Improving Extreme Precipitation Simulation
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 11031
Special Issue Editors
Interests: artificial Intelligence, numerical modeling;extended-range forecast;nonlinear dynamics;extreme events;complex network
Special Issues, Collections and Topics in MDPI journals
Interests: climate modeling; numerical simulation; cumulus parameterization; extreme precipitation
Special Issue Information
Dear Colleagues,
Extreme precipitation profoundly impacts global society. Each year, floods induced by extreme precipitation cause thousands of deaths globally, with billions of dollars in damages. Governments rely on both improved observations and more accurate forecasts to minimize the damage from these disasters. In recent decades, remote sensing has substantially improved our ability to monitor extreme precipitation events, while improved understanding of the physical mechanisms underlying extreme precipitation have greatly improved modeling capabilities. The numerical modeling community has applied these advances to significantly improve extreme precipitation simulation. However, persistent challenges remain, not only slowing down our further understanding of the mechanism of extreme precipitation processes but also hindering the production of highly reliable extreme precipitation event forecasting. Ultimately, they reduced the certainty and accuracy in the future projection of how extreme precipitation evolves in the future.
In recognition of these urgent needs to improve model skill in capturing extreme precipitation, the open-access journal Atmosphere is hosting a Special Issue to showcase the most recent findings related to extreme precipitation simulation, model improvement, parameterization scheme development, and process understanding. Given the recent research showing that global warming significantly exacerbates extreme precipitation events, this Special Issue is also an appropriate venue for papers that deal with extreme precipitation projection under future climate conditions. We also welcome any studies that apply the latest machine learning techniques to improve extreme precipitation simulation. Overall, this Special Issue aims to highlight the most recent developments, techniques, and physical understandings, as well as new evidence from observations. We also encourage relevant studies assessing the societal effects of extreme precipitation based on numerical model results.
Original results from model studies, model evaluation, model development, surveys, and review papers related to extreme precipitation are all welcome contributions. Authors are encouraged to include a section touching on future issues, opportunities, and/or concerns related to their topics, on the 5-, 10-, and 20-year horizons.
Dr. Qiguang Wang
Dr. Fengxue Qiao
Dr. Chao Sun
Guest Editors
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Keywords
- extreme precipitation
- model evaluation
- model development
- numerical simulation
- cumulus parameterization
- machine learning
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