Radar in atmospheric physics and in forecasting

正确的风暴演变的短期预测，关键取决于我们的能力，引入观测，特别是雷达观测，到目前用于预测的数值模式。 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对预报进行校正。 为了使这种技术成功，降水过程的模式描述和雷达观测必须兼容。 该提案的目的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制定微观物理学的尺度描述的颗粒尺寸分布，从一个完整的参数化可以只使用的时刻的分布，而没有任何参考尺寸分布本身。 雷达观测值也由分布的矩描述，因此，将使用与雷达观测值矩接近的预报矩进行参数化。 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。

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.