Liao Liang, Meneghini Robert
Goddard Earth Science Technology and Research, Morgan State University, Greenbelt, Maryland.
NASA Goddard Space Flight Center, Greenbelt, Maryland.
J Atmos Ocean Technol. 2019 May 1;36(5):883-902. doi: 10.1175/jtech-d-18-0210.1.
A physical evaluation of the rain profiling retrieval algorithms for the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) satellite is carried out by applying them to the hydrometeor profiles generated from measured raindrop size distributions (DSD). The DSD-simulated radar profiles are used as input to the algorithms, and their estimates of hydrometeors' parameters are compared with the same quantities derived directly from the DSD data (or truth). The retrieval accuracy is assessed by the degree to which the estimates agree with the truth. To check the validity and robustness of the retrievals, the profiles are constructed for cases ranging from fully correlated (or uniform) to totally uncorrelated DSDs along the columns. Investigation into the sensitivity of the retrieval results to the model assumptions is made to characterize retrieval uncertainties and identify error sources. Comparisons between the single- and dual-wavelength algorithm performance are carried out with either a single- or dual-wavelength constraint of the path integral or differential path integral attenuation. The results suggest that the DPR dual-wavelength algorithm generally provides accurate range-profiled estimates of rainfall rate and mass-weighted diameter with the dual-wavelength estimates superior in accuracy to those from the single-wavelength retrievals.
通过将全球降水测量（GPM）卫星上搭载的双频降水雷达（DPR）的降雨廓线反演算法应用于由实测雨滴大小分布（DSD）生成的水凝物廓线，对这些算法进行了物理评估。将DSD模拟的雷达廓线用作算法的输入，并将其对水凝物参数的估计与直接从DSD数据（或真值）得出的相同量进行比较。通过估计值与真值的一致程度来评估反演精度。为检验反演的有效性和稳健性，针对沿列从完全相关（或均匀）到完全不相关的DSD的情况构建了廓线。研究了反演结果对模型假设的敏感性，以表征反演不确定性并识别误差源。在路径积分或差分路径积分衰减的单波长或双波长约束下，对单波长和双波长算法性能进行了比较。结果表明，DPR双波长算法通常能提供准确的降雨率和质量加权直径的距离廓线估计，双波长估计的精度优于单波长反演。
