Preprints
https://doi.org/10.5194/egusphere-2023-2888
https://doi.org/10.5194/egusphere-2023-2888
16 Jan 2024
 | 16 Jan 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms

Laura Mary Tomkins, Sandra E. Yuter, and Matthew A. Miller

Abstract. Radar observations of winter storms often exhibit locally-enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally-enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field that is rescaled from reflectivity and incorporates an under and over estimate to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of datasets and applications.

Laura Mary Tomkins, Sandra E. Yuter, and Matthew A. Miller

Status: open (until 21 Feb 2024)

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Laura Mary Tomkins, Sandra E. Yuter, and Matthew A. Miller

Data sets

Northeast US Regional NEXRAD radar mosaics of winter storms from 1996-2023, part 1 Laura Tomkins, Sandra Yuter, Matthew Miller, Nicole Corbin, and Nicole Hoban https://doi.org/10.5061/dryad.zcrjdfnk6

Northeast US Regional NEXRAD radar mosaics of winter storms from 1996-2023, part 2 Laura Tomkins, Sandra Yuter, Matthew Miller, Nicole Corbin, and Nicole Hoban https://doi.org/10.5061/dryad.rbnzs7hj9

Video supplement

Supplemental videos for the paper "Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms" Laura Tomkins, Sandra Yuter, and Matthew Miller https://av.tib.eu/series/1524

Laura Mary Tomkins, Sandra E. Yuter, and Matthew A. Miller

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Short summary
We have created a new method to better identify enhanced features in radar data from winter storms. Unlike the clear-cut features seen in warm season storms, features in winter storms are often fuzzier with softer edges. Our technique is unique because it uses two adaptive thresholds that change based on the background radar values. It can identify both strong and subtle features in the radar data, and takes into account uncertainties in the detection process.