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

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. [ABSTRACT FROM AUTHOR]