A Comparison of Approaches to Objectively Identify Precipitation Structures within the Comma Head of Midlatitude Cyclones

Abstract Automated feature identification of storm structures is a useful tool for model validation and understanding the environment around the feature. Past studies have used automated identification to investigate precipitation bands within the comma head of extratropical cyclones, but the different algorithms have not been compared. This paper compares the past approaches and introduces a new adaptive feature identification algorithm to obtain a range of structures from precipitation cells to bands. Previous studies generally use a single threshold of base reflectivity over a large region or some other variable to isolate objects, while our algorithm applies a threshold to localized regions within the domain of interest, treating the precipitation objects as locally enhanced features. The new algorithm is first tested on a set of synthetically generated reflectivity fields with varying complexity and then evaluated for a few observed winter storm events in the northeast United States. While there is some sensitivity to the size of the localized region, this issue can be alleviated by doing multiple permutations of the box size and taking the union of the resulting objects. Image morphology can be used to further separate identified objects. When the new algorithm is compared to algorithms using single thresholds, there is improved detection of low-intensity objects and separation of high-intensity objects, advantages which are particularly relevant for identifying snow and rainbands within winter storms. Significance Statement Snowstorms often contain band-like structures featuring enhanced snowfall, which yield a variety of societal impacts. Precipitation structures in winter storms have a variety of shapes and durations, but the range of structures is not well understood. Manually identifying these structures by looking at radar images is one method, but this is time consuming and subjective. Some past approaches used to automatically identify precipitation structures have limitations. This study compares past methods and introduces a new method that can be applied to better understand the precipitation structures around a cyclone, focusing on storms in the northeast United States.