Machine Learning Detection of Melting Layers From Radar Observations

Abstract Melting layers in the atmosphere signify where falling ice hydrometeors melt into raindrops, and can be identified by discernible radar signatures. Accurate detection of melting layers is crucial to improving quantitative precipitation estimation, weather forecasts, microwave communication, and aviation risk assessments in a changing climate. Traditional detection algorithms based on fixed thresholds or a priori assumptions lack general robustness across diverse weather conditions, which can be addressed by leveraging machine learning techniques. This study presents a binary semantic segmentation U‐Net model for automatic detection of melting layers, using Ka‐band vertical profiling ground radar observations collected at the North Slope of Alaska between March 2015 and February 2016. An interactive data extraction tool, ClickCollect, has been developed to generate a labeled data set of melting layer boundaries from radar observations during all seasons. Results show that the U‐Net effectively detects 96% of the melting layer cases, and is applicable to complex weather conditions including heavy precipitation with velocity folding, multiple layer melting, and near‐surface melt layers. Compared to a traditional detection method, the U‐Net model increases the Probability of Detection by 57% and improves the mean Dice‐Sørensen coefficient from 0.69 to 0.91. Furthermore, the U‐Net model provides additional information of detection uncertainty based on ensemble predictions. The U‐Net model and the data extraction tool can be applied to similar profiling radar instruments in different regions of the world, contributing to an enhanced understanding of the distribution and variations of melting layers.