Development of a Met Office NERC cloud model (MONC)

开发英国气象局 NERC 云模型 (MONC)

• 批准号: NE/L01338X/1
• 负责人: Michele Weiland
• 金额: $ 16.74万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FL01338X%2F1
• 关键词: Development; Met; Office; NERC; cloud

Very high resolution (~10 to 50 m) modelling of clouds, precipitation and cloud feedbacks is one of the tools that is used very effectively to further our understanding of the interactions of aerosol, clouds and radiation processes.Within the UK one tool that is regularly used to conduct such research is the Met Office Large Eddy Model (LEM). The LEM and data generated by the LEM has been fundamental in the development and testing of the core functionalities in UK's main weather forecasting software, the Unified Mode (UM). As well as having a direct impact on the further development of the UM, over the past two decades the LEM has been regularly used by the Met Office and NERC scientists to conduct fundamental research into turbulence, clouds, convection and radiation.The aim of this project is to use the LEM as a basis for the creation a Met Office/NERC cloud model (MONC). The development of MONC will address any shortcomings of the LEM, as well as upgrade and modernise its current structure with a view to enabling it on modern supercomputers.

云、降水和云反馈的极高分辨率（~10至50米）模拟是一种非常有效的工具，可用来进一步了解气溶胶、云和辐射过程的相互作用，在联合王国，经常用于进行这类研究的工具之一是气象局的大涡模式（LEM）。LEM和LEM产生的数据一直是英国主要天气预报软件统一模式（UM）核心功能开发和测试的基础。在过去的二十年里，LEM不仅对UM的进一步发展产生了直接影响，而且还经常被英国气象局和NERC的科学家用于湍流、云、对流和辐射的基础研究。该项目的目的是使用LEM作为创建英国气象局/NERC云模型（MONC）的基础。MONC的开发将解决LEM的任何缺点，并对其现有结构进行升级和现代化，以便使其能够在现代超级计算机上运行。

项目成果

Fortran High-Level Synthesis: Reducing the Barriers to Accelerating HPC Codes on FPGAs

• DOI: 10.1109/fpl60245.2023.00010
• 发表时间: 2023-08
• 期刊: 2023 33rd International Conference on Field-Programmable Logic and Applications (FPL)
• 作者: Gabriel Rodriguez-Canal;Nick Brown;Timothy Dykes;Jessica R. Jones;U. Haus

A directive based hybrid met office NERC cloud model

基于指令的混合气象办公室 NERC 云模型

• DOI: 10.1145/2832105.2832115
• 发表时间: 2015
• 作者: Brown N

Fortran performance optimisation and auto-parallelisation by leveraging MLIR-based domain specific abstractions in Flang

通过利用 Flang 中基于 MLIR 的领域特定抽象来优化 Fortran 性能和自动并行化

• DOI: 10.1145/3624062.3624167
• 发表时间: 2023
• 作者: Brown N

Leveraging MPI RMA to optimize halo-swapping communications in MONC on Cray machines

• DOI: 10.1002/cpe.5008
• 发表时间: 2019-08-25
• 期刊: CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
• 影响因子: 2
• 作者: Brown, Nick;Bareford, Michael;Weiland, Michele
• 通讯作者: Weiland, Michele

Exploring the Versal AI Engines for Accelerating Stencil-based Atmospheric Advection Simulation

探索 Versal AI 引擎以加速基于模板的大气平流模拟

• DOI: 10.1145/3543622.3573047
• 发表时间: 2023
• 作者: Brown N