Articles | Volume 4, issue 4
https://doi.org/10.5194/wcd-4-1111-2023
© 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/wcd-4-1111-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Dec 2023
Research article |
| 14 Dec 2023
| 14 Dec 2023
Understanding the dependence of mean precipitation on convective treatment and horizontal resolution in tropical aquachannel experiments
Institute of Meteorology and Climate Research (IMK), Department Troposphere Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Peter Knippertz
Institute of Meteorology and Climate Research (IMK), Department Troposphere Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Yvonne Ruckstuhl
Meteorological Institute, Ludwig Maximilian University of Munich, Munich, Germany
Robert Redl
Meteorological Institute, Ludwig Maximilian University of Munich, Munich, Germany
Tijana Janjic
Meteorological Institute, Ludwig Maximilian University of Munich, Munich, Germany
Mathematical Institute for Machine Learning and Data Science, Katholische Universität Eichstätt-Ingolstadt, Ingolstadt, Germany
Corinna Hoose
Institute of Meteorology and Climate Research (IMK), Department Troposphere Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Hyunju Jung, Ann Kristin Naumann, and Bjorn Stevens
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Alberto Caldas-Alvarez, Samiro Khodayar, and Peter Knippertz
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The assimilation of observations using standard algorithms can lead to a violation of physical laws (e.g. mass conservation), which is shown to have a detrimental impact on the system's forecast. We use a neural network (NN) to correct this mass violation, using training data generated from expensive algorithms that can constrain such physical properties. We found that, in an idealized set-up, the NN can match the performance of these expensive algorithms at negligible computational costs.
Gregor Pante, Peter Knippertz, Andreas H. Fink, and Anke Kniffka
Atmos. Chem. Phys., 21, 35–55, https://doi.org/10.5194/acp-21-35-2021, https://doi.org/10.5194/acp-21-35-2021, 2021
Short summary
Short summary
Seasonal rainfall amounts along the densely populated West African Guinea coast have been decreasing during the past 35 years, with recently accelerating trends. We find strong indications that this is in part related to increasing human air pollution in the region. Given the fast increase in emissions, the political implications of this work are significant. Reducing air pollution locally and regionally would mitigate an imminent health crisis and socio-economic damage from reduced rainfall.
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Short summary
A narrow rainfall belt in the tropics is an important feature for large-scale circulations and the global water cycle. The accurate simulation of this rainfall feature has been a long-standing problem, with the reasons behind that unclear. We present a novel diagnostic tool that allows us to disentangle processes important for rainfall, which changes due to modifications in model. Using our diagnostic tool, one can potentially identify sources of uncertainty in weather and climate models.
A narrow rainfall belt in the tropics is an important feature for large-scale circulations and...
