Efficient Dealiasing of Doppler Velocities Using Local Environment Constraints

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  • 1 NOAA, Environmental Research Laboratories, National Severe Storms Laboratory, Norman, Oklahoma
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Abstract

A Doppler velocity dealiasing algorithm is described that processes one radial at a time by comparing that radial with a previous radial. This technique has worked reliably on numerous Doppler radar datasets for clear air, thunderstorm, and severe thunderstorm situations. It was also tested on four volume mans from severe weather environments with difficult aliasing problems to determine statistically how well the algorithm performs in a worst case environment. Of some 1.2 million velocities in these severe storms, 0.2% were improperly dealiased, and 93% of those were above 13 km height in the storm-top divergent region where shears were extreme. Every tornado, mesocyclone, gust front, microburst, and storm-top divergent signature was preserved, and could be readily discerned by human analyst. No adverse impact was observed on the signature and automated signature detection algorithms would therefore be freed from contamination by velocity aliasing. The velocity dealiasing algorithms described is adaptive and therefore efficient because simple cheeks are made initially, and progressively more sophisticated and time-consuming checks are used only if they are needed.

Abstract

A Doppler velocity dealiasing algorithm is described that processes one radial at a time by comparing that radial with a previous radial. This technique has worked reliably on numerous Doppler radar datasets for clear air, thunderstorm, and severe thunderstorm situations. It was also tested on four volume mans from severe weather environments with difficult aliasing problems to determine statistically how well the algorithm performs in a worst case environment. Of some 1.2 million velocities in these severe storms, 0.2% were improperly dealiased, and 93% of those were above 13 km height in the storm-top divergent region where shears were extreme. Every tornado, mesocyclone, gust front, microburst, and storm-top divergent signature was preserved, and could be readily discerned by human analyst. No adverse impact was observed on the signature and automated signature detection algorithms would therefore be freed from contamination by velocity aliasing. The velocity dealiasing algorithms described is adaptive and therefore efficient because simple cheeks are made initially, and progressively more sophisticated and time-consuming checks are used only if they are needed.

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