INVESTIGATION OF A WSR-88D Z-R RELATION FOR SNOWFALL IN NORTHERN UTAH
INVESTIGATION OF A WSR-88D Z-R RELATION FOR SNOWFALL IN NORTHERN UTAH
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犹他州北部降雪的 WSR-88D Z-R 关系调查
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发表时间：
2001
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通讯作者：
S. Vasiloff
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作者：
S. Vasiloff
在降水处理系统中，美国国家气象局（NWS）1988年天气监视雷达多普勒（WSR-88 D）的默认反射率-降雨率（Z-R）关系为Z= 300 R（PPS;富尔顿等人，1998）。默认的Z-R关系通常最适合对流降雨。最近，美国国家气象局雷达业务中心（NWS ROC）推荐了额外的Z-R关系，以改善非对流风暴的降水估计; Z= 75 R被推荐用于“大陆分水岭以西的冬季层状降水”（ROC 1999）。虽然预报员现在可以使用更广泛的Z-R关系，但需要新的程序进行实时Z-R调整，因为固定的Z-R会因风暴内和风暴间降水过程的大变化而产生误差（例如，Fujiyoshi等人，1990年）。这些变化取决于许多因素，包括粒子密度、下落速度以及冰和水的折射率。Rasmussen等人（2001年）在开发天气支持除冰决策系统（WSDDM）时认识到了这一点，该系统在实时采样期间使用实时雪量计数据来调整雷达降水估计。WSDDM系统集成了测量数据和雷达降水估计，并根据两个集成量的比值计算每个雷达体积的新Z-R系数。随着雷达距离的增加而增加雷达波束宽度和高度导致随着距离的增加雷达和仪表估计之间的去相关。在某些时候，光束将完全超过风暴的顶部。几项研究记录了补偿范围效应的努力。Seo等人（2000年）提出了一种利用垂直反射率剖面（VPR）实时调整雷达距离偏差的方法。Joss和Lee（1995年）根据山区地形的气候VPR推导出距离校正因子。虽然这些距离修正可以改善雷达QPE，但据信，鲁棒的实时仪表调整将消除对这种额外处理的需要。精确估算山区降水量的另一个重大挑战是
The National Weather Service (NWS) Weather Surveillance Radar 1988 Doppler (WSR-88D) default reflectivity-rain rate (Z-R) relation is Z=300R in the Precipitation Processing System (PPS; Fulton et al. 1998). The default Z-R relation typically works best with convective rainfall. Recently, the National Weather Service Radar Operations Center (NWS ROC) has recommended additional Z-R relations to improve precipitation estimates for non-convective storms; Z=75R is recommended for “winter stratiform precipitation west of the continental divide” (ROC 1999). Although a wider variety of Z-R relations is now available to forecasters, new procedures are needed for a real-time Z-R adjustment since a fixed Z-R is subject to error due to the large variations in precipitation processes within and among storms (e.g., Fujiyoshi et al. 1990). These variations depend on many factors including the particle density, fall speed, and the refractive indices for ice and water. Rasmussen et al. (2001) recognized this in the development of the Weather Support to Deicing Decision Making System (WSDDM) that uses real-time snow gauge data to adjust radar precipitation estimates during real-time sampling. The WSDDM system integrates gauge data and radar precipitation estimates and computes a new Z-R coefficient every radar volume based on the ratio of the two integrated quantities. Increasing radar beam width and height with increasing range from the radar results in a decorrelation between the radar and gauge estimates with increasing range. At some point, the beam will entirely over-shoot the top of the storm. Several studies have documented efforts to compensate for range effects. Seo et al. (2000) proposed a real-time adjustment of radar range biases using a vertical profile of reflectivity (VPR). Joss and Lee (1995) derived range correction factors based on climatological VPRs in mountainous terrain. While these range corrections may improve radar QPE, it is believed that a robust real-time gauge adjustment will eliminate the need for this extra processing. Another significant challenge to accurate precipitation estimates in mountainous terrain is the