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John W. Nielsen-Gammon
and
William L. Read

Abstract

Left-moving supercells, which rotate anticyclonically, are much less common than their right-moving counterparts but are nevertheless capable of producing severe weather. On 26 May 1992, a severe left-moving thunderstorm over east Texas developed within range of the WSR-88D (Weather Surveillance Radar-1988 Doppler) radar at League City, Texas. The evolution of the left-moving thunderstorm, including its split from its parent thunderstorm, is presented using standard WSR-88D products. The storm produced wind damage and large hail, whose presence in the thunderstorm caused a flare echo in the return signal. No automated WSR-88D algorithms exist to detect mesoanticyclones or flares, so the subjective interpretation of these radar signatures as indicators of severe weather can be critical for the proper issuance of warnings for such storms.

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Fuqing Zhang
,
Andrew M. Odins
, and
John W. Nielsen-Gammon

Abstract

A mesoscale model is used to investigate the mesoscale predictability of an extreme precipitation event over central Texas on 29 June 2002 that lasted through 7 July 2002. Both the intrinsic and practical aspects of warm-season predictability, especially quantitative precipitation forecasts up to 36 h, were explored through experiments with various grid resolutions, initial and boundary conditions, physics parameterization schemes, and the addition of small-scale, small-amplitude random initial errors. It is found that the high-resolution convective-resolving simulations (with grid spacing down to 3.3 km) do not produce the best simulation or forecast. It was also found that both the realistic initial condition uncertainty and model errors can result in large forecast errors for this warm-season flooding event. Thus, practically, there is room to gain higher forecast accuracy through improving the initial analysis with better data assimilation techniques or enhanced observations, and through improving the forecast model with better-resolved or -parameterized physical processes. However, even if a perfect forecast model is used, small-scale, small-amplitude initial errors, such as those in the form of undetectable random noise, can grow rapidly and subsequently contaminate the short-term deterministic mesoscale forecast within 36 h. This rapid error growth is caused by moist convection. The limited deterministic predictability of such a heavy precipitation event, both practically and intrinsically, illustrates the need for probabilistic forecasts at the mesoscales.

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