Hydrometeor Profile Characterization Method for Dual-Frequency Precipitation Radar Onboard the GPM
Hydrometeor Profile Characterization Method for Dual-Frequency Precipitation Radar Onboard the GPM
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DOI：
10.1109/tgrs.2012.2224352
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发表时间：
2013-06
影响因子：
8.2
通讯作者：
M. Le;V. Chandrasekar
中科院分区：
文献类型：
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作者：
M. Le;V. Chandrasekar
廓线分类是全球降水测量核心卫星上双频降水雷达微物理反演算法的一个关键模块。水凝物剖面特征化（HPC或熔融区检测）是剖面分类的重要组成部分。为了完成这一分类，测量双频比DFRM，定义为在两个频率通道（Ku-和Ka-波段）测量的反射率之间的差异，特性进行了研究，为不同的水凝物阶段。本文表明，DFRM配置文件可以用来检测冻结，混合相，和液体区域。本文提出了一种利用DFRm及其沿高度沿着的距离变化率进行DPR剖面分类的HPC模型。第二代机载降水雷达（APR-2）在美国宇航局非洲季风多学科分析，成因和快速增强过程，和若狭湾活动收集的数据被用于模型验证。多普勒速度的签名，以及在Ku波段的线性去极化比，可用于APR-2数据，用于交叉验证的目的。HPC模型和基于速度的估计值之间的熔化层顶部和底部的比较表明，它们比较好，偏差为2%。HPC方法在GPM-DPR观测分辨率的性能进行了评估，并被证明是适用于GPM-DPR分辨率的观测。从分析中可以推断，本文使用DFRm开发的方法是GPM-DPR HPC的一个很好的候选者。
Profile classification is a critical module in the microphysics retrieval algorithm for the dual-frequency precipitation radar (DPR) that will be onboard the Global Precipitation Measurement (GPM) Core satellite. Hydrometeor profile characterization (HPC or melting region detection) is an important part of profile classification. To accomplish this classification, characteristics of measured dual-frequency ratio DFRm, defined as the difference between measured reflectivity at two frequency channels (Ku- and Ka-bands), were studied for different hydrometeor phases. This paper shows that a DFRm profile can be used to detect the frozen, mixed-phase, and liquid regions. An HPC model is developed in this paper for DPR profile classification using DFRm and its range variability along the height. Data collected by the Second Generation Airborne Precipitation Radar (APR-2) in NASA African Monsoon Multidisciplinary Analysis, Genesis and Rapid Intensification Processes, and Wakasa Bay campaigns are employed in model validation. Signatures of Doppler velocity, as well as the linear depolarization ratio at Ku-band, available for APR-2 data, are used for cross-validation purpose. Comparison of the melting layer top and bottom between the HPC model and the velocity-based estimates shows that they compare well, with a 2% bias. The performance of the HPC method at GPM-DPR observation resolution is evaluated and is shown to be applicable to observation at GPM-DPR resolution. It can be inferred from the analysis presented that the methodology developed in this paper using DFRm is a good candidate for HPC for GPM-DPR.