Meteorological Applications of Fully Polarimetric Doppler Radar

拟议的研究涉及气象 极化灵敏雷达传感的应用，也就是说， 确定散射体集合的性质（例如， 水凝物），例如尺寸、形状和取向分布 从测量的散射或米勒极化矩阵。 是 他认为应该对全极化数据进行处理， 在进行时间平均和形成相关性之前， 基于矢量波散射的物理洞察力， 识别偏振参数的最佳组合。 这项工作的目的是开发、测试和应用新的 在相干和非相干两种情况下的极化数据处理算法 非相干散射区 这些算法的目的是1） 提高现有降水量估算的准确性; 2） 提供云和降水现象的新“特征” 例如雨与冰雹的区别; 3）改善概念 的理解和适用范围， 非相干偏振散射机制（散射矩阵与 Mueller矩阵描述）。

The proposed research is concerned with meteorological aplications of polarimetrically agile radar sensing, that is, determining properties of collections of scatterers (e.g. hydrometeors) such as size, shape, and orientation distributions from measured scattering or Mueller polarization matrices. It is argued that processing of fully polarimetric data should be done before time averages are made and correlations formed so that physical insight, based on vector wave scattering, can be focused on identifying an optimal combination of polarization parameters. The purpose of this work is to develop, test, and apply new polarimetric data processing algorithms in both, coherent and incoherent scattering regimes. The algorithms are aimed at 1) improving the accuracy of existing estimators of precipitation; 2) providing new "signatures" of cloud and precipitation phenomena e.g. rain vs. hail discrimination; 3) improving conceptual understanding and ranges of applicability of the coherent and incoherent polarimetric scattering regimes (scattering matrix vs. Mueller matrix description).