Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4399-2020
© Author(s) 2020. 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-13-4399-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
RadNet 1.0: exploring deep learning architectures for longwave radiative transfer
Ying Liu
CORRESPONDING AUTHOR
Department of Meteorology, Stockholm University, Stockholm, Sweden
Rodrigo Caballero
Department of Meteorology, Stockholm University, Stockholm, Sweden
Joy Merwin Monteiro
Department of Meteorology, Stockholm University, Stockholm, Sweden
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Sohan Suresan, Nili Harnik, and Rodrigo Caballero
Weather Clim. Dynam., 6, 789–806, https://doi.org/10.5194/wcd-6-789-2025, https://doi.org/10.5194/wcd-6-789-2025, 2025
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This study is an exploration of how extreme winter weather events across the Northern Hemisphere are influenced by the rare merging of the Atlantic and African jets, beyond such typical factors as the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO). We identify unique surface signals and changes in cyclone paths associated with such persistent winter jets merging over the Atlantic, offering insights into these extreme winter weather patterns.
Hardik M. Shah and Joy M. Monteiro
EGUsphere, https://doi.org/10.22541/essoar.174349794.49450607/v1, https://doi.org/10.22541/essoar.174349794.49450607/v1, 2025
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Atmospheric circulation changes contribute significantly to climate projection uncertainty, which is compounded by the limited understanding of how upper-tropospheric circulations influence near-surface events like heatwaves. This study develops a methodology to trace the imprint of upper-tropospheric circulations on the lower-tropospheric energy budget, and introduces a classification system enabling statistical summaries of the energetics that respect underlying circulation patterns.
Aleksa Stanković, Gabriele Messori, Joaquim G. Pinto, and Rodrigo Caballero
Weather Clim. Dynam., 5, 821–837, https://doi.org/10.5194/wcd-5-821-2024, https://doi.org/10.5194/wcd-5-821-2024, 2024
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The article studies extreme winds near the surface over the North Atlantic Ocean. These winds are caused by storms that pass through this region. The strongest storms that have occurred in the winters from 1950–2020 are studied in detail and compared to weaker but still strong storms. The analysis shows that the storms associated with the strongest winds are preceded by another older storm that travelled through the same region and made the conditions suitable for development of extreme winds.
Emma Holmberg, Gabriele Messori, Rodrigo Caballero, and Davide Faranda
Earth Syst. Dynam., 14, 737–765, https://doi.org/10.5194/esd-14-737-2023, https://doi.org/10.5194/esd-14-737-2023, 2023
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We analyse the duration of large-scale patterns of air movement in the atmosphere, referred to as persistence, and whether unusually persistent patterns favour warm-temperature extremes in Europe. We see no clear relationship between summertime heatwaves and unusually persistent patterns. This suggests that heatwaves do not necessarily require the continued flow of warm air over a region and that local effects could be important for their occurrence.
Sonja Murto, Rodrigo Caballero, Gunilla Svensson, and Lukas Papritz
Weather Clim. Dynam., 3, 21–44, https://doi.org/10.5194/wcd-3-21-2022, https://doi.org/10.5194/wcd-3-21-2022, 2022
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This study uses reanalysis data to investigate the role of atmospheric blocking, prevailing high-pressure systems and mid-latitude cyclones in driving high-Arctic wintertime warm extreme events. These events are mainly preceded by Ural and Scandinavian blocks, which are shown to be significantly influenced and amplified by cyclones in the North Atlantic. It also highlights processes that need to be well captured in climate models for improving their representation of Arctic wintertime climate.
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Short summary
The calculation of atmospheric radiative transfer is the most computationally expensive part of climate models. Reducing this computational burden could potentially improve our ability to simulate the earth's climate at finer scales. We propose using a statistical model – a deep neural network – to compute approximate radiative transfer in the earth's atmosphere. We demonstrate a significant reduction in computational burden as compared to a traditional scheme, especially when using GPUs.
The calculation of atmospheric radiative transfer is the most computationally expensive part of...