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Donald W. McCann

Abstract

Enhanced infrared satellite imagery is used to examine severe thunderstorms that display a warm spot accompanied by a signature called an enhanced-V. This enhanced-V is formed when strong upper level winds are diverted around an overshooting thunderstorm top. When a storm has an enhanced-V, it has a high probability of subsequently producing severe weather. Two rules for identification of an enhanced-V are established. The meridian lead time from enhanced-V identification to the first severe weather report is 30 min. A low false alarm ratio makes this identification technique a potential severe storm warning tool. However, a relatively low probability of detection indicates that there are many severe storms that do not show an enhanced-V.

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Donald W. McCann

Abstract

Moore and Lambert showed how a quantity called equivalent potential vorticity (EPV) can provide quantitative values to assess conditional symmetric instability (CSI), also known as slantwise instability. Expanding the EPV equation into three dimensions, the equation becomes a function of the geostrophic wind shell, the horizontal equivalent potential temperature gradient, the absolute geostrophic vorticity, and the vertical equivalent potential temperature gradient, all of which are easily computed from gridded data. The equation reduces further by recognizing that the geographic wind shear is a function of the horizontal equivalent potential temperature gradient in a saturated environment. This reduced equation is difficult to evaluate quantitatively because of its dependence on the local value of the moist adiabatic lapse rate. Nevertheless, it shows that horizontal temperature gradients will always act to promote slantwise convection. EPV is a mixture of upright potential instability and slantwise instability. While this is obviously a drawback if one were attempting to assess CSI separately from VPI by combining both instabilities, EPV becomes an all-purpose convection diagnostic tool. EPV is computed from numerical model grids at the National Severe Storms Forecast Center.

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Donald W. McCann

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Microbursts are small-scale phenomena that have been viewed by many meteorologists as difficult to predict. However, there exists sufficient knowledge of microburst evolution by some in the research and operational communities that can be applied on the mesoscale to provide some warning to the public and aviation. This paper introduces a wind index or WINDEX that is based on this knowledge. It can be easily computed from soundings. The WINDEX is calculated from soundings known to have been taken in microburst environments and previously presented in the literature. The WINDEX can also be computed from surface observations using appropriate assumptions. This paper shows how to use the hourly surface-based WINDEX information (data) by showing its application to the infamous DFW microburst on 2 August 1985 and for three consecutive days in August 1993. The surface-based WINDEX analyses reveal a common pattern first noted by Ladd (1989); that is, microbursts primarily occur with new convection on old thunderstorm outflow boundaries. When an outflow boundary moves perpendicular to the WINDEX contours, into an area of high WINDEX values, conditions are favorable for microbursts. With this conceptual model it is possible for forecasters to give one to two hours warning that microbursts are probable for a small area.

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Donald W. McCann

Abstract

Case studies are the typical means by which meteorologists pass on their knowledge of how to solve a particular weather-forecasting problem to other forecasters. A case study helps others recognize an important pattern and enhances the meteorologist in the meteorologist–machine mix. A neural network is an artificial-intelligence tool that excels in pattern recognition. This tool can become another means of enhancing a forecaster's pattern-recognition ability. Since neural networks are a relatively new tool to meteorologists, some basics are given before discussing a 3–7-h significant thunderstorm forecast developed with this technique. Two neural networks learned to forecast significant thunderstorms from fields of surface-based lifted index and surface moisture convergence. These networks are sensitive to the patterns that skilled forecasters recognize as occurring prior to strong thunderstorms. The two neural networks are combined operationally at the National Severe Storms Forecast Center into a single hourly product that enhances pattern-recognition skills. Examples of neural network products are shown, and their potential impact on significant thunderstorm forecasting is demonstrated.

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John A. Knox, Donald W. McCann, and Paul D. Williams
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John A. Knox, Donald W. McCann, and Paul D. Williams

Abstract

A new method of clear-air turbulence (CAT) forecasting based on the Lighthill–Ford theory of spontaneous imbalance and emission of inertia–gravity waves has been derived and applied on episodic and seasonal time scales. A scale analysis of this shallow-water theory for midlatitude synoptic-scale flows identifies advection of relative vorticity as the leading-order source term. Examination of leading- and second-order terms elucidates previous, more empirically inspired CAT forecast diagnostics. Application of the Lighthill–Ford theory to the Upper Mississippi and Ohio Valleys CAT outbreak of 9 March 2006 results in good agreement with pilot reports of turbulence. Application of Lighthill–Ford theory to CAT forecasting for the 3 November 2005–26 March 2006 period using 1-h forecasts of the Rapid Update Cycle (RUC) 2 1500 UTC model run leads to superior forecasts compared to the current operational version of the Graphical Turbulence Guidance (GTG1) algorithm, the most skillful operational CAT forecasting method in existence. The results suggest that major improvements in CAT forecasting could result if the methods presented herein become operational.

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