GSMaP Passive Microwave Precipitation Retrieval Algorithm : Algorithm Description and Validation
GSMaP Passive Microwave Precipitation Retrieval Algorithm : Algorithm Description and Validation
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DOI：
10.2151/jmsj.87a.119
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发表时间：
2009-03-01
影响因子：
3.1
通讯作者：
Takayabu, Yukari N.
中科院分区：
文献类型：
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作者：
Aonashi, Kazumasa;Awaka, Jun;Takayabu, Yukari N.
本文介绍了热带降雨测量任务 (TRMM) 微波成像仪 (TMI) 的降水反演算法，该算法是在全球卫星降水测绘项目 (GSMaP) 下通过改进作者之前的算法而开发的。 GMap 算法的基本思想是找到最佳降水量，使辐射传输模型 (RTM) 计算的亮温 (TB) 与观测到的 TB 最为吻合。 GMaP算法较作者之前的工作主要改进如下：（1）使用了与降水相关的变量模型（降水剖面、雨滴尺寸分布等）以及基于TRMM观测研究的降水检测和不均匀性估计方法； (2) 使用 37 和 85 GHz 的 TMI 偏振校正温度 (PCT) 散射信号 (PCT37、PCT85) 以及对陆地和沿海地区高降水 (降水顶层和冰冻层 (Dtop) 之间的厚度大于 6 km) 的散射信号校正。为了验证 GMap 算法，我们将 1998 年 TMI TB 的反演与 TRMM 降水雷达 (PR) 和戈达德剖面算法 (GPROF) 检索（2A12 版本 61）。结果表明：(1)在陆地和沿海地区，对于弱于10 mm h(-1)的高降水(Dtop > 4 km)，GSMaP反演与PR的一致性优于GPROF，而对于降水大于10 mm h(-1)的降水，GSMaP和GPROF均低估了PR降水率； (2) 在海洋上空，对于大于10 mm h(-1)的降水量，GSMaP反演与PR的一致性优于GPROF，而与PR和GPROF相比，GSMaP略高估了小于10 mm h(-1)的降水量； (3)GSMaP算法未能检测到副热带海洋上空一些弱于2mm·h(-1)的降水区域。1998年7月将不同降水相关变量模型的实验算法和利用散射信号的反演方法应用于TMI TB，以检验上述改进对GSMaP算法的效果。结果表明:降水剖面的改进缓解了陆地和沿海地区10 mm h(-1)以上降水的低估;新的物理相关变量模型的结合使用缓解了海洋10 mm h(-1)以上降水的低估;PCT37和散射信号校正的使用减少了高降水(Dtop > 4)的高估。 km) 陆地和沿海地区的风速低于 10 mm h(-1)。
This paper describes a precipitation-retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) that was developed under the Global Satellite Mapping of Precipitation project (GSMaP) by improving the authors' previous algorithm. The basic idea of the GSMaP algorithm is to find the optimal precipitation for which the brightness temperatures (TBs) calculated by the radiative-transfer model (RTM) fit best with the observed TBs. The main improvements of the GSMaP algorithm over the authors' previous work are as follows: (1) use of precipitation-related variable models (precipitation profiles, drop-size distribution, etc.) and precipitation detection and inhomogeneity estimation methods based on TRMM observation studies; (2) use of scattering signals of the TMI Polarization-Corrected Temperature (PCT) at 37 and 85 GHz (PCT37, PCT85) and scattering-signal correction for tall precipitation (thickness between precipitation top level and freezing level (Dtop) larger than 6 km) over land and coastal areas.In order to validate the GSMaP algorithm, we compared its retrievals from TMI TBs in 1998 with the TRMM Precipitation Radar (PR) and Goddard Profiling Algorithm (GPROF) retrievals (2A12 version 61). The results show that (1) over land and coastal areas, the GSMaP retrievals agreed better with PR than GPROF for tall precipitation (Dtop > 4 km) weaker than 10 mm h(-1), while both GSMaP and GPROF underestimated PR precipitation rates for precipitation heavier than 10 mm h(-1); (2) over ocean, the GSMaP retrievals agreed better with PR than GPROF for precipitation heavier than 10 mm h(-1), while GSMaP slightly overestimated precipitation weaker than 10 mm h(-1) compared to PR and GPROF; (3) The GSMaP algorithm failed to detect some precipitation areas weaker than 2 mm h(-1) over sub-tropical oceans.Experimental algorithms with different precipitation-related variable models and retrieval methods using scattering signals were applied to TMI TBs in July 1998 to examine the effect of the above improvements to the GSMaP algorithm. The results show that the improvement of the precipitation profile alleviated the underestimation of precipitation heavier than 10 mm h(-1) over land and coastal areas, that the combined use of new physical-related variable models alleviated the underestimation of precipitation heavier than 10 mm h(-1) over ocean, and that the use of PCT37 and scattering-signal correction reduced the overestimation of tall precipitation (Dtop > 4 km) weaker than 10 mm h(-1) over land and coastal areas.