation of
convective shower characteristics in a convection-permitting model,
Meteorol. Z., 27, 59–66, https://doi.org/10.1127/metz/2017/0817, 2018. a, b, c, d
Bronstert, A., Agarwal, A., Boessenkool, B., Crisologo, I., Fischer, M.,
Heistermann, M., Köhn-Reich, L., López-Tarazón, J. A., Moran, T., Ozturk,
U., Reinhardt-Imjela, C., and Wendi, D.: Forensic hydro-meteorological
analysis of an extreme flash flood: The 2016-05-29 event in Braunsbach, SW
Germany, Sci. Total Environ., 630, 977–991,
https://doi.org/10.1016/j.scitotenv.2018.02.241, 2018. a
Burghardt, B. J., Evans, C., and Roebber, P. J.: Assessing the Predictability
of Convection Initiation in the High Plains Using an Object-Based Approach,
Weather Forecast., 29, 403–418, https://doi.org/10.1175/WAF-D-13-00089.1, 2014. a
Caillaud, C., Somot, S., Alias, A., Bernard-Bouissières, I., Fumière,
Q., Laurantin, O., Seity, Y., and Ducrocq, V.: Modelling Mediterranean
heavy precipitation events at climate scale: an object-oriented evaluation of
the CNRM-AROME convection-permitting regional climate model, Clim.
Dynam., 56, 1717–1752, https://doi.org/10.1007/s00382-020-05558-y, 2021. a, b
Caine, S., Lane, T. P., May, P. T., Jakob, C., Siems, S. T., Manton, M. J., and
Pinto, J.: Statistical Assessment of Tropical Convection-Permitting Model
Simulations Using a Cell-Tracking Algorithm, Mon. Weather Rev., 141,
557–581, https://doi.org/10.1175/MWR-D-11-00274.1, 2013. a, b
Caldas-Alvarez, A., Augenstein, M., Ayzel, G., Barfus, K., Cherian, R., Dillenardt, L., Fauer, F., Feldmann, H., Heistermann, M., Karwat, A., Kaspar, F., Kreibich, H., Lucio-Eceiza, E. E., Meredith, E. P., Mohr, S., Niermann, D., Pfahl, S., Ruff, F., Rust, H. W., Schoppa, L., Schwitalla, T., Steidl, S., Thieken, A. H., Tradowsky, J. S., Wulfmeyer, V., and Quaas, J.: Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area, Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, 2022. a
Chen, D., Guo, J., Yao, D., Lin, Y., Zhao, C., Min, M., Xu, H., Liu, L., Huang,
X., Chen, T., and Zhai, P.: Mesoscale Convective Systems in the Asian
Monsoon Region From Advanced Himawari Imager: Algorithms and Preliminary
Results, J. Geophys. Res.-Atmos., 124, 2210–2234,
https://doi.org/10.1029/2018JD029707, 2019. a
Clark, A. J., Bullock, R. G., Jensen, T. L., Xue, M., and Kong, F.:
Application of Object-Based Time-Domain Diagnostics for Tracking
Precipitation Systems in Convection-Allowing Models, Weather
Forecast., 29, 517–542, https://doi.org/10.1175/WAF-D-13-00098.1, 2014. a
Davis, C., Brown, B., and Bullock, R.: Object-Based Verification of
Precipitation Forecasts. Part I: Methodology and Application to Mesoscale
Rain Areas, Mon. Weather Rev., 134, 1772–1784,
https://doi.org/10.1175/MWR3145.1, 2006. a
Davison, A. C. and Hinkley, D. V.: Bootstrap Methods and their Application,
Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge
University Press, https://doi.org/10.1017/CBO9780511802843, 1997. 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. M., 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 (data available at: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim, last access: 30 January 2023). a, b
Dixon, M. and Wiener, G.: TITAN: Thunderstorm Identification, Tracking,
Analysis, and Nowcasting – A Radar-based Methodology, J.
