Articles | Volume 18, issue 11
https://doi.org/10.5194/gmd-18-3509-2025
© 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/gmd-18-3509-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
13 Jun 2025
Development and technical paper |
| 13 Jun 2025
| 13 Jun 2025
DustNet (v1): skilful neural network predictions of dust aerosols over the Saharan desert
Trish E. Nowak
CORRESPONDING AUTHOR
Department of Mathematics and Statistics, University of Exeter, Exeter, EX4 QH, UK
Centre for Ecology and Conservation, University of Exeter, Penryn, TR10 9FE, UK
Andy T. Augousti
Department of Mechanical Engineering, Kingston University, London, SW15 3DW, UK
Benno I. Simmons
Centre for Ecology and Conservation, University of Exeter, Penryn, TR10 9FE, UK
Stefan Siegert
CORRESPONDING AUTHOR
Department of Mathematics and Statistics, University of Exeter, Exeter, EX4 QH, UK
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Paul Bell, Jennifer Catto, Anne Jones, and Stefan Siegert
EGUsphere, https://doi.org/10.5194/egusphere-2025-4110, https://doi.org/10.5194/egusphere-2025-4110, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Precipitation weather generators are statistical models that simulate local precipitation for downscaling applications. This study develops a precipitation weather generator for the UK that uses a recent dataset of discrete storm types which assigns areas to be thunderstorms, cyclones or fronts. Results show this dataset improves the weather generator, with improvement quantified using proper scoring rules, which score the weather generator on how close its output is to observations.
Jacob William Maddison, Jennifer Louise Catto, Sandra Hansen, Ching Ho Justin Ng, and Stefan Siegert
EGUsphere, https://doi.org/10.5194/egusphere-2025-2138, https://doi.org/10.5194/egusphere-2025-2138, 2025
Short summary
Short summary
Strong winds and heavy precipitation in extratropical cyclones can cause significant damage, and also considerable losses. Here, we estimate the worst case scenarios in terms of impacts that could occur in todays climate resulting from wind and precipitation in extratropical cyclones. We find impacts roughly 1.5 times more severe than any in the historical record for 14 countries considered in Northwestern/Central Europe. These damages would incur costs into the billions of pounds for insurers.
Jacob William Maddison, Jennifer Louise Catto, Sandra Hansen, Ching Ho Justin Ng, and Stefan Siegert
EGUsphere, https://doi.org/10.5194/egusphere-2024-686, https://doi.org/10.5194/egusphere-2024-686, 2024
Preprint archived
Short summary
Short summary
In this work we estimate the impact of the most extreme European windstorms that could occur in the current climate. Using a large dataset of windstorm footprints created seasonal forecast model output, we find windstorms that are more extreme than any previously observed for most of the countries considered. Impacts from these extreme windstorms are expected to be around 1.5 times stronger than the most extreme storm on record. This information is highly valuable in the insurance industry.
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Short summary
The DustNet model uses deep neural networks to accurately predict Saharan mineral dust transport in the atmosphere. It offers fast and precise forecasts with predictions achieved in just 2.1 s on a standard computer. This innovative approach outperforms traditional models, which take hours to produce a forecast and use high-energy supercomputers. By making high-quality dust monitoring accessible and efficient, DustNet can improve weather, climate, and air quality forecasts.
The DustNet model uses deep neural networks to accurately predict Saharan mineral dust transport...
