积雪微波降水相位探测的嵌套K-最近预报方法
A Nested K-Nearest Prognostic Approach for Microwave Precipitation Phase Detection over Snow Cover
摘要：
从空间监测降水相态的变化对于了解气候变化中地球冰冻圈的质量平衡非常重要。本文探讨了贝叶斯最近邻方法的预测检测降水及其阶段使用被动微波观测全球降水测量（GPM）卫星。该方法使用的加权欧氏距离度量搜索通过一个先验的数据库填充符合GPM辐射计和雷达观测以及辅助积雪数据。使用GPM官方降水产品、地面雷达的数据以及天气研究和预报模型的高保真模拟来评估算法性能。使用所提出的方法，我们证明了地面降水检测的命中概率可以达到0.80，而虚警概率保持在0.11以下。该算法在检测降雪方面比降雨方面表现出更高的技能，平均为10%。特别是，与其他表面类型相比，在干燥的积雪上，降水检测及其固相的概率增加了11%和8%。主要原因被发现是有关的能力，该算法在捕捉信号的增加液态水含量的雪云在辐射冷积雪覆盖的表面
英文摘要：
Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth's cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10 percent. In particular, the probability of precipitation detection and its solid phase increases by 11 and 8 percent, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces
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