Atmos. Ocean. Technol., 10, 785–797,
https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2, 1993. a, b
DWD: Deutscher Wetterdienst – Glossar – Niederschlagsintensität,
https://www.dwd.de/DE/service/lexikon/Functions/glossar.html?lv2=101812&lv3=101906,
last access: 15 December 2022. a
Einfalt, T., Denoeux, T., and Jacquet, G.: A radar rainfall forecasting method
designed for hydrological purposes, J. Hydrol., 114, 229–244,
https://doi.org/10.1016/0022-1694(90)90058-6, 1990. a, b
Fosser, G., Khodayar, S., and Berg, P.: Benefit of convection permitting
climate model simulations in the representation of convective precipitation,
Clim. Dynam., 44, 45–60, https://doi.org/10.1007/s00382-014-2242-1, 2015. a
Fowler, H. J., Lenderink, G., Prein, A. F., Westra, S., Allan, R. P., Ban, N.,
Barbero, R., Berg, P., Blenkinsop, S., Do, H. X., Guerreiro, S., Haerter,
J. O., Kendon, E. J., Lewis, E., Schär, C., Sharma, A., Villarini, G.,
Wasko, C., and Zhang, X.: Anthropogenic intensification of short-duration
rainfall extremes, Nat. Rev. Earth Environ., 2, 107–122,
https://doi.org/10.1038/s43017-020-00128-6, 2021. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis
for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017 (data available at: https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/, last access: 30 January 2023). a, b
Germann, U. and Zawadzki, I.: Scale-Dependence of the Predictability of
Precipitation from Continental Radar Images. Part I: Description of the
Methodology, Mon. Weather Rev., 130, 2859–2873,
https://doi.org/10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2, 2002. a
Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader,
J., Böttinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K.,
Glushak, K., Gayler, V., Haak, H., Hollweg, H.-D., Ilyina, T., Kinne, S., Kornblueh, L., Matei, D., Mauritsen, T., Mikolajewicz, U.,
Mueller, W., Notz, D., Pithan, F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur, R., Segschneider, J., Six, K. D., Stockhause, M., Timmreck, C., Wegner, J., Widmann, H., Wieners, K.-H., Claussen, M., Marotzke, J., and Stevens, B.:
Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations
for the Coupled Model Intercomparison Project phase 5, J. Adv.
Model. Earth Sy., 5, 572–597, 2013. a
Golding, B. W.: Nimrod: a system for generating automated very short range
forecasts, Meteorol. Appl., 5, 1–16,
https://doi.org/10.1017/S1350482798000577, 1998. a
Goldstein, H. and Healy, M. J. R.: The Graphical Presentation of a Collection
of Means, J. Roy. Stat. Soc. A, 158, 175–177, https://doi.org/10.2307/2983411, 1995. a, b
Hazeleger, W., Wang, X., Severijns, C., Ştefănescu, S., Bintanja,
R., Sterl, A., Wyser, K., Semmler, T., Yang, S., Van den Hurk, B., van Noije, T.,
van der Linden, E., and
van der Wiel, K:
EC-Earth V2. 2: description and validation of a new seamless earth system
prediction model, Clim. Dynam., 39, 2611–2629,
https://doi.org/10.1007/s00382-011-1228-5, 2012. a
He, T., Einfalt, T., Zhang, J., Hua, J., and Cai, Y.: New Algorithm for Rain
Cell Identification and Tracking in Rainfall Event Analysis, Atmosphere, 10,
532, https://doi.org/10.3390/atmos10090532, 2019. a
Hering, A., Morel, C., Galli, G., Sénési, S., Ambrosetti, P., and
Boscacci, M.: Nowcasting thunderstorms in the Alpine region using a radar
based adaptive thresholding scheme, in: Proceedings of ERAD, Copernicus GmbH, 1,
206–211, 2004. a
Hibino, K., Takayabu, I., Wakazuki, Y., and Ogata, T.: Physical responses of
convective heavy rainfall to future warming condition: Case study of the
Hiroshima event, Front. Earth Sci., 6, 35,
https://doi.org/10.3389/feart.2018.00035, 2018. a
Hirt, M. and Craig, G. C.: A cold pool perturbation scheme to improve
convective initiation in convection-permitting models, Q. J. Roy. Meteor. Soc., 147, 2429–2447, https://doi.org/10.1002/qj.4032,
2021. a, b
Hirt, M., Rasp, S., Blahak, U., and Craig, G. C.: Stochastic Parameterization
of Processes Leading to Convective Initiation in Kilometer-Scale Models,
Mon. Weather Rev., 147, 3917–3934, https://doi.org/10.1175/MWR-D-19-0060.1,
2019. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G.,
Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A.,
Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski,
S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S.,
Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M.,
Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R.,
Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution
climate change projections for European impact research, Reg. Environ.
Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014. a
Keil, C., Heinlein, F., and Craig, G. C.: The convective adjustment time-scale
as indicator of predictability of convective precipitation, Q. J. Roy. Meteor. Soc., 140, 480–490, https://doi.org/10.1002/qj.2143,
2014. a
Keil, C., Baur, F., Bachmann, K., Rasp, S., Schneider, L., and Barthlott, C.:
Relative contribution of soil moisture, boundary-layer and microphysical
perturbations on convective predictability in different weather regimes,
Q. J. Roy. Meteor. Soc., 145, 3102–3115,
https://doi.org/10.1002/qj.3607, 2019. a
Keller, M., Kröner, N., Fuhrer, O., Lüthi, D., Schmidli, J., Stengel, M., Stöckli, R., and Schär, C.: The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models, Atmos. Chem. Phys., 18, 5253–5264, https://doi.org/10.5194/acp-18-5253-2018, 2018. a
Knist, S., Goergen, K., and Simmer, C.: Evaluation and projected changes of
precipitation statistics in convection-permitting WRF climate simulations
over Central Europe, Clim. Dynam., 55, 1–17,
https://doi.org/10.1007/s00382-018-4147-x, 2018. a
Kröner, N., Kotlarski, S., Fischer, E., Lüthi, D., Zubler, E., and
Schär, C.: Separating climate change signals into thermodynamic,
lapse-rate and circulation effects: theory and application to the European
summer climate, Clim. Dynam., 48, 3425–3440,
https://doi.org/10.1007/s00382-016-3276-3, 2017. a
Lackmann, G. M.: The south-central US flood of May 2010: Present and future,
J. Climate, 26, 4688–4709, https://doi.org/10.1175/JCLI-D-12-00392.1, 2013. a
Li, L., Li, Y., and Li, Z.: Object-based tracking of precipitation systems in
western Canada: the importance of temporal resolution of source data,
Clim. Dynam., 55, 2421–2437, https://doi.org/10.1007/s00382-020-05388-y, 2020. a, b
Lucas-Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P.,
Lemonsu, A., Kotlarski, S., and Caillaud, C.: Convection-permitting modeling
with regional climate models: Latest developments and next steps, Wiley
Interdisciplinary Reviews: Climate Change, 12, e731, https://doi.org/10.1002/wcc.731,
2021. a
Mandapaka, P. V., Germann, U., Panziera, L., and Hering, A.: Can Lagrangian
extrapolation of radar fields be used for precipitation nowcasting over
complex alpine orography?, Weather Forecast., 27, 28–49,
https://doi.org/10.1175/WAF-D-11-00050.1, 2012. a
Mazza, E., Ulbrich, U., and Klein, R.: The tropical transition of the October
1996 medicane in the western Mediterranean Sea: A warm seclusion event,
Mon. Weather Rev., 145, 2575–2595, https://doi.org/10.1175/MWR-D-16-0474.1, 2017. a
Meredith, E. P., Ulbrich, U., and Rust, H. W.: The Diurnal Nature of Future
Extreme Precipitation Intensification, Geophys. Res. Lett., 46,
7680–7689, https://doi.org/10.1029/2019GL082385, 2019. a
Meredith, E. P., Ulbrich, U., and Rust, H. W.: Subhourly rainfall in a
convection-permitting model, Environ. Res. Lett., 15, 034031,
https://doi.org/10.1088/1748-9326/ab6787, 2020. a
Meredith, E. P., Ulbrich, U., Rust, H. W., and Truhetz, H.: Present and future
diurnal hourly precipitation in 0.11∘ EURO-CORDEX models and at
convection-permitting resolution, Environmental Research Communications, 3,
055002, https://doi.org/10.1088/2515-7620/abf15e, 2021. a
Meredith, E. P., Ulbrich, U., and Rust, H. W.: Pseudo global-warming
simulations with COSMO-CLM of a period of high convective activity over
Germany, World Data Centre for Climate [data set],
https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_1152_ds00302 (last access: 30 January 2023),
2022a. a
Meredith, E. P., Ulbrich, U., and Rust, H. W.: Data from “Cell tracking of
convective rainfall: sensitivity of climate-change signal to tracking
algorithm and cell definition (Cell-TAO v1.0)”, Zenodo [code],
https://doi.org/10.5281/zenodo.6977074, 2022b. a
Morel, C. and Senesi, S.: A climatology of mesoscale convective systems over
Europe using satellite infrared imagery. I: Methodology, Q. J. Roy. Meteor. Soc., 128, 1953–1971,
https://doi.org/10.1256/003590002320603485, 2002. a
Moseley, C., Berg, P., and Haerter, J. O.: Probing the precipitation life cycle
by iterative rain cell tracking, J. Geophys. Res.-Atmos., 118, 13361–13370, https://doi.org/10.1002/2013JD020868, 2013. a, b, c, d
Müller, S. K., Caillaud, C., Chan, S., de Vries, H., Bastin, S.,
Berthou, S., Brisson, E., Demory, M.-E., Feldmann, H., Goergen, K., Kartsios, S., Lind, P., Keuler, K., Pichelli, E., Raffa, M., Tölle, M.
