Use of advanced data/computational science techniques to improve and intergrate site-specific weather forecasts to support Shell's global operations

使用先进的数据/计算科学技术来改进和整合特定地点的天气预报，以支持壳牌的全球运营

• 批准号: 2625241
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2625241
• 关键词: Use; advanced; data; computational; science

The overall aim of this research project will be to investigate how weather forecasting models can be improved and interegrated to support decision-making in a variety of Shell's global operations, including Global Commercials, Trading & Supply and New Energies (Renewable Power and e-mobility).The underlying forecasting frameworks will be WRF (Weather Research and Forecasting Model) which is currently used by Shell to provide high-resolution, site-specific forecasts, as well as Imperial College's next-generation Gusto model, which used advanced discretisation methods on flexible meshes and it built using the highly scalable Firedrake code generation framework.

该研究项目的总体目标是研究如何改进和整合天气预报模型，以支持壳牌全球业务的各种决策，包括全球商业、贸易和供应以及新能源（可再生能源和电动汽车）。底层预报框架将是 WRF（天气研究和预报模型），壳牌目前使用该模型来提供高分辨率、特定地点的预报，以及 帝国理工学院的下一代 Gusto 模型在灵活的网格上使用了先进的离散化方法，并使用高度可扩展的 Firedrake 代码生成框架进行构建。