Articles | Volume 17, issue 11
https://doi.org/10.5194/essd-17-6405-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-6405-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hourly precipitation fields at 1 km resolution over Belgium from 1940 to 2016 based on the analog technique
Elke Debrie
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
Jonathan Demaeyer
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
Stéphane Vannitsem
CORRESPONDING AUTHOR
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
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Martin Bonte and Stéphane Vannitsem
Nonlin. Processes Geophys., 32, 139–165, https://doi.org/10.5194/npg-32-139-2025, https://doi.org/10.5194/npg-32-139-2025, 2025
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In recent years, there have been more and more floods due to intense precipitation, such as the July 2021 event in Belgium. Predicting precipitation is a difficult task, even just for the next few hours. This study focuses on a tool that assesses whether a given situation is stable or not (i.e., whether it is likely to stay as it is or could evolve in an unpredictable manner).
Stéphane Vannitsem, X. San Liang, and Carlos A. Pires
Earth Syst. Dynam., 16, 703–719, https://doi.org/10.5194/esd-16-703-2025, https://doi.org/10.5194/esd-16-703-2025, 2025
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Large-scale modes of variability are present in the climate system. These modes are known to have influences on each other but are usually viewed as linear influences. The nonlinear connections among a set of key climate indices are explored here using tools from information theory, which allow us to characterize the causality between indices. It was found that quadratic nonlinear dependencies between climate indices are present at low frequencies, reflecting the complex nature of their dynamics.
Anupama K. Xavier, Jonathan Demaeyer, and Stéphane Vannitsem
Earth Syst. Dynam., 15, 893–912, https://doi.org/10.5194/esd-15-893-2024, https://doi.org/10.5194/esd-15-893-2024, 2024
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This research focuses on understanding different atmospheric patterns like blocking, zonal, and transition regimes and analyzing their predictability. We used an idealized land–atmosphere coupled model to simulate Earth's atmosphere. Then we identified these blocking, zonal, and transition regimes using Gaussian mixture clustering and studied their predictability using Lyapunov exponents.
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024, https://doi.org/10.5194/npg-31-115-2024, 2024
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Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.
Michel Journée, Edouard Goudenhoofdt, Stéphane Vannitsem, and Laurent Delobbe
Hydrol. Earth Syst. Sci., 27, 3169–3189, https://doi.org/10.5194/hess-27-3169-2023, https://doi.org/10.5194/hess-27-3169-2023, 2023
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The exceptional flood of July 2021 in central Europe impacted Belgium severely. This study aims to characterize rainfall amounts in Belgium from 13 to 16 July 2021 based on observational data (i.e., rain gauge data and a radar-based rainfall product). The spatial and temporal distributions of rainfall during the event aredescribed. In order to document such a record-breaking event as much as possible, the rainfall data are shared with the scientific community on Zenodo for further studies.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
David Docquier, Stéphane Vannitsem, and Alessio Bellucci
Earth Syst. Dynam., 14, 577–591, https://doi.org/10.5194/esd-14-577-2023, https://doi.org/10.5194/esd-14-577-2023, 2023
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The climate system is strongly regulated by interactions between the ocean and atmosphere. However, many uncertainties remain in the understanding of these interactions. Our analysis uses a relatively novel approach to quantify causal links between the ocean surface and lower atmosphere based on satellite observations. We find that both the ocean and atmosphere influence each other but with varying intensity depending on the region, demonstrating the power of causal methods.
Stéphane Vannitsem
Nonlin. Processes Geophys., 30, 1–12, https://doi.org/10.5194/npg-30-1-2023, https://doi.org/10.5194/npg-30-1-2023, 2023
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The impact of climate change on weather pattern dynamics over the North Atlantic is explored through the lens of information theory. These tools allow the predictability of the succession of weather patterns and the irreversible nature of the dynamics to be clarified. It is shown that the predictability is increasing in the observations, while the opposite trend is found in model projections. The irreversibility displays an overall increase in time in both the observations and the model runs.
David Docquier, Stéphane Vannitsem, Alessio Bellucci, and Claude Frankignoul
EGUsphere, https://doi.org/10.5194/egusphere-2022-1340, https://doi.org/10.5194/egusphere-2022-1340, 2022
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Understanding whether variations in ocean heat content are driven by air-sea heat fluxes or by ocean dynamics is of crucial importance to enhance climate projections. We use a relatively novel causal method to quantify interactions between ocean heat budget terms based on climate models. We find that low-resolution models overestimate the influence of ocean dynamics in the upper ocean, and that changes in ocean heat content are dominated by air-sea fluxes at high resolution.