H., and Warrach-Sagi, K.: Evaluation
of Alpine-Mediterranean precipitation events in convection-permitting
regional climate models using a set of tracking algorithms, Clim. Dynam., 1–19, https://doi.org/10.1007/s00382-022-06555-z, 2022. a, b
Muñoz, C., Wang, L.-P., and Willems, P.: Enhanced object-based tracking
algorithm for convective rain storms and cells, Atmos. Res., 201,
144–158, https://doi.org/10.1016/j.atmosres.2017.10.027, 2018. a
NCL: The NCAR Command Language (Version 6.5.0), Boulder,
Colorado, UCAR/NCAR/CISL/TDD [code], https://doi.org/10.5065/D6WD3XH5, 2018. a
Nissen, K. M. and Ulbrich, U.: Increasing frequencies and changing characteristics of heavy precipitation events threatening infrastructure in Europe under climate change, Nat. Hazards Earth Syst. Sci., 17, 1177–1190, https://doi.org/10.5194/nhess-17-1177-2017, 2017. a
Novo, S., Martínez, D., and Puentes, O.: Tracking, analysis, and nowcasting of
Cuban convective cells as seen by radar, Meteorol. Appl., 21,
585–595, https://doi.org/10.1002/met.1380, 2014. a
Noyelle, R., Ulbrich, U., Becker, N., and Meredith, E. P.: Assessing the impact of sea surface temperatures on a simulated medicane using ensemble simulations, Nat. Hazards Earth Syst. Sci., 19, 941–955, https://doi.org/10.5194/nhess-19-941-2019, 2019. a
Pardowitz, T., Befort, D. J., Leckebusch, G. C., and Ulbrich, U.: Estimating
uncertainties from high resolution simulations of extreme wind storms and
consequences for impacts, Meteorol. Z., 25, 531–541,
https://doi.org/10.1127/metz/2016/0582, 2016. a
Parodi, A., Ferraris, L., Gallus, W., Maugeri, M., Molini, L., Siccardi, F., and Boni, G.: Ensemble cloud-resolving modelling of a historic back-building mesoscale convective system over Liguria: the San Fruttuoso case of 1915, Clim. Past, 13, 455–472, https://doi.org/10.5194/cp-13-455-2017, 2017. a
Piper, D., Kunz, M., Ehmele, F., Mohr, S., Mühr, B., Kron, A., and Daniell, J.: Exceptional sequence of severe thunderstorms and related flash floods in May and June 2016 in Germany – Part 1: Meteorological background, Nat. Hazards Earth Syst. Sci., 16, 2835–2850, https://doi.org/10.5194/nhess-16-2835-2016, 2016. a, b
Poujol, B., Prein, A. F., and Newman, A. J.: Kilometer-scale modeling projects
a tripling of Alaskan convective storms in future climate, Clim.
