Stochastic methods for climate and weather forecasting

气候和天气预报的随机方法

• 批准号: RGPIN-2015-04288
• 负责人: Khouider, Boualem
• 金额: $ 1.82万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635715
• 关键词: Stochastic; methods; climate; weather; forecasting

Climate and weather forecasting models (GCMs) are based on a discretization of the equations of atmospheric and oceanic flows: a system of partial differential equations for the conservation of mass, momentum, and energy plus a certain number of active and inactive tracers such as carbon dioxide, salinity, and water mixing ratios (vapour, cloud droplets, ice crystals, rain, snow, hail). Due to computer limitations, spatial mesh sizes ranging from 10 to 100 km and time steps of minutes to hours are typically used. On such coarse grids, many important physical processes, which greatly affect climate and weather variability, are not accounted for. Instead, subgrid models, or parameterizations, are used to represent the effects of the unresolved scales on the large scales.

气候和天气预测模型（GCM）基于大气和海洋流动方程的分散化：部分微分方程的系统，用于保护质量，动量和能量的局部差分方程，以及一定数量的活跃​​和不活跃的示踪剂，例如二氧化碳，二氧化碳，盐酸，盐度和水混合率（vapor，cloud cloud，cloud cloud droplets coper，scee consey sace sace，snow hac，snow hac，sage）。由于计算机的限制，通常使用10到100公里的空间网格尺寸，通常使用几分钟到小时的时间步骤。在这样的Corse网格上，许多重要的物理过程，极大地影响气候和天气变异性。取而代之的是，亚网格模型或参数用于表示未解决的尺度对大尺度的影响。