Utsumi Nobuyuki, Turk F Joseph, Haddad Ziad S, Kirstetter Pierre-Emmanuel, Kim Hyungjun
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California.
Nagamori Institute of Actuators, Kyoto University of Advanced Science, Kyoto, Japan.
J Hydrometeorol. 2020 Dec 23;22(1):95-112. doi: 10.1175/jhm-d-20-0160.1. Epub 2021 Jan 1.
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.
基于低地球轨道卫星被动微波（MW）观测的降水估计是理解全球气候的关键变量之一。然而，几乎所有此类降水估计的验证研究都仅关注地面降水率。本研究调查了两种基于被动微波的反演算法估计的垂直降水剖面，即发射率主成分（EPC）算法和戈达德剖面算法（GPROF）。使用GMI + 双频降水雷达组合算法作为参考产品，对从全球降水测量微波成像仪（GMI）估计的基于被动微波的凝结水含量剖面进行了验证。结果表明，EPC在中高纬度地区通常低估了以平均凝结水含量描述的凝结水含量剖面的幅度，低估幅度约为20% - 50%，而GPROF在中高纬度地区高估了约20% - 50%，在热带地区高估超过50%。EPC幅度偏差的部分原因与反演算法中降水类型（即对流性和层状）的表示有关。这表明一种单独的降水类型识别技术将有助于减轻这些偏差。与剖面幅度不同，这两种基于被动微波的反演对剖面形状的表示相对较好。垂直剖面估计性能与地面降水率之间的联合分析表明，基于被动微波的算法在一定程度上实现了地面降水率与相关垂直剖面之间合理的物理联系。