Nicolas Ghilain, Stéphane Vannitsem, Quentin Dalaiden, Hugues Goosse, Lesley De Cruz, and Wenguang Wei
Earth Syst. Sci. Data, 14, 1901–1916, https://doi.org/10.5194/essd-14-1901-2022, https://doi.org/10.5194/essd-14-1901-2022, 2022
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Modeling the climate at high resolution is crucial to represent the snowfall accumulation over the complex orography of the Antarctic coast. While ice cores provide a view constrained spatially but over centuries, climate models can give insight into its spatial distribution, either at high resolution over a short period or vice versa. We downscaled snowfall accumulation from climate model historical simulations (1850–present day) over Dronning Maud Land at 5.5 km using a statistical method.
Tommaso Alberti, Reik V. Donner, and Stéphane Vannitsem
Earth Syst. Dynam., 12, 837–855, https://doi.org/10.5194/esd-12-837-2021, https://doi.org/10.5194/esd-12-837-2021, 2021
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We provide a novel approach to diagnose the strength of the ocean–atmosphere coupling by using both a reduced order model and reanalysis data. Our findings suggest the ocean–atmosphere dynamics presents a rich variety of features, moving from a chaotic to a coherent coupled dynamics, mainly attributed to the atmosphere and only marginally to the ocean. Our observations suggest further investigations in characterizing the occurrence and spatial dependency of the ocean–atmosphere coupling.
Cited articles
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., and Ziese, M.: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present, Earth Syst. Sci. Data, 5, 71–99, https://doi.org/10.5194/essd-5-71-2013, 2013. a
Bell, V. A., Kay, A. L., Jones, R. G., and Moore, R. J.: Development of a high resolution grid-based river flow model for use with regional climate model output, Hydrol. Earth Syst. Sci., 11, 532–549, https://doi.org/10.5194/hess-11-532-2007, 2007. a
Blenkinsop, S., Fowler, H., Dubus, I., Nolan, B., and Hollis, J.: Developing climatic scenarios for pesticide fate modelling in Europe, Environmental Pollution, 154, 219–231, 2008. a
Debrie, E., Demaeyer, J., and Vannitsem, S.: RADCLIM-Analogs: High-Resolution Gridded Hourly Median Precipitation dataset for Belgium (1940–2016) Using the Analogue Technique, Zenodo [data set], https://doi.org/10.5281/zenodo.14965710, 2025. a, b, c
Demaeyer, J. and Debrie, E.: ElkeDebrie/RADCLIM-Analogs: RADCLIM-Analogs version 1.0 (v1.0), Zenodo [software], https://doi.org/10.5281/zenodo.17661222, 2025. a
Finnerty, B. D., Smith, M. B., Seo, D.-J., Koren, V., and Moglen, G. E.: Space-time scale sensitivity of the Sacramento model to radar-gage precipitation inputs, Journal of Hydrology, 203, 21–38, 1997. a
Foresti, L., Puigdomènech Treserras, B., Nerini, D., Atencia, A., Gabella, M., Sideris, I. V., Germann, U., and Zawadzki, I.: A quest for precipitation attractors in weather radar archives, Nonlin. Processes Geophys., 31, 259–286, https://doi.org/10.5194/npg-31-259-2024, 2024. a
Ghilain, N., Vannitsem, S., Dalaiden, Q., Goosse, H., De Cruz, L., and Wei, W.: Large ensemble of downscaled historical daily snowfall from an earth system model to 5.5 km resolution over Dronning Maud Land, Antarctica, Earth Syst. Sci. Data, 14, 1901–1916, https://doi.org/10.5194/essd-14-1901-2022, 2022. a
Goudenhoofdt, E. and Delobbe, L.: Generation and Verification of Rainfall Estimates from 10-Yr Volumetric Weather Radar Measurements, Journal of Hydrometeorology, 17, 1223–1242, https://doi.org/10.1175/JHM-D-15-0166.1, 2016. a
Guilbaud, S. and Obled, C.: Prévision quantitative des précipitations journalières par une technique de recherche de journées antérieures analogues: optimisation du critère d'analogie, Comptes Rendus de l'Académie des Sciences – Series IIA – Earth and Planetary Science, 327, 181–188, 1998. 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, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, 2020. a
