Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals
Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals
复制标题
GPM-Era 卫星被动微波反演估算降水垂直剖面的评估
DOI：
10.1175/jhm-d-20-0160.1
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发表时间：
2021
影响因子：
3.8
通讯作者：
Kim Hyungjun
中科院分区：
文献类型：
--
作者：
Utsumi Nobuyuki;Turk F. Joseph;Haddad Ziad S.;Kirstetter Pierre-Emmanuel;Kim Hyungjun
根据低地球轨道卫星的被动微波观测进行降水估算是了解全球气候的基本变量之一。然而，几乎所有的验证研究，这种降水估计只集中在地面降水率。本研究探讨两种被动微波反演算法，即，发射率主成分（EPC）算法和戈达德轮廓算法（GPROF）。使用GMI +双频降水雷达组合算法作为参考产品，对全球降水测量微波成像仪（GMI）估计的被动MW凝结水含量廓线进行了验证。结果表明，在中高纬地区，EPC普遍低估了由平均凝结水含量描述的凝结水含量廓线的大小，低估幅度约为20%~ 50%，而GPROF在中高纬地区高估了约20%~ 50%，在热带地区高估幅度超过50%。EPC震级偏差的一部分与降水类型的表示有关（即，对流和层状）。这表明，一个单独的降水类型识别技术将有助于减轻这些偏见。与轮廓的大小相反，轮廓形状相对较好地由这两个被动的基于MW的检索表示。垂直廓线和地面降水率的估计性能之间的联合分析表明，地面降水率和相关的垂直廓线之间的物理上合理的连接，在一定程度上实现了被动的MW为基础的算法。
Precipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.