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A Consistent Treatment of Microwave Emissivity and Radar Backscatter for Retrieval of Precipitation over Water Surfaces

机译:微波发射率和雷达反向散射的一致处理用于反演水面上的降水

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摘要

The Global Precipitation Measurement satellite’s Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) are designed to provide the most accurate instantaneous precipitation estimates currently available from space. The GPM Combined Algorithm (CORRA) plays a key role in this process by retrieving precipitation profiles that are consistent with GMI and DPR measurements; therefore it is desirable that the forward models in CORRA use the same geophysical input parameters. This study explores the feasibility of using internally consistent emissivity and surface backscatter cross section (σ0) models for water surfaces in CORRA. An empirical model for DPR Ku and Ka σ0 as a function of 10m wind speed and incidence angle is derived from GMI-only wind retrievals under clear conditions. This allows for the σ0 measurements, which are also influenced by path-integrated attenuation (PIA) from precipitation, to be used as input to CORRA and for wind speed to be retrieved as output. Comparisons to buoy data give a wind rmse of 3.7 m/s for Ku+GMI and 3.2 m/s for Ku+Ka+GMI retrievals under precipitation (compared to 1.3 m/s for clear-sky GMI-only), and there is a reduction in bias from the GANAL background data (−10%) to the Ku+GMI (−3%) and Ku+Ka+GMI (−5%) retrievals. Ku+GMI retrievals of precipitation increase slightly in light (< 1 mm/hr) and decrease in moderate to heavy precipitation (> 1mm/hr). The Ku+Ka+GMI retrievals, being additionally constrained by the Ka reflectivity, increase only slightly in moderate and heavy precipitation at low wind speeds (< 5 m/s) relative to retrievals using the surface reference estimate of PIA as input.
机译:全球降水测量卫星的微波成像仪(GMI)和双频降水雷达(DPR)旨在提供目前太空中最准确的瞬时降水估算。 GPM组合算法(CORRA)通过检索与GMI和DPR测量值一致的降水剖面,在此过程中发挥了关键作用。因此,希望CORRA中的正向模型使用相同的地球物理输入参数。这项研究探索了在CORRA中对水表面使用内部一致的发射率和表面反向散射截面(σ0)模型的可行性。由DMI Ku和Kaσ0随风速和入射角变化的经验模型是在明确条件下从仅GMI取风的情况下得出的。这允许将σ0测量值用作CORRA的输入,并将风速作为输出值,σ0测量值也受降水的路径积分衰减(PIA)的影响。与浮标数据的比较得出,在降水条件下,Ku + GMI的风均方根值为3.7 m / s,Ku + Ka + GMI的风速均方根值为3.2 m / s(相比之下,仅晴空GMI时为1.3 m / s)。从GANAL背景数据(−10%)到Ku + GMI(−3%)和Ku + Ka + GMI(−5%)检索的偏差减少。 Ku + GMI的降水量在轻度(<1 mm / hr)时略有增加,而中度至重度降水(> 1mm / hr)则减少。相对于使用PIA的表面参考估计作为输入的Ku + Ka + GMI检索,还受到Ka反射率的限制,在低风速(<5 m / s)的中等和强降水中,Ku + Ka + GMI检索仅略有增加。

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