Horton, P.: AtmoSwing: Analog Technique Model for Statistical Weather forecastING and downscalING (v2.1.0), Geosci. Model Dev., 12, 2915–2940, https://doi.org/10.5194/gmd-12-2915-2019, 2019. a, b
Horton, P., Obled, C., and Jaboyedoff, M.: The analogue method for precipitation prediction: finding better analogue situations at a sub-daily time step, Hydrol. Earth Syst. Sci., 21, 3307–3323, https://doi.org/10.5194/hess-21-3307-2017, 2017. a
Hoyer, S. and Joseph, H.: xarray: N-D labeled Arrays and Datasets in Python, Journal of Open Research Software, 5, https://doi.org/10.5334/jors.148, 2017. a
Huang, Y., Fu, Z., and Franzke, C. L. E.: Detecting causality from time series in a machine learning framework, Chaos: An Interdisciplinary Journal of Nonlinear Science, 30, 063116, https://doi.org/10.1063/5.0007670, 2020. a
Isotta, F. A., Vogel, R., and Frei, C.: Evaluation of European regional reanalyses and downscalings for precipitation in the Alpine region, Meteorologische Zeitschrift, 24, 15–37, 2015. a
Journée, M., Goudenhoofdt, E., Vannitsem, S., and Delobbe, L.: Quantitative rainfall analysis of the 2021 mid-July flood event in Belgium, Hydrol. Earth Syst. Sci., 27, 3169–3189, https://doi.org/10.5194/hess-27-3169-2023, 2023. a, b, c, d
Kendall, M. and Stuart, A.: The advanced theory of statistics: in three volumes. 3. Design and analysis, and time series, Griffin, https://books.google.be/books?id=VwGWzAEACAAJ (last access: 18 March 2025), 1983. a
Lewis, E., Quinn, N., Blenkinsop, S., Fowler, H. J., Freer, J., Tanguy, M., Hitt, O., Coxon, G., Bates, P., and Woods, R.: A rule based quality control method for hourly rainfall data and a 1 km resolution gridded hourly rainfall dataset for Great Britain: CEH-GEAR1hr, Journal of Hydrology, 564, 930–943, 2018. a
Lguensat, R., Tandeo, P., Ailliot, P., Pulido, M., and Fablet, R.: The analog data assimilation, Monthly Weather Review, 145, 4093–4107, 2017. a
Li, J. and Ding, R.: Temporal–Spatial Distribution of Atmospheric Predictability Limit by Local Dynamical Analogs, Monthly Weather Review, 139, 3265–3283, https://doi.org/10.1175/MWR-D-10-05020.1, 2011. a
Lorenz, E. N.: Atmospheric predictability as revealed by naturally occurring analogues, Journal of Atmospheric Sciences, 26, 636–646, 1969. a
Miles, A., Kirkham, J., Durant, M., Bourbeau, J., Onalan, T., Hamman, J., Patel, Z., shikharsg, Rocklin, M., raphael dussin, Schut, V., de Andrade, E. S., Abernathey, R., Noyes, C., sbalmer, pyup.io bot, Tran, T., Saalfeld, S., Swaney, J., Moore, J., Jevnik, J., Kelleher, J., Funke, J., Sakkis, G., Barnes, C., and Banihirwe, A.: zarr-developers/zarr-python: v2.4.0, Zenodo [code], https://doi.org/10.5281/zenodo.3773450, 2020. a
Overeem, A., van den Besselaar, E., van der Schrier, G., Meirink, J. F., van der Plas, E., and Leijnse, H.: EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset, Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, 2023. a
Raynaud, D., Hingray, B., Evin, G., Favre, A.-C., and Chardon, J.: Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues, Hydrol. Earth Syst. Sci., 24, 4339–4352, https://doi.org/10.5194/hess-24-4339-2020, 2020. a, b, c
RMI: Klimaattrend in België, https://www.meteo.be/nl/klimaat/klimaatverandering-in-belgie/klimaattrends-in-belgie, last access: 17 July 2025a. a
RMI: Klimaattrends in Ukkel, https://www.meteo.be/nl/klimaat/klimaatverandering-in-belgie/klimaattrends-in-ukkel/neerslag/extremiteitsindices/max-1-uur-neerslag, last access: 17 July 2025b. a
RMI: Klimaattrends in Ukkel, https://www.meteo.be/nl/klimaat/klimaatverandering-in-belgie/klimaattrends-in-ukkel/neerslag/extremiteitsindices/dagen-met-20mm-neerslag, last access: 17 July 2025c. a
Rozoff, C. M. and Alessandrini, S.: A Comparison between Analog Ensemble and Convolutional Neural Network Empirical-Statistical Downscaling Techniques for Reconstructing High-Resolution Near-Surface Wind, Energies, 15, https://doi.org/10.3390/en15051718, 2022. a
