Applications of artificial intelligence to satellite data for extreme weather forecasts

人工智能在极端天气预报卫星数据中的应用

• 批准号: 1953331
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1953331
• 关键词: Applications; artificial; intelligence; satellite; data

The frequency and resolution of Earth Observation data of clouds and other atmospheric/ocean variables is rapidly growing. To optimally deploy this information there is emerging interest to apply new approaches in machine learning to this data for faster weather forecasting suitable for emergency response and other applications. In this multidisciplinary project you will research the possibility of combining machine learning, the latest Earth Observation data and state of the art numerical weather prediction models to anticipate changes in weather. We are particularly interested in better forecasts for high impact extreme weather such as tropical cyclones, storms, cold and heat spells. The project meets two of the UKs industrial strategic priorities: the exploitation of satellite earth observation technology through the application of artificial intelligence. The industrial partner will make a contribution of £1000 per annum to the student and in-kind contribution in terms of annual industrial placement and expertise in artificial intelligence.

云和其他大气/海洋变量的地球观测数据的频率和分辨率正在迅速增长。为了最佳地部署这些信息，人们越来越有兴趣将机器学习的新方法应用于这些数据，以更快地进行适合紧急响应和其他应用的天气预报。在这个多学科项目中，您将研究将机器学习、最新的地球观测数据和最先进的数值天气预报模型相结合来预测天气变化的可能性。我们特别感兴趣的是对热带气旋、风暴、寒暑期等高影响极端天气的更好预报。该项目满足了英国的两个工业战略重点：通过人工智能的应用开发卫星地球观测技术。产业合作伙伴每年将向学生捐赠 1000 英镑，并在年度产业安置和人工智能专业知识方面提供实物捐助。

项目成果

A Comparison of techniques to optimise tropical cyclone ensemble predictions

优化热带气旋集合预测的技术比较

• 发表时间: 2020
• 作者: Mohan Smith