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.

气候和天气预报模型(GCM)是基于大气和海洋流动方程的离散化：质量、动量和能量守恒的偏微分方程组加上一定数量的活跃和不活跃的示踪物，如二氧化碳、盐度和水的混合比率(水蒸气、云滴、冰晶、雨、雪、冰雹)。由于计算机的限制，通常使用从10到100公里的空间网格大小和几分钟到几小时的时间步长。在这种粗糙的网格上，许多对气候和天气变异性有很大影响的重要物理过程没有被考虑在内。取而代之的是，子网格模型或参数化被用来表示大尺度上未分辨尺度的影响。