Satish, B. and Vasubandhu, M.: Sensitivity of Hydrological Simulations of Southeastern United States Watersheds to Temporal Aggregation of Rainfall, Journal of Hydrometeorology, 14, 1334–1344, 2013. a
Sugihara, G., May, R., Ye, H., hao Hsieh, C., Deyle, E., Fogarty, M., and Munch, S.: Detecting Causality in Complex Ecosystems, Science, 338, 496–500, https://doi.org/10.1126/science.1227079, 2012. a
Teweles, S. and Wobus, H. B.: Verification of Prognostic Charts, Bulletin of the American Meteorological Society, 35, 455––463, 1954. a
Toth, Z.: Long-range weather forecasting using an analog approach, Journal of Climate, 2, 594–607, 1989. a
Toth, Z.: Estimation of Atmospheric Predictability by Circulation Analogs, Monthly Weather Review, 119, 65–72, https://doi.org/10.1175/1520-0493(1991)119<0065:EOAPBC>2.0.CO;2, 1991. a
Tradowsky, J. S., Philip, S. Y., Kreienkamp, F., Kew, S. F., Lorenz, P., Arrighi, J., Bettmann, T., Caluwaerts, S., Chan, S. C., De Cruz, L., de Vries, H., Demuth, N., Ferrone, A., Fischer, E. M., Fowler, H. J., Goergen, K., Heinrich, D., Henrichs, Y., Kaspar, F., Lenderink, G., Nilson, E., Otto, F. E. L., Ragone, F., Seneviratne, S. I., Singh, R. K., Skålevåg, A., Termonia, P., Thalheimer, L., van Aalst, M., Van den Bergh, J., Van de Vyver, H., Vannitsem, S., van Oldenborgh, G. J., Van Schaeybroeck, B., Vautard, R., Vonk, D., and Wanders, N.: Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021, Climatic Change, 176, https://doi.org/10.1007/s10584-023-03502-7, 2023. a
Vannitsem, S. and Ekelmans, P.: Causal dependences between the coupled ocean–atmosphere dynamics over the tropical Pacific, the North Pacific and the North Atlantic, Earth Syst. Dynam., 9, 1063–1083, https://doi.org/10.5194/esd-9-1063-2018, 2018. a
Wetterhall, F., Halldin, S., and yu Xu, C.: Statistical precipitation downscaling in central Sweden with the analogue method, Journal of Hydrology, 306, 174–190, 2005. a
Wilks, D. S.: Statistical methods in the atmospheric sciences, vol. 100 of International geophysics series, Elsevier/Academic Press, Amsterdam; Boston, 4th Edn., ISBN 9780123850225, 2011. a
Winterrath, T., Brendel, C., Hafer, M., Junghänel, T., Klameth, A., Lengfeld, K., Walawender, E., Weigl, E., and Becker, A.: Radar-based gauge-adjusted one-hour precipitation sum climatology Version 2017.002, Deutscher Wetterdienst [dataset], https://doi.org/10.5676/DWD/RADKLIM_RW_V2017.002, 2018. a
Xavier, P. K. and Goswami, B. N.: An analog method for real-time forecasting of summer monsoon subseasonal variability, Monthly Weather Review, 135, 4149–4160, 2007. a
Yiou, P.: AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531–543, https://doi.org/10.5194/gmd-7-531-2014, 2014. a
Yiou, P. and Déandréis, C.: Stochastic ensemble climate forecast with an analogue model, Geosci. Model Dev., 12, 723–734, https://doi.org/10.5194/gmd-12-723-2019, 2019. a
Yu, L., Zhong, S., Pei, L., Bian, X., and Heilman, W. E.: Contribution of large-scale circulation anomalies to changes in extreme precipitation frequency in the United States, Environmental Research Letters, 11, 044003, https://doi.org/10.1088/1748-9326/11/4/044003, 2016. a
Short summary
In this project, we developed a gridded hourly precipitation dataset for Belgium, covering over 70 years (1940–2016). The data has a spatial resolution of one kilometer, which means it provides highly localized precipitation information. To estimate precipitation for a specific day in the past, we searched for days in the recent radar data period with similar weather patterns, known as the analog method. The median of the produced dataset is available for public use and can be found on Zenodo.
In this project, we developed a gridded hourly precipitation dataset for Belgium, covering over...
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