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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=612879
• 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.  Processes associated with clouds and precipitation are among those; they are also of paramount importance for the climate system and for the society. The latest (4th) report of the Intergovernmental Panel on Climate Change, once more, identified clouds and precipitation as two of the major uncertainties in GCMs. Clouds and water vapor affect the climate system in at least two different ways. They directly impact the radiation budget and force local and global atmospheric circulation by the latent heat associated with phase changes of water. Convective flows in the atmosphere, i.e, flows that are directly or indirectly induced by latent heat, occur on a wide spectrum of scales, ranging from mesoscale (50­ to 500 km) systems to planetary scale intra­seasonal oscillations, which have a huge impact on the global weather and climate system. The associated precipitation events and their timing are of vital importance in many largely populated places of the world. While radiative forcing is well understood, the amount of clouds and their optical depths as well as the concentrations of water vapor and rain in the atmosphere are very uncertain in GCMs because of inaccuracies associated with the representation of convection.  Warm and moist air tends to rise. As it rises, it cools down by expansion, becomes over­ saturated and starts to condensate and form clouds. The heat release from condensation overcomes most of the cooling by expansion and maintains the air parcels positively buoyant. The cloud droplets and ice crystals then grow into rain droplets, snow or hail particles that are big enough to overcome the cloud updrafts and fall as precipitation. The complexity of this phenomenon, known broadly as convection, is due to many factors that are highly variable and uncertain. In particular, as they rise, buoyant air parcels entrain non­ negligible amounts of environmental air and detrain some of their mass flux into the environment through very complex and poorly understood turbulent mixing processes. The aim of this proposal is to develop and use a hierarchy of stochastic models to represent these complex processes in GCMs.

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。 其中包括与云和降水相关的过程；对于气候系统和社会，它们也至关重要。政府间气候变化小组的最新报告（第四次）再次确定云和降水是GCMS中的两个主要不确定性。云和水蒸气至少以两种不同的方式影响气候系统。它们直接影响辐射预算，并通过与水相变相关的潜热局部和全球大气循环。在大气中，即直接或间接地通过潜热引起的连接流在各种尺度上发生，范围从中尺度（50至500 km）系统到行星尺度的季节内振荡，这对全球天气和气候系统产生巨大影响。在世界许多人口稠密的地方，相关的降水事件及其时机至关重要。虽然辐射强迫已被充分了解，但由于与便利性相关的不准确性，GCM中的云及其光学深度以及大气中的水蒸气和降雨的浓度非常不确定。 温暖而潮湿的空气往往会上升。随着它的上升，它会通过膨胀冷却，变得过度饱和并开始凝结并形成云。冷凝水的热量释放通过膨胀来克服大部分冷却，并保持空气包裹积极浮力。然后，云液滴和冰晶体生长成雨滴，雪或冰雹颗粒，这些粒子足够大，可以克服云的上升气流并随着降水而下降。这种现象的复杂性（广泛地称为转换）是由于许多高度可变和不确定的因素所致。特别是，随着它们的上升，浮力的空气包入口，通过非常复杂且理解不足的湍流混合过程，将其量的环境空气忽略不计，并将某些质量通量降低到环境中。该建议的目的是开发和使用随机模型的层次结构来表示GCM中的这些复杂过程。