G2TT
来源类型Projects
规范类型研究项目
Climate Advanced Forecasting of sub-seasonal Extremes
其他题名CAFE
Jürgen Kurths
开始日期2019-03-01
结束日期2023-02-28
项目经费252.788 €
资助机构EU, H2020
摘要

To advance the state of the art of sub-seasonal predictability of extreme events, it is crucial to invest in the new generation of young researchers with top-level interdisciplinary training, as the atmosphere and the ocean are coupled systems of an enormous complexity that require nonlinear physics and advanced statistical tools for their analysis. To progress in this field, it is also crucial to improve international coordination and collaboration. The CAFE network will address these needs by providing a structured training programme to 14 early stage researchers (ESRs) and by stipulating a unique opportunity to develop new collaborations among world-leading interdisciplinary research teams. The CAFE research is structured in three main areas: Atmospheric and oceanic processes (WP1), Analysis of extremes (WP2) and Predictability of climate variability (WP3), all centered at the sub-seasonal time scale. Comprising nine academic and two non-academic beneficiaries in four EU member states and one eligible non-associated third country, CAFE brings together an interdisciplinary team of scientists with complementary expertise in mathematics, statistics, physics, climatology, meteorology and oceanography. The CAFE training programme will provide the ESRs with a broad understanding of climate phenomena (atmospheric and oceanic dynamical processes), climate models, nonlinear physics and advanced data analysis tools. By providing top-level training in the wide range of skills required to undertake a successful career in physics, geosciences and data analysis, as well as a complete set of transferable skills, the CAFE network will improve the ESRs’ employability, opening for them a wide range of job opportunities, either in academia or in the private sector (weather agencies, risk analysis companies, insurance industry, etc.).

标签Nonlinear Data Analysis ; Extremes ; Atmosphere ; Weather ; Machine Learning ; Oceans ; Global ; RD4 - Complexity Science ; Complex Networks
关键词https://www.pik-potsdam.de/en/institute/departments/complexity-science/projects/643
URLPotsdam Institute for Climate Impact Research (Germany)
资源类型智库项目
条目标识符http://119.78.100.153/handle/2XGU8XDN/527609
推荐引用方式
GB/T 7714
Jürgen Kurths. Climate Advanced Forecasting of sub-seasonal Extremes. 2019.
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