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Impact of non-uniform beam filling on spaceborne cloud and precipitation radar retrieval algorithms

机译:光束不均匀填充对星云和降水雷达检索算法的影响

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In this presentation we will discuss the performance of classification and retrieval algorithms for spaceborne cloud and precipitation radars such as the Global Precipitation Measurement mission [1] Dual-frequency Precipitation Radar (GPM/DPR) [2], and notional radar for the Aerosol/Clouds/Ecosystem (ACE) [1] mission and related concepts. Spaceborne radar measurements are simulated either from Airborne Precipitation Radar 2~(nd) Generation (APR-2, [3]) observations, or from atmospheric model outputs via instrument simulators contained in the NASA Earth Observing Systems Simulators Suite (NEOS~3). Both methods account for the three dimensional nature of the scattering field at resolutions smaller than that of the spaceborne radar under consideration. We will focus on the impact of non-homogeneities of the field of hydrometeors within the beam. We will discuss also the performance of methods to identify and mitigate such conditions, and the resulting improvements in retrieval accuracy. The classification and retrieval algorithms analyzed in this study are those derived from APR-2's Suite of Processing and Retrieval Algorithms (ASPRA); here generalized to operate on an arbitrary set of radar configuration parameters to study the expected performance of spaceborne cloud and precipitation radars. The presentation will highlight which findings extend to other algorithm families and which ones do not.
机译:在本演示中,我们将讨论星云和降水雷达的分类和检索算法的性能,例如全球降水测量任务[1]双频降水雷达(GPM / DPR)[2],以及用于气溶胶/云/生态系统(ACE)[1]的任务和相关概念。星载雷达的测量结果可以从机载降水雷达第2代(APR-2,[3])观测结果中模拟,也可以通过NASA地球观测系统模拟器套件(NEOS〜3)中包含的仪器模拟器从大气模型输出中得到模拟。两种方法都以比所考虑的星载雷达小的分辨率来解释散射场的三维特性。我们将关注梁内水凝物场的非均匀性的影响。我们还将讨论识别和缓解这种情况的方法的性能,以及由此带来的检索准确性的提高。本研究中分析的分类和检索算法是从APR-2的处理和检索算法套件(ASPRA)中获得的;在这里,我们一般可以对任意一组雷达配置参数进行操作,以研究星云和降水雷达的预期性能。该演讲将重点介绍哪些发现扩展到其他算法家族,哪些没有。

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