Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2961-2024
© 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-17-2961-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
16 Apr 2024
Model experiment description paper |
| 16 Apr 2024
| 16 Apr 2024
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Clément Bouvier
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Daan van den Broek
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Madeleine Ekblom
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Victoria A. Sinclair
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
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Diego Aliaga, Victoria A. Sinclair, Marcos Andrade, Paulo Artaxo, Samara Carbone, Evgeny Kadantsev, Paolo Laj, Alfred Wiedensohler, Radovan Krejci, and Federico Bianchi
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We investigate the origin of air masses sampled at Mount Chacaltaya, Bolivia. Three-quarters of the measured air has not been influenced by the surface in the previous 4 d. However, it is rare that, at any given time, the sampled air has not been influenced at all by the surface, and often the sampled air has multiple origins. The influence of the surface is more prevalent during day than night. Furthermore, during the 6-month study, one-third of the air masses originated from Amazonia.
Nahid Atashi, Dariush Rahimi, Victoria A. Sinclair, Martha A. Zaidan, Anton Rusanen, Henri Vuollekoski, Markku Kulmala, Timo Vesala, and Tareq Hussein
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Dew formation potential during a long-term period (1979–2018) was assessed in Iran to identify dew formation zones and to investigate the impacts of long-term variation in meteorological parameters on dew formation. Six dew formation zones were identified based on cluster analysis of the time series of the simulated dew yield. The distribution of dew formation zones in Iran was closely aligned with topography and sources of moisture. The dew formation trend was significantly negative.
Janne Lampilahti, Katri Leino, Antti Manninen, Pyry Poutanen, Anna Franck, Maija Peltola, Paula Hietala, Lisa Beck, Lubna Dada, Lauriane Quéléver, Ronja Öhrnberg, Ying Zhou, Madeleine Ekblom, Ville Vakkari, Sergej Zilitinkevich, Veli-Matti Kerminen, Tuukka Petäjä, and Markku Kulmala
Atmos. Chem. Phys., 21, 7901–7915, https://doi.org/10.5194/acp-21-7901-2021, https://doi.org/10.5194/acp-21-7901-2021, 2021
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Using airborne measurements we observed increased number concentrations of sub-25 nm particles in the upper residual layer. These particles may be entrained into the well-mixed boundary layer and observed at the surface. We attribute our observations to new particle formation in the topmost part of the residual layer.
Pirkka Ollinaho, Glenn D. Carver, Simon T. K. Lang, Lauri Tuppi, Madeleine Ekblom, and Heikki Järvinen
Geosci. Model Dev., 14, 2143–2160, https://doi.org/10.5194/gmd-14-2143-2021, https://doi.org/10.5194/gmd-14-2143-2021, 2021
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OpenEnsemble 1.0 is a novel dataset that aims to open ensemble or probabilistic weather forecasting research up to the academic community. The dataset contains atmospheric states that are required for running model forecasts of atmospheric evolution. Our capacity to observe the atmosphere is limited; thus, a single reconstruction of the atmospheric state contains some errors. Our dataset provides sets of 50 slightly different atmospheric states so that these errors can be taken into account.
Lauri Tuppi, Pirkka Ollinaho, Madeleine Ekblom, Vladimir Shemyakin, and Heikki Järvinen
Geosci. Model Dev., 13, 5799–5812, https://doi.org/10.5194/gmd-13-5799-2020, https://doi.org/10.5194/gmd-13-5799-2020, 2020
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This paper presents general guidelines on how to utilise computer algorithms efficiently in order to tune weather models so that they would produce better forecasts. The main conclusions are that the computer algorithms work most efficiently with a suitable cost function, certain forecast length and ensemble size. We expect that our results will facilitate the use of algorithmic methods in the tuning of weather models.
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Short summary
An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
An analytical initial background state has been developed for moist baroclinic wave simulation...
