Radar in atmospheric physics and in forecasting

雷达在大气物理和预报中的应用

• 批准号: 139414-2006
• 负责人: Zawadzki, Isztar
• 金额: $ 2.65万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=391602
• 关键词: Radar; atmospheric; physics; forecasting

The correct short term prediction of the evolution of a storm is critically dependent on our ability to introduce observations, particularly radar observations, into the numerical models that are presently used for forecasting.  For this a data assimilation techniques have been developed that allow to combine current observations with numerical forecast initiated in the past so that the forecast is corrected by all available radar observations.  For this technique to be successful model description of the precipitation processes and radar observations must be compatible.  The objective of this proposal is to optimise the equations that represent the interactions between precipitating particles (the physical processes that describe properties of rain, snow, melting snow, hail, etc., called parameterization of microphysics in cloud models) in such a manner as to make radar observations describable by these equations and make the data assimilation as insensible as possible to uncertainties in measurements and in the description of the physical processes. Our previous studies of the physics of precipitation from radar data will be guiding us in our quest. The starting point of this work is a formulation of microphysics in terms of a scaling description of particle size distribution from which a full parameterization can be made using only the moments of the distributions without any reference to size distribution itself.  Radar observations are also described by the moments of the distribution, thus, the parameterization will be made using the prognostic moments close to the radar observed moments.  We will derive the microphysical equations by developing mathematical descriptions of radar observations and from these derive the parameterization. We will test our developments on simple situations observed by radar in which each process can be reasonably isolated.

对风暴演变的正确短期预测关键取决于我们引入观测的能力，特别是雷达观测，目前用于预报的数值模式。为此，开发了一种数据同化技术，可以将联合收割机当前观测与过去启动的数值预报相结合，以便通过所有可用的雷达观测来校正预报。为此，降水过程的模式描述和雷达观测必须兼容。本建议的目的是优化表示降水粒子之间相互作用的方程（描述雨、雪、融化的雪、冰雹等性质的物理过程，称为云模型中微物理学的参数化），使雷达观测可以用这些方程描述，并使数据同化尽可能不受测量和物理过程描述中的不确定性的影响。我们以前对雷达数据降水物理学的研究将指导我们的探索。这项工作的出发点是微物理学的一个公式，它是根据颗粒尺寸分布的标度描述，从这个公式中可以只使用分布的矩来进行完全的参数化，而不涉及尺寸分布本身。雷达观测也由分布的矩来描述，因此，参数化将利用与雷达观测矩接近的预报矩进行，我们将通过发展雷达观测的数学描述导出微物理方程，并从这些导出参数化。我们将在雷达观察到的简单情况下测试我们的发展，其中每个过程都可以合理地隔离。