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: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=364663
• 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在很大程度上取决于我们在目前用于预报的数值模式中引入观测，特别是雷达观测的能力。为此，已经发展了一种数据同化技术，可以将当前观测与过去开始的数值预报结合起来，以便所有可用的雷达观测对预报进行校正。要使这种技术成功，降水过程的模式描述和雷达观测必须是相容的。本提案的目标是优化代表降水粒子之间相互作用的方程（描述雨、雪、融雪、冰雹等特性的物理过程，称为云模式中的微物理参数化），使雷达观测可以用这些方程来描述，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。我们以前从雷达数据中对降水物理的研究将指导我们的探索。这项工作的出发点是一个微物理学的公式，根据粒度分布的尺度描述，从一个完整的参数化可以只使用分布的矩，而不需要任何参考尺寸分布本身。雷达观测也是用分布的矩来描述的，因此，将使用接近雷达观测矩的预测矩进行参数化。我们将通过发展雷达观测的数学描述来推导微物理方程，并从中推导参数化。我们将在雷达观察到的简单情况下测试我们的开发，其中每个过程都可以合理地隔离。