Dynam., 55, 3543–3564, https://doi.org/10.1007/s00382-020-05466-1,
2020a. a, b
Poujol, B., Sobolowski, S., Mooney, P., and Berthou, S.: A physically based
precipitation separation algorithm for convection-permitting models over
complex topography, Q. J. Roy. Meteor. Soc.,
146, 748–761, https://doi.org/10.1002/qj.3706, 2020b. a, b
Purr, C., Brisson, E., and Ahrens, B.: Convective rain cell characteristics and
scaling in climate projections for Germany, Int. J.
Climatol., 41, 3174–3185, https://doi.org/10.1002/joc.7012, 2021. a, b, c, d
Rasmussen, R., Ikeda, K., Liu, C., Gochis, D., Clark, M., Dai, A., Gutmann,
E., Dudhia, J., Chen, F., Barlage, M., Yates, D., and Zhang, G.: Climate change impacts on the
water balance of the Colorado headwaters: High-resolution regional climate
model simulations, J. Hydrometeorol., 15, 1091–1116,
https://doi.org/10.1175/JHM-D-13-0118.1, 2014. a
Rasp, S., Selz, T., and Craig, G. C.: Variability and Clustering of
Midlatitude Summertime Convection: Testing the Craig and Cohen Theory in a
Convection-Permitting Ensemble with Stochastic Boundary Layer Perturbations,
J. Atmos. Sci., 75, 691–706,
https://doi.org/10.1175/JAS-D-17-0258.1, 2018.
a
Raupach, T. H., Martynov, A., Nisi, L., Hering, A., Barton, Y., and Martius, O.: Object-based analysis of simulated thunderstorms in Switzerland: application and validation of automated thunderstorm tracking with simulation data, Geosci. Model Dev., 14, 6495–6514, https://doi.org/10.5194/gmd-14-6495-2021, 2021. a, b
Rezacova, D., Zacharov, P., and Sokol, Z.: Uncertainty in the area-related QPF
for heavy convective precipitation, Atmos. Res., 93, 238–246,
https://doi.org/10.1016/j.atmosres.2008.12.005, 2009. a, b
Rockel, B., Will, A., and Hense, A.: The regional climate model COSMO-CLM
(CCLM), Meteorol. Z., 17, 347–348, https://doi.org/10.1127/0941-2948/2008/0309,
2008. a, b
Schär, C., Frei, C., Lüthi, D., and Davies, H. C.: Surrogate
climate-change scenarios for regional climate models, Geophys. Res.
Lett., 23, 669–672, https://doi.org/10.1029/96GL00265, 1996. a, b
Skinner, P. S., Wheatley, D. M., Knopfmeier, K. H., Reinhart, A. E., Choate,
J. J., Jones, T. A., Creager, G. J., Dowell, D. C., Alexander, C. R., Ladwig,
T. T., Wicker, L. J., Heinselman, P. L., Minnis, P., and Palikonda, R.:
Object-Based Verification of a Prototype Warn-on-Forecast System, Weather
Forecast., 33, 1225–1250, https://doi.org/10.1175/WAF-D-18-0020.1, 2018. a
Stein, T. H. M., Hogan, R. J., Hanley, K. E., Nicol, J. C., Lean, H. W., Plant,
R. S., Clark, P. A., and Halliwell, C. E.: The Three-Dimensional Morphology
of Simulated and Observed Convective Storms over Southern England, Mon. Weather Rev., 142, 3264–3283, https://doi.org/10.1175/MWR-D-13-00372.1, 2014. a, b
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design, B. Am. Meteorol. Soc., 93, 485,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in
large-scale models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Clim. Change, 109,
5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011. a
Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.:
The CNRM-CM5. 1 global climate model: description and basic evaluation,
Clim. Dynam., 40, 2091–2121, https://doi.org/10.1007/s00382-011-1259-y, 2013. a
Wasko, C., Sharma, A., and Westra, S.: Reduced spatial extent of extreme storms
at higher temperatures, Geophys. Res. Lett., 43, 4026–4032, 2016. a
Woo, W.-C. and Wong, W.-K.: Operational Application of Optical Flow Techniques
to Radar-Based Rainfall Nowcasting, Atmosphere, 8, 48,
https://doi.org/10.3390/atmos8030048, 2017. a
Short summary
Cell-tracking algorithms allow for the study of properties of a convective cell across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm's criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Cell-tracking algorithms allow for the study of properties of a convective cell across its...