Articles | Volume 15, issue 6
https://doi.org/10.5194/essd-15-2635-2023
© 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/essd-15-2635-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The EUPPBench postprocessing benchmark dataset v1.0
Jonathan Demaeyer
CORRESPONDING AUTHOR
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
European Meteorological Network (EUMETNET), Brussels, Belgium
Jonas Bhend
Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
Sebastian Lerch
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Cristina Primo
Deutscher Wetterdienst, Offenbach, Germany
Bert Van Schaeybroeck
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
Aitor Atencia
GeoSphere Austria, Vienna, Austria
Zied Ben Bouallègue
European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Jieyu Chen
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Markus Dabernig
GeoSphere Austria, Vienna, Austria
Gavin Evans
Met Office, Exeter, United Kingdom
Jana Faganeli Pucer
Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Ben Hooper
Met Office, Exeter, United Kingdom
Nina Horat
Institute of Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
David Jobst
Institute of Mathematics and Applied Informatics, University of Hildesheim, Hildesheim, Germany
Janko Merše
Slovenian Environment Agency, Ljubljana, Slovenia
Peter Mlakar
Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Slovenian Environment Agency, Ljubljana, Slovenia
Annette Möller
Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
Olivier Mestre
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Météo-France, Toulouse, France
Maxime Taillardat
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Météo-France, Toulouse, France
Stéphane Vannitsem
Meteorological and Climatological Information Service, Royal Meteorological Institute of Belgium, Brussels, Belgium
European Meteorological Network (EUMETNET), Brussels, Belgium
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Nicolas Ghilain, Stéphane Vannitsem, Quentin Dalaiden, Hugues Goosse, Lesley De Cruz, and Wenguang Wei
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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.
Gerard van der Schrier, Richard P. Allan, Albert Ossó, Pedro M. Sousa, Hans Van de Vyver, Bert Van Schaeybroeck, Roberto Coscarelli, Angela A. Pasqua, Olga Petrucci, Mary Curley, Mirosław Mietus, Janusz Filipiak, Petr Štěpánek, Pavel Zahradníček, Rudolf Brázdil, Ladislava Řezníčková, Else J. M. van den Besselaar, Ricardo Trigo, and Enric Aguilar
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The 1921 drought was the most severe drought to hit Europe since the start of the 20th century. Here the climatological description of the drought is coupled to an overview of its impacts, sourced from newspapers, and an analysis of its drivers. The area from Ireland to the Ukraine was affected but hardest hit was the triangle between Brussels, Paris and Lyon. The drought impacts lingered on until well into autumn and winter, affecting water supply and agriculture and livestock farming.
Guillaume Evin, Matthieu Lafaysse, Maxime Taillardat, and Michaël Zamo
Nonlin. Processes Geophys., 28, 467–480, https://doi.org/10.5194/npg-28-467-2021, https://doi.org/10.5194/npg-28-467-2021, 2021
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Forecasting the height of new snow is essential for avalanche hazard surveys, road and ski resort management, tourism attractiveness, etc. Météo-France operates a probabilistic forecasting system using a numerical weather prediction system and a snowpack model. It provides better forecasts than direct diagnostics but exhibits significant biases. Post-processing methods can be applied to provide automatic forecasting products from this system.
Tommaso Alberti, Reik V. Donner, and Stéphane Vannitsem
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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.
Sara Top, Lola Kotova, Lesley De Cruz, Svetlana Aniskevich, Leonid Bobylev, Rozemien De Troch, Natalia Gnatiuk, Anne Gobin, Rafiq Hamdi, Arne Kriegsmann, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck, Viesturs Zandersons, Philippe De Maeyer, Piet Termonia, and Steven Caluwaerts
Geosci. Model Dev., 14, 1267–1293, https://doi.org/10.5194/gmd-14-1267-2021, https://doi.org/10.5194/gmd-14-1267-2021, 2021
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Detailed climate data are needed to assess the impact of climate change on human and natural systems. The performance of two high-resolution regional climate models, ALARO-0 and REMO2015, was investigated over central Asia, a vulnerable region where detailed climate information is scarce. In certain subregions the produced climate data are suitable for impact studies, but bias adjustment is required for subregions where significant biases have been identified.
Stephan Hemri, Sebastian Lerch, Maxime Taillardat, Stéphane Vannitsem, and Daniel S. Wilks
Nonlin. Processes Geophys., 27, 519–521, https://doi.org/10.5194/npg-27-519-2020, https://doi.org/10.5194/npg-27-519-2020, 2020
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Short summary
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.
A benchmark dataset is proposed to compare different statistical postprocessing methods used in...
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