Parallel Paradigms for Numerical Weather Prediction

数值天气预报的并行范式

• 批准号: NE/R008795/1
• 负责人: Colin Cotter
• 金额: $ 66.87万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR008795%2F1
• 关键词: Parallel; Paradigms; Numerical; Weather; Prediction

Weather forecasts and climate simulations require dedicated highperformance supercomputers to run. Advances in the power ofsupercomputers bring the possibility of simulating the atmosphere athigher resolution (i.e. with more detail) without having to waitlonger for the answer. It has been consistently shown that increasingthe resolution of atmosphere models results in more accurate weatherforecasts and climate simulations. However, getting models that canmake full use of state-of-the-art supercomputers is very challenging.The Met Office is in the process of installing a new Cray XC40supercomputer which which will deliver 16 petaflops (16 quadrillionarithmetic operations per second)peak processingpower by using 4800000 individual processors computingtogether at the same time (in parallel). In the next few decadessupercomputers are expected to deliver more and more computing power, by using more and more processors. The main thing that slows downcomputations on these massively parallel supercomputers iscommunicating data between processors. Unfortunately, the physics of theatmosphere means that the weather in one location is intrinsicallylinked with the weather at all other locations on the globe; thismeans that a lot of data communication between processors is required.Scientists who develop atmosphere models are currently grappling withthe fact that we are close to the limit of what is possible in termsof resolution and simulation speed, due to the communicationrequirements of the mathematical algorithms that are used to solve theequations that predict how the weather evolves in time. At the moment,these algorithms use geographic parallelism: the globe is divided upinto overlapping pieces and each piece is given to a differentprocessor, which must communicate data to processors that sharegeographic locations on the overlaps. To speed up a model, we need touse more and more processors on smaller and smaller regions. Thespeed-up is eventually limited when there are so many overlapping regionsthat all of the globe iscovered by overlaps, and the model spends all of the timecommunicating.This means that it is time to invent new mathematical algorithms thatcan make better use of the parallel computer. In this project we will developalgorithms that are time-parallel as well asgeographic-parallel. Instead of advancing the forecast of the modelforwards step by step in time, these methods produce several differentestimates of the weather at the next step, before combining themtogether to make a more accurate solution. Each of these differentestimates can be independently calculated, which introduces additionalparallel computation into the model.This project is in close partnership with the Met Office. Ifsuccessful, these algorithms will lead to faster and higher resolutionweather forecast and climate prediction models at the Met Office,leading to more accurate forecasts for government, industry and thegeneral public. The Met Office provides forecasts for customers acrossthe transport sector, particularly for aviation planning (so thataeroplanes can avoid headwinds and make use of tailwinds) andpredictions of the motion of volcanic ash clouds. It also providesforecasts for retail and leisure, insurers, the Ministry of Defence, and theEnvironment Agency (including flood forecasting). More accurateforecasts will allow all of these business organisations to plan furtherinto the future, avoiding risks and unnecessary costs.

天气预报和气候模拟需要专用的高性能超级计算机来运行。超级计算机能力的进步带来了以更高分辨率（即更多细节）模拟大气的可能性，而不必等待更长时间的答案。事实一直表明，提高大气模型的分辨率可以使天气预报和气候模拟更加准确。然而，获得能够充分利用最先进的超级计算机的模型非常具有挑战性。 英国气象局正在安装一台新的 Cray XC40 超级计算机，该计算机将通过使用 4800000 个单独的处理器同时（并行）计算来提供 16 petaflops（每秒 16 万亿次算术运算）的峰值处理能力。在接下来的几十年里，超级计算机预计将通过使用越来越多的处理器来提供越来越多的计算能力。减慢这些大规模并行超级计算机计算速度的主要因素是处理器之间的数据通信。不幸的是，大气物理学意味着一个地方的天气与地球上所有其他地方的天气有着内在的联系；这意味着处理器之间需要大量数据通信。开发大气模型的科学家目前正在努力解决这样一个事实：由于用于求解预测天气随时间变化的方程的数学算法的通信要求，我们在分辨率和模拟速度方面已接近极限。目前，这些算法使用地理并行性：地球被划分为重叠的部分，每个部分都分配给不同的处理器，该处理器必须将数据传输到在重叠部分共享地理位置的处理器。为了加速模型，我们需要在越来越小的区域上使用越来越多的处理器。当重叠区域太多以至于整个地球都被重叠覆盖时，加速最终会受到限制，并且模型将所有时间都花在通信上。这意味着是时候发明新的数学算法来更好地利用并行计算机了。在这个项目中，我们将开发时间并行和地理并行的算法。这些方法不是在时间上一步步推进模型的预测，而是在下一步中产生几种不同的天气估计，然后将它们组合在一起形成更准确的解决方案。这些不同的估计值中的每一个都可以独立计算，这在模型中引入了额外的并行计算。该项目与英国气象局密切合作。如果成功，这些算法将为英国气象局带来更快、更高分辨率的天气预报和气候预测模型，从而为政府、行业和公众提供更准确的预测。英国气象局为整个运输部门的客户提供预报，特别是航空规划（以便飞机可以避免逆风并利用顺风）和火山灰云运动的预测。它还为零售和休闲、保险公司、国防部和环境局提供预测（包括洪水预报）。更准确的预测将使所有这些商业组织能够进一步规划未来，避免风险和不必要的成本。

项目成果

Performance of parallel-in-time integration for Rayleigh Bénard convection

瑞利贝纳德对流的时间并行积分性能

• DOI: 10.1007/s00791-020-00332-3
• 发表时间: 2020
• 期刊: Computing and Visualization in Science
• 作者: Clarke A

Energy-enstrophy conserving compatible finite element schemes for the rotating shallow water equations with slip boundary conditions

具有滑移边界条件的旋转浅水方程能量熵守恒兼容有限元格式

• DOI: 10.1016/j.jcp.2018.06.071
• 发表时间: 2018
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Bauer W

Rotating Shallow Water Flow Under Location Uncertainty With a Structure-Preserving Discretization

具有结构保持离散化的位置不确定性下的旋转浅水流

• DOI: 10.1029/2021ms002492
• 发表时间: 2021
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Brecht R

Selective decay for the rotating shallow-water equations with a structure-preserving discretization

具有结构保持离散化的旋转浅水方程的选择性衰减

• DOI: 10.1063/5.0062573
• 发表时间: 2021
• 期刊: Physics of Fluids
• 影响因子: 4.6
• 作者: Brecht R

The 'recovered space' advection scheme for lowest-order compatible finite element methods

最低阶兼容有限元方法的“恢复空间”平流方案

• DOI: 10.1016/j.jcp.2019.04.013
• 发表时间: 2019
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Bendall T