Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimetric signal is challenging. The present study develops a new method, called process identification based on vertical gradient signs (PIVSs), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions, we then identify regions dominated by (i) vapor deposition, (ii) aggregation and riming and (iii) snowflake sublimation and possibly snowflake breakup, based on the sign of the local vertical gradients of the reflectivity Z_H and the differential reflectivity Z_DR. The method is then applied to data from two frontal snowfall events, namely one in coastal Adélie Land, Antarctica, and one in the Taebaek Mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations, using a multi-angle snowflake camera, and with the output of a hydrometeor classification, based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. In particular, we are able to automatically derive and discuss the altitude and thickness of the layers where each process prevails for both case studies. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g., Z_H and Z_DR distribution for each process). We, finally, highlight the potential for extensive application to cold precipitation events in different meteorological contexts.

中文翻译：

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从极化雷达变量的欧拉垂直梯度识别降雪微物理过程

极化雷达系统通常用于研究降水的微物理。虽然它们提供具有大空间覆盖的连续测量，但从极化信号中检索有关控制降雪演变的微物理过程的信息具有挑战性。本研究开发了一种新方法，称为基于垂直梯度符号 (PIVS) 的过程识别，可在空间上识别双极化多普勒降雪中主要微物理过程（聚集和边缘、气相沉积和升华的晶体生长）的发生。雷达扫描。我们首先推导出一个分析框架来评估雷达变量的局部垂直梯度在哪些气象条件下可靠地告知微物理过程。在这样的条件下，Z _H和微分反射率Z _DR. 然后将该方法应用于两次锋面降雪事件的数据，即一次在南极洲阿德利地沿海，另一次在韩国太白山脉。该方法的有效性是通过将其结果与雪花观测、使用多角度雪花相机以及基于极化雷达信号的水凝物分类的输出进行比较来评估的。该方法的应用进一步使更好地表征和理解降雪在两个不同的地理和气象背景下如何形成、增长和衰减成为可能。特别是，我们能够自动推导出和讨论两个案例研究中每个过程占优势的层的高度和厚度。我们从方法输出的统计分析中推断出雷达变量方面的一些微物理特征（例如，每个过程的Z _H和Z _DR分布）。最后，我们强调了在不同气象背景下广泛应用于冷降水事件的潜力。