Radar in atmospheric physics and in forecasting

点击翻译按钮获取中文摘要

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.